From 9d646db9d885d7d86fa5f40aa29a48147379ed70 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Tue, 17 Jan 2023 23:28:36 -0800 Subject: [PATCH 01/69] print '?' if a letter can't be encoded using the system default encoding (#859) --- whisper/transcribe.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/whisper/transcribe.py b/whisper/transcribe.py index c76b5c2d6..d95d3336d 100644 --- a/whisper/transcribe.py +++ b/whisper/transcribe.py @@ -1,5 +1,6 @@ import argparse import os +import sys import warnings from typing import List, Optional, Tuple, Union, TYPE_CHECKING @@ -165,7 +166,10 @@ def add_segment( } ) if verbose: - print(f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}") + line = f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}\n" + # compared to just `print(line)`, this replaces any character not representable using + # the system default encoding with an '?', avoiding UnicodeEncodeError. + sys.stderr.buffer.write(line.encode(sys.getdefaultencoding(), errors="replace")) # show the progress bar when verbose is False (otherwise the transcribed text will be printed) num_frames = mel.shape[-1] From 8135a7c31ccda72d03569429b4d79fd3a059d62a Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Wed, 18 Jan 2023 10:30:18 -0800 Subject: [PATCH 02/69] verbose outputs from pytest --- .github/workflows/test.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index e9c3466d3..c5b4eeccd 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -23,4 +23,4 @@ jobs: - run: echo "$CONDA/envs/test/bin" >> $GITHUB_PATH - run: pip install pytest - run: pip install . - - run: pytest -k 'not test_transcribe or test_transcribe[tiny]' + - run: pytest --durations=0 -vv -k 'not test_transcribe or test_transcribe[tiny] or test_transcribe[tiny.en]' From ea1c266709125f68a89acdc0c6ddbde6773eb0ed Mon Sep 17 00:00:00 2001 From: Markus Hennerbichler Date: Wed, 18 Jan 2023 18:41:11 +0000 Subject: [PATCH 03/69] Fix bug where mm is mistakenly replaced with hmm in e.g. 20mm (#659) Co-authored-by: Jong Wook Kim --- tests/test_normalizer.py | 1 + whisper/normalizers/english.json | 1 - 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_normalizer.py b/tests/test_normalizer.py index a6c16cb0f..3785e6cab 100644 --- a/tests/test_normalizer.py +++ b/tests/test_normalizer.py @@ -84,6 +84,7 @@ def test_text_normalizer(): assert std("he's like") == "he is like" assert std("she's been like") == "she has been like" assert std("10km") == "10 km" + assert std("10mm") == "10 mm" assert std("RC232") == "rc 232" assert ( diff --git a/whisper/normalizers/english.json b/whisper/normalizers/english.json index bd84ae73f..74a1c3521 100644 --- a/whisper/normalizers/english.json +++ b/whisper/normalizers/english.json @@ -1737,6 +1737,5 @@ "yoghurt": "yogurt", "yoghurts": "yogurts", "mhm": "hmm", - "mm": "hmm", "mmm": "hmm" } \ No newline at end of file From 12e1089462a2ea92e9ade6145e7be5b6883676ff Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Fri, 20 Jan 2023 00:54:05 -0800 Subject: [PATCH 04/69] use stdout for printing transcription progress (#867) --- whisper/transcribe.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/whisper/transcribe.py b/whisper/transcribe.py index d95d3336d..62ef5fe5c 100644 --- a/whisper/transcribe.py +++ b/whisper/transcribe.py @@ -169,7 +169,8 @@ def add_segment( line = f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}\n" # compared to just `print(line)`, this replaces any character not representable using # the system default encoding with an '?', avoiding UnicodeEncodeError. - sys.stderr.buffer.write(line.encode(sys.getdefaultencoding(), errors="replace")) + sys.stdout.buffer.write(line.encode(sys.getdefaultencoding(), errors="replace")) + sys.stdout.flush() # show the progress bar when verbose is False (otherwise the transcribed text will be printed) num_frames = mel.shape[-1] From 9f7aba609971434b9de2a8d34ca2de766976904d Mon Sep 17 00:00:00 2001 From: zer0-x <65136727+zer0-x@users.noreply.github.com> Date: Sat, 21 Jan 2023 12:09:39 +0300 Subject: [PATCH 05/69] Handle XDG_CACHE_HOME properly for download_root (#864) Co-authored-by: Jong Wook Kim --- whisper/__init__.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/whisper/__init__.py b/whisper/__init__.py index 2a1fb4ec6..cb334065e 100644 --- a/whisper/__init__.py +++ b/whisper/__init__.py @@ -94,9 +94,14 @@ def load_model(name: str, device: Optional[Union[str, torch.device]] = None, dow if device is None: device = "cuda" if torch.cuda.is_available() else "cpu" if download_root is None: - download_root = os.getenv( - "XDG_CACHE_HOME", - os.path.join(os.path.expanduser("~"), ".cache", "whisper") + download_root = os.path.join( + os.getenv( + "XDG_CACHE_HOME", + os.path.join( + os.path.expanduser("~"), ".cache" + ) + ), + "whisper" ) if name in _MODELS: From da600abd2b296a5450770b872c3765d0a5a5c769 Mon Sep 17 00:00:00 2001 From: Aaryan YVS <51906812+Aaryan369@users.noreply.github.com> Date: Sun, 22 Jan 2023 13:28:38 +0530 Subject: [PATCH 06/69] Added --output_format option (#333) * Added --output option --output option will help select the output files that will be generated. Corrected the logic, which wrongly shows progress bar when verbose is set to False * Changed output_files variable * Changed back the tqdm verbose * refactor output format handling Co-authored-by: Jong Wook Kim Co-authored-by: Jong Wook Kim --- whisper/transcribe.py | 21 ++----- whisper/utils.py | 125 ++++++++++++++++++++++++++++-------------- 2 files changed, 89 insertions(+), 57 deletions(-) diff --git a/whisper/transcribe.py b/whisper/transcribe.py index 62ef5fe5c..02952dfe2 100644 --- a/whisper/transcribe.py +++ b/whisper/transcribe.py @@ -11,7 +11,7 @@ from .audio import SAMPLE_RATE, N_FRAMES, HOP_LENGTH, pad_or_trim, log_mel_spectrogram from .decoding import DecodingOptions, DecodingResult from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer -from .utils import exact_div, format_timestamp, optional_int, optional_float, str2bool, write_txt, write_vtt, write_srt +from .utils import exact_div, format_timestamp, optional_int, optional_float, str2bool, get_writer if TYPE_CHECKING: from .model import Whisper @@ -260,6 +260,7 @@ def cli(): parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default") parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference") parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs") + parser.add_argument("--output_format", "-f", type=str, default="all", choices=["txt", "vtt", "srt", "json", "all"], help="format of the output file; if not specified, all available formats will be produced") parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages") parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')") @@ -286,6 +287,7 @@ def cli(): model_name: str = args.pop("model") model_dir: str = args.pop("model_dir") output_dir: str = args.pop("output_dir") + output_format: str = args.pop("output_format") device: str = args.pop("device") os.makedirs(output_dir, exist_ok=True) @@ -308,22 +310,11 @@ def cli(): from . import load_model model = load_model(model_name, device=device, download_root=model_dir) + writer = get_writer(output_format, output_dir) + for audio_path in args.pop("audio"): result = transcribe(model, audio_path, temperature=temperature, **args) - - audio_basename = os.path.basename(audio_path) - - # save TXT - with open(os.path.join(output_dir, audio_basename + ".txt"), "w", encoding="utf-8") as txt: - write_txt(result["segments"], file=txt) - - # save VTT - with open(os.path.join(output_dir, audio_basename + ".vtt"), "w", encoding="utf-8") as vtt: - write_vtt(result["segments"], file=vtt) - - # save SRT - with open(os.path.join(output_dir, audio_basename + ".srt"), "w", encoding="utf-8") as srt: - write_srt(result["segments"], file=srt) + writer(result, audio_path) if __name__ == '__main__': diff --git a/whisper/utils.py b/whisper/utils.py index 233d3d4ff..8315e7f45 100644 --- a/whisper/utils.py +++ b/whisper/utils.py @@ -1,5 +1,7 @@ +import json +import os import zlib -from typing import Iterator, TextIO +from typing import Callable, TextIO def exact_div(x, y): @@ -45,44 +47,83 @@ def format_timestamp(seconds: float, always_include_hours: bool = False, decimal return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}" -def write_txt(transcript: Iterator[dict], file: TextIO): - for segment in transcript: - print(segment['text'].strip(), file=file, flush=True) - - -def write_vtt(transcript: Iterator[dict], file: TextIO): - print("WEBVTT\n", file=file) - for segment in transcript: - print( - f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n" - f"{segment['text'].strip().replace('-->', '->')}\n", - file=file, - flush=True, - ) - - -def write_srt(transcript: Iterator[dict], file: TextIO): - """ - Write a transcript to a file in SRT format. - - Example usage: - from pathlib import Path - from whisper.utils import write_srt - - result = transcribe(model, audio_path, temperature=temperature, **args) - - # save SRT - audio_basename = Path(audio_path).stem - with open(Path(output_dir) / (audio_basename + ".srt"), "w", encoding="utf-8") as srt: - write_srt(result["segments"], file=srt) - """ - for i, segment in enumerate(transcript, start=1): - # write srt lines - print( - f"{i}\n" - f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> " - f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n" - f"{segment['text'].strip().replace('-->', '->')}\n", - file=file, - flush=True, - ) +class ResultWriter: + extension: str + + def __init__(self, output_dir: str): + self.output_dir = output_dir + + def __call__(self, result: dict, audio_path: str): + audio_basename = os.path.basename(audio_path) + output_path = os.path.join(self.output_dir, audio_basename + "." + self.extension) + + with open(output_path, "w", encoding="utf-8") as f: + self.write_result(result, file=f) + + def write_result(self, result: dict, file: TextIO): + raise NotImplementedError + + +class WriteTXT(ResultWriter): + extension: str = "txt" + + def write_result(self, result: dict, file: TextIO): + for segment in result["segments"]: + print(segment['text'].strip(), file=file, flush=True) + + +class WriteVTT(ResultWriter): + extension: str = "vtt" + + def write_result(self, result: dict, file: TextIO): + print("WEBVTT\n", file=file) + for segment in result["segments"]: + print( + f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n" + f"{segment['text'].strip().replace('-->', '->')}\n", + file=file, + flush=True, + ) + + +class WriteSRT(ResultWriter): + extension: str = "srt" + + def write_result(self, result: dict, file: TextIO): + for i, segment in enumerate(result["segments"], start=1): + # write srt lines + print( + f"{i}\n" + f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> " + f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n" + f"{segment['text'].strip().replace('-->', '->')}\n", + file=file, + flush=True, + ) + + +class WriteJSON(ResultWriter): + extension: str = "json" + + def write_result(self, result: dict, file: TextIO): + json.dump(result, file) + + +def get_writer(output_format: str, output_dir: str) -> Callable[[dict, TextIO], None]: + writers = { + "txt": WriteTXT, + "vtt": WriteVTT, + "srt": WriteSRT, + "json": WriteJSON, + } + + if output_format == "all": + all_writers = [writer(output_dir) for writer in writers.values()] + + def write_all(result: dict, file: TextIO): + for writer in all_writers: + writer(result, file) + + return write_all + + return writers[output_format](output_dir) From f5bfe004eccc3837a0d198baf7602ec7bccffafd Mon Sep 17 00:00:00 2001 From: Niels Mayer Date: Sun, 22 Jan 2023 00:27:17 -0800 Subject: [PATCH 07/69] Add TSV formatted output in transcript, using integer start/end times in milliseconds. (#228) * Add CSV format output in transcript, containing lines of characters formatted like: , , * for easier reading by spreadsheets importing CSV, the third column of the CSV file is delimited by quotes, and any quote characters that might be in the transcript (which would interfere with parsing the third column as a string) are converted to "''". * fix syntax error * docstring edit Co-authored-by: Jong Wook Kim Co-authored-by: Jong Wook Kim --- whisper/transcribe.py | 2 +- whisper/utils.py | 21 +++++++++++++++++++++ 2 files changed, 22 insertions(+), 1 deletion(-) diff --git a/whisper/transcribe.py b/whisper/transcribe.py index 02952dfe2..c0404416e 100644 --- a/whisper/transcribe.py +++ b/whisper/transcribe.py @@ -260,7 +260,7 @@ def cli(): parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default") parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference") parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs") - parser.add_argument("--output_format", "-f", type=str, default="all", choices=["txt", "vtt", "srt", "json", "all"], help="format of the output file; if not specified, all available formats will be produced") + parser.add_argument("--output_format", "-f", type=str, default="all", choices=["txt", "vtt", "srt", "tsv", "json", "all"], help="format of the output file; if not specified, all available formats will be produced") parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages") parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')") diff --git a/whisper/utils.py b/whisper/utils.py index 8315e7f45..8293641b2 100644 --- a/whisper/utils.py +++ b/whisper/utils.py @@ -102,6 +102,25 @@ def write_result(self, result: dict, file: TextIO): ) +class WriteTSV(ResultWriter): + """ + Write a transcript to a file in TSV (tab-separated values) format containing lines like: + \t\t + + Using integer milliseconds as start and end times means there's no chance of interference from + an environment setting a language encoding that causes the decimal in a floating point number + to appear as a comma; also is faster and more efficient to parse & store, e.g., in C++. + """ + extension: str = "tsv" + + def write_result(self, result: dict, file: TextIO): + print("start", "end", "text", sep="\t", file=file) + for segment in result["segments"]: + print(round(1000 * segment['start']), file=file, end="\t") + print(round(1000 * segment['end']), file=file, end="\t") + print(segment['text'].strip().replace("\t", " "), file=file, flush=True) + + class WriteJSON(ResultWriter): extension: str = "json" @@ -114,6 +133,7 @@ def get_writer(output_format: str, output_dir: str) -> Callable[[dict, TextIO], "txt": WriteTXT, "vtt": WriteVTT, "srt": WriteSRT, + "tsv": WriteTSV, "json": WriteJSON, } @@ -127,3 +147,4 @@ def write_all(result: dict, file: TextIO): return write_all return writers[output_format](output_dir) + From 7f1ef223abea88cf9d0b8067316601ac7436094e Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Tue, 24 Jan 2023 10:12:04 -0800 Subject: [PATCH 08/69] handle printing even if sys.stdout.buffer is not available (#887) --- whisper/transcribe.py | 11 +++-------- whisper/utils.py | 13 +++++++++++++ 2 files changed, 16 insertions(+), 8 deletions(-) diff --git a/whisper/transcribe.py b/whisper/transcribe.py index c0404416e..c5bea7b26 100644 --- a/whisper/transcribe.py +++ b/whisper/transcribe.py @@ -1,8 +1,7 @@ import argparse import os -import sys import warnings -from typing import List, Optional, Tuple, Union, TYPE_CHECKING +from typing import Optional, Tuple, Union, TYPE_CHECKING import numpy as np import torch @@ -11,7 +10,7 @@ from .audio import SAMPLE_RATE, N_FRAMES, HOP_LENGTH, pad_or_trim, log_mel_spectrogram from .decoding import DecodingOptions, DecodingResult from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer -from .utils import exact_div, format_timestamp, optional_int, optional_float, str2bool, get_writer +from .utils import exact_div, format_timestamp, make_safe, optional_int, optional_float, str2bool, get_writer if TYPE_CHECKING: from .model import Whisper @@ -166,11 +165,7 @@ def add_segment( } ) if verbose: - line = f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}\n" - # compared to just `print(line)`, this replaces any character not representable using - # the system default encoding with an '?', avoiding UnicodeEncodeError. - sys.stdout.buffer.write(line.encode(sys.getdefaultencoding(), errors="replace")) - sys.stdout.flush() + print(make_safe(f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}")) # show the progress bar when verbose is False (otherwise the transcribed text will be printed) num_frames = mel.shape[-1] diff --git a/whisper/utils.py b/whisper/utils.py index 8293641b2..5dacc173c 100644 --- a/whisper/utils.py +++ b/whisper/utils.py @@ -1,8 +1,21 @@ import json import os +import sys import zlib from typing import Callable, TextIO +system_encoding = sys.getdefaultencoding() + +if system_encoding != "utf-8": + def make_safe(string): + # replaces any character not representable using the system default encoding with an '?', + # avoiding UnicodeEncodeError (https://github.com/openai/whisper/discussions/729). + return string.encode(system_encoding, errors="replace").decode(system_encoding) +else: + def make_safe(string): + # utf-8 can encode any Unicode code point, so no need to do the round-trip encoding + return string + def exact_div(x, y): assert x % y == 0 From 55f690af7914c672c69733b7e04ef5a41b2b2774 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Tue, 24 Jan 2023 11:11:08 -0800 Subject: [PATCH 09/69] Release 20230124 --- whisper/version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/whisper/version.py b/whisper/version.py index 8c605f4d5..c9782e774 100644 --- a/whisper/version.py +++ b/whisper/version.py @@ -1 +1 @@ -__version__ = "20230117" +__version__ = "20230124" From a6b36ede1f060860d5676a543176a6439d91eae6 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Tue, 24 Jan 2023 14:05:57 -0800 Subject: [PATCH 10/69] drop python 3.7 support (#889) --- whisper/decoding.py | 19 ++++++------------- whisper/tokenizer.py | 38 +++++++++++++------------------------- whisper/transcribe.py | 25 ++++++++++++++----------- 3 files changed, 33 insertions(+), 49 deletions(-) diff --git a/whisper/decoding.py b/whisper/decoding.py index bb70cc024..983c898a3 100644 --- a/whisper/decoding.py +++ b/whisper/decoding.py @@ -252,11 +252,10 @@ def __init__(self, temperature: float, eot: int): self.eot = eot def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: - temperature = self.temperature - if temperature == 0: + if self.temperature == 0: next_tokens = logits.argmax(dim=-1) else: - next_tokens = Categorical(logits=logits / temperature).sample() + next_tokens = Categorical(logits=logits / self.temperature).sample() logprobs = F.log_softmax(logits.float(), dim=-1) current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens] @@ -511,10 +510,8 @@ def _verify_options(self, options: DecodingOptions) -> DecodingOptions: def _get_initial_tokens(self) -> Tuple[int]: tokens = list(self.sot_sequence) - prefix = self.options.prefix - prompt = self.options.prompt - if prefix: + if prefix := self.options.prefix: prefix_tokens = ( self.tokenizer.encode(" " + prefix.strip()) if isinstance(prefix, str) else prefix ) @@ -523,7 +520,7 @@ def _get_initial_tokens(self) -> Tuple[int]: prefix_tokens = prefix_tokens[-max_prefix_len:] tokens = tokens + prefix_tokens - if prompt: + if prompt := self.options.prompt: prompt_tokens = ( self.tokenizer.encode(" " + prompt.strip()) if isinstance(prompt, str) else prompt ) @@ -698,13 +695,9 @@ def decode(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOpt result: Union[DecodingResult, List[DecodingResult]] The result(s) of decoding contained in `DecodingResult` dataclass instance(s) """ - single = mel.ndim == 2 - if single: + if single := mel.ndim == 2: mel = mel.unsqueeze(0) result = DecodingTask(model, options).run(mel) - - if single: - result = result[0] - return result + return result[0] if single else result diff --git a/whisper/tokenizer.py b/whisper/tokenizer.py index a27cb359e..7b4605f3c 100644 --- a/whisper/tokenizer.py +++ b/whisper/tokenizer.py @@ -1,6 +1,6 @@ import os from dataclasses import dataclass -from functools import lru_cache +from functools import lru_cache, cached_property from typing import List, Optional, Tuple, Union import numpy as np @@ -156,43 +156,35 @@ def decode_with_timestamps(self, tokens) -> str: outputs = [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs] return "".join(outputs) - @property - @lru_cache() + @cached_property def eot(self) -> int: return self.tokenizer.eos_token_id - @property - @lru_cache() + @cached_property def sot(self) -> int: return self._get_single_token_id("<|startoftranscript|>") - @property - @lru_cache() + @cached_property def sot_lm(self) -> int: return self._get_single_token_id("<|startoflm|>") - @property - @lru_cache() + @cached_property def sot_prev(self) -> int: return self._get_single_token_id("<|startofprev|>") - @property - @lru_cache() + @cached_property def no_speech(self) -> int: return self._get_single_token_id("<|nospeech|>") - @property - @lru_cache() + @cached_property def no_timestamps(self) -> int: return self._get_single_token_id("<|notimestamps|>") - @property - @lru_cache() + @cached_property def timestamp_begin(self) -> int: return self.tokenizer.all_special_ids[-1] + 1 - @property - @lru_cache() + @cached_property def language_token(self) -> int: """Returns the token id corresponding to the value of the `language` field""" if self.language is None: @@ -210,8 +202,7 @@ def language_token(self) -> int: raise KeyError(f"Language {self.language} not found in tokenizer.") - @property - @lru_cache() + @cached_property def all_language_tokens(self) -> Tuple[int]: result = [] for token, token_id in zip( @@ -222,18 +213,15 @@ def all_language_tokens(self) -> Tuple[int]: result.append(token_id) return tuple(result) - @property - @lru_cache() + @cached_property def all_language_codes(self) -> Tuple[str]: return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens) - @property - @lru_cache() + @cached_property def sot_sequence_including_notimestamps(self) -> Tuple[int]: return tuple(list(self.sot_sequence) + [self.no_timestamps]) - @property - @lru_cache() + @cached_property def non_speech_tokens(self) -> Tuple[int]: """ Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech diff --git a/whisper/transcribe.py b/whisper/transcribe.py index c5bea7b26..80bdd7965 100644 --- a/whisper/transcribe.py +++ b/whisper/transcribe.py @@ -26,6 +26,7 @@ def transcribe( logprob_threshold: Optional[float] = -1.0, no_speech_threshold: Optional[float] = 0.6, condition_on_previous_text: bool = True, + initial_prompt: Optional[str] = None, **decode_options, ): """ @@ -138,10 +139,11 @@ def decode_with_fallback(segment: torch.Tensor) -> DecodingResult: all_segments = [] prompt_reset_since = 0 - initial_prompt = decode_options.pop("initial_prompt", None) or [] - if initial_prompt: - initial_prompt = tokenizer.encode(" " + initial_prompt.strip()) - all_tokens.extend(initial_prompt) + if initial_prompt is not None: + initial_prompt_tokens = tokenizer.encode(" " + initial_prompt.strip()) + all_tokens.extend(initial_prompt_tokens) + else: + initial_prompt_tokens = [] def add_segment( *, start: float, end: float, text_tokens: torch.Tensor, result: DecodingResult @@ -243,7 +245,11 @@ def add_segment( pbar.update(min(num_frames, seek) - previous_seek_value) previous_seek_value = seek - return dict(text=tokenizer.decode(all_tokens[len(initial_prompt):]), segments=all_segments, language=language) + return dict( + text=tokenizer.decode(all_tokens[len(initial_prompt_tokens):]), + segments=all_segments, + language=language + ) def cli(): @@ -292,21 +298,18 @@ def cli(): args["language"] = "en" temperature = args.pop("temperature") - temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback") - if temperature_increment_on_fallback is not None: - temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback)) + if (increment := args.pop("temperature_increment_on_fallback")) is not None: + temperature = tuple(np.arange(temperature, 1.0 + 1e-6, increment)) else: temperature = [temperature] - threads = args.pop("threads") - if threads > 0: + if (threads := args.pop("threads")) > 0: torch.set_num_threads(threads) from . import load_model model = load_model(model_name, device=device, download_root=model_dir) writer = get_writer(output_format, output_dir) - for audio_path in args.pop("audio"): result = transcribe(model, audio_path, temperature=temperature, **args) writer(result, audio_path) From 4e635c66441dfae3c6c72365f802293afffca3ef Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Tue, 24 Jan 2023 14:45:56 -0800 Subject: [PATCH 11/69] Update README.md about Python 3.8+ requirement --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8f6de2b02..376c41d89 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ A Transformer sequence-to-sequence model is trained on various speech processing ## Setup -We used Python 3.9.9 and [PyTorch](https://pytorch.org/) 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.7 or later and recent PyTorch versions. The codebase also depends on a few Python packages, most notably [HuggingFace Transformers](https://huggingface.co/docs/transformers/index) for their fast tokenizer implementation and [ffmpeg-python](https://github.com/kkroening/ffmpeg-python) for reading audio files. You can download and install (or update to) the latest release of Whisper with the following command: +We used Python 3.9.9 and [PyTorch](https://pytorch.org/) 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.8 or later and recent PyTorch versions. The codebase also depends on a few Python packages, most notably [HuggingFace Transformers](https://huggingface.co/docs/transformers/index) for their fast tokenizer implementation and [ffmpeg-python](https://github.com/kkroening/ffmpeg-python) for reading audio files. You can download and install (or update to) the latest release of Whisper with the following command: pip install -U openai-whisper From 5c1a8c10e762bf9c29fcf6b3e40f17bc8ab09864 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Fri, 27 Jan 2023 00:01:49 -0800 Subject: [PATCH 12/69] clarify that 3.11 is not supported --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 376c41d89..807bf4826 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ A Transformer sequence-to-sequence model is trained on various speech processing ## Setup -We used Python 3.9.9 and [PyTorch](https://pytorch.org/) 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.8 or later and recent PyTorch versions. The codebase also depends on a few Python packages, most notably [HuggingFace Transformers](https://huggingface.co/docs/transformers/index) for their fast tokenizer implementation and [ffmpeg-python](https://github.com/kkroening/ffmpeg-python) for reading audio files. You can download and install (or update to) the latest release of Whisper with the following command: +We used Python 3.9.9 and [PyTorch](https://pytorch.org/) 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.8-3.10 and recent PyTorch versions. The codebase also depends on a few Python packages, most notably [HuggingFace Transformers](https://huggingface.co/docs/transformers/index) for their fast tokenizer implementation and [ffmpeg-python](https://github.com/kkroening/ffmpeg-python) for reading audio files. You can download and install (or update to) the latest release of Whisper with the following command: pip install -U openai-whisper From 7858aa9c08d98f75575035ecd6481f462d66ca27 Mon Sep 17 00:00:00 2001 From: Andrey Chernykh Date: Thu, 2 Feb 2023 06:46:51 +0700 Subject: [PATCH 13/69] Fix infinite loop caused by incorrect timestamp tokens prediction (#914) * Fix infinite loop caused by incorrect timestamp tokens prediction https://github.com/openai/whisper/discussions/810 * Update decoding.py --------- Co-authored-by: Jong Wook Kim --- whisper/decoding.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/whisper/decoding.py b/whisper/decoding.py index 983c898a3..7613a0c5e 100644 --- a/whisper/decoding.py +++ b/whisper/decoding.py @@ -412,7 +412,8 @@ def apply(self, logits: Tensor, tokens: Tensor): # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly for k in range(tokens.shape[0]): - seq = [t for t in tokens[k, self.sample_begin :].tolist()] + sampled_tokens = tokens[k, self.sample_begin :] + seq = [t for t in sampled_tokens.tolist()] last_was_timestamp = len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin penultimate_was_timestamp = len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin @@ -422,6 +423,11 @@ def apply(self, logits: Tensor, tokens: Tensor): else: # cannot be normal text tokens logits[k, : self.tokenizer.eot] = -np.inf + timestamps = sampled_tokens[sampled_tokens.ge(self.tokenizer.timestamp_begin)] + if timestamps.numel() > 0: + # timestamps shouldn't decrease; forbid timestamp tokens smaller than the last + logits[k, self.tokenizer.timestamp_begin : timestamps[-1]] = -np.inf + if tokens.shape[1] == self.sample_begin: # suppress generating non-timestamp tokens at the beginning logits[:, : self.tokenizer.timestamp_begin] = -np.inf From 3e1780fd37686666f568be9c99f5b5e3e4f2eb92 Mon Sep 17 00:00:00 2001 From: Roman Vasilenko <101987068+roman-vasi1enko@users.noreply.github.com> Date: Fri, 3 Mar 2023 19:41:59 -0500 Subject: [PATCH 14/69] Update README.md (#894) Fixed a few typos and made general improvements for clarity. Co-authored-by: Jong Wook Kim --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 807bf4826..eba82ce22 100644 --- a/README.md +++ b/README.md @@ -5,14 +5,14 @@ [[Model card]](https://github.com/openai/whisper/blob/main/model-card.md) [[Colab example]](https://colab.research.google.com/github/openai/whisper/blob/master/notebooks/LibriSpeech.ipynb) -Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification. +Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification. ## Approach ![Approach](https://raw.githubusercontent.com/openai/whisper/main/approach.png) -A Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. All of these tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing for a single model to replace many different stages of a traditional speech processing pipeline. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets. +A Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. These tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing a single model to replace many stages of a traditional speech-processing pipeline. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets. ## Setup @@ -68,9 +68,9 @@ There are five model sizes, four with English-only versions, offering speed and | medium | 769 M | `medium.en` | `medium` | ~5 GB | ~2x | | large | 1550 M | N/A | `large` | ~10 GB | 1x | -For English-only applications, the `.en` models tend to perform better, especially for the `tiny.en` and `base.en` models. We observed that the difference becomes less significant for the `small.en` and `medium.en` models. +The `.en` models for English-only applications tend to perform better, especially for the `tiny.en` and `base.en` models. We observed that the difference becomes less significant for the `small.en` and `medium.en` models. -Whisper's performance varies widely depending on the language. The figure below shows a WER (Word Error Rate) breakdown by languages of Fleurs dataset, using the `large-v2` model. More WER and BLEU scores corresponding to the other models and datasets can be found in Appendix D in [the paper](https://arxiv.org/abs/2212.04356). The smaller is better. +Whisper's performance varies widely depending on the language. The figure below shows a WER (Word Error Rate) breakdown by languages of the Fleurs dataset using the `large-v2` model. More WER and BLEU scores corresponding to the other models and datasets can be found in Appendix D in [the paper](https://arxiv.org/abs/2212.04356). The smaller, the better. ![WER breakdown by language](https://raw.githubusercontent.com/openai/whisper/main/language-breakdown.svg) @@ -144,4 +144,4 @@ Please use the [🙌 Show and tell](https://github.com/openai/whisper/discussion ## License -The code and the model weights of Whisper are released under the MIT License. See [LICENSE](https://github.com/openai/whisper/blob/main/LICENSE) for further details. +Whisper's code and model weights are released under the MIT License. See [LICENSE](https://github.com/openai/whisper/blob/main/LICENSE) for further details. From eab8d920edf3947294c466f3912c24ed4b191264 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Mon, 6 Mar 2023 14:32:32 -0500 Subject: [PATCH 15/69] Decoding improvements (#1033) * suppress task tokens (transcribe/translate) * not ignoring the last segment ending with one timestamp --- whisper/decoding.py | 8 +++++++- whisper/tokenizer.py | 8 ++++++++ whisper/transcribe.py | 30 +++++++++++++++--------------- 3 files changed, 30 insertions(+), 16 deletions(-) diff --git a/whisper/decoding.py b/whisper/decoding.py index 7613a0c5e..7c51f251c 100644 --- a/whisper/decoding.py +++ b/whisper/decoding.py @@ -549,7 +549,13 @@ def _get_suppress_tokens(self) -> Tuple[int]: assert isinstance(suppress_tokens, list), "suppress_tokens must be a list" suppress_tokens.extend( - [self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm] + [ + self.tokenizer.transcribe, + self.tokenizer.translate, + self.tokenizer.sot, + self.tokenizer.sot_prev, + self.tokenizer.sot_lm + ] ) if self.tokenizer.no_speech is not None: # no-speech probability is collected separately diff --git a/whisper/tokenizer.py b/whisper/tokenizer.py index 7b4605f3c..7efa2d4af 100644 --- a/whisper/tokenizer.py +++ b/whisper/tokenizer.py @@ -160,6 +160,14 @@ def decode_with_timestamps(self, tokens) -> str: def eot(self) -> int: return self.tokenizer.eos_token_id + @cached_property + def transcribe(self) -> int: + return self._get_single_token_id("<|transcribe|>") + + @cached_property + def translate(self) -> int: + return self._get_single_token_id("<|translate|>") + @cached_property def sot(self) -> int: return self._get_single_token_id("<|startoftranscript|>") diff --git a/whisper/transcribe.py b/whisper/transcribe.py index 80bdd7965..d155b20df 100644 --- a/whisper/transcribe.py +++ b/whisper/transcribe.py @@ -197,35 +197,35 @@ def add_segment( timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin) consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0].add_(1) if len(consecutive) > 0: # if the output contains two consecutive timestamp tokens + if ended_with_single_timestamp := timestamp_tokens[-2:].tolist() == [False, True]: + consecutive = consecutive.tolist() + [len(tokens)] last_slice = 0 for current_slice in consecutive: sliced_tokens = tokens[last_slice:current_slice] - start_timestamp_position = ( - sliced_tokens[0].item() - tokenizer.timestamp_begin - ) - end_timestamp_position = ( - sliced_tokens[-1].item() - tokenizer.timestamp_begin - ) + start_timestamp_pos = sliced_tokens[0].item() - tokenizer.timestamp_begin + end_timestamp_pos = sliced_tokens[-1].item() - tokenizer.timestamp_begin add_segment( - start=timestamp_offset + start_timestamp_position * time_precision, - end=timestamp_offset + end_timestamp_position * time_precision, + start=timestamp_offset + start_timestamp_pos * time_precision, + end=timestamp_offset + end_timestamp_pos * time_precision, text_tokens=sliced_tokens[1:-1], result=result, ) last_slice = current_slice - last_timestamp_position = ( - tokens[last_slice - 1].item() - tokenizer.timestamp_begin - ) - seek += last_timestamp_position * input_stride + if ended_with_single_timestamp: + # single timestamp at the end means no speech after the last timestamp. + seek += segment.shape[-1] + else: + # otherwise, ignore the unfinished segment and seek to the last timestamp + last_timestamp_pos = tokens[last_slice - 1].item() - tokenizer.timestamp_begin + seek += last_timestamp_pos * input_stride all_tokens.extend(tokens[: last_slice + 1].tolist()) else: duration = segment_duration timestamps = tokens[timestamp_tokens.nonzero().flatten()] if len(timestamps) > 0 and timestamps[-1].item() != tokenizer.timestamp_begin: # no consecutive timestamps but it has a timestamp; use the last one. - # single timestamp at the end means no speech after the last timestamp. - last_timestamp_position = timestamps[-1].item() - tokenizer.timestamp_begin - duration = last_timestamp_position * time_precision + last_timestamp_pos = timestamps[-1].item() - tokenizer.timestamp_begin + duration = last_timestamp_pos * time_precision add_segment( start=timestamp_offset, From 500d0fe9668fae5fe2af2b6a3c4950f8a29aa145 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Mon, 6 Mar 2023 17:00:49 -0500 Subject: [PATCH 16/69] word-level timestamps in `transcribe()` (#869) * word-level timestamps in `transcribe()` * moving to `timing.py` * numba implementation for dtw, replacing dtw-python * triton implementation for dtw * add test for dtw implementations * triton implementation of median_filter * a simple word-level timestamps test * add scipy as dev dependency * installs an older version of Triton if CUDA < 11.4 * fix broken merge * loosen nvcc version match regex * find_alignment() function * miscellaneous improvements * skip median filtering when the input is too small * Expose punctuation options in cli and transcribe() (#973) * fix merge error * fix merge error 2 * annotating that word_timestamps is experimental --------- Co-authored-by: ryanheise --- .github/workflows/test.yml | 5 +- requirements.txt | 1 + setup.py | 20 ++- tests/conftest.py | 14 ++ tests/test_timing.py | 87 +++++++++++ tests/test_transcribe.py | 14 +- whisper/__init__.py | 22 +++ whisper/audio.py | 4 + whisper/model.py | 13 +- whisper/timing.py | 305 +++++++++++++++++++++++++++++++++++++ whisper/tokenizer.py | 43 ++++++ whisper/transcribe.py | 160 ++++++++++++------- whisper/triton_ops.py | 92 +++++++++++ whisper/utils.py | 67 +++++--- 14 files changed, 769 insertions(+), 78 deletions(-) create mode 100644 tests/conftest.py create mode 100644 tests/test_timing.py create mode 100644 whisper/timing.py create mode 100644 whisper/triton_ops.py diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index c5b4eeccd..f06bff79a 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -21,6 +21,5 @@ jobs: - run: conda install -n test ffmpeg python=${{ matrix.python-version }} pytorch=${{ matrix.pytorch-version }} cpuonly -c pytorch - uses: actions/checkout@v2 - run: echo "$CONDA/envs/test/bin" >> $GITHUB_PATH - - run: pip install pytest - - run: pip install . - - run: pytest --durations=0 -vv -k 'not test_transcribe or test_transcribe[tiny] or test_transcribe[tiny.en]' + - run: pip install .["dev"] + - run: pytest --durations=0 -vv -k 'not test_transcribe or test_transcribe[tiny] or test_transcribe[tiny.en]' -m 'not requires_cuda' diff --git a/requirements.txt b/requirements.txt index a46140353..45ecbe769 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,4 @@ +numba numpy torch tqdm diff --git a/setup.py b/setup.py index 0e822ab9e..a548c8d35 100644 --- a/setup.py +++ b/setup.py @@ -1,4 +1,5 @@ import os +import sys import pkg_resources from setuptools import setup, find_packages @@ -9,6 +10,21 @@ def read_version(fname="whisper/version.py"): return locals()["__version__"] +requirements = [] +if sys.platform.startswith("linux"): + triton_requirement = "triton>=2.0.0.dev20221202" + try: + import re + import subprocess + version_line = subprocess.check_output(["nvcc", "--version"]).strip().split(b"\n")[-1] + major, minor = re.findall(rb"([\d]+)\.([\d]+)", version_line)[0] + if (int(major), int(minor)) < (11, 4): + # the last version supporting CUDA < 11.4 + triton_requirement = "triton==2.0.0.dev20221011" + except (IndexError, OSError, subprocess.SubprocessError): + pass + requirements.append(triton_requirement) + setup( name="openai-whisper", py_modules=["whisper"], @@ -22,7 +38,7 @@ def read_version(fname="whisper/version.py"): url="https://github.com/openai/whisper", license="MIT", packages=find_packages(exclude=["tests*"]), - install_requires=[ + install_requires=requirements + [ str(r) for r in pkg_resources.parse_requirements( open(os.path.join(os.path.dirname(__file__), "requirements.txt")) @@ -32,5 +48,5 @@ def read_version(fname="whisper/version.py"): "console_scripts": ["whisper=whisper.transcribe:cli"], }, include_package_data=True, - extras_require={"dev": ["pytest"]}, + extras_require={"dev": ["pytest", "scipy"]}, ) diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 000000000..31f1d6b48 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,14 @@ +import random as rand + +import numpy +import pytest + + +def pytest_configure(config): + config.addinivalue_line("markers", "requires_cuda") + + +@pytest.fixture +def random(): + rand.seed(42) + numpy.random.seed(42) diff --git a/tests/test_timing.py b/tests/test_timing.py new file mode 100644 index 000000000..50a2583f6 --- /dev/null +++ b/tests/test_timing.py @@ -0,0 +1,87 @@ +import pytest +import numpy as np +import scipy.ndimage +import torch + +from whisper.timing import dtw_cpu, dtw_cuda, median_filter + + +sizes = [ + (10, 20), (32, 16), (123, 1500), (234, 189), +] +shapes = [ + (10,), (1, 15), (4, 5, 345), (6, 12, 240, 512), +] + + +@pytest.mark.parametrize("N, M", sizes) +def test_dtw(N: int, M: int): + steps = np.concatenate([np.zeros(N - 1), np.ones(M - 1)]) + np.random.shuffle(steps) + x = np.random.random((N, M)).astype(np.float32) + + i, j, k = 0, 0, 0 + trace = [] + while True: + x[i, j] -= 1 + trace.append((i, j)) + + if k == len(steps): + break + + if k + 1 < len(steps) and steps[k] != steps[k + 1]: + i += 1 + j += 1 + k += 2 + continue + + if steps[k] == 0: + i += 1 + if steps[k] == 1: + j += 1 + k += 1 + + trace = np.array(trace).T + dtw_trace = dtw_cpu(x) + + assert np.allclose(trace, dtw_trace) + + +@pytest.mark.requires_cuda +@pytest.mark.parametrize("N, M", sizes) +def test_dtw_cuda_equivalence(N: int, M: int): + x_numpy = np.random.randn(N, M).astype(np.float32) + x_cuda = torch.from_numpy(x_numpy).cuda() + + trace_cpu = dtw_cpu(x_numpy) + trace_cuda = dtw_cuda(x_cuda) + + assert np.allclose(trace_cpu, trace_cuda) + + +@pytest.mark.parametrize("shape", shapes) +def test_median_filter(shape): + x = torch.randn(*shape) + + for filter_width in [3, 5, 7, 13]: + filtered = median_filter(x, filter_width) + + # using np.pad to reflect-pad, because Scipy's behavior is different near the edges. + pad_width = filter_width // 2 + padded_x = np.pad(x, [(0, 0)] * (x.ndim - 1) + [(pad_width, pad_width)], mode="reflect") + scipy_filtered = scipy.ndimage.median_filter(padded_x, [1] * (x.ndim - 1) + [filter_width]) + scipy_filtered = scipy_filtered[..., pad_width:-pad_width] + + assert np.allclose(filtered, scipy_filtered) + + +@pytest.mark.requires_cuda +@pytest.mark.parametrize("shape", shapes) +def test_median_filter_equivalence(shape): + x = torch.randn(*shape) + + for filter_width in [3, 5, 7, 13]: + filtered_cpu = median_filter(x, filter_width) + filtered_gpu = median_filter(x.cuda(), filter_width).cpu() + + assert np.allclose(filtered_cpu, filtered_gpu) diff --git a/tests/test_transcribe.py b/tests/test_transcribe.py index f5d66c37f..9802f734b 100644 --- a/tests/test_transcribe.py +++ b/tests/test_transcribe.py @@ -13,10 +13,22 @@ def test_transcribe(model_name: str): audio_path = os.path.join(os.path.dirname(__file__), "jfk.flac") language = "en" if model_name.endswith(".en") else None - result = model.transcribe(audio_path, language=language, temperature=0.0) + result = model.transcribe(audio_path, language=language, temperature=0.0, word_timestamps=True) assert result["language"] == "en" transcription = result["text"].lower() assert "my fellow americans" in transcription assert "your country" in transcription assert "do for you" in transcription + + timing_checked = False + for segment in result["segments"]: + for timing in segment["words"]: + assert timing["start"] < timing["end"] + if timing["word"].strip(" ,") == "Americans": + assert timing["start"] <= 1.8 + assert timing["end"] >= 1.8 + print(timing) + timing_checked = True + + assert timing_checked diff --git a/whisper/__init__.py b/whisper/__init__.py index cb334065e..26d1e0eaf 100644 --- a/whisper/__init__.py +++ b/whisper/__init__.py @@ -29,6 +29,23 @@ "large": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt", } +# base85-encoded (n_layers, n_heads) boolean arrays indicating the cross-attention heads that are +# highly correlated to the word-level timing, i.e. the alignment between audio and text tokens. +_ALIGNMENT_HEADS = { + "tiny.en": b"ABzY8J1N>@0{>%R00Bk>$p{7v037`oCl~+#00", + "tiny": b"ABzY8bu8Lr0{>%RKn9Fp%m@SkK7Kt=7ytkO", + "base.en": b"ABzY8;40c<0{>%RzzG;p*o+Vo09|#PsxSZm00", + "base": b"ABzY8KQ!870{>%RzyTQH3`Q^yNP!>##QT-?_)10{>%RpeA61k&I|OI3I$65C{;;pbCHh0B{qLQ;+}v00", + "small": b"ABzY8DmU6=0{>%Rpa?J`kvJ6qF(V^F86#Xh7JUGMK}P%R7%R7}kK1fFL7w6%<-Pf*t^=N)Qr&0RR9", + "large-v1": b"ABzY8r9j$a0{>%R7#4sLmoOs{s)o3~84-RPdcFk!JR%R7=D0pU<_bnWW*tkYAhobTNnu$jnkEkXqp)j;w1Tzk)UH3X%SZd&fFZ2fC2yj', + "large": b'ABzY8zd+h!0{>%R7=D0pU<_bnWW*tkYAhobTNnu$jnkEkXqp)j;w1Tzk)UH3X%SZd&fFZ2fC2yj', +} + + def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]: os.makedirs(root, exist_ok=True) @@ -106,8 +123,10 @@ def load_model(name: str, device: Optional[Union[str, torch.device]] = None, dow if name in _MODELS: checkpoint_file = _download(_MODELS[name], download_root, in_memory) + alignment_heads = _ALIGNMENT_HEADS[name] elif os.path.isfile(name): checkpoint_file = open(name, "rb").read() if in_memory else name + alignment_heads = None else: raise RuntimeError(f"Model {name} not found; available models = {available_models()}") @@ -119,4 +138,7 @@ def load_model(name: str, device: Optional[Union[str, torch.device]] = None, dow model = Whisper(dims) model.load_state_dict(checkpoint["model_state_dict"]) + if alignment_heads is not None: + model.set_alignment_heads(alignment_heads) + return model.to(device) diff --git a/whisper/audio.py b/whisper/audio.py index de8a19514..964d41579 100644 --- a/whisper/audio.py +++ b/whisper/audio.py @@ -18,6 +18,10 @@ N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000: number of samples in a chunk N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH) # 3000: number of frames in a mel spectrogram input +N_SAMPLES_PER_TOKEN = HOP_LENGTH * 2 # the initial convolutions has stride 2 +FRAMES_PER_SECOND = exact_div(SAMPLE_RATE, HOP_LENGTH) # 100 mel frames in 1s (10ms each) +TOKENS_PER_SECOND = exact_div(SAMPLE_RATE, N_SAMPLES_PER_TOKEN) # 50 audio tokens in 1s (20ms each) + def load_audio(file: str, sr: int = SAMPLE_RATE): """ diff --git a/whisper/model.py b/whisper/model.py index be73a4a87..a1ab2e349 100644 --- a/whisper/model.py +++ b/whisper/model.py @@ -1,3 +1,5 @@ +import base64 +import gzip from dataclasses import dataclass from typing import Dict from typing import Iterable, Optional @@ -8,8 +10,8 @@ from torch import Tensor from torch import nn -from .transcribe import transcribe as transcribe_function from .decoding import detect_language as detect_language_function, decode as decode_function +from .transcribe import transcribe as transcribe_function @dataclass @@ -213,6 +215,15 @@ def __init__(self, dims: ModelDimensions): self.dims.n_text_head, self.dims.n_text_layer, ) + # use the last half layers for alignment by default; see `set_alignment_heads()` below + all_heads = torch.zeros(self.dims.n_text_layer, self.dims.n_text_head, dtype=torch.bool) + all_heads[self.dims.n_text_layer // 2:] = True + self.register_buffer("alignment_heads", all_heads.to_sparse(), persistent=False) + + def set_alignment_heads(self, dump: bytes): + array = np.frombuffer(gzip.decompress(base64.b85decode(dump)), dtype=bool).copy() + mask = torch.from_numpy(array).reshape(self.dims.n_text_layer, self.dims.n_text_head) + self.register_buffer("alignment_heads", mask.to_sparse(), persistent=False) def embed_audio(self, mel: torch.Tensor): return self.encoder(mel) diff --git a/whisper/timing.py b/whisper/timing.py new file mode 100644 index 000000000..98927aa04 --- /dev/null +++ b/whisper/timing.py @@ -0,0 +1,305 @@ +import subprocess +import warnings +from dataclasses import dataclass +from typing import List, TYPE_CHECKING + +import numba +import numpy as np +import torch +import torch.nn.functional as F + +from .audio import HOP_LENGTH, SAMPLE_RATE, TOKENS_PER_SECOND +from .tokenizer import Tokenizer + +if TYPE_CHECKING: + from .model import Whisper + + +def median_filter(x: torch.Tensor, filter_width: int): + """Apply a median filter of width `filter_width` along the last dimension of `x`""" + pad_width = filter_width // 2 + if x.shape[-1] <= pad_width: + # F.pad requires the padding width to be smaller than the input dimension + return x + + if (ndim := x.ndim) <= 2: + # `F.pad` does not support 1D or 2D inputs for reflect padding but supports 3D and 4D + x = x[None, None, :] + + assert filter_width > 0 and filter_width % 2 == 1, "`filter_width` should be an odd number" + + result = None + x = F.pad(x, (filter_width // 2, filter_width // 2, 0, 0), mode="reflect") + if x.is_cuda: + try: + from .triton_ops import median_filter_cuda + result = median_filter_cuda(x, filter_width) + except (RuntimeError, subprocess.CalledProcessError): + warnings.warn( + "Failed to launch Triton kernels, likely due to missing CUDA toolkit; " + "falling back to a slower median kernel implementation..." + ) + + if result is None: + # sort() is faster than torch.median (https://github.com/pytorch/pytorch/issues/51450) + result = x.unfold(-1, filter_width, 1).sort()[0][..., filter_width // 2] + + if ndim <= 2: + result = result[0, 0] + + return result + +@numba.jit +def backtrace(trace: np.ndarray): + i = trace.shape[0] - 1 + j = trace.shape[1] - 1 + trace[0, :] = 2 + trace[:, 0] = 1 + + result = [] + while i > 0 or j > 0: + result.append((i - 1, j - 1)) + + if trace[i, j] == 0: + i -= 1 + j -= 1 + elif trace[i, j] == 1: + i -= 1 + elif trace[i, j] == 2: + j -= 1 + else: + raise ValueError("Unexpected trace[i, j]") + + result = np.array(result) + return result[::-1, :].T + + +@numba.jit(nopython=True, parallel=True) +def dtw_cpu(x: np.ndarray): + N, M = x.shape + cost = np.ones((N + 1, M + 1), dtype=np.float32) * np.inf + trace = -np.ones((N + 1, M + 1), dtype=np.float32) + + cost[0, 0] = 0 + for j in range(1, M + 1): + for i in range(1, N + 1): + c0 = cost[i - 1, j - 1] + c1 = cost[i - 1, j] + c2 = cost[i, j - 1] + + if c0 < c1 and c0 < c2: + c, t = c0, 0 + elif c1 < c0 and c1 < c2: + c, t = c1, 1 + else: + c, t = c2, 2 + + cost[i, j] = x[i - 1, j - 1] + c + trace[i, j] = t + + return backtrace(trace) + + +def dtw_cuda(x, BLOCK_SIZE=1024): + from .triton_ops import dtw_kernel + + M, N = x.shape + assert M < BLOCK_SIZE, f"M should be smaller than {BLOCK_SIZE=}" + + x_skew = F.pad(x, (0, M + 1), value=np.inf).flatten()[: M * (N + M)].reshape(M, N + M) + x_skew = x_skew.T.contiguous() + cost = torch.ones(N + M + 2, M + 2) * np.inf + cost[0, 0] = 0 + cost = cost.cuda() + trace = torch.zeros_like(cost, dtype=torch.int32) + + dtw_kernel[(1,)]( + cost, + trace, + x_skew, + x_skew.stride(0), + cost.stride(0), + trace.stride(0), + N, + M, + BLOCK_SIZE=BLOCK_SIZE + ) + + trace = trace.T.flatten()[:(M + 1) * (M + N + 3)].reshape(M + 1, M + N + 3)[:, :N + 1] + return backtrace(trace.cpu().numpy()) + + +def dtw(x: torch.Tensor) -> np.ndarray: + if x.is_cuda: + try: + return dtw_cuda(x) + except (RuntimeError, subprocess.CalledProcessError): + warnings.warn( + "Failed to launch Triton kernels, likely due to missing CUDA toolkit; " + "falling back to a slower DTW implementation..." + ) + + return dtw_cpu(x.double().cpu().numpy()) + + +@dataclass +class WordTiming: + word: str + tokens: List[int] + start: float + end: float + probability: float + + +def find_alignment( + model: "Whisper", + tokenizer: Tokenizer, + text_tokens: List[int], + mel: torch.Tensor, + num_frames: int, + *, + medfilt_width: int = 7, + qk_scale: float = 1.0, +) -> List[WordTiming]: + tokens = torch.tensor( + [ + *tokenizer.sot_sequence, + tokenizer.no_timestamps, + *text_tokens, + tokenizer.eot, + ] + ).to(model.device) + + # install hooks on the cross attention layers to retrieve the attention weights + QKs = [None] * model.dims.n_text_layer + hooks = [ + block.cross_attn.register_forward_hook( + lambda _, ins, outs, index=i: QKs.__setitem__(index, outs[-1][0]) + ) + for i, block in enumerate(model.decoder.blocks) + ] + + with torch.no_grad(): + logits = model(mel.unsqueeze(0), tokens.unsqueeze(0))[0] + token_probs = logits[len(tokenizer.sot_sequence):, :tokenizer.eot].softmax(dim=-1) + text_token_probs = token_probs[np.arange(len(text_tokens)), text_tokens].tolist() + + for hook in hooks: + hook.remove() + + # heads * tokens * frames + weights = torch.stack([QKs[l][h] for l, h in model.alignment_heads.indices().T]) + weights = weights[:, :, : num_frames // 2] + weights = (weights * qk_scale).softmax(dim=-1) + std, mean = torch.std_mean(weights, dim=-2, keepdim=True, unbiased=False) + weights = (weights - mean) / std + weights = median_filter(weights, medfilt_width) + + matrix = weights.mean(axis=0) + matrix = matrix[len(tokenizer.sot_sequence):-1] + text_indices, time_indices = dtw(-matrix) + + words, word_tokens = tokenizer.split_to_word_tokens(text_tokens + [tokenizer.eot]) + word_boundaries = np.pad(np.cumsum([len(t) for t in word_tokens[:-1]]), (1, 0)) + + jumps = np.pad(np.diff(text_indices), (1, 0), constant_values=1).astype(bool) + jump_times = time_indices[jumps] / TOKENS_PER_SECOND + start_times = jump_times[word_boundaries[:-1]] + end_times = jump_times[word_boundaries[1:]] + word_probabilities = [ + np.mean(text_token_probs[i:j]) for i, j in zip(word_boundaries[:-1], word_boundaries[1:]) + ] + + # hack: ensure the first and second word is not longer than twice the median word duration. + # a better segmentation algorithm based on VAD should be able to replace this. + word_durations = end_times - start_times + word_durations = word_durations[word_durations.nonzero()] + if len(word_durations) > 0: + median_duration = np.median(word_durations) + max_duration = median_duration * 2 + if len(word_durations) >= 2 and word_durations[1] > max_duration: + end_times[0] = start_times[1] = max(end_times[2] / 2, end_times[2] - max_duration) + if len(word_durations) >= 1 and end_times[0] - start_times[0] > max_duration: + start_times[0] = max(0, end_times[0] - max_duration) + + return [ + WordTiming(word, tokens, start, end, probability) + for word, tokens, start, end, probability in zip( + words, word_tokens, start_times, end_times, word_probabilities + ) + ] + + +def merge_punctuations(alignment: List[WordTiming], prepended: str, appended: str): + # merge prepended punctuations + i = len(alignment) - 2 + j = len(alignment) - 1 + while i >= 0: + previous = alignment[i] + following = alignment[j] + if previous.word.startswith(" ") and previous.word.strip() in prepended: + # prepend it to the following word + following.word = previous.word + following.word + following.tokens = previous.tokens + following.tokens + previous.word = "" + previous.tokens = [] + else: + j = i + i -= 1 + + # merge appended punctuations + i = 0 + j = 1 + while j < len(alignment): + previous = alignment[i] + following = alignment[j] + if not previous.word.endswith(" ") and following.word in appended: + # append it to the previous word + previous.word = previous.word + following.word + previous.tokens = previous.tokens + following.tokens + following.word = "" + following.tokens = [] + else: + i = j + j += 1 + + +def add_word_timestamps( + *, + segments: List[dict], + model: "Whisper", + tokenizer: Tokenizer, + mel: torch.Tensor, + num_frames: int, + prepend_punctuations: str = "\"\'“¿([{-", + append_punctuations: str = "\"\'.。,，!！?？:：”)]}、", + **hyperparams, +): + if len(segments) == 0: + return + + text_tokens = [t for segment in segments for t in segment["tokens"]] + alignment = find_alignment(model, tokenizer, text_tokens, mel, num_frames, **hyperparams) + merge_punctuations(alignment, prepend_punctuations, append_punctuations) + + time_offset = segments[0]["seek"] * HOP_LENGTH / SAMPLE_RATE + token_sources = np.repeat(np.arange(len(segments)), [len(s["tokens"]) for s in segments]) + + for segment in segments: + segment["words"] = [] + + word_boundaries = np.pad(np.cumsum([len(w.tokens) for w in alignment]), (1, 0)) + for i, timing in enumerate(alignment): + if timing.word: + segment = segments[token_sources[word_boundaries[i]]] + start = round(time_offset + timing.start, 2) + end = round(time_offset + timing.end, 2) + segment["words"].append( + dict(word=timing.word, start=start, end=end, probability=timing.probability) + ) + + for segment in segments: + if len(words := segment["words"]) > 0: + # adjust the segment-level timestamps based on the word-level timestamps + segment["start"] = words[0]["start"] + segment["end"] = words[-1]["end"] diff --git a/whisper/tokenizer.py b/whisper/tokenizer.py index 7efa2d4af..ea1117f76 100644 --- a/whisper/tokenizer.py +++ b/whisper/tokenizer.py @@ -1,4 +1,5 @@ import os +import string from dataclasses import dataclass from functools import lru_cache, cached_property from typing import List, Optional, Tuple, Union @@ -265,6 +266,48 @@ def _get_single_token_id(self, text) -> int: assert len(tokens) == 1, f"{text} is not encoded as a single token" return tokens[0] + def split_to_word_tokens(self, tokens: List[int]): + if self.language in {"zh", "ja", "th", "lo", "my"}: + # These languages don't typically use spaces, so it is difficult to split words + # without morpheme analysis. Here, we instead split words at any + # position where the tokens are decoded as valid unicode points + return self.split_tokens_on_unicode(tokens) + + return self.split_tokens_on_spaces(tokens) + + def split_tokens_on_unicode(self, tokens: List[int]): + words = [] + word_tokens = [] + current_tokens = [] + + for token in tokens: + current_tokens.append(token) + decoded = self.decode_with_timestamps(current_tokens) + if "\ufffd" not in decoded: + words.append(decoded) + word_tokens.append(current_tokens) + current_tokens = [] + + return words, word_tokens + + def split_tokens_on_spaces(self, tokens: List[int]): + subwords, subword_tokens_list = self.split_tokens_on_unicode(tokens) + words = [] + word_tokens = [] + + for subword, subword_tokens in zip(subwords, subword_tokens_list): + special = subword_tokens[0] >= self.eot + with_space = subword.startswith(" ") + punctuation = subword.strip() in string.punctuation + if special or with_space or punctuation or len(words) == 0: + words.append(subword) + word_tokens.append(subword_tokens) + else: + words[-1] = words[-1] + subword + word_tokens[-1].extend(subword_tokens) + + return words, word_tokens + @lru_cache(maxsize=None) def build_tokenizer(name: str = "gpt2"): diff --git a/whisper/transcribe.py b/whisper/transcribe.py index d155b20df..d7c048749 100644 --- a/whisper/transcribe.py +++ b/whisper/transcribe.py @@ -7,8 +7,9 @@ import torch import tqdm -from .audio import SAMPLE_RATE, N_FRAMES, HOP_LENGTH, pad_or_trim, log_mel_spectrogram +from .audio import HOP_LENGTH, N_FRAMES, SAMPLE_RATE, FRAMES_PER_SECOND, log_mel_spectrogram, pad_or_trim from .decoding import DecodingOptions, DecodingResult +from .timing import add_word_timestamps from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer from .utils import exact_div, format_timestamp, make_safe, optional_int, optional_float, str2bool, get_writer @@ -27,6 +28,9 @@ def transcribe( no_speech_threshold: Optional[float] = 0.6, condition_on_previous_text: bool = True, initial_prompt: Optional[str] = None, + word_timestamps: bool = False, + prepend_punctuations: str = "\"\'“¿([{-", + append_punctuations: str = "\"\'.。,，!！?？:：”)]}、", **decode_options, ): """ @@ -63,6 +67,21 @@ def transcribe( disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop, such as repetition looping or timestamps going out of sync. + word_timestamps: bool + Extract word-level timestamps using the cross-attention pattern and dynamic time warping, + and include the timestamps for each word in each segment. + + prepend_punctuations: str + If word_timestamps is True, merge these punctuation symbols with the next word + + append_punctuations: str + If word_timestamps is True, merge these punctuation symbols with the previous word + + initial_prompt: Optional[str] + Optional text to provide as a prompt for the first window. This can be used to provide, or + "prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns + to make it more likely to predict those word correctly. + decode_options: dict Keyword arguments to construct `DecodingOptions` instances @@ -90,16 +109,19 @@ def transcribe( else: if verbose: print("Detecting language using up to the first 30 seconds. Use `--language` to specify the language") - segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype) - _, probs = model.detect_language(segment) + mel_segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype) + _, probs = model.detect_language(mel_segment) decode_options["language"] = max(probs, key=probs.get) if verbose is not None: print(f"Detected language: {LANGUAGES[decode_options['language']].title()}") - language = decode_options["language"] - task = decode_options.get("task", "transcribe") + language: str = decode_options["language"] + task: str = decode_options.get("task", "transcribe") tokenizer = get_tokenizer(model.is_multilingual, language=language, task=task) + if word_timestamps and task == "translate": + warnings.warn("Word-level timestamps on translations may not be reliable.") + def decode_with_fallback(segment: torch.Tensor) -> DecodingResult: temperatures = [temperature] if isinstance(temperature, (int, float)) else temperature decode_result = None @@ -145,42 +167,35 @@ def decode_with_fallback(segment: torch.Tensor) -> DecodingResult: else: initial_prompt_tokens = [] - def add_segment( - *, start: float, end: float, text_tokens: torch.Tensor, result: DecodingResult + def new_segment( + *, start: float, end: float, tokens: torch.Tensor, result: DecodingResult ): - text = tokenizer.decode([token for token in text_tokens if token < tokenizer.eot]) - if len(text.strip()) == 0: # skip empty text output - return - - all_segments.append( - { - "id": len(all_segments), - "seek": seek, - "start": start, - "end": end, - "text": text, - "tokens": text_tokens.tolist(), - "temperature": result.temperature, - "avg_logprob": result.avg_logprob, - "compression_ratio": result.compression_ratio, - "no_speech_prob": result.no_speech_prob, - } - ) - if verbose: - print(make_safe(f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}")) - - # show the progress bar when verbose is False (otherwise the transcribed text will be printed) + text_tokens = [token for token in tokens.tolist() if token < tokenizer.eot] + return { + "id": len(all_segments), + "seek": seek, + "start": start, + "end": end, + "text": tokenizer.decode(text_tokens), + "tokens": text_tokens, + "temperature": result.temperature, + "avg_logprob": result.avg_logprob, + "compression_ratio": result.compression_ratio, + "no_speech_prob": result.no_speech_prob, + } + + # show the progress bar when verbose is False (if True, transcribed text will be printed) num_frames = mel.shape[-1] - previous_seek_value = seek - with tqdm.tqdm(total=num_frames, unit='frames', disable=verbose is not False) as pbar: while seek < num_frames: - timestamp_offset = float(seek * HOP_LENGTH / SAMPLE_RATE) - segment = pad_or_trim(mel[:, seek:], N_FRAMES).to(model.device).to(dtype) - segment_duration = segment.shape[-1] * HOP_LENGTH / SAMPLE_RATE + time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE) + mel_segment = mel[:, seek:] + segment_size = min(mel_segment.shape[-1], N_FRAMES) + segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE + mel_segment = pad_or_trim(mel_segment, N_FRAMES).to(model.device).to(dtype) decode_options["prompt"] = all_tokens[prompt_reset_since:] - result: DecodingResult = decode_with_fallback(segment) + result: DecodingResult = decode_with_fallback(mel_segment) tokens = torch.tensor(result.tokens) if no_speech_threshold is not None: @@ -191,29 +206,36 @@ def add_segment( should_skip = False if should_skip: - seek += segment.shape[-1] # fast-forward to the next segment boundary + seek += segment_size # fast-forward to the next segment boundary continue + previous_seek = seek + current_segments = [] + current_tokens = [] + timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin) consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0].add_(1) if len(consecutive) > 0: # if the output contains two consecutive timestamp tokens if ended_with_single_timestamp := timestamp_tokens[-2:].tolist() == [False, True]: consecutive = consecutive.tolist() + [len(tokens)] + last_slice = 0 for current_slice in consecutive: sliced_tokens = tokens[last_slice:current_slice] start_timestamp_pos = sliced_tokens[0].item() - tokenizer.timestamp_begin end_timestamp_pos = sliced_tokens[-1].item() - tokenizer.timestamp_begin - add_segment( - start=timestamp_offset + start_timestamp_pos * time_precision, - end=timestamp_offset + end_timestamp_pos * time_precision, - text_tokens=sliced_tokens[1:-1], + current_segments.append(new_segment( + start=time_offset + start_timestamp_pos * time_precision, + end=time_offset + end_timestamp_pos * time_precision, + tokens=sliced_tokens, result=result, - ) + )) + current_tokens.append(sliced_tokens.tolist()) last_slice = current_slice + if ended_with_single_timestamp: # single timestamp at the end means no speech after the last timestamp. - seek += segment.shape[-1] + seek += segment_size else: # otherwise, ignore the unfinished segment and seek to the last timestamp last_timestamp_pos = tokens[last_slice - 1].item() - tokenizer.timestamp_begin @@ -227,23 +249,54 @@ def add_segment( last_timestamp_pos = timestamps[-1].item() - tokenizer.timestamp_begin duration = last_timestamp_pos * time_precision - add_segment( - start=timestamp_offset, - end=timestamp_offset + duration, - text_tokens=tokens, + current_segments.append(new_segment( + start=time_offset, + end=time_offset + duration, + tokens=tokens, result=result, - ) - - seek += segment.shape[-1] - all_tokens.extend(tokens.tolist()) + )) + current_tokens.append(tokens.tolist()) + seek += segment_size if not condition_on_previous_text or result.temperature > 0.5: # do not feed the prompt tokens if a high temperature was used prompt_reset_since = len(all_tokens) + if word_timestamps: + add_word_timestamps( + segments=current_segments, + model=model, + tokenizer=tokenizer, + mel=mel_segment, + num_frames=segment_size, + prepend_punctuations=prepend_punctuations, + append_punctuations=append_punctuations, + ) + word_end_timestamps = [w["end"] for s in current_segments for w in s["words"]] + if len(consecutive) > 0 and len(word_end_timestamps) > 0: + seek_shift = round((word_end_timestamps[-1] - time_offset) * FRAMES_PER_SECOND) + if seek_shift > 0: + seek = previous_seek + seek_shift + + if verbose: + for segment in current_segments: + start, end, text = segment["start"], segment["end"], segment["text"] + line = f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}" + print(make_safe(line)) + + # if a segment is instantaneous or does not contain text, clear it + for i, segment in enumerate(current_segments): + if segment["start"] == segment["end"] or segment["text"].strip() == "": + segment["text"] = "" + segment["tokens"] = [] + segment["words"] = [] + current_tokens[i] = [] + + all_segments.extend(current_segments) + all_tokens.extend([token for segment in current_tokens for token in segment]) + # update progress bar - pbar.update(min(num_frames, seek) - previous_seek_value) - previous_seek_value = seek + pbar.update(min(num_frames, seek) - previous_seek) return dict( text=tokenizer.decode(all_tokens[len(initial_prompt_tokens):]), @@ -282,6 +335,9 @@ def cli(): parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed") parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed") parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence") + parser.add_argument("--word_timestamps", type=str2bool, default=False, help="(experimental) extract word-level timestamps and refine the results based on them") + parser.add_argument("--prepend_punctuations", type=str, default="\"\'“¿([{-", help="if word_timestamps is True, merge these punctuation symbols with the next word") + parser.add_argument("--append_punctuations", type=str, default="\"\'.。,，!！?？:：”)]}、", help="if word_timestamps is True, merge these punctuation symbols with the previous word") parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS") args = parser.parse_args().__dict__ diff --git a/whisper/triton_ops.py b/whisper/triton_ops.py new file mode 100644 index 000000000..d829e204a --- /dev/null +++ b/whisper/triton_ops.py @@ -0,0 +1,92 @@ +import math + +import numpy as np +import torch +from functools import lru_cache + +try: + import triton + import triton.language as tl +except ImportError: + raise RuntimeError("triton import failed; try `pip install --pre triton`") + + +@triton.jit +def dtw_kernel(cost, trace, x, x_stride, cost_stride, trace_stride, N, M, BLOCK_SIZE: tl.constexpr): + offsets = tl.arange(0, BLOCK_SIZE) + mask = offsets < M + + for k in range(1, N + M + 1): # k = i + j + tl.debug_barrier() + + p0 = cost + (k - 1) * cost_stride + p1 = cost + k * cost_stride + p2 = cost + k * cost_stride + 1 + + c0 = tl.load(p0 + offsets, mask=mask) + c1 = tl.load(p1 + offsets, mask=mask) + c2 = tl.load(p2 + offsets, mask=mask) + + x_row = tl.load(x + (k - 1) * x_stride + offsets, mask=mask, other=0) + cost_row = x_row + tl.minimum(tl.minimum(c0, c1), c2) + + cost_ptr = cost + (k + 1) * cost_stride + 1 + tl.store(cost_ptr + offsets, cost_row, mask=mask) + + trace_ptr = trace + (k + 1) * trace_stride + 1 + tl.store(trace_ptr + offsets, 2, mask=mask & (c2 <= c0) & (c2 <= c1)) + tl.store(trace_ptr + offsets, 1, mask=mask & (c1 <= c0) & (c1 <= c2)) + tl.store(trace_ptr + offsets, 0, mask=mask & (c0 <= c1) & (c0 <= c2)) + + +@lru_cache(maxsize=None) +def median_kernel(filter_width: int): + @triton.jit + def kernel(y, x, x_stride, y_stride, BLOCK_SIZE: tl.constexpr): # x.shape[-1] == filter_width + row_idx = tl.program_id(0) + offsets = tl.arange(0, BLOCK_SIZE) + mask = offsets < y_stride + + x_ptr = x + row_idx * x_stride + y_ptr = y + row_idx * y_stride + + LOAD_ALL_ROWS_HERE + + BUBBLESORT_HERE + + tl.store(y_ptr + offsets, MIDDLE_ROW_HERE, mask=mask) + + kernel = triton.JITFunction(kernel.fn) + kernel.src = kernel.src.replace(" LOAD_ALL_ROWS_HERE", "\n".join([ + f" row{i} = tl.load(x_ptr + offsets + {i}, mask=mask)" + for i in range(filter_width) + ])) + kernel.src = kernel.src.replace(" BUBBLESORT_HERE", "\n\n".join([ + "\n\n".join([ + "\n".join([ + f" smaller = tl.where(row{j} < row{j + 1}, row{j}, row{j + 1})", + f" larger = tl.where(row{j} > row{j + 1}, row{j}, row{j + 1})", + f" row{j} = smaller", + f" row{j + 1} = larger", + ]) + for j in range(filter_width - i - 1) + ]) + for i in range(filter_width // 2 + 1) + ])) + kernel.src = kernel.src.replace("MIDDLE_ROW_HERE", f"row{filter_width // 2}") + + return kernel + + +def median_filter_cuda(x: torch.Tensor, filter_width: int): + """Apply a median filter of given width along the last dimension of x""" + slices = x.contiguous().unfold(-1, filter_width, 1) + grid = np.prod(slices.shape[:-2]) + + kernel = median_kernel(filter_width) + y = torch.empty_like(slices[..., 0]) + + BLOCK_SIZE = 1 << (y.stride(-2) - 1).bit_length() + kernel[(grid,)](y, x, x.stride(-2), y.stride(-2), BLOCK_SIZE=BLOCK_SIZE) + + return y diff --git a/whisper/utils.py b/whisper/utils.py index 5dacc173c..8ee912932 100644 --- a/whisper/utils.py +++ b/whisper/utils.py @@ -85,34 +85,63 @@ def write_result(self, result: dict, file: TextIO): print(segment['text'].strip(), file=file, flush=True) -class WriteVTT(ResultWriter): +class SubtitlesWriter(ResultWriter): + always_include_hours: bool + decimal_marker: str + + def iterate_result(self, result: dict): + for segment in result["segments"]: + segment_start = self.format_timestamp(segment["start"]) + segment_end = self.format_timestamp(segment["end"]) + segment_text = segment['text'].strip().replace('-->', '->') + + if word_timings := segment.get("words", None): + all_words = [timing["word"] for timing in word_timings] + all_words[0] = all_words[0].strip() # remove the leading space, if any + last = segment_start + for i, this_word in enumerate(word_timings): + start = self.format_timestamp(this_word["start"]) + end = self.format_timestamp(this_word["end"]) + if last != start: + yield last, start, segment_text + + yield start, end, "".join( + [f"{word}" if j == i else word for j, word in enumerate(all_words)] + ) + last = end + + if last != segment_end: + yield last, segment_end, segment_text + else: + yield segment_start, segment_end, segment_text + + def format_timestamp(self, seconds: float): + return format_timestamp( + seconds=seconds, + always_include_hours=self.always_include_hours, + decimal_marker=self.decimal_marker, + ) + + +class WriteVTT(SubtitlesWriter): extension: str = "vtt" + always_include_hours: bool = False + decimal_marker: str = '.' def write_result(self, result: dict, file: TextIO): print("WEBVTT\n", file=file) - for segment in result["segments"]: - print( - f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n" - f"{segment['text'].strip().replace('-->', '->')}\n", - file=file, - flush=True, - ) + for start, end, text in self.iterate_result(result): + print(f"{start} --> {end}\n{text}\n", file=file, flush=True) -class WriteSRT(ResultWriter): +class WriteSRT(SubtitlesWriter): extension: str = "srt" + always_include_hours: bool = True + decimal_marker: str = ',' def write_result(self, result: dict, file: TextIO): - for i, segment in enumerate(result["segments"], start=1): - # write srt lines - print( - f"{i}\n" - f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> " - f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n" - f"{segment['text'].strip().replace('-->', '->')}\n", - file=file, - flush=True, - ) + for i, (start, end, text) in enumerate(self.iterate_result(result), start=1): + print(f"{i}\n{start} --> {end}\n{text}\n", file=file, flush=True) class WriteTSV(ResultWriter): From b80bcf610d89960bc658b61af9c333fc6d978d78 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Mon, 6 Mar 2023 18:50:37 -0500 Subject: [PATCH 17/69] apply formatting with `black` (#1038) * applying black (with the default 88-column limit) * add flake8 * add isort * fix isort --- .flake8 | 4 + .github/workflows/test.yml | 3 + pyproject.toml | 8 ++ setup.py | 12 +- tests/test_audio.py | 2 +- tests/test_normalizer.py | 5 +- tests/test_timing.py | 21 +++- tests/test_transcribe.py | 4 +- whisper/__init__.py | 50 ++++---- whisper/__main__.py | 1 - whisper/audio.py | 20 +++- whisper/decoding.py | 198 +++++++++++++++++++++++--------- whisper/model.py | 74 ++++++++---- whisper/normalizers/__init__.py | 4 +- whisper/normalizers/basic.py | 11 +- whisper/normalizers/english.py | 21 ++-- whisper/timing.py | 50 +++++--- whisper/tokenizer.py | 28 +++-- whisper/transcribe.py | 143 ++++++++++++++++------- whisper/triton_ops.py | 65 +++++++---- whisper/utils.py | 36 ++++-- 21 files changed, 533 insertions(+), 227 deletions(-) create mode 100644 .flake8 create mode 100644 pyproject.toml diff --git a/.flake8 b/.flake8 new file mode 100644 index 000000000..c61c928fa --- /dev/null +++ b/.flake8 @@ -0,0 +1,4 @@ +[flake8] +per-file-ignores = + */__init__.py: F401 + diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index f06bff79a..bca49b575 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -22,4 +22,7 @@ jobs: - uses: actions/checkout@v2 - run: echo "$CONDA/envs/test/bin" >> $GITHUB_PATH - run: pip install .["dev"] + - run: black --check --diff -t py38 --include '(\.pyi?)$' . + - run: isort --check --diff . + - run: flake8 --ignore E203,W503,W504,E501,E731,E741 . - run: pytest --durations=0 -vv -k 'not test_transcribe or test_transcribe[tiny] or test_transcribe[tiny.en]' -m 'not requires_cuda' diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 000000000..84637eb2e --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,8 @@ +[tool.black] + +[tool.isort] +profile = "black" +include_trailing_comma = true +line_length = 88 +multi_line_output = 3 + diff --git a/setup.py b/setup.py index a548c8d35..736c1ac67 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ import sys import pkg_resources -from setuptools import setup, find_packages +from setuptools import find_packages, setup def read_version(fname="whisper/version.py"): @@ -16,7 +16,10 @@ def read_version(fname="whisper/version.py"): try: import re import subprocess - version_line = subprocess.check_output(["nvcc", "--version"]).strip().split(b"\n")[-1] + + version_line = ( + subprocess.check_output(["nvcc", "--version"]).strip().split(b"\n")[-1] + ) major, minor = re.findall(rb"([\d]+)\.([\d]+)", version_line)[0] if (int(major), int(minor)) < (11, 4): # the last version supporting CUDA < 11.4 @@ -38,7 +41,8 @@ def read_version(fname="whisper/version.py"): url="https://github.com/openai/whisper", license="MIT", packages=find_packages(exclude=["tests*"]), - install_requires=requirements + [ + install_requires=requirements + + [ str(r) for r in pkg_resources.parse_requirements( open(os.path.join(os.path.dirname(__file__), "requirements.txt")) @@ -48,5 +52,5 @@ def read_version(fname="whisper/version.py"): "console_scripts": ["whisper=whisper.transcribe:cli"], }, include_package_data=True, - extras_require={"dev": ["pytest", "scipy"]}, + extras_require={"dev": ["pytest", "scipy", "black", "flake8", "isort"]}, ) diff --git a/tests/test_audio.py b/tests/test_audio.py index ad6a6dacb..dfd78bc09 100644 --- a/tests/test_audio.py +++ b/tests/test_audio.py @@ -2,7 +2,7 @@ import numpy as np -from whisper.audio import load_audio, log_mel_spectrogram, SAMPLE_RATE +from whisper.audio import SAMPLE_RATE, load_audio, log_mel_spectrogram def test_audio(): diff --git a/tests/test_normalizer.py b/tests/test_normalizer.py index 3785e6cab..3bc65744c 100644 --- a/tests/test_normalizer.py +++ b/tests/test_normalizer.py @@ -1,7 +1,10 @@ import pytest from whisper.normalizers import EnglishTextNormalizer -from whisper.normalizers.english import EnglishNumberNormalizer, EnglishSpellingNormalizer +from whisper.normalizers.english import ( + EnglishNumberNormalizer, + EnglishSpellingNormalizer, +) @pytest.mark.parametrize("std", [EnglishNumberNormalizer(), EnglishTextNormalizer()]) diff --git a/tests/test_timing.py b/tests/test_timing.py index 50a2583f6..9bab838e8 100644 --- a/tests/test_timing.py +++ b/tests/test_timing.py @@ -1,16 +1,21 @@ -import pytest import numpy as np +import pytest import scipy.ndimage import torch from whisper.timing import dtw_cpu, dtw_cuda, median_filter - sizes = [ - (10, 20), (32, 16), (123, 1500), (234, 189), + (10, 20), + (32, 16), + (123, 1500), + (234, 189), ] shapes = [ - (10,), (1, 15), (4, 5, 345), (6, 12, 240, 512), + (10,), + (1, 15), + (4, 5, 345), + (6, 12, 240, 512), ] @@ -68,8 +73,12 @@ def test_median_filter(shape): # using np.pad to reflect-pad, because Scipy's behavior is different near the edges. pad_width = filter_width // 2 - padded_x = np.pad(x, [(0, 0)] * (x.ndim - 1) + [(pad_width, pad_width)], mode="reflect") - scipy_filtered = scipy.ndimage.median_filter(padded_x, [1] * (x.ndim - 1) + [filter_width]) + padded_x = np.pad( + x, [(0, 0)] * (x.ndim - 1) + [(pad_width, pad_width)], mode="reflect" + ) + scipy_filtered = scipy.ndimage.median_filter( + padded_x, [1] * (x.ndim - 1) + [filter_width] + ) scipy_filtered = scipy_filtered[..., pad_width:-pad_width] assert np.allclose(filtered, scipy_filtered) diff --git a/tests/test_transcribe.py b/tests/test_transcribe.py index 9802f734b..e5d530783 100644 --- a/tests/test_transcribe.py +++ b/tests/test_transcribe.py @@ -13,7 +13,9 @@ def test_transcribe(model_name: str): audio_path = os.path.join(os.path.dirname(__file__), "jfk.flac") language = "en" if model_name.endswith(".en") else None - result = model.transcribe(audio_path, language=language, temperature=0.0, word_timestamps=True) + result = model.transcribe( + audio_path, language=language, temperature=0.0, word_timestamps=True + ) assert result["language"] == "en" transcription = result["text"].lower() diff --git a/whisper/__init__.py b/whisper/__init__.py index 26d1e0eaf..379133b6a 100644 --- a/whisper/__init__.py +++ b/whisper/__init__.py @@ -10,11 +10,10 @@ from .audio import load_audio, log_mel_spectrogram, pad_or_trim from .decoding import DecodingOptions, DecodingResult, decode, detect_language -from .model import Whisper, ModelDimensions +from .model import ModelDimensions, Whisper from .transcribe import transcribe from .version import __version__ - _MODELS = { "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt", "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt", @@ -41,12 +40,11 @@ "medium.en": b"ABzY8usPae0{>%R7%R7}kK1fFL7w6%<-Pf*t^=N)Qr&0RR9", "large-v1": b"ABzY8r9j$a0{>%R7#4sLmoOs{s)o3~84-RPdcFk!JR%R7=D0pU<_bnWW*tkYAhobTNnu$jnkEkXqp)j;w1Tzk)UH3X%SZd&fFZ2fC2yj', - "large": b'ABzY8zd+h!0{>%R7=D0pU<_bnWW*tkYAhobTNnu$jnkEkXqp)j;w1Tzk)UH3X%SZd&fFZ2fC2yj', + "large-v2": b"ABzY8zd+h!0{>%R7=D0pU<_bnWW*tkYAhobTNnu$jnkEkXqp)j;w1Tzk)UH3X%SZd&fFZ2fC2yj", + "large": b"ABzY8zd+h!0{>%R7=D0pU<_bnWW*tkYAhobTNnu$jnkEkXqp)j;w1Tzk)UH3X%SZd&fFZ2fC2yj", } - def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]: os.makedirs(root, exist_ok=True) @@ -62,10 +60,18 @@ def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]: if hashlib.sha256(model_bytes).hexdigest() == expected_sha256: return model_bytes if in_memory else download_target else: - warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") + warnings.warn( + f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" + ) with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: - with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop: + with tqdm( + total=int(source.info().get("Content-Length")), + ncols=80, + unit="iB", + unit_scale=True, + unit_divisor=1024, + ) as loop: while True: buffer = source.read(8192) if not buffer: @@ -76,7 +82,9 @@ def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]: model_bytes = open(download_target, "rb").read() if hashlib.sha256(model_bytes).hexdigest() != expected_sha256: - raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.") + raise RuntimeError( + "Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model." + ) return model_bytes if in_memory else download_target @@ -86,7 +94,12 @@ def available_models() -> List[str]: return list(_MODELS.keys()) -def load_model(name: str, device: Optional[Union[str, torch.device]] = None, download_root: str = None, in_memory: bool = False) -> Whisper: +def load_model( + name: str, + device: Optional[Union[str, torch.device]] = None, + download_root: str = None, + in_memory: bool = False, +) -> Whisper: """ Load a Whisper ASR model @@ -111,15 +124,8 @@ def load_model(name: str, device: Optional[Union[str, torch.device]] = None, dow if device is None: device = "cuda" if torch.cuda.is_available() else "cpu" if download_root is None: - download_root = os.path.join( - os.getenv( - "XDG_CACHE_HOME", - os.path.join( - os.path.expanduser("~"), ".cache" - ) - ), - "whisper" - ) + default = os.path.join(os.path.expanduser("~"), ".cache") + download_root = os.path.join(os.getenv("XDG_CACHE_HOME", default), "whisper") if name in _MODELS: checkpoint_file = _download(_MODELS[name], download_root, in_memory) @@ -128,9 +134,13 @@ def load_model(name: str, device: Optional[Union[str, torch.device]] = None, dow checkpoint_file = open(name, "rb").read() if in_memory else name alignment_heads = None else: - raise RuntimeError(f"Model {name} not found; available models = {available_models()}") + raise RuntimeError( + f"Model {name} not found; available models = {available_models()}" + ) - with (io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb")) as fp: + with ( + io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb") + ) as fp: checkpoint = torch.load(fp, map_location=device) del checkpoint_file diff --git a/whisper/__main__.py b/whisper/__main__.py index bc9b04a39..d14f2058e 100644 --- a/whisper/__main__.py +++ b/whisper/__main__.py @@ -1,4 +1,3 @@ from .transcribe import cli - cli() diff --git a/whisper/audio.py b/whisper/audio.py index 964d41579..a19b7ab0d 100644 --- a/whisper/audio.py +++ b/whisper/audio.py @@ -16,11 +16,13 @@ HOP_LENGTH = 160 CHUNK_LENGTH = 30 N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000: number of samples in a chunk -N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH) # 3000: number of frames in a mel spectrogram input +N_FRAMES = exact_div( + N_SAMPLES, HOP_LENGTH +) # 3000: number of frames in a mel spectrogram input N_SAMPLES_PER_TOKEN = HOP_LENGTH * 2 # the initial convolutions has stride 2 -FRAMES_PER_SECOND = exact_div(SAMPLE_RATE, HOP_LENGTH) # 100 mel frames in 1s (10ms each) -TOKENS_PER_SECOND = exact_div(SAMPLE_RATE, N_SAMPLES_PER_TOKEN) # 50 audio tokens in 1s (20ms each) +FRAMES_PER_SECOND = exact_div(SAMPLE_RATE, HOP_LENGTH) # 10ms per audio frame +TOKENS_PER_SECOND = exact_div(SAMPLE_RATE, N_SAMPLES_PER_TOKEN) # 20ms per audio token def load_audio(file: str, sr: int = SAMPLE_RATE): @@ -59,7 +61,9 @@ def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1): """ if torch.is_tensor(array): if array.shape[axis] > length: - array = array.index_select(dim=axis, index=torch.arange(length, device=array.device)) + array = array.index_select( + dim=axis, index=torch.arange(length, device=array.device) + ) if array.shape[axis] < length: pad_widths = [(0, 0)] * array.ndim @@ -89,11 +93,15 @@ def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor: ) """ assert n_mels == 80, f"Unsupported n_mels: {n_mels}" - with np.load(os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")) as f: + with np.load( + os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz") + ) as f: return torch.from_numpy(f[f"mel_{n_mels}"]).to(device) -def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS): +def log_mel_spectrogram( + audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS +): """ Compute the log-Mel spectrogram of diff --git a/whisper/decoding.py b/whisper/decoding.py index 7c51f251c..ff9261e04 100644 --- a/whisper/decoding.py +++ b/whisper/decoding.py @@ -1,5 +1,5 @@ from dataclasses import dataclass, field -from typing import Dict, List, Tuple, Iterable, Optional, Sequence, Union, TYPE_CHECKING +from typing import TYPE_CHECKING, Dict, Iterable, List, Optional, Sequence, Tuple, Union import numpy as np import torch @@ -16,7 +16,9 @@ @torch.no_grad() -def detect_language(model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None) -> Tuple[Tensor, List[dict]]: +def detect_language( + model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None +) -> Tuple[Tensor, List[dict]]: """ Detect the spoken language in the audio, and return them as list of strings, along with the ids of the most probable language tokens and the probability distribution over all language tokens. @@ -31,8 +33,13 @@ def detect_language(model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None) """ if tokenizer is None: tokenizer = get_tokenizer(model.is_multilingual) - if tokenizer.language is None or tokenizer.language_token not in tokenizer.sot_sequence: - raise ValueError(f"This model doesn't have language tokens so it can't perform lang id") + if ( + tokenizer.language is None + or tokenizer.language_token not in tokenizer.sot_sequence + ): + raise ValueError( + "This model doesn't have language tokens so it can't perform lang id" + ) single = mel.ndim == 2 if single: @@ -70,31 +77,36 @@ def detect_language(model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None) @dataclass(frozen=True) class DecodingOptions: - task: str = "transcribe" # whether to perform X->X "transcribe" or X->English "translate" - language: Optional[str] = None # language that the audio is in; uses detected language if None + # whether to perform X->X "transcribe" or X->English "translate" + task: str = "transcribe" + + # language that the audio is in; uses detected language if None + language: Optional[str] = None # sampling-related options temperature: float = 0.0 sample_len: Optional[int] = None # maximum number of tokens to sample - best_of: Optional[int] = None # number of independent samples to collect, when t > 0 - beam_size: Optional[int] = None # number of beams in beam search, when t == 0 - patience: Optional[float] = None # patience in beam search (https://arxiv.org/abs/2204.05424) + best_of: Optional[int] = None # number of independent sample trajectories, if t > 0 + beam_size: Optional[int] = None # number of beams in beam search, if t == 0 + patience: Optional[float] = None # patience in beam search (arxiv:2204.05424) - # options for ranking generations (either beams or best-of-N samples) - length_penalty: Optional[float] = None # "alpha" in Google NMT, None defaults to length norm + # "alpha" in Google NMT, or None for length norm, when ranking generations + # to select which to return among the beams or best-of-N samples + length_penalty: Optional[float] = None - # prompt, prefix, and token suppression - prompt: Optional[Union[str, List[int]]] = None # text or tokens for the previous context - prefix: Optional[Union[str, List[int]]] = None # text or tokens to prefix the current context - suppress_blank: bool = True # this will suppress blank outputs + # text or tokens to feed as the prompt or the prefix; for more info: + # https://github.com/openai/whisper/discussions/117#discussioncomment-3727051 + prompt: Optional[Union[str, List[int]]] = None # for the previous context + prefix: Optional[Union[str, List[int]]] = None # to prefix the current context # list of tokens ids (or comma-separated token ids) to suppress # "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()` suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1" + suppress_blank: bool = True # this will suppress blank outputs # timestamp sampling options - without_timestamps: bool = False # use <|notimestamps|> to sample text tokens only - max_initial_timestamp: Optional[float] = 1.0 # the initial timestamp cannot be later than this + without_timestamps: bool = False # use <|notimestamps|> to sample text tokens only + max_initial_timestamp: Optional[float] = 1.0 # implementation details fp16: bool = True # use fp16 for most of the calculation @@ -158,7 +170,9 @@ def rearrange_kv_cache(self, source_indices): class SequenceRanker: - def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]) -> List[int]: + def rank( + self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]] + ) -> List[int]: """ Given a list of groups of samples and their cumulative log probabilities, return the indices of the samples in each group to select as the final result @@ -196,7 +210,9 @@ class TokenDecoder: def reset(self): """Initialize any stateful variables for decoding a new sequence""" - def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: + def update( + self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor + ) -> Tuple[Tensor, bool]: """Specify how to select the next token, based on the current trace and logits Parameters @@ -251,7 +267,9 @@ def __init__(self, temperature: float, eot: int): self.temperature = temperature self.eot = eot - def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: + def update( + self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor + ) -> Tuple[Tensor, bool]: if self.temperature == 0: next_tokens = logits.argmax(dim=-1) else: @@ -274,7 +292,13 @@ def finalize(self, tokens: Tensor, sum_logprobs: Tensor): class BeamSearchDecoder(TokenDecoder): - def __init__(self, beam_size: int, eot: int, inference: Inference, patience: Optional[float] = None): + def __init__( + self, + beam_size: int, + eot: int, + inference: Inference, + patience: Optional[float] = None, + ): self.beam_size = beam_size self.eot = eot self.inference = inference @@ -282,12 +306,16 @@ def __init__(self, beam_size: int, eot: int, inference: Inference, patience: Opt self.max_candidates: int = round(beam_size * self.patience) self.finished_sequences = None - assert self.max_candidates > 0, f"Invalid beam size ({beam_size}) or patience ({patience})" + assert ( + self.max_candidates > 0 + ), f"Invalid beam size ({beam_size}) or patience ({patience})" def reset(self): self.finished_sequences = None - def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: + def update( + self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor + ) -> Tuple[Tensor, bool]: if tokens.shape[0] % self.beam_size != 0: raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0") @@ -331,7 +359,9 @@ def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[ # add newly finished sequences to self.finished_sequences assert len(self.finished_sequences) == len(finished_sequences) - for previously_finished, newly_finished in zip(self.finished_sequences, finished_sequences): + for previously_finished, newly_finished in zip( + self.finished_sequences, finished_sequences + ): for seq in sorted(newly_finished, key=newly_finished.get, reverse=True): if len(previously_finished) >= self.max_candidates: break # the candidate list is full @@ -339,7 +369,8 @@ def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[ # mark as completed if all audio has enough number of samples completed = all( - len(sequences) >= self.max_candidates for sequences in self.finished_sequences + len(sequences) >= self.max_candidates + for sequences in self.finished_sequences ) return tokens, completed @@ -347,7 +378,9 @@ def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor): # collect all finished sequences, including patience, and add unfinished ones if not enough sum_logprobs = sum_logprobs.cpu() for i, sequences in enumerate(self.finished_sequences): - if len(sequences) < self.beam_size: # when not enough sequences are finished + if ( + len(sequences) < self.beam_size + ): # when not enough sequences are finished for j in list(np.argsort(sum_logprobs[i]))[::-1]: sequence = preceding_tokens[i, j].tolist() + [self.eot] sequences[tuple(sequence)] = sum_logprobs[i][j].item() @@ -355,7 +388,8 @@ def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor): break tokens: List[List[Tensor]] = [ - [torch.tensor(seq) for seq in sequences.keys()] for sequences in self.finished_sequences + [torch.tensor(seq) for seq in sequences.keys()] + for sequences in self.finished_sequences ] sum_logprobs: List[List[float]] = [ list(sequences.values()) for sequences in self.finished_sequences @@ -399,7 +433,10 @@ def apply(self, logits: Tensor, tokens: Tensor): class ApplyTimestampRules(LogitFilter): def __init__( - self, tokenizer: Tokenizer, sample_begin: int, max_initial_timestamp_index: Optional[int] + self, + tokenizer: Tokenizer, + sample_begin: int, + max_initial_timestamp_index: Optional[int], ): self.tokenizer = tokenizer self.sample_begin = sample_begin @@ -414,8 +451,12 @@ def apply(self, logits: Tensor, tokens: Tensor): for k in range(tokens.shape[0]): sampled_tokens = tokens[k, self.sample_begin :] seq = [t for t in sampled_tokens.tolist()] - last_was_timestamp = len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin - penultimate_was_timestamp = len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin + last_was_timestamp = ( + len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin + ) + penultimate_was_timestamp = ( + len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin + ) if last_was_timestamp: if penultimate_was_timestamp: # has to be non-timestamp @@ -423,7 +464,9 @@ def apply(self, logits: Tensor, tokens: Tensor): else: # cannot be normal text tokens logits[k, : self.tokenizer.eot] = -np.inf - timestamps = sampled_tokens[sampled_tokens.ge(self.tokenizer.timestamp_begin)] + timestamps = sampled_tokens[ + sampled_tokens.ge(self.tokenizer.timestamp_begin) + ] if timestamps.numel() > 0: # timestamps shouldn't decrease; forbid timestamp tokens smaller than the last logits[k, self.tokenizer.timestamp_begin : timestamps[-1]] = -np.inf @@ -434,13 +477,17 @@ def apply(self, logits: Tensor, tokens: Tensor): # apply the `max_initial_timestamp` option if self.max_initial_timestamp_index is not None: - last_allowed = self.tokenizer.timestamp_begin + self.max_initial_timestamp_index + last_allowed = ( + self.tokenizer.timestamp_begin + self.max_initial_timestamp_index + ) logits[:, last_allowed + 1 :] = -np.inf # if sum of probability over timestamps is above any other token, sample timestamp logprobs = F.log_softmax(logits.float(), dim=-1) for k in range(tokens.shape[0]): - timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp(dim=-1) + timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp( + dim=-1 + ) max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max() if timestamp_logprob > max_text_token_logprob: logits[k, : self.tokenizer.timestamp_begin] = -np.inf @@ -456,7 +503,9 @@ def __init__(self, model: "Whisper", options: DecodingOptions): self.model = model language = options.language or "en" - tokenizer = get_tokenizer(model.is_multilingual, language=language, task=options.task) + tokenizer = get_tokenizer( + model.is_multilingual, language=language, task=options.task + ) self.tokenizer: Tokenizer = tokenizer self.options: DecodingOptions = self._verify_options(options) @@ -496,9 +545,13 @@ def __init__(self, model: "Whisper", options: DecodingOptions): precision = CHUNK_LENGTH / model.dims.n_audio_ctx # usually 0.02 seconds max_initial_timestamp_index = None if options.max_initial_timestamp: - max_initial_timestamp_index = round(self.options.max_initial_timestamp / precision) + max_initial_timestamp_index = round( + self.options.max_initial_timestamp / precision + ) self.logit_filters.append( - ApplyTimestampRules(tokenizer, self.sample_begin, max_initial_timestamp_index) + ApplyTimestampRules( + tokenizer, self.sample_begin, max_initial_timestamp_index + ) ) def _verify_options(self, options: DecodingOptions) -> DecodingOptions: @@ -509,7 +562,9 @@ def _verify_options(self, options: DecodingOptions) -> DecodingOptions: raise ValueError("best_of with greedy sampling (T=0) is not compatible") if options.patience is not None and options.beam_size is None: raise ValueError("patience requires beam_size to be given") - if options.length_penalty is not None and not (0 <= options.length_penalty <= 1): + if options.length_penalty is not None and not ( + 0 <= options.length_penalty <= 1 + ): raise ValueError("length_penalty (alpha) should be a value between 0 and 1") return options @@ -519,7 +574,9 @@ def _get_initial_tokens(self) -> Tuple[int]: if prefix := self.options.prefix: prefix_tokens = ( - self.tokenizer.encode(" " + prefix.strip()) if isinstance(prefix, str) else prefix + self.tokenizer.encode(" " + prefix.strip()) + if isinstance(prefix, str) + else prefix ) if self.sample_len is not None: max_prefix_len = self.n_ctx // 2 - self.sample_len @@ -528,9 +585,15 @@ def _get_initial_tokens(self) -> Tuple[int]: if prompt := self.options.prompt: prompt_tokens = ( - self.tokenizer.encode(" " + prompt.strip()) if isinstance(prompt, str) else prompt + self.tokenizer.encode(" " + prompt.strip()) + if isinstance(prompt, str) + else prompt + ) + tokens = ( + [self.tokenizer.sot_prev] + + prompt_tokens[-(self.n_ctx // 2 - 1) :] + + tokens ) - tokens = [self.tokenizer.sot_prev] + prompt_tokens[-(self.n_ctx // 2 - 1) :] + tokens return tuple(tokens) @@ -554,7 +617,7 @@ def _get_suppress_tokens(self) -> Tuple[int]: self.tokenizer.translate, self.tokenizer.sot, self.tokenizer.sot_prev, - self.tokenizer.sot_lm + self.tokenizer.sot_lm, ] ) if self.tokenizer.no_speech is not None: @@ -567,14 +630,21 @@ def _get_audio_features(self, mel: Tensor): if self.options.fp16: mel = mel.half() - if mel.shape[-2:] == (self.model.dims.n_audio_ctx, self.model.dims.n_audio_state): + if mel.shape[-2:] == ( + self.model.dims.n_audio_ctx, + self.model.dims.n_audio_state, + ): # encoded audio features are given; skip audio encoding audio_features = mel else: audio_features = self.model.encoder(mel) - if audio_features.dtype != (torch.float16 if self.options.fp16 else torch.float32): - return TypeError(f"audio_features has an incorrect dtype: {audio_features.dtype}") + if audio_features.dtype != ( + torch.float16 if self.options.fp16 else torch.float32 + ): + return TypeError( + f"audio_features has an incorrect dtype: {audio_features.dtype}" + ) return audio_features @@ -583,7 +653,9 @@ def _detect_language(self, audio_features: Tensor, tokens: Tensor): lang_probs = None if self.options.language is None or self.options.task == "lang_id": - lang_tokens, lang_probs = self.model.detect_language(audio_features, self.tokenizer) + lang_tokens, lang_probs = self.model.detect_language( + audio_features, self.tokenizer + ) languages = [max(probs, key=probs.get) for probs in lang_probs] if self.options.language is None: tokens[:, self.sot_index + 1] = lang_tokens # write language tokens @@ -600,7 +672,9 @@ def _main_loop(self, audio_features: Tensor, tokens: Tensor): for i in range(self.sample_len): logits = self.inference.logits(tokens, audio_features) - if i == 0 and self.tokenizer.no_speech is not None: # save no_speech_probs + if ( + i == 0 and self.tokenizer.no_speech is not None + ): # save no_speech_probs probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1) no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist() @@ -634,8 +708,12 @@ def run(self, mel: Tensor) -> List[DecodingResult]: languages, language_probs = self._detect_language(audio_features, tokens) if self.options.task == "lang_id": return [ - DecodingResult(audio_features=features, language=language, language_probs=probs) - for features, language, probs in zip(audio_features, languages, language_probs) + DecodingResult( + audio_features=features, language=language, language_probs=probs + ) + for features, language, probs in zip( + audio_features, languages, language_probs + ) ] # repeat the audio & text tensors by the group size, for beam search or best-of-n sampling @@ -656,7 +734,8 @@ def run(self, mel: Tensor) -> List[DecodingResult]: # get the final candidates for each group, and slice between the first sampled token and EOT tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs) tokens: List[List[Tensor]] = [ - [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] for s in tokens + [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] + for s in tokens ] # select the top-ranked sample in each group @@ -665,9 +744,18 @@ def run(self, mel: Tensor) -> List[DecodingResult]: texts: List[str] = [tokenizer.decode(t).strip() for t in tokens] sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)] - avg_logprobs: List[float] = [lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs)] + avg_logprobs: List[float] = [ + lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs) + ] - fields = (texts, languages, tokens, audio_features, avg_logprobs, no_speech_probs) + fields = ( + texts, + languages, + tokens, + audio_features, + avg_logprobs, + no_speech_probs, + ) if len(set(map(len, fields))) != 1: raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}") @@ -682,12 +770,16 @@ def run(self, mel: Tensor) -> List[DecodingResult]: temperature=self.options.temperature, compression_ratio=compression_ratio(text), ) - for text, language, tokens, features, avg_logprob, no_speech_prob in zip(*fields) + for text, language, tokens, features, avg_logprob, no_speech_prob in zip( + *fields + ) ] @torch.no_grad() -def decode(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions()) -> Union[DecodingResult, List[DecodingResult]]: +def decode( + model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions() +) -> Union[DecodingResult, List[DecodingResult]]: """ Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s). diff --git a/whisper/model.py b/whisper/model.py index a1ab2e349..3457fcfc6 100644 --- a/whisper/model.py +++ b/whisper/model.py @@ -1,16 +1,15 @@ import base64 import gzip from dataclasses import dataclass -from typing import Dict -from typing import Iterable, Optional +from typing import Dict, Iterable, Optional import numpy as np import torch import torch.nn.functional as F -from torch import Tensor -from torch import nn +from torch import Tensor, nn -from .decoding import detect_language as detect_language_function, decode as decode_function +from .decoding import decode as decode_function +from .decoding import detect_language as detect_language_function from .transcribe import transcribe as transcribe_function @@ -36,12 +35,16 @@ def forward(self, x: Tensor) -> Tensor: class Linear(nn.Linear): def forward(self, x: Tensor) -> Tensor: return F.linear( - x, self.weight.to(x.dtype), None if self.bias is None else self.bias.to(x.dtype) + x, + self.weight.to(x.dtype), + None if self.bias is None else self.bias.to(x.dtype), ) class Conv1d(nn.Conv1d): - def _conv_forward(self, x: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor: + def _conv_forward( + self, x: Tensor, weight: Tensor, bias: Optional[Tensor] + ) -> Tensor: return super()._conv_forward( x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype) ) @@ -87,7 +90,9 @@ def forward( wv, qk = self.qkv_attention(q, k, v, mask) return self.out(wv), qk - def qkv_attention(self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None): + def qkv_attention( + self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None + ): n_batch, n_ctx, n_state = q.shape scale = (n_state // self.n_head) ** -0.25 q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale @@ -110,11 +115,15 @@ def __init__(self, n_state: int, n_head: int, cross_attention: bool = False): self.attn = MultiHeadAttention(n_state, n_head) self.attn_ln = LayerNorm(n_state) - self.cross_attn = MultiHeadAttention(n_state, n_head) if cross_attention else None + self.cross_attn = ( + MultiHeadAttention(n_state, n_head) if cross_attention else None + ) self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None n_mlp = n_state * 4 - self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state)) + self.mlp = nn.Sequential( + Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state) + ) self.mlp_ln = LayerNorm(n_state) def forward( @@ -132,7 +141,9 @@ def forward( class AudioEncoder(nn.Module): - def __init__(self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int): + def __init__( + self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int + ): super().__init__() self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1) self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1) @@ -163,14 +174,19 @@ def forward(self, x: Tensor): class TextDecoder(nn.Module): - def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int): + def __init__( + self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int + ): super().__init__() self.token_embedding = nn.Embedding(n_vocab, n_state) self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state)) self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( - [ResidualAttentionBlock(n_state, n_head, cross_attention=True) for _ in range(n_layer)] + [ + ResidualAttentionBlock(n_state, n_head, cross_attention=True) + for _ in range(n_layer) + ] ) self.ln = LayerNorm(n_state) @@ -185,14 +201,19 @@ def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None): the encoded audio features to be attended on """ offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0 - x = self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]] + x = ( + self.token_embedding(x) + + self.positional_embedding[offset : offset + x.shape[-1]] + ) x = x.to(xa.dtype) for block in self.blocks: x = block(x, xa, mask=self.mask, kv_cache=kv_cache) x = self.ln(x) - logits = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float() + logits = ( + x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1) + ).float() return logits @@ -216,13 +237,19 @@ def __init__(self, dims: ModelDimensions): self.dims.n_text_layer, ) # use the last half layers for alignment by default; see `set_alignment_heads()` below - all_heads = torch.zeros(self.dims.n_text_layer, self.dims.n_text_head, dtype=torch.bool) - all_heads[self.dims.n_text_layer // 2:] = True + all_heads = torch.zeros( + self.dims.n_text_layer, self.dims.n_text_head, dtype=torch.bool + ) + all_heads[self.dims.n_text_layer // 2 :] = True self.register_buffer("alignment_heads", all_heads.to_sparse(), persistent=False) def set_alignment_heads(self, dump: bytes): - array = np.frombuffer(gzip.decompress(base64.b85decode(dump)), dtype=bool).copy() - mask = torch.from_numpy(array).reshape(self.dims.n_text_layer, self.dims.n_text_head) + array = np.frombuffer( + gzip.decompress(base64.b85decode(dump)), dtype=bool + ).copy() + mask = torch.from_numpy(array).reshape( + self.dims.n_text_layer, self.dims.n_text_head + ) self.register_buffer("alignment_heads", mask.to_sparse(), persistent=False) def embed_audio(self, mel: torch.Tensor): @@ -231,7 +258,9 @@ def embed_audio(self, mel: torch.Tensor): def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor): return self.decoder(tokens, audio_features) - def forward(self, mel: torch.Tensor, tokens: torch.Tensor) -> Dict[str, torch.Tensor]: + def forward( + self, mel: torch.Tensor, tokens: torch.Tensor + ) -> Dict[str, torch.Tensor]: return self.decoder(tokens, self.encoder(mel)) @property @@ -260,8 +289,9 @@ def install_kv_cache_hooks(self, cache: Optional[dict] = None): hooks = [] def save_to_cache(module, _, output): - if module not in cache or output.shape[1] > self.decoder.positional_embedding.shape[0]: - cache[module] = output # save as-is, for the first token or cross attention + if module not in cache or output.shape[1] > self.dims.n_text_ctx: + # save as-is, for the first token or cross attention + cache[module] = output else: cache[module] = torch.cat([cache[module], output], dim=1).detach() return cache[module] diff --git a/whisper/normalizers/__init__.py b/whisper/normalizers/__init__.py index 0b10d5a9d..896d5e336 100644 --- a/whisper/normalizers/__init__.py +++ b/whisper/normalizers/__init__.py @@ -1,2 +1,2 @@ -from .basic import BasicTextNormalizer -from .english import EnglishTextNormalizer +from .basic import BasicTextNormalizer as BasicTextNormalizer +from .english import EnglishTextNormalizer as EnglishTextNormalizer diff --git a/whisper/normalizers/basic.py b/whisper/normalizers/basic.py index ef8d249bf..a82403203 100644 --- a/whisper/normalizers/basic.py +++ b/whisper/normalizers/basic.py @@ -48,13 +48,16 @@ def remove_symbols(s: str): Replace any other markers, symbols, punctuations with a space, keeping diacritics """ return "".join( - " " if unicodedata.category(c)[0] in "MSP" else c for c in unicodedata.normalize("NFKC", s) + " " if unicodedata.category(c)[0] in "MSP" else c + for c in unicodedata.normalize("NFKC", s) ) class BasicTextNormalizer: def __init__(self, remove_diacritics: bool = False, split_letters: bool = False): - self.clean = remove_symbols_and_diacritics if remove_diacritics else remove_symbols + self.clean = ( + remove_symbols_and_diacritics if remove_diacritics else remove_symbols + ) self.split_letters = split_letters def __call__(self, s: str): @@ -66,6 +69,8 @@ def __call__(self, s: str): if self.split_letters: s = " ".join(regex.findall(r"\X", s, regex.U)) - s = re.sub(r"\s+", " ", s) # replace any successive whitespace characters with a space + s = re.sub( + r"\s+", " ", s + ) # replace any successive whitespace characters with a space return s diff --git a/whisper/normalizers/english.py b/whisper/normalizers/english.py index d5c2bb4eb..4932042bc 100644 --- a/whisper/normalizers/english.py +++ b/whisper/normalizers/english.py @@ -84,7 +84,8 @@ def __init__(self): name.replace("y", "ies"): (value, "s") for name, value in self.tens.items() } self.tens_ordinal = { - name.replace("y", "ieth"): (value, "th") for name, value in self.tens.items() + name.replace("y", "ieth"): (value, "th") + for name, value in self.tens.items() } self.tens_suffixed = {**self.tens_plural, **self.tens_ordinal} @@ -108,7 +109,10 @@ def __init__(self): self.multipliers_ordinal = { name + "th": (value, "th") for name, value in self.multipliers.items() } - self.multipliers_suffixed = {**self.multipliers_plural, **self.multipliers_ordinal} + self.multipliers_suffixed = { + **self.multipliers_plural, + **self.multipliers_ordinal, + } self.decimals = {*self.ones, *self.tens, *self.zeros} self.preceding_prefixers = { @@ -128,7 +132,8 @@ def __init__(self): "cents": "¢", } self.prefixes = set( - list(self.preceding_prefixers.values()) + list(self.following_prefixers.values()) + list(self.preceding_prefixers.values()) + + list(self.following_prefixers.values()) ) self.suffixers = { "per": {"cent": "%"}, @@ -218,7 +223,9 @@ def output(result: Union[str, int]): if value is None: value = ones elif isinstance(value, str) or prev in self.ones: - if prev in self.tens and ones < 10: # replace the last zero with the digit + if ( + prev in self.tens and ones < 10 + ): # replace the last zero with the digit assert value[-1] == "0" value = value[:-1] + str(ones) else: @@ -522,14 +529,14 @@ def __call__(self, s: str): s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis s = re.sub(self.ignore_patterns, "", s) - s = re.sub(r"\s+'", "'", s) # standardize when there's a space before an apostrophe + s = re.sub(r"\s+'", "'", s) # when there's a space before an apostrophe for pattern, replacement in self.replacers.items(): s = re.sub(pattern, replacement, s) s = re.sub(r"(\d),(\d)", r"\1\2", s) # remove commas between digits s = re.sub(r"\.([^0-9]|$)", r" \1", s) # remove periods not followed by numbers - s = remove_symbols_and_diacritics(s, keep=".%$¢€£") # keep some symbols for numerics + s = remove_symbols_and_diacritics(s, keep=".%$¢€£") # keep numeric symbols s = self.standardize_numbers(s) s = self.standardize_spellings(s) @@ -538,6 +545,6 @@ def __call__(self, s: str): s = re.sub(r"[.$¢€£]([^0-9])", r" \1", s) s = re.sub(r"([^0-9])%", r"\1 ", s) - s = re.sub(r"\s+", " ", s) # replace any successive whitespace characters with a space + s = re.sub(r"\s+", " ", s) # replace any successive whitespaces with a space return s diff --git a/whisper/timing.py b/whisper/timing.py index 98927aa04..39f38728a 100644 --- a/whisper/timing.py +++ b/whisper/timing.py @@ -1,7 +1,7 @@ import subprocess import warnings from dataclasses import dataclass -from typing import List, TYPE_CHECKING +from typing import TYPE_CHECKING, List import numba import numpy as np @@ -26,13 +26,16 @@ def median_filter(x: torch.Tensor, filter_width: int): # `F.pad` does not support 1D or 2D inputs for reflect padding but supports 3D and 4D x = x[None, None, :] - assert filter_width > 0 and filter_width % 2 == 1, "`filter_width` should be an odd number" + assert ( + filter_width > 0 and filter_width % 2 == 1 + ), "`filter_width` should be an odd number" result = None x = F.pad(x, (filter_width // 2, filter_width // 2, 0, 0), mode="reflect") if x.is_cuda: try: from .triton_ops import median_filter_cuda + result = median_filter_cuda(x, filter_width) except (RuntimeError, subprocess.CalledProcessError): warnings.warn( @@ -49,6 +52,7 @@ def median_filter(x: torch.Tensor, filter_width: int): return result + @numba.jit def backtrace(trace: np.ndarray): i = trace.shape[0] - 1 @@ -106,7 +110,9 @@ def dtw_cuda(x, BLOCK_SIZE=1024): M, N = x.shape assert M < BLOCK_SIZE, f"M should be smaller than {BLOCK_SIZE=}" - x_skew = F.pad(x, (0, M + 1), value=np.inf).flatten()[: M * (N + M)].reshape(M, N + M) + x_skew = ( + F.pad(x, (0, M + 1), value=np.inf).flatten()[: M * (N + M)].reshape(M, N + M) + ) x_skew = x_skew.T.contiguous() cost = torch.ones(N + M + 2, M + 2) * np.inf cost[0, 0] = 0 @@ -122,10 +128,12 @@ def dtw_cuda(x, BLOCK_SIZE=1024): trace.stride(0), N, M, - BLOCK_SIZE=BLOCK_SIZE + BLOCK_SIZE=BLOCK_SIZE, ) - trace = trace.T.flatten()[:(M + 1) * (M + N + 3)].reshape(M + 1, M + N + 3)[:, :N + 1] + trace = trace.T.flatten()[: (M + 1) * (M + N + 3)].reshape(M + 1, M + N + 3)[ + :, : N + 1 + ] return backtrace(trace.cpu().numpy()) @@ -181,8 +189,10 @@ def find_alignment( with torch.no_grad(): logits = model(mel.unsqueeze(0), tokens.unsqueeze(0))[0] - token_probs = logits[len(tokenizer.sot_sequence):, :tokenizer.eot].softmax(dim=-1) - text_token_probs = token_probs[np.arange(len(text_tokens)), text_tokens].tolist() + sampled_logits = logits[len(tokenizer.sot_sequence) :, : tokenizer.eot] + token_probs = sampled_logits.softmax(dim=-1) + text_token_probs = token_probs[np.arange(len(text_tokens)), text_tokens] + text_token_probs = text_token_probs.tolist() for hook in hooks: hook.remove() @@ -196,7 +206,7 @@ def find_alignment( weights = median_filter(weights, medfilt_width) matrix = weights.mean(axis=0) - matrix = matrix[len(tokenizer.sot_sequence):-1] + matrix = matrix[len(tokenizer.sot_sequence) : -1] text_indices, time_indices = dtw(-matrix) words, word_tokens = tokenizer.split_to_word_tokens(text_tokens + [tokenizer.eot]) @@ -207,7 +217,8 @@ def find_alignment( start_times = jump_times[word_boundaries[:-1]] end_times = jump_times[word_boundaries[1:]] word_probabilities = [ - np.mean(text_token_probs[i:j]) for i, j in zip(word_boundaries[:-1], word_boundaries[1:]) + np.mean(text_token_probs[i:j]) + for i, j in zip(word_boundaries[:-1], word_boundaries[1:]) ] # hack: ensure the first and second word is not longer than twice the median word duration. @@ -218,7 +229,8 @@ def find_alignment( median_duration = np.median(word_durations) max_duration = median_duration * 2 if len(word_durations) >= 2 and word_durations[1] > max_duration: - end_times[0] = start_times[1] = max(end_times[2] / 2, end_times[2] - max_duration) + boundary = max(end_times[2] / 2, end_times[2] - max_duration) + end_times[0] = start_times[1] = boundary if len(word_durations) >= 1 and end_times[0] - start_times[0] > max_duration: start_times[0] = max(0, end_times[0] - max_duration) @@ -271,19 +283,20 @@ def add_word_timestamps( tokenizer: Tokenizer, mel: torch.Tensor, num_frames: int, - prepend_punctuations: str = "\"\'“¿([{-", - append_punctuations: str = "\"\'.。,，!！?？:：”)]}、", - **hyperparams, + prepend_punctuations: str = "\"'“¿([{-", + append_punctuations: str = "\"'.。,，!！?？:：”)]}、", + **kwargs, ): if len(segments) == 0: return text_tokens = [t for segment in segments for t in segment["tokens"]] - alignment = find_alignment(model, tokenizer, text_tokens, mel, num_frames, **hyperparams) + alignment = find_alignment(model, tokenizer, text_tokens, mel, num_frames, **kwargs) merge_punctuations(alignment, prepend_punctuations, append_punctuations) time_offset = segments[0]["seek"] * HOP_LENGTH / SAMPLE_RATE - token_sources = np.repeat(np.arange(len(segments)), [len(s["tokens"]) for s in segments]) + segment_lengths = [len(s["tokens"]) for s in segments] + token_sources = np.repeat(np.arange(len(segments)), segment_lengths) for segment in segments: segment["words"] = [] @@ -295,7 +308,12 @@ def add_word_timestamps( start = round(time_offset + timing.start, 2) end = round(time_offset + timing.end, 2) segment["words"].append( - dict(word=timing.word, start=start, end=end, probability=timing.probability) + dict( + word=timing.word, + start=start, + end=end, + probability=timing.probability, + ) ) for segment in segments: diff --git a/whisper/tokenizer.py b/whisper/tokenizer.py index ea1117f76..cacb18d3c 100644 --- a/whisper/tokenizer.py +++ b/whisper/tokenizer.py @@ -1,7 +1,7 @@ import os import string from dataclasses import dataclass -from functools import lru_cache, cached_property +from functools import cached_property, lru_cache from typing import List, Optional, Tuple, Union import numpy as np @@ -138,7 +138,9 @@ class Tokenizer: def encode(self, text, **kwargs): return self.tokenizer.encode(text, **kwargs) - def decode(self, token_ids: Union[int, List[int], np.ndarray, torch.Tensor], **kwargs): + def decode( + self, token_ids: Union[int, List[int], np.ndarray, torch.Tensor], **kwargs + ): return self.tokenizer.decode(token_ids, **kwargs) def decode_with_timestamps(self, tokens) -> str: @@ -154,8 +156,9 @@ def decode_with_timestamps(self, tokens) -> str: outputs.append([]) else: outputs[-1].append(token) - outputs = [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs] - return "".join(outputs) + return "".join( + [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs] + ) @cached_property def eot(self) -> int: @@ -197,7 +200,7 @@ def timestamp_begin(self) -> int: def language_token(self) -> int: """Returns the token id corresponding to the value of the `language` field""" if self.language is None: - raise ValueError(f"This tokenizer does not have language token configured") + raise ValueError("This tokenizer does not have language token configured") additional_tokens = dict( zip( @@ -242,8 +245,10 @@ def non_speech_tokens(self) -> Tuple[int]: keeping basic punctuations like commas, periods, question marks, exclamation points, etc. """ - symbols = list("\"#()*+/:;<=>@[\\]^_`{|}~「」『』") - symbols += "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split() + symbols = list('"#()*+/:;<=>@[\\]^_`{|}~「」『』') + symbols += ( + "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split() + ) # symbols that may be a single token or multiple tokens depending on the tokenizer. # In case they're multiple tokens, suppress the first token, which is safe because: @@ -255,7 +260,10 @@ def non_speech_tokens(self) -> Tuple[int]: # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word result = {self.tokenizer.encode(" -")[0], self.tokenizer.encode(" '")[0]} for symbol in symbols + list(miscellaneous): - for tokens in [self.tokenizer.encode(symbol), self.tokenizer.encode(" " + symbol)]: + for tokens in [ + self.tokenizer.encode(symbol), + self.tokenizer.encode(" " + symbol), + ]: if len(tokens) == 1 or symbol in miscellaneous: result.add(tokens[0]) @@ -367,4 +375,6 @@ def get_tokenizer( if task is not None: sot_sequence.append(transcribe if task == "transcribe" else translate) - return Tokenizer(tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence)) + return Tokenizer( + tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence) + ) diff --git a/whisper/transcribe.py b/whisper/transcribe.py index d7c048749..20f01477e 100644 --- a/whisper/transcribe.py +++ b/whisper/transcribe.py @@ -1,17 +1,32 @@ import argparse import os import warnings -from typing import Optional, Tuple, Union, TYPE_CHECKING +from typing import TYPE_CHECKING, Optional, Tuple, Union import numpy as np import torch import tqdm -from .audio import HOP_LENGTH, N_FRAMES, SAMPLE_RATE, FRAMES_PER_SECOND, log_mel_spectrogram, pad_or_trim +from .audio import ( + FRAMES_PER_SECOND, + HOP_LENGTH, + N_FRAMES, + SAMPLE_RATE, + log_mel_spectrogram, + pad_or_trim, +) from .decoding import DecodingOptions, DecodingResult from .timing import add_word_timestamps from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer -from .utils import exact_div, format_timestamp, make_safe, optional_int, optional_float, str2bool, get_writer +from .utils import ( + exact_div, + format_timestamp, + get_writer, + make_safe, + optional_float, + optional_int, + str2bool, +) if TYPE_CHECKING: from .model import Whisper @@ -29,8 +44,8 @@ def transcribe( condition_on_previous_text: bool = True, initial_prompt: Optional[str] = None, word_timestamps: bool = False, - prepend_punctuations: str = "\"\'“¿([{-", - append_punctuations: str = "\"\'.。,，!！?？:：”)]}、", + prepend_punctuations: str = "\"'“¿([{-", + append_punctuations: str = "\"'.。,，!！?？:：”)]}、", **decode_options, ): """ @@ -108,12 +123,16 @@ def transcribe( decode_options["language"] = "en" else: if verbose: - print("Detecting language using up to the first 30 seconds. Use `--language` to specify the language") + print( + "Detecting language using up to the first 30 seconds. Use `--language` to specify the language" + ) mel_segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype) _, probs = model.detect_language(mel_segment) decode_options["language"] = max(probs, key=probs.get) if verbose is not None: - print(f"Detected language: {LANGUAGES[decode_options['language']].title()}") + print( + f"Detected language: {LANGUAGES[decode_options['language']].title()}" + ) language: str = decode_options["language"] task: str = decode_options.get("task", "transcribe") @@ -123,7 +142,9 @@ def transcribe( warnings.warn("Word-level timestamps on translations may not be reliable.") def decode_with_fallback(segment: torch.Tensor) -> DecodingResult: - temperatures = [temperature] if isinstance(temperature, (int, float)) else temperature + temperatures = ( + [temperature] if isinstance(temperature, (int, float)) else temperature + ) decode_result = None for t in temperatures: @@ -140,9 +161,15 @@ def decode_with_fallback(segment: torch.Tensor) -> DecodingResult: decode_result = model.decode(segment, options) needs_fallback = False - if compression_ratio_threshold is not None and decode_result.compression_ratio > compression_ratio_threshold: + if ( + compression_ratio_threshold is not None + and decode_result.compression_ratio > compression_ratio_threshold + ): needs_fallback = True # too repetitive - if logprob_threshold is not None and decode_result.avg_logprob < logprob_threshold: + if ( + logprob_threshold is not None + and decode_result.avg_logprob < logprob_threshold + ): needs_fallback = True # average log probability is too low if not needs_fallback: @@ -186,7 +213,9 @@ def new_segment( # show the progress bar when verbose is False (if True, transcribed text will be printed) num_frames = mel.shape[-1] - with tqdm.tqdm(total=num_frames, unit='frames', disable=verbose is not False) as pbar: + with tqdm.tqdm( + total=num_frames, unit="frames", disable=verbose is not False + ) as pbar: while seek < num_frames: time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE) mel_segment = mel[:, seek:] @@ -201,7 +230,10 @@ def new_segment( if no_speech_threshold is not None: # no voice activity check should_skip = result.no_speech_prob > no_speech_threshold - if logprob_threshold is not None and result.avg_logprob > logprob_threshold: + if ( + logprob_threshold is not None + and result.avg_logprob > logprob_threshold + ): # don't skip if the logprob is high enough, despite the no_speech_prob should_skip = False @@ -214,22 +246,35 @@ def new_segment( current_tokens = [] timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin) - consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0].add_(1) - if len(consecutive) > 0: # if the output contains two consecutive timestamp tokens - if ended_with_single_timestamp := timestamp_tokens[-2:].tolist() == [False, True]: + consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[ + 0 + ].add_(1) + if ( + len(consecutive) > 0 + ): # if the output contains two consecutive timestamp tokens + if ended_with_single_timestamp := timestamp_tokens[-2:].tolist() == [ + False, + True, + ]: consecutive = consecutive.tolist() + [len(tokens)] last_slice = 0 for current_slice in consecutive: sliced_tokens = tokens[last_slice:current_slice] - start_timestamp_pos = sliced_tokens[0].item() - tokenizer.timestamp_begin - end_timestamp_pos = sliced_tokens[-1].item() - tokenizer.timestamp_begin - current_segments.append(new_segment( - start=time_offset + start_timestamp_pos * time_precision, - end=time_offset + end_timestamp_pos * time_precision, - tokens=sliced_tokens, - result=result, - )) + start_timestamp_pos = ( + sliced_tokens[0].item() - tokenizer.timestamp_begin + ) + end_timestamp_pos = ( + sliced_tokens[-1].item() - tokenizer.timestamp_begin + ) + current_segments.append( + new_segment( + start=time_offset + start_timestamp_pos * time_precision, + end=time_offset + end_timestamp_pos * time_precision, + tokens=sliced_tokens, + result=result, + ) + ) current_tokens.append(sliced_tokens.tolist()) last_slice = current_slice @@ -238,23 +283,32 @@ def new_segment( seek += segment_size else: # otherwise, ignore the unfinished segment and seek to the last timestamp - last_timestamp_pos = tokens[last_slice - 1].item() - tokenizer.timestamp_begin + last_timestamp_pos = ( + tokens[last_slice - 1].item() - tokenizer.timestamp_begin + ) seek += last_timestamp_pos * input_stride all_tokens.extend(tokens[: last_slice + 1].tolist()) else: duration = segment_duration timestamps = tokens[timestamp_tokens.nonzero().flatten()] - if len(timestamps) > 0 and timestamps[-1].item() != tokenizer.timestamp_begin: + if ( + len(timestamps) > 0 + and timestamps[-1].item() != tokenizer.timestamp_begin + ): # no consecutive timestamps but it has a timestamp; use the last one. - last_timestamp_pos = timestamps[-1].item() - tokenizer.timestamp_begin + last_timestamp_pos = ( + timestamps[-1].item() - tokenizer.timestamp_begin + ) duration = last_timestamp_pos * time_precision - current_segments.append(new_segment( - start=time_offset, - end=time_offset + duration, - tokens=tokens, - result=result, - )) + current_segments.append( + new_segment( + start=time_offset, + end=time_offset + duration, + tokens=tokens, + result=result, + ) + ) current_tokens.append(tokens.tolist()) seek += segment_size @@ -272,9 +326,13 @@ def new_segment( prepend_punctuations=prepend_punctuations, append_punctuations=append_punctuations, ) - word_end_timestamps = [w["end"] for s in current_segments for w in s["words"]] + word_end_timestamps = [ + w["end"] for s in current_segments for w in s["words"] + ] if len(consecutive) > 0 and len(word_end_timestamps) > 0: - seek_shift = round((word_end_timestamps[-1] - time_offset) * FRAMES_PER_SECOND) + seek_shift = round( + (word_end_timestamps[-1] - time_offset) * FRAMES_PER_SECOND + ) if seek_shift > 0: seek = previous_seek + seek_shift @@ -293,21 +351,24 @@ def new_segment( current_tokens[i] = [] all_segments.extend(current_segments) - all_tokens.extend([token for segment in current_tokens for token in segment]) + all_tokens.extend( + [token for segment in current_tokens for token in segment] + ) # update progress bar pbar.update(min(num_frames, seek) - previous_seek) return dict( - text=tokenizer.decode(all_tokens[len(initial_prompt_tokens):]), + text=tokenizer.decode(all_tokens[len(initial_prompt_tokens) :]), segments=all_segments, - language=language + language=language, ) def cli(): from . import available_models + # fmt: off parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe") parser.add_argument("--model", default="small", choices=available_models(), help="name of the Whisper model to use") @@ -339,6 +400,7 @@ def cli(): parser.add_argument("--prepend_punctuations", type=str, default="\"\'“¿([{-", help="if word_timestamps is True, merge these punctuation symbols with the next word") parser.add_argument("--append_punctuations", type=str, default="\"\'.。,，!！?？:：”)]}、", help="if word_timestamps is True, merge these punctuation symbols with the previous word") parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS") + # fmt: on args = parser.parse_args().__dict__ model_name: str = args.pop("model") @@ -350,7 +412,9 @@ def cli(): if model_name.endswith(".en") and args["language"] not in {"en", "English"}: if args["language"] is not None: - warnings.warn(f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead.") + warnings.warn( + f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead." + ) args["language"] = "en" temperature = args.pop("temperature") @@ -363,6 +427,7 @@ def cli(): torch.set_num_threads(threads) from . import load_model + model = load_model(model_name, device=device, download_root=model_dir) writer = get_writer(output_format, output_dir) @@ -371,5 +436,5 @@ def cli(): writer(result, audio_path) -if __name__ == '__main__': +if __name__ == "__main__": cli() diff --git a/whisper/triton_ops.py b/whisper/triton_ops.py index d829e204a..edd456414 100644 --- a/whisper/triton_ops.py +++ b/whisper/triton_ops.py @@ -1,8 +1,7 @@ -import math +from functools import lru_cache import numpy as np import torch -from functools import lru_cache try: import triton @@ -12,7 +11,9 @@ @triton.jit -def dtw_kernel(cost, trace, x, x_stride, cost_stride, trace_stride, N, M, BLOCK_SIZE: tl.constexpr): +def dtw_kernel( + cost, trace, x, x_stride, cost_stride, trace_stride, N, M, BLOCK_SIZE: tl.constexpr +): offsets = tl.arange(0, BLOCK_SIZE) mask = offsets < M @@ -42,37 +43,53 @@ def dtw_kernel(cost, trace, x, x_stride, cost_stride, trace_stride, N, M, BLOCK_ @lru_cache(maxsize=None) def median_kernel(filter_width: int): @triton.jit - def kernel(y, x, x_stride, y_stride, BLOCK_SIZE: tl.constexpr): # x.shape[-1] == filter_width + def kernel( + y, x, x_stride, y_stride, BLOCK_SIZE: tl.constexpr + ): # x.shape[-1] == filter_width row_idx = tl.program_id(0) offsets = tl.arange(0, BLOCK_SIZE) mask = offsets < y_stride - x_ptr = x + row_idx * x_stride + x_ptr = x + row_idx * x_stride # noqa: F841 y_ptr = y + row_idx * y_stride - LOAD_ALL_ROWS_HERE + LOAD_ALL_ROWS_HERE # noqa: F821 - BUBBLESORT_HERE + BUBBLESORT_HERE # noqa: F821 - tl.store(y_ptr + offsets, MIDDLE_ROW_HERE, mask=mask) + tl.store(y_ptr + offsets, MIDDLE_ROW_HERE, mask=mask) # noqa: F821 kernel = triton.JITFunction(kernel.fn) - kernel.src = kernel.src.replace(" LOAD_ALL_ROWS_HERE", "\n".join([ - f" row{i} = tl.load(x_ptr + offsets + {i}, mask=mask)" - for i in range(filter_width) - ])) - kernel.src = kernel.src.replace(" BUBBLESORT_HERE", "\n\n".join([ - "\n\n".join([ - "\n".join([ - f" smaller = tl.where(row{j} < row{j + 1}, row{j}, row{j + 1})", - f" larger = tl.where(row{j} > row{j + 1}, row{j}, row{j + 1})", - f" row{j} = smaller", - f" row{j + 1} = larger", - ]) - for j in range(filter_width - i - 1) - ]) - for i in range(filter_width // 2 + 1) - ])) + kernel.src = kernel.src.replace( + " LOAD_ALL_ROWS_HERE", + "\n".join( + [ + f" row{i} = tl.load(x_ptr + offsets + {i}, mask=mask)" + for i in range(filter_width) + ] + ), + ) + kernel.src = kernel.src.replace( + " BUBBLESORT_HERE", + "\n\n".join( + [ + "\n\n".join( + [ + "\n".join( + [ + f" smaller = tl.where(row{j} < row{j + 1}, row{j}, row{j + 1})", + f" larger = tl.where(row{j} > row{j + 1}, row{j}, row{j + 1})", + f" row{j} = smaller", + f" row{j + 1} = larger", + ] + ) + for j in range(filter_width - i - 1) + ] + ) + for i in range(filter_width // 2 + 1) + ] + ), + ) kernel.src = kernel.src.replace("MIDDLE_ROW_HERE", f"row{filter_width // 2}") return kernel diff --git a/whisper/utils.py b/whisper/utils.py index 8ee912932..7712d3131 100644 --- a/whisper/utils.py +++ b/whisper/utils.py @@ -7,11 +7,14 @@ system_encoding = sys.getdefaultencoding() if system_encoding != "utf-8": + def make_safe(string): # replaces any character not representable using the system default encoding with an '?', # avoiding UnicodeEncodeError (https://github.com/openai/whisper/discussions/729). return string.encode(system_encoding, errors="replace").decode(system_encoding) + else: + def make_safe(string): # utf-8 can encode any Unicode code point, so no need to do the round-trip encoding return string @@ -43,7 +46,9 @@ def compression_ratio(text) -> float: return len(text_bytes) / len(zlib.compress(text_bytes)) -def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = '.'): +def format_timestamp( + seconds: float, always_include_hours: bool = False, decimal_marker: str = "." +): assert seconds >= 0, "non-negative timestamp expected" milliseconds = round(seconds * 1000.0) @@ -57,7 +62,9 @@ def format_timestamp(seconds: float, always_include_hours: bool = False, decimal milliseconds -= seconds * 1_000 hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else "" - return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}" + return ( + f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}" + ) class ResultWriter: @@ -68,7 +75,9 @@ def __init__(self, output_dir: str): def __call__(self, result: dict, audio_path: str): audio_basename = os.path.basename(audio_path) - output_path = os.path.join(self.output_dir, audio_basename + "." + self.extension) + output_path = os.path.join( + self.output_dir, audio_basename + "." + self.extension + ) with open(output_path, "w", encoding="utf-8") as f: self.write_result(result, file=f) @@ -82,7 +91,7 @@ class WriteTXT(ResultWriter): def write_result(self, result: dict, file: TextIO): for segment in result["segments"]: - print(segment['text'].strip(), file=file, flush=True) + print(segment["text"].strip(), file=file, flush=True) class SubtitlesWriter(ResultWriter): @@ -93,7 +102,7 @@ def iterate_result(self, result: dict): for segment in result["segments"]: segment_start = self.format_timestamp(segment["start"]) segment_end = self.format_timestamp(segment["end"]) - segment_text = segment['text'].strip().replace('-->', '->') + segment_text = segment["text"].strip().replace("-->", "->") if word_timings := segment.get("words", None): all_words = [timing["word"] for timing in word_timings] @@ -106,7 +115,10 @@ def iterate_result(self, result: dict): yield last, start, segment_text yield start, end, "".join( - [f"{word}" if j == i else word for j, word in enumerate(all_words)] + [ + f"{word}" if j == i else word + for j, word in enumerate(all_words) + ] ) last = end @@ -126,7 +138,7 @@ def format_timestamp(self, seconds: float): class WriteVTT(SubtitlesWriter): extension: str = "vtt" always_include_hours: bool = False - decimal_marker: str = '.' + decimal_marker: str = "." def write_result(self, result: dict, file: TextIO): print("WEBVTT\n", file=file) @@ -137,7 +149,7 @@ def write_result(self, result: dict, file: TextIO): class WriteSRT(SubtitlesWriter): extension: str = "srt" always_include_hours: bool = True - decimal_marker: str = ',' + decimal_marker: str = "," def write_result(self, result: dict, file: TextIO): for i, (start, end, text) in enumerate(self.iterate_result(result), start=1): @@ -153,14 +165,15 @@ class WriteTSV(ResultWriter): an environment setting a language encoding that causes the decimal in a floating point number to appear as a comma; also is faster and more efficient to parse & store, e.g., in C++. """ + extension: str = "tsv" def write_result(self, result: dict, file: TextIO): print("start", "end", "text", sep="\t", file=file) for segment in result["segments"]: - print(round(1000 * segment['start']), file=file, end="\t") - print(round(1000 * segment['end']), file=file, end="\t") - print(segment['text'].strip().replace("\t", " "), file=file, flush=True) + print(round(1000 * segment["start"]), file=file, end="\t") + print(round(1000 * segment["end"]), file=file, end="\t") + print(segment["text"].strip().replace("\t", " "), file=file, flush=True) class WriteJSON(ResultWriter): @@ -189,4 +202,3 @@ def write_all(result: dict, file: TextIO): return write_all return writers[output_format](output_dir) - From c6e4e5efb3a1743bc24368528367d9fc8494f959 Mon Sep 17 00:00:00 2001 From: Local State Date: Mon, 6 Mar 2023 20:48:14 -0500 Subject: [PATCH 18/69] remove auxiliary audio extension (#1021) Co-authored-by: Jong Wook Kim --- whisper/utils.py | 1 + 1 file changed, 1 insertion(+) diff --git a/whisper/utils.py b/whisper/utils.py index 7712d3131..490bdd19f 100644 --- a/whisper/utils.py +++ b/whisper/utils.py @@ -75,6 +75,7 @@ def __init__(self, output_dir: str): def __call__(self, result: dict, audio_path: str): audio_basename = os.path.basename(audio_path) + audio_basename = os.path.splitext(audio_basename)[0] output_path = os.path.join( self.output_dir, audio_basename + "." + self.extension ) From 8180fde939369f2e39bbafb4f155e20cc2157bd0 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Mon, 6 Mar 2023 18:50:41 -0800 Subject: [PATCH 19/69] Release 20230306 --- CHANGELOG.md | 26 ++++++++++++++++++++++++++ whisper/version.py | 2 +- 2 files changed, 27 insertions(+), 1 deletion(-) create mode 100644 CHANGELOG.md diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 000000000..ae6f2b1e6 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,26 @@ +# CHANGELOG + + +## [v20230306](https://github.com/openai/whisper/releases/tag/v20230306) + +* #1021: remove auxiliary audio extension +* #1038: apply formatting with `black`, `isort`, and `flake8` +* #869: word-level timestamps in `transcribe()` +* #1033: Decoding improvements +* #894: Update README.md +* #914: Fix infinite loop caused by incorrect timestamp tokens prediction +* #889: drop python 3.7 support + +## [v20230124](https://github.com/openai/whisper/releases/tag/v20230124) + +* #887: handle printing even if sys.stdout.buffer is not available +* #228: Add TSV formatted output in transcript, using integer start/end time in milliseconds +* #333: Added `--output_format` option +* #864: Handle `XDG_CACHE_HOME` properly for `download_root` +* #867: use stdout for printing transcription progress +* #659: Fix bug where mm is mistakenly replaced with hmm in e.g. 20mm +* #859: print '?' if a letter can't be encoded using the system default encoding + +## [v20230117](https://github.com/openai/whisper/releases/tag/v20230117) + +The first versioned release available on [PyPI](https://pypi.org/project/openai-whisper/) diff --git a/whisper/version.py b/whisper/version.py index c9782e774..66f1e62a5 100644 --- a/whisper/version.py +++ b/whisper/version.py @@ -1 +1 @@ -__version__ = "20230124" +__version__ = "20230306" From 4b0d5e58d0bc07977b078241e122afaa3310d1f2 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Tue, 7 Mar 2023 04:47:46 -0800 Subject: [PATCH 20/69] Update setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 736c1ac67..5bd594824 100644 --- a/setup.py +++ b/setup.py @@ -36,7 +36,7 @@ def read_version(fname="whisper/version.py"): long_description=open("README.md", encoding="utf-8").read(), long_description_content_type="text/markdown", readme="README.md", - python_requires=">=3.7", + python_requires=">=3.8", author="OpenAI", url="https://github.com/openai/whisper", license="MIT", From 924e1f8e06c02b6c0036ff742c09f1b6c509521c Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Tue, 7 Mar 2023 14:31:40 -0500 Subject: [PATCH 21/69] Try installing triton only if linux & x86_64 (#1051) --- setup.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 5bd594824..249b831b8 100644 --- a/setup.py +++ b/setup.py @@ -1,4 +1,5 @@ import os +import platform import sys import pkg_resources @@ -11,8 +12,8 @@ def read_version(fname="whisper/version.py"): requirements = [] -if sys.platform.startswith("linux"): - triton_requirement = "triton>=2.0.0.dev20221202" +if sys.platform.startswith("linux") and platform.machine() == "x86_64": + triton_requirement = "triton==2.0.0" try: import re import subprocess From 38e990d853b77690e544e71a2d2df0de3de63f80 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Tue, 7 Mar 2023 19:56:31 -0500 Subject: [PATCH 22/69] Use triton==2.0.0 (#1053) --- setup.py | 16 +--------------- 1 file changed, 1 insertion(+), 15 deletions(-) diff --git a/setup.py b/setup.py index 249b831b8..1161d815a 100644 --- a/setup.py +++ b/setup.py @@ -13,21 +13,7 @@ def read_version(fname="whisper/version.py"): requirements = [] if sys.platform.startswith("linux") and platform.machine() == "x86_64": - triton_requirement = "triton==2.0.0" - try: - import re - import subprocess - - version_line = ( - subprocess.check_output(["nvcc", "--version"]).strip().split(b"\n")[-1] - ) - major, minor = re.findall(rb"([\d]+)\.([\d]+)", version_line)[0] - if (int(major), int(minor)) < (11, 4): - # the last version supporting CUDA < 11.4 - triton_requirement = "triton==2.0.0.dev20221011" - except (IndexError, OSError, subprocess.SubprocessError): - pass - requirements.append(triton_requirement) + requirements.append("triton==2.0.0") setup( name="openai-whisper", From 919a713499d4c4f76ecf448617ada8e9ac008fbb Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Tue, 7 Mar 2023 23:08:45 -0500 Subject: [PATCH 23/69] attempt to fix the repetition/hallucination issue identified in #1046 (#1052) * attempt to fix the repetition/hallucination issue identified in #1046 * zero-pad the audio instead of spectrogram * formatting fix * delete debug print --- whisper/audio.py | 23 +++++++++++++++++------ whisper/transcribe.py | 42 +++++++++++++++++++++--------------------- 2 files changed, 38 insertions(+), 27 deletions(-) diff --git a/whisper/audio.py b/whisper/audio.py index a19b7ab0d..513ab7c9d 100644 --- a/whisper/audio.py +++ b/whisper/audio.py @@ -1,6 +1,6 @@ import os from functools import lru_cache -from typing import Union +from typing import Optional, Union import ffmpeg import numpy as np @@ -15,10 +15,8 @@ N_MELS = 80 HOP_LENGTH = 160 CHUNK_LENGTH = 30 -N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000: number of samples in a chunk -N_FRAMES = exact_div( - N_SAMPLES, HOP_LENGTH -) # 3000: number of frames in a mel spectrogram input +N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000 samples in a 30-second chunk +N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH) # 3000 frames in a mel spectrogram input N_SAMPLES_PER_TOKEN = HOP_LENGTH * 2 # the initial convolutions has stride 2 FRAMES_PER_SECOND = exact_div(SAMPLE_RATE, HOP_LENGTH) # 10ms per audio frame @@ -100,7 +98,10 @@ def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor: def log_mel_spectrogram( - audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS + audio: Union[str, np.ndarray, torch.Tensor], + n_mels: int = N_MELS, + padding: int = 0, + device: Optional[Union[str, torch.device]] = None, ): """ Compute the log-Mel spectrogram of @@ -113,6 +114,12 @@ def log_mel_spectrogram( n_mels: int The number of Mel-frequency filters, only 80 is supported + padding: int + Number of zero samples to pad to the right + + device: Optional[Union[str, torch.device]] + If given, the audio tensor is moved to this device before STFT + Returns ------- torch.Tensor, shape = (80, n_frames) @@ -123,6 +130,10 @@ def log_mel_spectrogram( audio = load_audio(audio) audio = torch.from_numpy(audio) + if device is not None: + audio = audio.to(device) + if padding > 0: + audio = F.pad(audio, (0, padding)) window = torch.hann_window(N_FFT).to(audio.device) stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True) magnitudes = stft[..., :-1].abs() ** 2 diff --git a/whisper/transcribe.py b/whisper/transcribe.py index 20f01477e..773e6365e 100644 --- a/whisper/transcribe.py +++ b/whisper/transcribe.py @@ -11,6 +11,7 @@ FRAMES_PER_SECOND, HOP_LENGTH, N_FRAMES, + N_SAMPLES, SAMPLE_RATE, log_mel_spectrogram, pad_or_trim, @@ -116,7 +117,9 @@ def transcribe( if dtype == torch.float32: decode_options["fp16"] = False - mel = log_mel_spectrogram(audio) + # Pad 30-seconds of silence to the input audio, for slicing + mel = log_mel_spectrogram(audio, padding=N_SAMPLES) + content_frames = mel.shape[-1] - N_FRAMES if decode_options.get("language", None) is None: if not model.is_multilingual: @@ -212,14 +215,13 @@ def new_segment( } # show the progress bar when verbose is False (if True, transcribed text will be printed) - num_frames = mel.shape[-1] with tqdm.tqdm( - total=num_frames, unit="frames", disable=verbose is not False + total=content_frames, unit="frames", disable=verbose is not False ) as pbar: - while seek < num_frames: + while seek < content_frames: time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE) - mel_segment = mel[:, seek:] - segment_size = min(mel_segment.shape[-1], N_FRAMES) + mel_segment = mel[:, seek : seek + N_FRAMES] + segment_size = min(N_FRAMES, content_frames - seek) segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE mel_segment = pad_or_trim(mel_segment, N_FRAMES).to(model.device).to(dtype) @@ -246,20 +248,18 @@ def new_segment( current_tokens = [] timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin) - consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[ - 0 - ].add_(1) - if ( - len(consecutive) > 0 - ): # if the output contains two consecutive timestamp tokens - if ended_with_single_timestamp := timestamp_tokens[-2:].tolist() == [ - False, - True, - ]: - consecutive = consecutive.tolist() + [len(tokens)] + single_timestamp_ending = timestamp_tokens[-2:].tolist() == [False, True] + + consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0] + consecutive.add_(1) + if len(consecutive) > 0: + # if the output contains two consecutive timestamp tokens + slices = consecutive.tolist() + if single_timestamp_ending: + slices.append(len(tokens)) last_slice = 0 - for current_slice in consecutive: + for current_slice in slices: sliced_tokens = tokens[last_slice:current_slice] start_timestamp_pos = ( sliced_tokens[0].item() - tokenizer.timestamp_begin @@ -278,7 +278,7 @@ def new_segment( current_tokens.append(sliced_tokens.tolist()) last_slice = current_slice - if ended_with_single_timestamp: + if single_timestamp_ending: # single timestamp at the end means no speech after the last timestamp. seek += segment_size else: @@ -329,7 +329,7 @@ def new_segment( word_end_timestamps = [ w["end"] for s in current_segments for w in s["words"] ] - if len(consecutive) > 0 and len(word_end_timestamps) > 0: + if not single_timestamp_ending and len(word_end_timestamps) > 0: seek_shift = round( (word_end_timestamps[-1] - time_offset) * FRAMES_PER_SECOND ) @@ -356,7 +356,7 @@ def new_segment( ) # update progress bar - pbar.update(min(num_frames, seek) - previous_seek) + pbar.update(min(content_frames, seek) - previous_seek) return dict( text=tokenizer.decode(all_tokens[len(initial_prompt_tokens) :]), From 26807ec6d3cd9d3ce153d143dc840ea312dc1fad Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Tue, 7 Mar 2023 20:36:29 -0800 Subject: [PATCH 24/69] Release 20230307 --- CHANGELOG.md | 34 ++++++++++++++++++++-------------- whisper/version.py | 2 +- 2 files changed, 21 insertions(+), 15 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index ae6f2b1e6..9dd8a3850 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,25 +1,31 @@ # CHANGELOG +## [v20230307](https://github.com/openai/whisper/releases/tag/v202303067) + +* Fix the repetition/hallucination issue identified in #1046 ([#1052](https://github.com/openai/whisper/pull/1052)) +* Use triton==2.0.0 ([#1053](https://github.com/openai/whisper/pull/1053)) +* Install triton in x86_64 linux only ([#1051](https://github.com/openai/whisper/pull/1051)) +* update setup.py to specify python >= 3.8 requirement ## [v20230306](https://github.com/openai/whisper/releases/tag/v20230306) -* #1021: remove auxiliary audio extension -* #1038: apply formatting with `black`, `isort`, and `flake8` -* #869: word-level timestamps in `transcribe()` -* #1033: Decoding improvements -* #894: Update README.md -* #914: Fix infinite loop caused by incorrect timestamp tokens prediction -* #889: drop python 3.7 support +* remove auxiliary audio extension ([#1021](https://github.com/openai/whisper/pull/1021)) +* apply formatting with `black`, `isort`, and `flake8` ([#1038](https://github.com/openai/whisper/pull/1038)) +* word-level timestamps in `transcribe()` ([#869](https://github.com/openai/whisper/pull/869)) +* Decoding improvements ([#1033](https://github.com/openai/whisper/pull/1033)) +* Update README.md ([#894](https://github.com/openai/whisper/pull/894)) +* Fix infinite loop caused by incorrect timestamp tokens prediction ([#914](https://github.com/openai/whisper/pull/914)) +* drop python 3.7 support ([#889](https://github.com/openai/whisper/pull/889)) ## [v20230124](https://github.com/openai/whisper/releases/tag/v20230124) -* #887: handle printing even if sys.stdout.buffer is not available -* #228: Add TSV formatted output in transcript, using integer start/end time in milliseconds -* #333: Added `--output_format` option -* #864: Handle `XDG_CACHE_HOME` properly for `download_root` -* #867: use stdout for printing transcription progress -* #659: Fix bug where mm is mistakenly replaced with hmm in e.g. 20mm -* #859: print '?' if a letter can't be encoded using the system default encoding +* handle printing even if sys.stdout.buffer is not available ([#887](https://github.com/openai/whisper/pull/887)) +* Add TSV formatted output in transcript, using integer start/end time in milliseconds ([#228](https://github.com/openai/whisper/pull/228)) +* Added `--output_format` option ([#333](https://github.com/openai/whisper/pull/333)) +* Handle `XDG_CACHE_HOME` properly for `download_root` ([#864](https://github.com/openai/whisper/pull/864)) +* use stdout for printing transcription progress ([#867](https://github.com/openai/whisper/pull/867)) +* Fix bug where mm is mistakenly replaced with hmm in e.g. 20mm ([#659](https://github.com/openai/whisper/pull/659)) +* print '?' if a letter can't be encoded using the system default encoding ([#859](https://github.com/openai/whisper/pull/859)) ## [v20230117](https://github.com/openai/whisper/releases/tag/v20230117) diff --git a/whisper/version.py b/whisper/version.py index 66f1e62a5..8cbb6c8d0 100644 --- a/whisper/version.py +++ b/whisper/version.py @@ -1 +1 @@ -__version__ = "20230306" +__version__ = "20230307" From aac47c98349b98cec5ca7b1be53960fb59f4436b Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Tue, 7 Mar 2023 20:43:49 -0800 Subject: [PATCH 25/69] fix typo --- CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 9dd8a3850..522d9acea 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,6 @@ # CHANGELOG -## [v20230307](https://github.com/openai/whisper/releases/tag/v202303067) +## [v20230307](https://github.com/openai/whisper/releases/tag/v20230307) * Fix the repetition/hallucination issue identified in #1046 ([#1052](https://github.com/openai/whisper/pull/1052)) * Use triton==2.0.0 ([#1053](https://github.com/openai/whisper/pull/1053)) From 38f2f4d99d297c2fc09f9f5f28eaa06b017e24a3 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Wed, 8 Mar 2023 18:34:07 -0500 Subject: [PATCH 26/69] fix all_tokens handling that caused more repetitions and discrepancy in JSON (#1060) --- tests/test_transcribe.py | 1 + whisper/timing.py | 2 +- whisper/transcribe.py | 22 ++++++++++++---------- 3 files changed, 14 insertions(+), 11 deletions(-) diff --git a/tests/test_transcribe.py b/tests/test_transcribe.py index e5d530783..96d04ebff 100644 --- a/tests/test_transcribe.py +++ b/tests/test_transcribe.py @@ -17,6 +17,7 @@ def test_transcribe(model_name: str): audio_path, language=language, temperature=0.0, word_timestamps=True ) assert result["language"] == "en" + assert result["text"] == "".join([s["text"] for s in result["segments"]]) transcription = result["text"].lower() assert "my fellow americans" in transcription diff --git a/whisper/timing.py b/whisper/timing.py index 39f38728a..7bc2b9a61 100644 --- a/whisper/timing.py +++ b/whisper/timing.py @@ -290,7 +290,7 @@ def add_word_timestamps( if len(segments) == 0: return - text_tokens = [t for segment in segments for t in segment["tokens"]] + text_tokens = [t for s in segments for t in s["tokens"] if t < tokenizer.eot] alignment = find_alignment(model, tokenizer, text_tokens, mel, num_frames, **kwargs) merge_punctuations(alignment, prepend_punctuations, append_punctuations) diff --git a/whisper/transcribe.py b/whisper/transcribe.py index 773e6365e..ed6d8205d 100644 --- a/whisper/transcribe.py +++ b/whisper/transcribe.py @@ -200,14 +200,14 @@ def decode_with_fallback(segment: torch.Tensor) -> DecodingResult: def new_segment( *, start: float, end: float, tokens: torch.Tensor, result: DecodingResult ): - text_tokens = [token for token in tokens.tolist() if token < tokenizer.eot] + tokens = tokens.tolist() + text_tokens = [token for token in tokens if token < tokenizer.eot] return { - "id": len(all_segments), "seek": seek, "start": start, "end": end, "text": tokenizer.decode(text_tokens), - "tokens": text_tokens, + "tokens": tokens, "temperature": result.temperature, "avg_logprob": result.avg_logprob, "compression_ratio": result.compression_ratio, @@ -245,7 +245,6 @@ def new_segment( previous_seek = seek current_segments = [] - current_tokens = [] timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin) single_timestamp_ending = timestamp_tokens[-2:].tolist() == [False, True] @@ -275,7 +274,6 @@ def new_segment( result=result, ) ) - current_tokens.append(sliced_tokens.tolist()) last_slice = current_slice if single_timestamp_ending: @@ -287,7 +285,6 @@ def new_segment( tokens[last_slice - 1].item() - tokenizer.timestamp_begin ) seek += last_timestamp_pos * input_stride - all_tokens.extend(tokens[: last_slice + 1].tolist()) else: duration = segment_duration timestamps = tokens[timestamp_tokens.nonzero().flatten()] @@ -309,7 +306,6 @@ def new_segment( result=result, ) ) - current_tokens.append(tokens.tolist()) seek += segment_size if not condition_on_previous_text or result.temperature > 0.5: @@ -348,11 +344,17 @@ def new_segment( segment["text"] = "" segment["tokens"] = [] segment["words"] = [] - current_tokens[i] = [] - all_segments.extend(current_segments) + all_segments.extend( + [ + {"id": i, **segment} + for i, segment in enumerate( + current_segments, start=len(all_segments) + ) + ] + ) all_tokens.extend( - [token for segment in current_tokens for token in segment] + [token for segment in current_segments for token in segment["tokens"]] ) # update progress bar From c4b50c0824b780a843a9043899af0c62385dbe65 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Wed, 8 Mar 2023 18:46:38 -0500 Subject: [PATCH 27/69] kwargs in decode() for convenience (#1061) * kwargs in decode() for convenience * formatting fix --- whisper/decoding.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/whisper/decoding.py b/whisper/decoding.py index ff9261e04..81cd8452b 100644 --- a/whisper/decoding.py +++ b/whisper/decoding.py @@ -1,4 +1,4 @@ -from dataclasses import dataclass, field +from dataclasses import dataclass, field, replace from typing import TYPE_CHECKING, Dict, Iterable, List, Optional, Sequence, Tuple, Union import numpy as np @@ -778,7 +778,10 @@ def run(self, mel: Tensor) -> List[DecodingResult]: @torch.no_grad() def decode( - model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions() + model: "Whisper", + mel: Tensor, + options: DecodingOptions = DecodingOptions(), + **kwargs, ) -> Union[DecodingResult, List[DecodingResult]]: """ Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s). @@ -802,6 +805,9 @@ def decode( if single := mel.ndim == 2: mel = mel.unsqueeze(0) + if kwargs: + options = replace(options, **kwargs) + result = DecodingTask(model, options).run(mel) return result[0] if single else result From ad3250a846fe7553a25064a2dc593e492dadf040 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Wed, 8 Mar 2023 15:48:57 -0800 Subject: [PATCH 28/69] Release 20230308 --- CHANGELOG.md | 6 ++++++ whisper/version.py | 2 +- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 522d9acea..51697a0f6 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,11 @@ # CHANGELOG +## [v20230308](https://github.com/openai/whisper/releases/tag/v20230308) + +* kwargs in decode() for convenience ([#1061](https://github.com/openai/whisper/pull/1061)) +* fix all_tokens handling that caused more repetitions and discrepancy in JSON ([#1060](https://github.com/openai/whisper/pull/1060)) +* fix typo in CHANGELOG.md + ## [v20230307](https://github.com/openai/whisper/releases/tag/v20230307) * Fix the repetition/hallucination issue identified in #1046 ([#1052](https://github.com/openai/whisper/pull/1052)) diff --git a/whisper/version.py b/whisper/version.py index 8cbb6c8d0..a0e37f977 100644 --- a/whisper/version.py +++ b/whisper/version.py @@ -1 +1 @@ -__version__ = "20230307" +__version__ = "20230308" From 839639a223b92ad61851baae9ad8a695ccb41ce5 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Mon, 13 Mar 2023 05:34:16 -0400 Subject: [PATCH 29/69] Use tiktoken (#1044) * use tiktoken==0.3.0 * formatting * tuple should be safer * Update whisper/tokenizer.py Co-authored-by: Ruhollah Majdoddin * use tiktoken 0.3.1 * reflecting suggestions * cleanup * bypassing load_tiktoken_bpe to avoid blobfile dep --------- Co-authored-by: Ruhollah Majdoddin --- MANIFEST.in | 2 - requirements.txt | 2 +- tests/test_transcribe.py | 7 +- whisper/assets/gpt2.tiktoken | 50256 +++++++++++++++ whisper/assets/gpt2/merges.txt | 50001 --------------- whisper/assets/gpt2/special_tokens_map.json | 1 - whisper/assets/gpt2/tokenizer_config.json | 1 - whisper/assets/gpt2/vocab.json | 1 - whisper/assets/multilingual.tiktoken | 50257 ++++++++++++++++ whisper/assets/multilingual/added_tokens.json | 1 - whisper/assets/multilingual/merges.txt | 50000 --------------- .../multilingual/special_tokens_map.json | 1 - .../assets/multilingual/tokenizer_config.json | 1 - whisper/assets/multilingual/vocab.json | 1 - whisper/tokenizer.py | 165 +- 15 files changed, 100601 insertions(+), 100096 deletions(-) create mode 100644 whisper/assets/gpt2.tiktoken delete mode 100644 whisper/assets/gpt2/merges.txt delete mode 100644 whisper/assets/gpt2/special_tokens_map.json delete mode 100644 whisper/assets/gpt2/tokenizer_config.json delete mode 100644 whisper/assets/gpt2/vocab.json create mode 100644 whisper/assets/multilingual.tiktoken delete mode 100644 whisper/assets/multilingual/added_tokens.json delete mode 100644 whisper/assets/multilingual/merges.txt delete mode 100644 whisper/assets/multilingual/special_tokens_map.json delete mode 100644 whisper/assets/multilingual/tokenizer_config.json delete mode 100644 whisper/assets/multilingual/vocab.json diff --git a/MANIFEST.in b/MANIFEST.in index f091bded7..08134dede 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -2,6 +2,4 @@ include requirements.txt include README.md include LICENSE include whisper/assets/* -include whisper/assets/gpt2/* -include whisper/assets/multilingual/* include whisper/normalizers/english.json diff --git a/requirements.txt b/requirements.txt index 45ecbe769..09ad0e340 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,5 +3,5 @@ numpy torch tqdm more-itertools -transformers>=4.19.0 +tiktoken==0.3.1 ffmpeg-python==0.2.0 diff --git a/tests/test_transcribe.py b/tests/test_transcribe.py index 96d04ebff..e4f8fd0f7 100644 --- a/tests/test_transcribe.py +++ b/tests/test_transcribe.py @@ -4,6 +4,7 @@ import torch import whisper +from whisper.tokenizer import get_tokenizer @pytest.mark.parametrize("model_name", whisper.available_models()) @@ -24,6 +25,11 @@ def test_transcribe(model_name: str): assert "your country" in transcription assert "do for you" in transcription + tokenizer = get_tokenizer(model.is_multilingual) + all_tokens = [t for s in result["segments"] for t in s["tokens"]] + assert tokenizer.decode(all_tokens) == result["text"] + assert tokenizer.decode_with_timestamps(all_tokens).startswith("<|0.00|>") + timing_checked = False for segment in result["segments"]: for timing in segment["words"]: @@ -31,7 +37,6 @@ def test_transcribe(model_name: str): if timing["word"].strip(" ,") == "Americans": assert timing["start"] <= 1.8 assert timing["end"] >= 1.8 - print(timing) timing_checked = True assert timing_checked diff --git a/whisper/assets/gpt2.tiktoken b/whisper/assets/gpt2.tiktoken new file mode 100644 index 000000000..3480e077d --- /dev/null +++ b/whisper/assets/gpt2.tiktoken @@ -0,0 +1,50256 @@ +IQ== 0 +Ig== 1 +Iw== 2 +JA== 3 +JQ== 4 +Jg== 5 +Jw== 6 +KA== 7 +KQ== 8 +Kg== 9 +Kw== 10 +LA== 11 +LQ== 12 +Lg== 13 +Lw== 14 +MA== 15 +MQ== 16 +Mg== 17 +Mw== 18 +NA== 19 +NQ== 20 +Ng== 21 +Nw== 22 +OA== 23 +OQ== 24 +Og== 25 +Ow== 26 +PA== 27 +PQ== 28 +Pg== 29 +Pw== 30 +QA== 31 +QQ== 32 +Qg== 33 +Qw== 34 +RA== 35 +RQ== 36 +Rg== 37 +Rw== 38 +SA== 39 +SQ== 40 +Sg== 41 +Sw== 42 +TA== 43 +TQ== 44 +Tg== 45 +Tw== 46 +UA== 47 +UQ== 48 +Ug== 49 +Uw== 50 +VA== 51 +VQ== 52 +Vg== 53 +Vw== 54 +WA== 55 +WQ== 56 +Wg== 57 +Ww== 58 +XA== 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m -Ġc on -Ġ W -Ġ R -he r -Ġw as -Ġ r -o d -Ġ F -u l -at e -Ġa t -r i -p p -o re -ĠT he -Ġs e -u s -Ġp ro -Ġh a -u m -Ġa re -Ġd e -a in -an d -Ġo r -ig h -es t -is t -a b -r om -Ġ N -t h -Ġc om -Ġ G -u n -o p -0 0 -Ġ L -Ġn ot -es s -Ġe x -Ġ v -re s -Ġ E -e w -it y -an t -Ġb y -e l -o s -or t -o c -q u -Ġf rom -Ġha ve -Ġs u -i ve -ou ld -Ġs h -Ġth is -n t -r a -p e -igh t -ar t -m ent -Ġa l -u st -en d -- - -al l -Ġ O -ac k -Ġc h -Ġ le -i es -re d -ar d -â Ģ -ou t -Ġ J -Ġa b -e ar -i v -al ly -ou r -o st -g h -p t -Ġp l -as t -Ġc an -a k -om e -u d -T he -Ġh is -Ġd o -Ġg o -Ġh as -g e -' t -Ġ U -r ou -Ġs a -Ġ j -Ġb ut -Ġw or -Ġa ll -e ct -Ġ k -am e -Ġw ill -o k -Ġw he -Ġthe y -id e -0 1 -f f -ic h -p l -t her -Ġt r -. . -Ġin t -i e -u re -ag e -Ġn e -i al -a p -in e -ic e -Ġm e -Ġo ut -an s -on e -on g -ion s -Ġwh o -Ġ K -Ġu p -Ġthe ir -Ġa d -Ġ 3 -Ġu s -at ed -ou s -Ġm ore -u e -o g -ĠS t -in d -i ke -Ġs o -im e -p er -. " -b er -i z -a ct -Ġon e -Ġsa id -Ġ - -a re -Ġyou r -c c -ĠT h -Ġc l -e p -a ke -ab le -i p -Ġcon t -Ġwh ich -i a -Ġ im -Ġab out -Ġwe re -ver y -u b -Ġh ad -Ġ en -Ġcom p -, " -ĠI n -Ġu n -Ġa g -i re -ac e -a u -ar y -Ġw ould -as s -r y -Ġ âĢ -c l -o ok -e re -s o -Ġ V -ig n -i b -Ġof f -Ġt e -v en -Ġ Y -i le -o se -it e -or m -Ġ2 01 -Ġre s -Ġm an -Ġp er -Ġo ther -or d -ul t -Ġbe en -Ġl ike -as e -an ce -k s -ay s -ow n -en ce -Ġd is -ct ion -Ġan y -Ġa pp -Ġs p -in t -res s -ation s -a il -Ġ 4 -ic al -Ġthe m -Ġhe r -ou nt -ĠC h -Ġa r -Ġ if -Ġthe re -Ġp e -Ġy ear -a v -Ġm y -Ġs ome -Ġwhe n -ou gh -ac h -Ġth an -r u -on d -ic k -Ġo ver -ve l -Ġ qu -Ċ Ċ -Ġs c -re at -re e -ĠI t -ou nd -p ort -Ġal so -Ġp art -f ter -Ġk n -Ġbe c -Ġt ime -en s -Ġ 5 -op le -Ġwh at -Ġn o -d u -m er -an g -Ġn ew --- -- -Ġg et -or y -it ion -ing s -Ġj ust -Ġint o -Ġ 0 -ent s -o ve -t e -Ġpe ople -Ġp re -Ġit s -Ġre c -Ġt w -i an -ir st -ar k -or s -Ġwor k -ad e -o b -Ġs he -Ġo ur -w n -in k -l ic -Ġ1 9 -ĠH e -is 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n -Ġth rough -ĠT r -_ _ -Ġw ay -Ġd on -Ġ , -Ġ1 0 -as ed -Ġas s -ub lic -Ġre g -ĠA nd -i x -Ġ very -Ġin clud -ot her -Ġim p -ot h -Ġsu b -ĠâĢ Ķ -Ġbe ing -ar g -ĠW h -= = -ib le -Ġdo es -an ge -r am -Ġ 9 -er t -p s -it ed -ation al -Ġb r -Ġd own -Ġman y -ak ing -Ġc all -ur ing -it ies -Ġp h -ic s -al s -Ġde c -at ive -en er -Ġbe fore -il ity -Ġwe ll -Ġm uch -ers on -Ġth ose -Ġsu ch -Ġ ke -Ġ end -ĠB ut -as on -t ing -Ġl ong -e f -Ġth ink -y s -Ġbe l -Ġs m -it s -a x -Ġo wn -Ġpro v -Ġs et -if e -ment s -b le -w ard -Ġsh ow -Ġp res -m s -om et -Ġo b -Ġs ay -ĠS h -t s -f ul -Ġe ff -Ġg u -Ġin st -u nd -re n -c ess -Ġ ent -ĠY ou -Ġgo od -Ġst art -in ce -Ġm ade -t t -st em -ol og -u p -Ġ | -um p -Ġhe l -ver n -ul ar -u ally -Ġa c -Ġm on -Ġl ast -Ġ2 00 -1 0 -Ġst ud -u res -ĠA r -sel f -ar s -mer ic -u es -c y -Ġm in -oll ow -Ġc ol -i o -Ġm od -Ġc ount -ĠC om -he s -Ġf in -a ir -i er -âĢ Ķ -re ad -an k -at ch -e ver -Ġst r -Ġpo int -or k -ĠN ew -Ġs ur -o ol -al k -em ent -Ġus ed -ra ct -we en -Ġs 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ub -Or ange -Ġfingert ips -ĠWood en -ĠK idd -ĠJ PM -ĠDam n -C ow -c odes -48 2 -Ġiniti ating -ĠEl k -ĠCut ting -Ġabsent ee -ĠV ance -ĠLil ith -G UI -Ġobsc ured -Ġdwar ves -ĠCh op -ĠB oko -Val ues -Ġmult imedia -Ġbrew ed -Reg ular -CRIP TION -ĠMort al -Ġa pex -Ġtravel er -Ġbo ils -Ġspray ing -Rep resent -ĠStars hip -4 28 -Ġdisappro val -Ġshadow y -Ġlament ed -ĠRe place -ĠFran ç -67 7 -d or -Ġunst oppable -Ġcoh orts -gy n -ĠClass ics -ĠAm ph -Ġsl uggish -ĠAdd iction -ĠPad res -Ġins cription -Ġin human -min us -ĠJere miah -at ars -Ter ror -ĠT os -ĠSh arma -ast a -c atch -Ġpl umbing -ĠTim bers -Sh ar -H al -ĠO sc -Ġcou pling -hum ans -Ġsp onge -Ġid ols -ĠSp a -ĠAdv ocate -ĠBe ats -lu a -Ġtick ing -Ġload er -ĠG ron -8 10 -Ġstim ulated -Ġside bar -ĠManufact urer -ore And -19 73 -Ġpra ises -ĠFl ores -dis able -ĠElect rical -ra ise -E th -Ġmigr ated -Ġlect urer -K ids -ĠCa vern -Ġk ettle -Ġgly c -ĠMand ela -ĠF ully -å§ « -FIN EST -Ġsquee zing -ĠRy der -amp oo -oreAnd Online -Inst oreAndOnline 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-Ġreform ing -Ġfrust rations -ĠDead pool -ĠBel ichick -ract or -ĠRagnar ok -ĠD rupal -ĠApp roximately -19 20 -ĠHub ble -arm or -ĠSar as -ĠJon as -Ġnostalg ic -Ġfeas ibility -Sah aran -Ġorb iting -Ġ9 70 -R u -Ġsh in -ĠInvestig ators -Ġinconsist encies -ĠP AN -B G -Ġgraz ing -Ġdetect ors -ĠStart up -ĠFun ny -ĠNa omi -Consider ing -Ġh og -ut f -ce mic -Ġfort ified -ĠFun ctions -Ġcod ec -nut rition -H at -" ! -micro soft -55 8 -ĠTh in -ĠA CE -Al ias -ĠO PS -p apers -P K -ãĢ İ -Ġimpro bable -N orthern -equ al -Ġlook out -Ġty res -ĠMod ified -ĠK op -Abs olutely -Ġbuild up -sil ver -Ġaud i -Ġgro tesque -ĠSab er -ĠPres byter -ON Y -Ġglac iers -ĠSho als -ĠK ass -ĠH RC -ĠNic ol -ĠL unch -ĠF oss -âĸ Ĵ -AD RA -ĠOne Plus -o ing -ground s -Ġincident al -Ġdatas ets -68 9 -ĠClarks on -Ġassemb ling -ĠCorrect ions -Ġdrink ers -Ġqual ifiers -Ġle ash -Ġunf ounded -ĠH undred -Ġkick off -T i -Ġrecon cil -ĠGr ants -ĠCompl iance -ĠDexter ity -Ġ19 06 -w arn -D allas -Max imum -n ard -av ia -be aut -ens itivity -tr ace -Ġpione ers -ĠF ract -ãĢ ı -Ġpre cept -Ġgloss y -ĠI EEE -Ac ross -Ġ6 80 -S leep -che on -Ġsatir ical -ĠMin otaur -ĠCla ude -Ġr é -ape go -Ġcar rot -ĠSem in -ino a -Ġz o -Ind ependent -Ġdiagn oses -ĠC ue -M AR -Ġrend ition -ĠK ik -Ġpath ology -Ġselect s -Link edIn -Ġass ay -ĠD res -Ġtext ual -post ed -IT AL -ĠM aul -N eal -Ġinter connected -Ġerr atic -ĠVir us -Ġ5 30 -Ġenvironmental ists -ĠP helps -Ġeng agements -ĠIN ST -Ġeconom ical -nox ious -Ġg earing -izz y -Ġfavor ably -ĠMcG ill -T erm -Ġh anged -Ġball park -ĠRe yes -Ġbe ware -ĠP sal -ĠMass acre -q i -Ġin accessible -acly sm -Ġfr ay -ill ac -Ġbitter ly -ĠCert ification -Mich igan -Ġir respective -al ore -Em pty -Ġendorse ments -Ġund et -f g -equ ipped -Ġmerc iless -ĠC ust -Ġimm ature -Ġvou cher -ĠBlack well -Ñ ı -h awk -dis ciplinary -ile e -ĠMak oto -ĠD ude -ãĥĩ ãĤ£ -Y ears -Ġin ver -Ġsh aman -ĠY ong -ip el -ell en -ĠCath y -br ids -Ġs arc -65 1 -N ear -Ġground work -Ġam az -Ġ4 15 -ĠHunting ton -hew s -ĠB ung -Ġarbit rarily -ĠW it -ĠAl berto -Ġdis qualified -best os -46 1 -Ġp c -Ġ28 4 -ro bat -Rob in -Ġh ugs -ĠTrans ition -ĠOcc asionally -Ġ3 26 -ĠWh ilst -ĠLe y -Ġspaces hip -cs v -Ġun successfully -ĠA u -le ck -ĠWing ed -ĠGrizz lies -. � -Ġne arer -ĠSorce ress -ĠInd igo -El se -8 40 -let es -Co ach -Ġup bringing -ĠK es -Ġseparat ist -Ġrac ists -Ġch ained -Ġabst inence -lear ning -Ġrein stated -Ġsymm etry -Ġremind ers -ĠChe vy -Ġm ont -Ġexempl ary -ĠT OR -Z X -Ġqual itative -ĠSt amp -ĠSav annah -ĠRoss i -Ġp aed -Ġdispens aries -ĠWall s -ĠCh ronic -Ġcompliment ary -ĠBeir ut -Ġ+ --- -igs list -Ġcrypt ographic -mas ters -ĠCap itals -Ġmax imal -Ġent ropy -Point s -Ġcombat ants -l ip -ĠGl ob -ĠB MC -ph ase -th ank -HT TP -Ġcomm uter -Ġ\( \ -.. / -ĠReg ener -ĠDO I -ĠActiv ision -Ġsl it -os al -RE M -Ġch ants -Y u -Ke ys -Bre xit -ĠFor ced -Ari zona -Ġsquad ron -IS O -ĠMal one -Ġ3 38 -Ġcontrast ing -Ġt idal -Ġlib el -Ġimpl anted -Ġupro ar -ĠC ater -Ġpropos itions -M anchester -ĠEuro s -it amin -G il -ĠEl ven -ĠSe 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", -al on -K rist -Ġmeas urable -Ġimagin able -Ġapost les -Y N -7 60 -Ġster oid -Ġspecific ity -ĠL ocated -ĠBeck er -ĠE du -ĠDiet ary -uts ch -ĠMar ilyn -Ġbl ister -ĠM EP -ĠK oz -ĠC MS -y ahoo -ĠCar ney -Ġbo asting -ĠC aleb -By te -read s -ad en -Pro blem -ĠWood ward -S we -S up -ĠK GB -Set up -Ġtac it -Ġret ribution -Ġd ues -ĠM ü -. ? -ä¸ Ń -p ots -Ġcame o -ĠP AL -educ ation -A my -like ly -g ling -Ġconstitution ally -ĠHam m -ĠSpe ak -Ġwid gets -br ate -Ġcra ppy -ĠI ter -Ġanticip ating -ĠB out -P ixel -ĠY ep -ĠLaur ie -Ġh ut -Ġbullet in -ĠSal vation -Ġch ats -ear able -Honest ly -AL TH -onse qu -c ult -isco very -ovy ch -Ġse lves -ĠSat oshi -S ounds -Ġconver gence -ĠRosen berg -19 74 -Ġnas al -Ġfull est -Ġfer ocious -x us -ist e -AM S -Ġlobb ied -Ġso othing -ĠGun n -t oday -0 24 -Ġinspir ational -ĠN BN -p b -g ewater -or ah -all owed -ĠCol iseum -Ġspecial izing -Ġinsane ly -ĠT ape -del ay -Ġt arn -ĠP ound -Ġmel anch -Ġdeploy ments -il and -Ġless en -Ġfur ry -ĠUE FA -Ġblood shed -ĠMe ier -ither ing -Ġhe irs -ĠJ aw -ax ter -ĠPublic ations -Ġal ters -int ention -ĠWinc hester -d etermination -ĠLif etime -th in -Mon ster -7 80 -Ġapprox imation -Ġsuper markets -ĠSecond s -or os -h uge -Ġb ribe -ĠLIM ITED -un ed -Ġmis interpret -ĠIn jury -Ġ3 67 -Ġthreshold s -ĠCarn ival -Ġgastro intestinal -Ġguid eline -Ġde ceived -f eatures -Ġpurported ly -ĠRon nie -ĠNew t -Ġsp acious -as us -Ġsuperhero es -ĠCyn thia -le gged -k amp -ch io -Ġth umbnail -ĠShir ley -ill ation -Ġshe ds -ĠZ y -E PA -Ġdam s -Ġy awn -n ah -ĠPe ggy -ĠE rie -ĠJu ventus -ĠF ountain -r x -don ald -al bum -ĠComp rehensive -Ġc aching -ĠU z -ulner ability -ĠPrinc iple -ĠJ ian -ing ers -cast s -ĠOs iris -ch art -t ile -ĠTiff any -ĠPatt on -ĠWh ip -Ġovers ized -J e -ĠCind erella -ĠB orders -ĠDa esh -M ah -Ġdog ma -Ġcommun ists -v u -Coun cil -Ġfresh water -Ġw ounding -Ġdeb acle -Ġyoung ster -Ġthread ed -ĠB ots -ĠSav ings -ãģ Ĥ -ol ing -oh o -Ġillum ination -M RI -Ġlo osen -tr ump -ag ency -ur ion -Ġmoment arily -ĠCh un -ĠBud apest -ĠAl ley -D isk -Ġaston ished -ĠCon quer -ĠAccount ing -h aving -ĠWe in -ĠAl right -Ġrev olver -Ġdel usion -Ġrelic s -Ġad herent -qu ant -Ġhand made -or io -Ġcomb ating -c oded -Ġquad ru -re th -N ik -ĠTrib al -ĠMyster ious -Ġin hal -ĠWin ning -ĠClass ification -ch anged -Ġun ab -Ġsc orn -icip ated -w l -ond uctor -Ġrein forcing -ĠChild hood -an ova -Ġadventure r -Ġdoctor al -ĠStrateg ies -Ġengulf ed -ĠEnc ounter -Ġl ashes -Crit ical -ric ular -ĠU TF -oci ation -check ing -ĠConsult ing -Run time -per iod -ĠAs gard -Ġdist illed -ĠPas adena -ĠD ying -ĠCOUN TY -Ġgran ite -Ġsm ack -Ġparach ute -ĠS UR -Virgin ia -ĠF urious -78 7 -ĠO kin -Ġcam el -ĠM bps -19 72 -ĠCh ao -ĠC yan -j oice -ef er -ĠW rap -ĠDeb ate -S eg -Ġfore arm -ĠIgn ore -Ġtim estamp -Ġprob ing -ĠNo on -ĠGra il -f en -Ġdorm ant -ĠFirst ly -ĠE ighth -ĠH UN -ĠDes ire -or as -Girl s -ĠDes mond -z ar -am ines -O AD -exec ute -Ġbo obs -ĠAT L -_ ( -Chel sea -Ġmasturb ation -ĠCo C -Ġdestroy er -ĠCh omsky -Ġsc atter 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a/whisper/assets/multilingual/added_tokens.json +++ /dev/null @@ -1 +0,0 @@ -{"<|endoftext|>": 50257} diff --git a/whisper/assets/multilingual/merges.txt b/whisper/assets/multilingual/merges.txt deleted file mode 100644 index afde9f4fa..000000000 --- a/whisper/assets/multilingual/merges.txt +++ /dev/null @@ -1,50000 +0,0 @@ -Ġ t -Ġ a -Ġt h -i n -e r -Ġ w -Ġ s -o u -Ġth e -r e -o n -a t -e n -Ġ c -i t -i s -Ġ b -n d -Ġ d -Ġ m -Ġ h -Ġ o -in g -e s -Ġ p -Ġt o -a n -Ġ f -o r -l l -Ġ I -Ġ l -Ġ y -a r -Ġ g -Ġy ou -e d -Ġa nd -Ġ in -Ġo f -a s -Ġ n -o m -i c -Ġth at -u s -e t -v e -a l -o w -l e -Ġ is -Ġ e -Ġ it -o t -' s -Ġb e -i on -Ġ T -Ġw h -Ġ A -en t -Ġ S -Ġ re -a y -Ġw e -Ġ on -er e -Ġh a -u t -a c -i d -i g -o s -k e -v er -i m -Ġ Ð -ĠT h -a m -a ll -Ġf or -e l -c h -r o -Ġth is -Ġs t -Ġ W -Ġ u -a d -ou t -i r -l d -c t -Ġ k -i f -Ġg o -. . -Ð ¾ -it h -l y -h t -q u -Ġ - -Ġd o -Ġ j -Ġha ve -Ġ B -Ġa n -Ġw ith -Ġa re -Ġ r -Ġd e -Ġs e -Ġs o -Ġ v -s t -i ll -u r -Ġl i -Ġ M -es t -o d -all y -' t -us t -Ġa s -Ġ C -c e -Ġm e -Ð ° -Ð µ -i l -Ġ H -Ġw as -t er -t h -Ġc an -an t -Ġc om -ou r -ig ht -Ġ Y -at ion -ĠA nd -o l -Ġs h -Ñ Ĥ -o p -s e -Ġn ot -ĠS o -Ġn e -u n -Ġa b -Ġli ke -Ġa t -Ġ D -i e -Ġh e -Ġc on -Ġc h -o re -Ġa l -Ġo r -Ġ qu -Ġ O -om e -r a -u l -Ġ N -p p -Ġyou r -ou ld -Ġ P -Ġf r -g e -er s -' re -Ð ¸ -Ġthe y -Ġwh at -us e -Ġa ll -ĠTh e -Ġ L -es s -e m -Ġk n -Ġj ust -ar t -Ġp ro -ver y -u m -Ġl o -Ġ ì -Ġm y -o k -Ġe x -a b -Ġth ere -Ġb ut -Ġkn ow -Ġs u -Ġ G -Ñ ģ -Ġ E -Ġm a -о Ð -Ġ en -Ġab out -ĠI t -is t -Ġw or -r i -in d -Ġon e -at e -a nd -in k -Ġl e -or t -' m -Ġ F -ic h -Ñ Ģ -id e -Ġg et -Ġ out -.. . -Ġw ill -ã ģ -i ve -Ð ½ -Ġfr om -a in -ĠW e -Ġu p -p e -re s -c a -Ġ R -Ġ if -Ġp l -Ġd on -ac k -Ġ 1 -Ġ " -Ġt r -Ġ us -ĠW h -it y -Ġ J -ĠY ou -Ġh ere -h er -Ġs ome -ou g -a k -ar d -Ġgo ing -Ġu n -m ent -Ġth ink -Ġp e -en d -Ġ ( -ca use -Ġt im -as t -à © -Ġ our -Ġw ant -am e -i es -Ġ ë -u d -in e -Ġre ally -Ġt e -Ġse e -c i -Ġb y -s o -u re -os e -Ġ [ -a re -Ġm ore -a h -on e -c k -op le -а Ð -Ġthe n -Ġth ing -Ġthe m -v en -ou nd -os t -on g -e ct -Ġr ight -a g -Ġin t -Ġpe ople -Ġwh en -ou s -p l -Ġtim e -Ġ im -Ġwh o -Ġ 2 -a p -Ġbe cause -h ing -Ġn o -ic e -Ġlo ok -Ġh as -Ġw ould -Ġh ow -ac t -Ġf e -n t -oug h -Ġp r -ĠB ut -Ġs ay -Ñ ĥ -Ġn ow -Ġm an -Ġ very -Ġwor k -i z -Ġ K -i v -it t -Ġa r -e p -Ġc l -Ġwh ich -Ġc o -an s -' ve -Ġs a -f f -' ll -Ġan y -Ġa ct -Ġy e -b er -ac h -a ge -p er -Ġal so -f er -Ġthe se -Ġa d -е Ð -th er -ac e -ic k -a ke -re at -i re -u e -Ġa g -Ġ U -u ch -ion s -r y -0 0 -n a -Ġd id -Ġqu e -Ġha d -Ġe very -ĠH e -Ġl a -Ġw ay -Ġs p -b le -ĠTh is -as s -Ġthe ir -it e -Ġne ed -Ġp art -Ġw ere -Ġb ack -i p -ow n -om et -b e -as e -Ġma ke -ir st -i a -en ce -an g -an k -Ġg ot -Ġp re -Ġcon t -Ġo ther -p t -ĠTh at -o g -Ġgo od -Ġint o -al k -Ġbe en -Ġa m -Ġo ver -u ally -Ġ â -ì Ŀ -Ġu nd -h e -w ay -Ġg r -Ñ Į -Ġd if -Ġp er -Ñ ı -ĠI n -Ġt w -on d -ar s -in t -or m -Ġl ot -Ġwh ere -Ġ à -Ġ V -Ġs omet -Ð » -en s -Ġg u -Ġa c -u g -Ñ ĭ -Ä ± -Ġf irst -re e -Ġh is -itt le -Ġim p -Ġm o -a v -Ġl ittle -ĠWh at -Ġm uch -Ġ z -Ġ ê -ab le -ĠÐ ¿ -Ġp o -Ġcom p -n e -Ġd is -Ġl et -an ce -Ġh er -Ġthing s -Ġst art -ul t -Ġa pp -Ġre s -Ġf o -Ġc ould -Ġin ter -Ġth ose -Ġd es -Ġwe ll -Ġtw o -Ġk ind -x t -res s -el y -à ¤ -Ġb r -Ġth r -ĠÐ ² -Ġ i -is h -Ġdif fer -Ġ ro -ĠS t -Ġsomet hing -Ġt ake -Ġb o -y s -Ġsh e -Ġt alk -l o -Ñ ĩ -Ġe ven -Ð º -ã Ģ -ĠÐ ½ -Ġb u -ĠI f -Ġd own -ĠC h -ad e -ation s -Ġ use -or d -Ġof f -Ġact ually -Ġs pe -d u -at ed -at er -os s -n ing -à ¼ -Ġdo es -Ġ Ñģ -Ġne w -Ġb et -ve l -c ess -p le -Ġha pp -t ing -on na -Ġ es -Ġd ay -Ġon ly -ig n -k ay -s el -ent s -ou nt -i ld -i le -Ġs c -Ġh im -Ġag ain -v ing -Ġg onna -Ġcom m -Ġh el -ot her -Ġ ke -ic al -Ġ 3 -Ġe l -Ġthr ough -Ġcom e -ar k -d ay -i er -à ³ -Ġth an -ĠThe y -Ġm ay -Ġs er -í ķ -Ġc all -Ġdiffer ent -Ġsh ould -ĠTh ere -ar y -ĠN ow -ã Ĥ -th ing -w e -or y -f ter -Ġp ut -or s -i al -ë ĭ -Ġund er -Ġin c -ĠY e -u b -f orm -Ġv ide -à ¸ -ver s -Ġfe el -à ¡ -od y -f t -f ore -Ġe m -g et -Ġsa id -it ion -Ġre c -i ous -at ch -Ġtr y -Ġhel p -Ġsh ow -Ð ´ -Ġb it -u ll -Ð ² -ÑĤ о -g r -Ġpl ay -if e -a il -ĠYe ah -Ġqu est -Ġman y -Ġp ers -Ġg reat -Ã Ń -Ġ est -n g -Ġâ Ļ -t y -l a -ĠO h -Ġ × -à ® -ĠB e -ad y -Ġm ost -ct ion -ĠN o -Ġdo ing -Ġbe ing -Ġto o -c es -Ġb l -. 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"ĠгоÑĤов": 17137, "Ġiter": 17138, "Ġcurse": 17139, "ôn": 17140, "merce": 17141, "aran": 17142, "Ġleak": 17143, "çµIJ": 17144, "Ġabsence": 17145, "Ñģкий": 17146, "Ġreaders": 17147, "aler": 17148, "Ġbeneath": 17149, "ango": 17150, "hetic": 17151, "Ġfinns": 17152, "Ġpoop": 17153, "Ġduplic": 17154, "Hi": 17155, "igs": 17156, "ologically": 17157, "opp": 17158, "Ġdizer": 17159, "ĠAllen": 17160, "Ġgli": 17161, "Ġacceleration": 17162, "Ġvitamin": 17163, "ãĥŃ": 17164, "vä": 17165, "ĠAccess": 17166, "à®Ļ": 17167, "rás": 17168, "Ġappreciated": 17169, "Ġnah": 17170, "Ġposter": 17171, "Ġtale": 17172, "Ġhighlighted": 17173, "æĸĩ": 17174, "żeli": 17175, "Ġblockchain": 17176, "Ġmicrow": 17177, "Ġcinema": 17178, "ĠChang": 17179, "ĠSearch": 17180, "usters": 17181, "ĠZero": 17182, "ĠDivision": 17183, "ÑĢаÑģ": 17184, "Ġscare": 17185, "Ġjelly": 17186, "ĠAdministration": 17187, "SO": 17188, "Ġlined": 17189, "Ġê°Ħ": 17190, "Ġgeben": 17191, "Ġsoda": 17192, "Ġwinners": 17193, "³¼": 17194, "ÙĴ": 17195, "ĠAmb": 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"Ġbinary": 17434, "Ġì¶Ķ": 17435, "Ġserial": 17436, "Ġion": 17437, "Ġunemployment": 17438, "Ġodds": 17439, "ĠFab": 17440, "ĠFBI": 17441, "BRUN": 17442, "Ġweights": 17443, "νο": 17444, "atile": 17445, "Ġnurses": 17446, "Ġinvolvement": 17447, "ĠíĶ¼": 17448, "Ġgovernance": 17449, "ĠâĤ¬": 17450, "ÑĢÑĥп": 17451, "ierra": 17452, "íĺķ": 17453, "ĠJerry": 17454, "Ġbeard": 17455, "Ġsalvation": 17456, "ĠAlong": 17457, "gentle": 17458, "ĠKi": 17459, "bol": 17460, "ĠPlat": 17461, "Ġhasht": 17462, "è¿ij": 17463, "Ġware": 17464, "Ġpartie": 17465, "ycz": 17466, "Ġintr": 17467, "Fih": 17468, "nent": 17469, "Ġcheat": 17470, "ilen": 17471, "Ġë¯": 17472, "orie": 17473, "Ġfácil": 17474, "etric": 17475, "Ġaffecting": 17476, "unciation": 17477, "Ġaffairs": 17478, "Ġbee": 17479, "Ġviewing": 17480, "Ġorang": 17481, "ĠLan": 17482, "ĠСÑĤ": 17483, "ä¸ĸ": 17484, "ĠMes": 17485, "ĥģ": 17486, "erie": 17487, "Ġespa": 17488, "Ġinterpre": 17489, "Ġpossess": 17490, "Ġpurely": 17491, "rito": 17492, "found": 17493, "asma": 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"Ġjuris": 17785, "Ġpowered": 17786, "zial": 17787, "Ġwelfare": 17788, "Ġupgrad": 17789, "Ġmożna": 17790, "ĠCase": 17791, "cular": 17792, "ĶìĿ´": 17793, "ãĥģ": 17794, "ĠGuess": 17795, "Ġcycles": 17796, "ä¾ĭ": 17797, "給": 17798, "rock": 17799, "umi": 17800, "Ġelite": 17801, "Ġquè": 17802, "åł±": 17803, "ÑĤом": 17804, "Ġshore": 17805, "gunta": 17806, "Ġku": 17807, "Ġfaithful": 17808, "ĠJeremy": 17809, "aid": 17810, "à·": 17811, "ugal": 17812, "å°įåķĬ": 17813, "ĠVel": 17814, "Ġvrai": 17815, "stell": 17816, "¨¸": 17817, "Ġkol": 17818, "è½": 17819, "Ġquanto": 17820, "ĠзаÑĢ": 17821, "Ġ2002": 17822, "esy": 17823, "Ġreserve": 17824, "ĠмоменÑĤ": 17825, "Ġdeployed": 17826, "Ġdefining": 17827, "Ġsau": 17828, "Ġgaat": 17829, "\")": 17830, "Ġtransmit": 17831, "Ġpublishing": 17832, "Ġranking": 17833, "Ġoffense": 17834, "Ġ46": 17835, "pin": 17836, "ĠTaking": 17837, "Ġentitled": 17838, "Ġgenuinely": 17839, "Ġvariations": 17840, "Ġfinde": 17841, "Ġtau": 17842, "Ġunfortunate": 17843, "ĠRah": 17844, 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"Ġnitrogen": 17903, "ê·¼": 17904, "Ġabi": 17905, "Ġvillain": 17906, "Ġblanket": 17907, "ĠSend": 17908, "Ġbeaten": 17909, "²Ħ": 17910, "Ġvolunt": 17911, "Ġscholar": 17912, "ĠEmperor": 17913, "Ġ43": 17914, "vable": 17915, "ĠDus": 17916, "ĠGU": 17917, "Ġtargeting": 17918, "www": 17919, "Ġamendment": 17920, "ìĨĮë": 17921, "Ġting": 17922, "Ġnasty": 17923, "Ġgauge": 17924, "ĠÑĢод": 17925, "ĠHans": 17926, "Your": 17927, "αν": 17928, "Ġprojet": 17929, "ĠHawaii": 17930, "Ġsuspicious": 17931, "Ġschw": 17932, "Ġremoval": 17933, "Ġintrig": 17934, "ĠMU": 17935, "Ġponto": 17936, "ा": 17937, "ĠобÑĢаз": 17938, "Ġguessing": 17939, "pace": 17940, "Ġmothers": 17941, "Ġmillimeter": 17942, "ление": 17943, "没æľī": 17944, "Ġavailability": 17945, "icz": 17946, "æѤ": 17947, "Ġfract": 17948, "Ġbases": 17949, "km": 17950, "ĠBTS": 17951, "ĠField": 17952, "Ġdzie": 17953, "Ġsegundo": 17954, "ĠëĤĺëĬĶ": 17955, "Ġlegitimate": 17956, "imas": 17957, "Ġвн": 17958, "Ġcorruption": 17959, "Ġsmash": 17960, "ĠValent": 17961, "Ġaligned": 17962, "ĠPennsylvania": 17963, "Ġgab": 17964, "ĠEun": 17965, "enth": 17966, "ĠMorning": 17967, "Ġcandle": 17968, "Ġbackpack": 17969, "ĠIslamic": 17970, "ações": 17971, "Ġencry": 17972, "Ġmushrooms": 17973, "íĮĮ": 17974, "dit": 17975, "Ġtransit": 17976, "ĠWisconsin": 17977, "Ġparticipated": 17978, "ĠIls": 17979, "Ġunfold": 17980, "¶Ģë": 17981, "Ġprofits": 17982, "Ġwarming": 17983, "ĠGang": 17984, "Ġnetworking": 17985, "Ġmega": 17986, "Ġthoroughly": 17987, "lements": 17988, "ĠHm": 17989, "Ġdeciding": 17990, "Ġemotionally": 17991, "Ġexhausted": 17992, "ĠÐŁÐ¾ÑĤ": 17993, "cido": 17994, "ĠHTML": 17995, "Ġcopyright": 17996, "Ġmelody": 17997, "yim": 17998, "Ġanders": 17999, "oshop": 18000, "Ġë³¼": 18001, "Ġathlete": 18002, "ĠGE": 18003, "Ġfrequent": 18004, "Ġdesires": 18005, "Ġneeding": 18006, "ĠYun": 18007, "Ġrifle": 18008, "Ġlover": 18009, "'T": 18010, "Ġdense": 18011, "Ġtão": 18012, "Ġnotified": 18013, "Ġidi": 18014, "ìĹŃ": 18015, "íĨ": 18016, "Ġinteracting": 18017, "Ġrapport": 18018, "еÑĢи": 18019, "ski": 18020, "Ġbesser": 18021, "Ġmanufacturer": 18022, "ĠKyle": 18023, "Ġaccountable": 18024, "ĠSak": 18025, "ĠPil": 18026, "ĠDomin": 18027, "Ġpresum": 18028, "ĠÐĴÑģе": 18029, "Ġvinegar": 18030, "Ġguaranteed": 18031, "çľĭåĪ°": 18032, "Ġhandled": 18033, "éŁ³": 18034, "cat": 18035, "Ġcivilization": 18036, "Ġaccomp": 18037, "ĠVM": 18038, "émon": 18039, "Ġdeze": 18040, "Ġgrades": 18041, "Ġsollte": 18042, "Ġstaring": 18043, "×IJת": 18044, "arnt": 18045, "Ġhorizon": 18046, "Ġtravail": 18047, "hour": 18048, "第ä¸Ģ": 18049, "ĠED": 18050, "ĠDak": 18051, "Ġny": 18052, "Ġconve": 18053, "ĠCham": 18054, "Ġfirms": 18055, "ĠLiu": 18056, "ĠÑģÑĤÑĢан": 18057, "Ġlibert": 18058, "Ġlenses": 18059, "Ġintake": 18060, "ĠвÑĭб": 18061, "Ġmensen": 18062, "hel": 18063, "Ġpractition": 18064, "Ġ350": 18065, "ãĤ³": 18066, "FO": 18067, "Ġbeds": 18068, "Ġancestors": 18069, "ĠìĹĦì²Ń": 18070, "Ġdisturb": 18071, "ĠLastly": 18072, "ĠSupport": 18073, "ีà¹ī": 18074, "ĠCorona": 18075, "Ġenthusi": 18076, "Ġвозм": 18077, "ĠìĤ¬ëŀĮë": 18078, "Ġ52": 18079, "bird": 18080, "Ġreduces": 18081, "ĠìŀĪìĿĦ": 18082, "ĠGene": 18083, "êµIJ": 18084, "ÄĻp": 18085, "ĠÃľber": 18086, "Ġconcerning": 18087, "user": 18088, "Ġconcentrate": 18089, "ĠWHAT": 18090, "ishop": 18091, "onymous": 18092, "nold": 18093, "Ġsuggesting": 18094, "©°": 18095, "ĠFish": 18096, "........": 18097, "Ġvessel": 18098, "Ġtrabajo": 18099, "ãģµ": 18100, "ĠOcean": 18101, "å§IJ": 18102, "yg": 18103, "Ġtowns": 18104, "del": 18105, "Ġterrifying": 18106, "ĠçalÄ±ÅŁ": 18107, "Ġsino": 18108, "Ġeats": 18109, "Ġgez": 18110, "Ġgeme": 18111, "ĠìĻĦ": 18112, "Ġcompart": 18113, "Ġimplementing": 18114, "ĠPotter": 18115, "ĠGermans": 18116, "ĠgÅĤ": 18117, "Ġtennis": 18118, "Ġcarpet": 18119, "auer": 18120, "ĠSaudi": 18121, "yeong": 18122, "Ġcurry": 18123, "ĠForest": 18124, "Ñĭл": 18125, "Ġfifteen": 18126, "Ġbolts": 18127, "Ġ{\\": 18128, "¬´": 18129, "Ġsettlement": 18130, "Ġlange": 18131, "Ġbam": 18132, "Get": 18133, "íķĻ": 18134, "Ġswap": 18135, "ĠKhan": 18136, "Ġcommence": 18137, "Ġquarantine": 18138, "Ġscored": 18139, "çĸ": 18140, "Ġ1950": 18141, "Ġthicker": 18142, "Ġsûr": 18143, "åı£": 18144, "ĠLarry": 18145, "Ġallez": 18146, "ìĭľëĬĶ": 18147, "Ġgü": 18148, "Ġspectacular": 18149, "//": 18150, "both": 18151, "Ġstats": 18152, "妳": 18153, "ĠNancy": 18154, "Ġbunu": 18155, "Ġcrust": 18156, "Ġactivated": 18157, "Ġê·¸ëŀ": 18158, "outhe": 18159, "Ġports": 18160, "Ġneural": 18161, "Ġjaw": 18162, "Ġobservations": 18163, "Ġvoit": 18164, "aban": 18165, "ải": 18166, "¦¬ë¥¼": 18167, "omes": 18168, "à¯ĭ": 18169, "qui": 18170, "Ġkindness": 18171, "Ðij": 18172, "Ġ41": 18173, "Ġmoderate": 18174, "Ġangels": 18175, "ĠTamb": 18176, "èt": 18177, "Ġchlor": 18178, "ĠBilly": 18179, "ì²ĺë": 18180, "acon": 18181, "Ġselecting": 18182, "ĠDelta": 18183, "Ġnull": 18184, "denly": 18185, "Ġciud": 18186, "Ġtendency": 18187, "Ġbreakdown": 18188, "Ġmint": 18189, "ÑĦоÑĢм": 18190, "orph": 18191, "Ġdawn": 18192, "spr": 18193, "ĠWILL": 18194, "ächlich": 18195, "Ġpuppy": 18196, "700": 18197, "Ġத": 18198, "Ġfails": 18199, "ĠConc": 18200, "Ġrelatives": 18201, "Ġinviting": 18202, "Ġautonom": 18203, "Ġcomposed": 18204, "Ġunity": 18205, "Ġdecis": 18206, "Ġaccessories": 18207, "ĠCass": 18208, "Ġbist": 18209, "ĠTip": 18210, "째": 18211, "Ġpunt": 18212, "Ġráp": 18213, "éĢ²": 18214, "ANK": 18215, "ãģļ": 18216, "exist": 18217, "Ġcompatible": 18218, "Ġner": 18219, "ĠемÑĥ": 18220, "Ġaplic": 18221, "Ġbapt": 18222, "Ġfailing": 18223, "ĠTamam": 18224, "Ġoscill": 18225, "Ġletzten": 18226, "Ġrepeatedly": 18227, "Ġjungle": 18228, "ĠPush": 18229, "hai": 18230, "Ġη": 18231, "Ġdeadly": 18232, "Ñıж": 18233, "wiÄħ": 18234, "ĠCommon": 18235, "ĠÎķ": 18236, "Ġskate": 18237, "TC": 18238, "ĠMini": 18239, "Ġhobby": 18240, "ần": 18241, "Ġroutes": 18242, "Ġamigos": 18243, "Ġconjun": 18244, "Ġpartnerships": 18245, "Ġnovo": 18246, "Ġaver": 18247, "Ġpouvez": 18248, "bridge": 18249, "Ġpreoc": 18250, "him": 18251, "Ġturb": 18252, "Ġsob": 18253, "ĠSnap": 18254, "Ġì°¸": 18255, 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"ĠìĿ´ê±´": 21867, "ĠpaÅĦst": 21868, "они": 21869, "itta": 21870, "Ġconfirmation": 21871, "ĠBrooklyn": 21872, "Ġnoodle": 21873, "fund": 21874, "itud": 21875, "Ġgrandparents": 21876, "Ġbarbecue": 21877, "ειÏĤ": 21878, "Ġá": 21879, "Ġballot": 21880, "ĠVeter": 21881, "Ġpipes": 21882, "igious": 21883, "ĠGraph": 21884, "ested": 21885, "Ġë¸Įë": 21886, "ĠKE": 21887, "ãģ¡ãĤĩãģ£ãģ¨": 21888, "Ġeins": 21889, "Ġhatred": 21890, "ãģijãģ©": 21891, "Ġdang": 21892, "eeee": 21893, "Ġarchae": 21894, "ĠJesse": 21895, "Ġdetected": 21896, "Ġseni": 21897, "burgh": 21898, "Ġdisplacement": 21899, "Ġdop": 21900, "Ġconditioning": 21901, "ĠнеÑģколÑĮко": 21902, "Ġdisturbing": 21903, "PH": 21904, "Ġthinner": 21905, "Ġwounded": 21906, "ĠCuando": 21907, "Ġcushion": 21908, "Ġwhites": 21909, "Ġpreferences": 21910, "Ġì¤Ģë¹Ħ": 21911, "Ġkaż": 21912, "ĠGate": 21913, "ĠPath": 21914, "dles": 21915, "à¸Ħร": 21916, "imore": 21917, "Ġë³´ìŬ": 21918, "Ġdisciplines": 21919, "á»ı": 21920, "Ġmesma": 21921, "ĠìĥĪë": 21922, "Ġìĭ¬": 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"ĠgÃ¥": 22098, "試": 22099, "ת×Ļ": 22100, "ĠWayne": 22101, "Ġinstagram": 22102, "Ġrated": 22103, "paper": 22104, "Ġbild": 22105, "Ġpretending": 22106, "Ġobserving": 22107, "ĠÑģамом": 22108, "Ġtror": 22109, "Ġorganisms": 22110, "Ġfalta": 22111, "Ġhometown": 22112, "ç±": 22113, "Ġíĭ": 22114, "Ġcheg": 22115, "Ġì¡": 22116, "Ġcomma": 22117, "isé": 22118, "Ġlikelihood": 22119, "avored": 22120, "Ġgeldi": 22121, "ников": 22122, "Ġmedio": 22123, "Ġjakie": 22124, "ĠJup": 22125, "Ġgreenhouse": 22126, "Ġspit": 22127, "кое": 22128, "Ġкаж": 22129, "ĠGram": 22130, "ĠConference": 22131, "Ġdeficit": 22132, "sın": 22133, "inse": 22134, "uÄŁ": 22135, "Ġricht": 22136, "Ġcoincidence": 22137, "åıį": 22138, "Ġeurop": 22139, "Ġbutterfly": 22140, "pread": 22141, "Ġìĸ¼": 22142, "èĢ¶": 22143, "Ġwavel": 22144, "ĠInfin": 22145, "ĠPlanet": 22146, "Ġselfie": 22147, "ientras": 22148, "Ġarrog": 22149, "oser": 22150, "idal": 22151, "ł×Ĺ׳×ķ": 22152, "ütün": 22153, "Ġfreshman": 22154, "ĠMachine": 22155, "ÏĥÏĦ": 22156, "ĠDia": 22157, "ìĿ´ëĭ¤": 22158, "ãģĵãģĨ": 22159, "nea": 22160, "Ġlisting": 22161, "Ġconfigure": 22162, "utor": 22163, "Up": 22164, "tschaft": 22165, "rière": 22166, "Ġupwards": 22167, "ĠÑħоÑĩÑĥ": 22168, "Ġsweep": 22169, "Br": 22170, "Ġexpressing": 22171, "Ġunhappy": 22172, "Ġmandatory": 22173, "gender": 22174, "ĠAÃŃ": 22175, "Ġindicators": 22176, "Ġoils": 22177, "note": 22178, "Ġsegur": 22179, "ожеÑĤ": 22180, "ynasty": 22181, "Ġdistances": 22182, "Ġmerge": 22183, "BERT": 22184, "Ġsurrender": 22185, "Ġbuat": 22186, "ĠAwards": 22187, "Ġseñor": 22188, "odox": 22189, "Ġflavour": 22190, "Ġabdom": 22191, "Ġconfigur": 22192, "86": 22193, "ĠDIY": 22194, "Ġrigid": 22195, "°ĺ": 22196, "Ġcorporation": 22197, "Ġgroom": 22198, "jaw": 22199, "ĠNear": 22200, "ило": 22201, "Ġopera": 22202, "ĠInnov": 22203, "иÑĢа": 22204, "ĵ±": 22205, "Ġspecified": 22206, "Ġcosm": 22207, "ĠFreedom": 22208, "Ġclown": 22209, "ĠNem": 22210, "Ġвол": 22211, "Ñijн": 22212, "Ġcharger": 22213, "à¹ģล": 22214, "Ġinfluential": 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26135, "achte": 26136, "ĠKol": 26137, "elujah": 26138, "Ġhav": 26139, "ĠCrime": 26140, "sek": 26141, "Ġë¡ľ": 26142, "ienna": 26143, "ĠGor": 26144, "èĽ": 26145, "ĠпоÑĤÑĢ": 26146, "ĠкажеÑĤÑģÑı": 26147, "ĠLift": 26148, "ĠSort": 26149, "ĠPsal": 26150, "Ġping": 26151, "ĵĿ": 26152, "phis": 26153, "ĠFUCK": 26154, "ĠSyn": 26155, "Ġbamboo": 26156, "¬ìĺģ": 26157, "cuts": 26158, "Ġmmm": 26159, "Ġfunktioniert": 26160, "Ġ_": 26161, "ÃŃcio": 26162, "Stop": 26163, "Ġimaginary": 26164, "Ġnotamment": 26165, "ĠInitiative": 26166, "ãĥ¥": 26167, "ĠKurt": 26168, "Ġloosen": 26169, "Ġbuscar": 26170, "çģ«": 26171, "Ġzelf": 26172, "Ġprops": 26173, "åĽī": 26174, "Ġmoeten": 26175, "Ġmilli": 26176, "Ġhalls": 26177, "ĠMatch": 26178, "Ġbrackets": 26179, "ĠCou": 26180, "æ¦Ĥ": 26181, "ĠÐľÐ°ÑĢ": 26182, "ISA": 26183, "Ġcigarette": 26184, "Ġcompetitions": 26185, "ĠMIN": 26186, "Ġbehö": 26187, "voor": 26188, "Ġust": 26189, "ĠZi": 26190, "ĠOcc": 26191, "ulates": 26192, "Ġballoons": 26193, "Ġpronto": 26194, "ĠMiy": 26195, 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26252, "Ġinterconnect": 26253, "éIJĺ": 26254, "OSH": 26255, "æŃ²": 26256, "Ġbrass": 26257, "Ġignoring": 26258, "ä»ĬæĹ¥": 26259, "infect": 26260, "Ġprojekt": 26261, "oret": 26262, "ÏĦαν": 26263, "ĠÑĤип": 26264, "Ġmutta": 26265, "Ġunboxing": 26266, "Ħ°": 26267, "å¡Ĭ": 26268, "Ġadvised": 26269, "ĠDenver": 26270, "Ġseverely": 26271, "ĠMhm": 26272, "Ġflipped": 26273, "Ġpien": 26274, "Ġkommun": 26275, "ĠFRE": 26276, "Ġà®ĩà®°": 26277, "ainted": 26278, "Ġknives": 26279, "Ġhabl": 26280, "Ġgeworden": 26281, "arettes": 26282, "CS": 26283, "ĠмаленÑĮ": 26284, "Ġgalax": 26285, "Ġninete": 26286, "ê±°ëĤĺ": 26287, "Ġsis": 26288, "Ġadvisory": 26289, "Ġdrilling": 26290, "ĠWouldn": 26291, "ünf": 26292, "gestellt": 26293, "ĠHelen": 26294, "Ġ×ŀ×IJ": 26295, "apolis": 26296, "Ġrzeczy": 26297, "Ġterra": 26298, "Ġhep": 26299, "Ġalgún": 26300, "ikk": 26301, "Ġastronom": 26302, "ĠStarbucks": 26303, "kÄħ": 26304, "Ġpatrol": 26305, "Ġì½Ķ": 26306, "Ġgon": 26307, "ĠãĢIJ": 26308, "Ġsonst": 26309, "Ġencounters": 26310, 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"ãģ¾ãģĽãĤĵ": 30250, "Ġutterly": 30251, "Ġsynthesis": 30252, "Ġlesbian": 30253, "Ġretrieve": 30254, "Ġmaneira": 30255, "Ġimpair": 30256, "Ġmentoring": 30257, "ĠSouls": 30258, "ĠGoPro": 30259, "ÑĢаÑĤÑĮ": 30260, "Ġcose": 30261, "ĠSSD": 30262, "IRE": 30263, "Ġupfront": 30264, "ĠAun": 30265, "Ġgamer": 30266, "Ġlitt": 30267, "Ġaggression": 30268, "ĠLikewise": 30269, "ĠBetty": 30270, "ĠDart": 30271, "ĠDLC": 30272, "ishment": 30273, "ìŀ¥ìĿĦ": 30274, "Ġ对": 30275, "ç»ı": 30276, "cream": 30277, "ĠBabylon": 30278, "Ġnug": 30279, "brar": 30280, "Ġaynı": 30281, "amily": 30282, "bike": 30283, "ahahaha": 30284, "loyd": 30285, "Ġmira": 30286, "Ġperme": 30287, "ĠGaming": 30288, "Ġfirmware": 30289, "Ma": 30290, "Ġassisted": 30291, "atics": 30292, "Ġìķŀìľ¼ë¡ľ": 30293, "ĠMental": 30294, "niejs": 30295, "ĠIz": 30296, "owÄħ": 30297, "Ġtougher": 30298, "Ġdeed": 30299, "èĭ¦": 30300, "Ġstylish": 30301, "ĠTools": 30302, "ĠHamp": 30303, "Ġsunscreen": 30304, "Ġarticulate": 30305, "iye": 30306, "иÑĦ": 30307, 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30364, "onent": 30365, "BB": 30366, "ĠIncre": 30367, "Ġsini": 30368, "ÃŁer": 30369, "èg": 30370, "maal": 30371, "generation": 30372, "Ġminorities": 30373, "Ġllevar": 30374, "Ġnomination": 30375, "Ġconsid": 30376, "Ġ×ľ×¢": 30377, "muÅŁ": 30378, "ĠEsc": 30379, "Ġnumerator": 30380, "Ġkaik": 30381, "Ġktórych": 30382, "iesen": 30383, "Ġvê": 30384, "ĠUSS": 30385, "ĠPrivate": 30386, "Ġодно": 30387, "Ġalém": 30388, "ÃŃtulo": 30389, "Ġlimb": 30390, "Ġforgiven": 30391, "Ġdisclosure": 30392, "ÏĦί": 30393, "Ġningún": 30394, "Ġtherapeutic": 30395, "Ġnegotiating": 30396, "ĠNike": 30397, "enseful": 30398, "Ġincap": 30399, "Ġflagship": 30400, "town": 30401, "âĪ": 30402, "ĠÏĢολ": 30403, "Ġwolves": 30404, "Ġviolations": 30405, "ĠArnold": 30406, "Ġintervene": 30407, "Ġheater": 30408, "Ġrecursos": 30409, "Ġmaid": 30410, "ê²¼": 30411, "ĠдавайÑĤе": 30412, "ĠCelebr": 30413, "Ġcape": 30414, "ĠSty": 30415, "ainen": 30416, "site": 30417, "bij": 30418, "ĠполÑĮз": 30419, "Ġframed": 30420, "Ġpublishers": 30421, "ĠÑĩÑĥÑĤÑĮ": 30422, "Ġtemptation": 30423, "Ġcerteza": 30424, "Ġexempt": 30425, "ìĬ¹": 30426, "selling": 30427, "ĠTask": 30428, "hoon": 30429, "ĠCoc": 30430, "ĠParks": 30431, "Ġrepetition": 30432, "ĠÑĤÑĥда": 30433, "Ġensl": 30434, "ĠdeÄŁiÅŁ": 30435, "ĠOrlando": 30436, "ĠMainten": 30437, "æŃ¢": 30438, "ocument": 30439, "ĠHC": 30440, "Ġscooter": 30441, "ĠнапиÑģ": 30442, "Ġtighter": 30443, "Ġtease": 30444, "Ġremoves": 30445, "Ġkijken": 30446, "ĠÑģÑĥÑīеÑģÑĤв": 30447, "Ġthé": 30448, "ĠвÑĭглÑıд": 30449, "Ġrelieve": 30450, "Ġmitä": 30451, "Ġstationary": 30452, "öff": 30453, "pable": 30454, "Ġarter": 30455, "Ġdéf": 30456, "rative": 30457, "Ġconect": 30458, "Ġsaddle": 30459, "ĠDiane": 30460, "Ġcommemor": 30461, "fendim": 30462, "SÃŃ": 30463, "Ġíģ´ë": 30464, "Ġmange": 30465, "atte": 30466, "Ġarrogant": 30467, "Ġrobotic": 30468, "ĠgiÃł": 30469, "æĺ¯çļĦ": 30470, "Ġneighbourhood": 30471, "isson": 30472, "Ġдвиж": 30473, "ĠRI": 30474, "ĠNorman": 30475, "brand": 30476, "amation": 30477, "Ġrazor": 30478, "Ġmurders": 30479, "ĠÑĤÑĥ": 30480, "Ġwszystkim": 30481, "Ġutilities": 30482, "Ġmicroscop": 30483, "ê¿": 30484, "Ġdaqui": 30485, "ollar": 30486, "ĠÐĶавайÑĤе": 30487, "Ġannée": 30488, "Ġkilometres": 30489, "Ġhomosexual": 30490, "Ġarchitects": 30491, "ãģ¡ãģ¯": 30492, "Ġniye": 30493, "LER": 30494, "Ġmicrophones": 30495, "ĠStunden": 30496, "Ġconsecutive": 30497, "ienda": 30498, "vänd": 30499, "DER": 30500, "Ġlifts": 30501, "ĠMeat": 30502, "Ġsavez": 30503, "íĸĪëįĺ": 30504, "Men": 30505, "Ġdismant": 30506, "거를": 30507, "Ġinsulation": 30508, "Ġscall": 30509, "Ġspooky": 30510, "Ġparc": 30511, "Ġballet": 30512, "ĠWhatsApp": 30513, "Ġfranc": 30514, "Ġdeliberate": 30515, "ĠíħĮ": 30516, "Ġmars": 30517, "ĠZur": 30518, "Pr": 30519, "disciplinary": 30520, "Ġobsession": 30521, "ме": 30522, "Ġmarching": 30523, "ĠEmergency": 30524, "iguous": 30525, "Ġszy": 30526, "ĠLands": 30527, "Ġboarding": 30528, "ĠпоÑĩÑĤи": 30529, "Ġenvy": 30530, "Ġcompassionate": 30531, "Ġmerci": 30532, "Ġdesirable": 30533, "dale": 30534, "Ġcanım": 30535, "ĠAntar": 30536, "temps": 30537, "Ġconfigured": 30538, "ĠCompared": 30539, "neh": 30540, "icating": 30541, "Ġnickel": 30542, "ÙĪÙĤ": 30543, "ÙĥÙĪÙĨ": 30544, "opes": 30545, "Ġformulas": 30546, "ĠÐķÑģÑĤÑĮ": 30547, "Ġpobl": 30548, "ĠPJ": 30549, "ĠLud": 30550, "ä»ĬåĽŀ": 30551, "ĠBrid": 30552, "ĠHog": 30553, "ĠBris": 30554, "Jen": 30555, "Ġshading": 30556, "ĠYas": 30557, "Ġdisturbed": 30558, "Ġrecommending": 30559, "Ġcé": 30560, "ĠHOW": 30561, "ìĹĪìĸ´": 30562, "Ġreversed": 30563, "ĠInterestingly": 30564, "ioxid": 30565, "åħŃ": 30566, "Ġìĺ¤ì¼ĢìĿ´": 30567, "ếu": 30568, "xx": 30569, "Ġouais": 30570, "ĠYouTubers": 30571, "ĠRosa": 30572, "ĠHaupt": 30573, "jadi": 30574, "Ġvlogs": 30575, "Ġcultura": 30576, "ĠLeadership": 30577, "ĠHep": 30578, "Ġillum": 30579, "´ëıĻ": 30580, "Ġcustomized": 30581, "Ġmarca": 30582, "Ġquatro": 30583, "Ġнаг": 30584, "ĠSpaceX": 30585, "ĠEigen": 30586, "asting": 30587, "ĠolduÄŁu": 30588, "Ġforts": 30589, "ãģī": 30590, "riment": 30591, "iencia": 30592, "Ġtenir": 30593, "roffen": 30594, "Ġ1979": 30595, "Ġcie": 30596, "ĠëIJĺê³ł": 30597, "Ġescri": 30598, "ÏĮÏĤ": 30599, "íı¬": 30600, "uzzy": 30601, "Cong": 30602, "ìĿ¸ìĿ´": 30603, "Great": 30604, "sil": 30605, "éch": 30606, "ãģ¨ãģĭ": 30607, "Ġmultic": 30608, "ĠDisk": 30609, "²ķ": 30610, "Ġfazla": 30611, "Ġlevant": 30612, "Ġabajo": 30613, "urry": 30614, "stru": 30615, "Ġ먹ëĬĶ": 30616, "Ġaccessory": 30617, "Ġдвиг": 30618, "ĠRid": 30619, "2019": 30620, "Ġdownstream": 30621, "æķ¸": 30622, "Ġkaz": 30623, "utan": 30624, "Ġcharcoal": 30625, "Ġafect": 30626, "wu": 30627, "Ġcontexts": 30628, "Ġfeared": 30629, "ĠìĦ¤": 30630, "Ġhistories": 30631, "Ġfas": 30632, "ensible": 30633, "Ġcocoa": 30634, "illar": 30635, "geons": 30636, "Ġspirituality": 30637, "ĠPew": 30638, "Ġpharmacy": 30639, "Ġpassions": 30640, "Ġbos": 30641, "Ġallá": 30642, "Ġthriving": 30643, "ĠReact": 30644, "Ġoccupy": 30645, "Ġwithdrawal": 30646, "Ġallowance": 30647, "ĠFraktion": 30648, "Ġbuddies": 30649, "Ġidle": 30650, "Ġdissolved": 30651, "Ġprevalent": 30652, "Ġmilitar": 30653, "Ġsensing": 30654, "Ġpojaw": 30655, "Ġancora": 30656, "Ġabundant": 30657, "Ġhairst": 30658, "ãģĤãĤĮ": 30659, "Ġtwee": 30660, "Ġnächste": 30661, "ĠMöglichkeit": 30662, "Ġhoo": 30663, "ufficient": 30664, "Ġfantast": 30665, "Ġedible": 30666, "Ġëĸ¨ìĸ´ì": 30667, "ìĽĥ": 30668, "Ġvein": 30669, "ucci": 30670, "Ġdevotion": 30671, "Ġconcealer": 30672, "income": 30673, "Ġrecycled": 30674, "ĠìĬ¤íĥĢ": 30675, "Ġpontos": 30676, "Ġdessus": 30677, "Ġvérit": 30678, "Ġreflections": 30679, "ĠAA": 30680, "Ġtakeaway": 30681, "bare": 30682, "ĠContact": 30683, "eil": 30684, "ĠHear": 30685, "Ġmirac": 30686, "ĠGerilim": 30687, "ĠÑģамÑĭй": 30688, "Ġvivo": 30689, "Ġkilograms": 30690, "ĠCrim": 30691, "ût": 30692, "78": 30693, "Ġsincerely": 30694, "raz": 30695, "Ġë³µ": 30696, "Ġarriv": 30697, "Ġconception": 30698, "ĠPersian": 30699, "Ġsjäl": 30700, "Ġstarring": 30701, "ĠìķĦ무": 30702, "ĠForever": 30703, "еÑģÑĤÑĮ": 30704, "Ġveil": 30705, "Ġsubtit": 30706, "odka": 30707, "ĠоÑĤноÑĪ": 30708, "Ġcooks": 30709, "енÑı": 30710, "Kay": 30711, "Ġniños": 30712, "ĠPhone": 30713, "Ġstitching": 30714, "Ġfingerprint": 30715, "é¢ĺ": 30716, "λά": 30717, "Ġdedicate": 30718, "ĠLob": 30719, "Ġblacks": 30720, "ĠBle": 30721, "bout": 30722, "ĠÄijang": 30723, "Ġeks": 30724, "Ġsquash": 30725, "ĠKü": 30726, "odi": 30727, "ĠnÆ°á»Ľc": 30728, "Ġvoyage": 30729, "Ġplayful": 30730, "ĠØ¥ÙĦÙī": 30731, "anic": 30732, "Ġcondemn": 30733, "ĠBöyle": 30734, "ĠPolize": 30735, "ãĤ¿ãĥ¼": 30736, "Ġayuda": 30737, "Ġpam": 30738, "à¹Ħà¸Ľ": 30739, "ĠKathy": 30740, "един": 30741, "нова": 30742, "Ġbrig": 30743, "eger": 30744, "Ġeagle": 30745, "Ġvisions": 30746, "ĠíķŃìĥģ": 30747, "Ġshitty": 30748, "Ġhott": 30749, "ĠBritt": 30750, "utors": 30751, "ENTE": 30752, "æĽ²": 30753, "Ġphon": 30754, "ĠBing": 30755, "ĠподдеÑĢж": 30756, "spring": 30757, "æĸ¯": 30758, "etten": 30759, "Ġpilgr": 30760, "Ġediyor": 30761, "енÑĤÑĭ": 30762, "aggio": 30763, "Ġjul": 30764, "Ġcomprend": 30765, "teil": 30766, "Ġز": 30767, "Ġperformers": 30768, "Ġinfamous": 30769, "ĠMK": 30770, "çª": 30771, "æ³ģ": 30772, "otle": 30773, "eff": 30774, "ĠHash": 30775, "Ġcoward": 30776, "ĠBRA": 30777, "ĠDD": 30778, "Ġcomida": 30779, "Ġplata": 30780, "Ġflap": 30781, "ĠMehr": 30782, "ribution": 30783, "ĠYemen": 30784, "Ġmysteries": 30785, "ĠÄ°yi": 30786, "Ġstell": 30787, "Ġeyeliner": 30788, "Ġdeles": 30789, "Ġnailed": 30790, "Ġillnesses": 30791, "Ġstacks": 30792, "Ġtrabajar": 30793, "flower": 30794, "ciu": 30795, "Ġcrude": 30796, "Ġsubstantially": 30797, "Ġhomem": 30798, "Ġnephew": 30799, "Ġstamps": 30800, "Ġcarbs": 30801, "ÑĮÑĤе": 30802, "mooth": 30803, "Ġtunnels": 30804, "acie": 30805, "æ³¢": 30806, "ĠSeñ": 30807, "ĠHera": 30808, "ĠìķĦëĭĪìĹIJìļĶ": 30809, "ĠWyoming": 30810, "ĠHDMI": 30811, "ĠLis": 30812, "ución": 30813, "Ġsteer": 30814, "оÑİ": 30815, "иÑĤа": 30816, "NT": 30817, "Ġìĸ¼êµ´": 30818, "Ġpalms": 30819, "Ġneon": 30820, "ованиÑı": 30821, "Ġfiltering": 30822, "Ġjouer": 30823, "ĠHö": 30824, "ĠнеÑģ": 30825, "ê²łìĸ´ìļĶ": 30826, "Ġ81": 30827, "Ġstoryline": 30828, "Ġprzep": 30829, "Ġthanking": 30830, "ĠBoeing": 30831, "Ġsoftly": 30832, "jem": 30833, "алÑĮнÑĭÑħ": 30834, "Ġflashlight": 30835, "ĠпÑĥ": 30836, "ĠWOMAN": 30837, "ắc": 30838, "ÃŃch": 30839, "Ġluxurious": 30840, "Ġwün": 30841, "Ġimpactful": 30842, "Ġconson": 30843, "reu": 30844, "irring": 30845, "ifter": 30846, "Ġconstituents": 30847, "èIJ½": 30848, "Ġ94": 30849, "ĠTou": 30850, "gom": 30851, "ĠìĥĿê°ģìĿĦ": 30852, "Ġstereotypes": 30853, "Ġmożli": 30854, "åĪĨ享": 30855, "Ĥ¨": 30856, "Ġpencils": 30857, "ĠÑģлож": 30858, "Ġihrem": 30859, "ĠBesch": 30860, "ĠKoh": 30861, "ĠEntscheid": 30862, "Ġlek": 30863, "Ġförs": 30864, "Ġtotalmente": 30865, "Ġlively": 30866, "Ġentropy": 30867, "Ġdiscern": 30868, "ĠÐĹна": 30869, "Ġdov": 30870, "Ġmythology": 30871, "è¨ĺå¾Ĺ": 30872, "apanese": 30873, "Ġapproximate": 30874, "аÑĤив": 30875, "ifiable": 30876, "ĠSeo": 30877, "åĢĴ": 30878, "´ìĭ¬íŀĪ": 30879, "Ġìĺ·": 30880, "Ġtemporal": 30881, "ĠiT": 30882, "Ġestat": 30883, "ким": 30884, "Ġsprink": 30885, "Ġgrund": 30886, "Ġinfantry": 30887, "Ġschaffen": 30888, "ç´Ħ": 30889, "Ġank": 30890, "riages": 30891, "ĠYeon": 30892, "ĠMoroc": 30893, "Ġinvasive": 30894, "ģĶ": 30895, "Ġparenting": 30896, "ĠRis": 30897, "ibile": 30898, "Ġmods": 30899, "å½¢": 30900, "ĠпÑĢовеÑĢ": 30901, "ĠThing": 30902, "ĠWherever": 30903, "Ġacknowledging": 30904, "Ġpawn": 30905, "ummer": 30906, "orb": 30907, "69": 30908, "Ġretrouve": 30909, "Ġrelies": 30910, "ĠHighway": 30911, "Ġawe": 30912, "ãģ§ãģĻãģĭ": 30913, "itaire": 30914, "Ġapplicant": 30915, "Ġaisle": 30916, "worm": 30917, "Ġpayload": 30918, "Ġcarre": 30919, "ĠBach": 30920, "æł¼": 30921, "Ġì¹ľêµ¬ë": 30922, "ние": 30923, "ĠitÃŃs": 30924, "onnaise": 30925, "sol": 30926, "èı¯": 30927, "algia": 30928, "Ġrocking": 30929, "Ġbesten": 30930, "rites": 30931, "^^": 30932, "иной": 30933, "Ġbaixo": 30934, "Ġ기ìĸµ": 30935, "оÑĤÑĢи": 30936, "sim": 30937, "Ġincarn": 30938, "ëĭ¤ìĿĮ": 30939, "Ġlick": 30940, "sided": 30941, "Ġ71": 30942, "forder": 30943, "Ġresonance": 30944, "Ġtegen": 30945, "Ġmetaph": 30946, "owser": 30947, "Ġ×IJ׳×Ĺ׳×ķ": 30948, "?ãĢį": 30949, "Ġspielen": 30950, "Ġvolley": 30951, "ĶìĿ´íģ¬ìĹħ": 30952, "looked": 30953, "Ġsentenced": 30954, "Ġmultiplying": 30955, "Ġideals": 30956, "Ġwahrscheinlich": 30957, "Ġdeposits": 30958, "bilir": 30959, "Ġeffet": 30960, "illon": 30961, "Īë§Į": 30962, "Ġtestimon": 30963, "Ġzawsze": 30964, "ĠпÑĢоÑĨеÑģÑģ": 30965, "ĠLav": 30966, "ä¸įéĮ¯": 30967, "Ġtravailler": 30968, "Ġlaisse": 30969, "ĠMountains": 30970, "ĠÑĢоб": 30971, "Ġexamined": 30972, "itus": 30973, "Was": 30974, "лÑĭ": 30975, "Ġattributed": 30976, "ĠìĬ¹": 30977, "ĠBaron": 30978, "Ġgep": 30979, "Ġattent": 30980, "ĠCollection": 30981, "Ġtheat": 30982, "ĠCai": 30983, "Ġwells": 30984, "Ġhumano": 30985, "çĹħ": 30986, "ĠHast": 30987, "ĠÑħоÑĤÑı": 30988, "czas": 30989, "Ġpermits": 30990, "Ġlegg": 30991, "Ġepo": 30992, "ĠFen": 30993, "Ġthi": 30994, "ĠFoi": 30995, "Ġélect": 30996, "Ġ83": 30997, "Ġoverth": 30998, "Ġè¬Ŀè¬Ŀ": 30999, "Ġtenant": 31000, "è²·": 31001, "Next": 31002, "Ġpraised": 31003, "security": 31004, "ĠImpact": 31005, "为ä»Ģä¹Ī": 31006, "Ġvouch": 31007, "Ġnegó": 31008, "Ġunve": 31009, "Ġcriticize": 31010, "ĠKenya": 31011, "Ġtactic": 31012, "Ġlogr": 31013, "Ġpois": 31014, "Ġpapa": 31015, "speaks": 31016, "ðŁij": 31017, "ispers": 31018, "Ġsurplus": 31019, "Ġcolder": 31020, "åįĹ": 31021, "åIJ¬": 31022, "plets": 31023, "ĠVienna": 31024, "ĠLead": 31025, "Ġaerial": 31026, "ĠTah": 31027, "енÑĤов": 31028, "ĠGreeks": 31029, "Cam": 31030, "Ġmáxim": 31031, "Ġkuin": 31032, "chio": 31033, "Ġdemonstrates": 31034, "anos": 31035, "ĠCert": 31036, "ĠÑįн": 31037, "Ġblogs": 31038, "ĠìĦľìļ¸": 31039, "Ġbeams": 31040, "иков": 31041, "Ġprompted": 31042, "Ġfrightening": 31043, "ĠPorsche": 31044, "ãģĪãģ¦": 31045, "larını": 31046, "Ġchilling": 31047, "isphere": 31048, "Ġflashing": 31049, "ĠKard": 31050, "bread": 31051, "Ġexh": 31052, "Ġtycker": 31053, "Ġecological": 31054, "ĠMae": 31055, "Ġ×ŀ×IJ×ķ×ĵ": 31056, "ĠëĤĺëıĦ": 31057, "лон": 31058, "yss": 31059, "Ġpergunt": 31060, "Ġprix": 31061, "izzard": 31062, "Ġcancers": 31063, "Ġ91": 31064, "susp": 31065, "ĠItem": 31066, "ÅŁa": 31067, "Ġpest": 31068, "ĠtakÄħ": 31069, "Ġlymph": 31070, "ĠPatri": 31071, "fill": 31072, "Ġreconna": 31073, "Ġoptimism": 31074, "Ġmimic": 31075, "Ġì²ľ": 31076, "ĠMadame": 31077, "ocy": 31078, "lining": 31079, "åijĬ訴": 31080, "erme": 31081, "Ġfolders": 31082, "ĠczÅĤ": 31083, "uchar": 31084, "Ġcurso": 31085, "Ġbreach": 31086, "ниÑĤÑĮ": 31087, "ĠpamiÄĻ": 31088, "Ġelig": 31089, "Ġautop": 31090, "Flow": 31091, "Ġprogrammed": 31092, "ĠProcess": 31093, "Ġfigur": 31094, "ĠSF": 31095, "ĠEles": 31096, "Ġprogrammes": 31097, "Ġdizzy": 31098, "ìĭľê°Ħ": 31099, "Ġлибо": 31100, "Ġsniff": 31101, "ĠSebastian": 31102, "ĠHye": 31103, "Ġ4000": 31104, "Ġpermite": 31105, "æ¢Ŀ": 31106, "ĠзаÑī": 31107, "Ġguit": 31108, "ĠDais": 31109, "Ġaccordance": 31110, "Ġmodular": 31111, "ogeneous": 31112, "æĭį": 31113, "Ġpouquinho": 31114, "Ġartillery": 31115, "Ġlubric": 31116, "Ġvolcan": 31117, "ĠNH": 31118, "ðŁ¤": 31119, "Ġdean": 31120, "Rh": 31121, "Ġministre": 31122, "åĿIJ": 31123, "ĠInv": 31124, "ĠBulgar": 31125, "ĠDaten": 31126, "èİ": 31127, "Im": 31128, "Ġoriginated": 31129, "ĠNixon": 31130, "integr": 31131, "Ġlacks": 31132, "ĠNacht": 31133, "ìĸ´ëĤĺ": 31134, "camera": 31135, "Ġradish": 31136, "kiye": 31137, "Ġanges": 31138, "Ġpréf": 31139, "juk": 31140, "ĠBee": 31141, "ĠBU": 31142, "ĠвоÑģп": 31143, "ĠBT": 31144, "êmes": 31145, "ĠStück": 31146, "ĠInk": 31147, "æĪĸèĢħ": 31148, "ĠSergeant": 31149, "ĠMultip": 31150, "Ġhiçbir": 31151, "ĠСам": 31152, "ĠDé": 31153, "olph": 31154, "ìĸ¸": 31155, "Ġimpat": 31156, "ĠìķĬê³ł": 31157, "ĠÑĤакого": 31158, "ĠнавеÑĢное": 31159, "Ġunpredictable": 31160, "Ġmend": 31161, "ĠìĹĨìĸ´ìļĶ": 31162, "ĠjakieÅĽ": 31163, "Ġanni": 31164, "Ġdonné": 31165, "ĠKirsty": 31166, "Ġrectangular": 31167, "Ġempezar": 31168, "ĠExchange": 31169, "ê°Ķ": 31170, "Ġéconom": 31171, "ãģĵãĤĵ": 31172, "elin": 31173, "reibt": 31174, "Ġ×Ķפ": 31175, "Ġcemetery": 31176, "Ġespañol": 31177, "olin": 31178, "лÑİд": 31179, "Ġgrâce": 31180, "allen": 31181, "ĠPhilos": 31182, "ĠErst": 31183, "ĠìĥĪ": 31184, "ĠVid": 31185, "Give": 31186, "OH": 31187, "μο": 31188, "ĠPare": 31189, "Ġmetabolism": 31190, "Ġmaple": 31191, "Ġaxle": 31192, "ĠDy": 31193, "Ġkomme": 31194, "Ïİν": 31195, "Ġgreatness": 31196, "Ġverified": 31197, "Ġspé": 31198, "ĠFahrenheit": 31199, "ĠBren": 31200, "ĠConfeder": 31201, "Ġhistoire": 31202, "Ġeliminating": 31203, "ĠAdding": 31204, "ĠAbi": 31205, "æĿİ": 31206, "Ġhospitality": 31207, "tim": 31208, "Ġbonito": 31209, "Ġpartes": 31210, "ĠдÑĢÑĥгиÑħ": 31211, "ĠShay": 31212, "ĠSed": 31213, "Ġregrets": 31214, "Ñıми": 31215, "Ġtenants": 31216, "éĢŁ": 31217, "ĠPTS": 31218, "Ġdevi": 31219, "ĠLate": 31220, "uez": 31221, "Ġsöyl": 31222, "ãĤ»": 31223, "Ġìŀ¬ë°Į": 31224, "Ġtoggle": 31225, "Ġmasking": 31226, "алÑĮного": 31227, "Ġpersön": 31228, "Ġamerican": 31229, "fik": 31230, "ĠRGB": 31231, "enson": 31232, "ĠKA": 31233, "wwww": 31234, "ĠÑĢег": 31235, "metics": 31236, "Ġeducator": 31237, "ãĤ·ãĥ«ãĤ¯": 31238, "park": 31239, "елÑĮзÑı": 31240, "arus": 31241, "ÑĢеÑĤ": 31242, "Ġfeito": 31243, "Ġchoir": 31244, "Ġlargo": 31245, "Ġeens": 31246, "Ġwatts": 31247, "ĠSingle": 31248, "Ġsusceptible": 31249, "icer": 31250, "ĠвклÑİÑĩ": 31251, "Ġpus": 31252, "íĻĺ": 31253, "Eng": 31254, "Ġfantas": 31255, "Ġspecification": 31256, "Ġconfronted": 31257, "ĠColumbus": 31258, "ивеÑĤ": 31259, "arım": 31260, "Ġcaffeine": 31261, "munition": 31262, "Ġmigrants": 31263, "lide": 31264, "itations": 31265, "ĠGeme": 31266, "ẫ": 31267, "Ġplanner": 31268, "Ġstimulate": 31269, "Ġaproxim": 31270, "ceu": 31271, "ĠNom": 31272, "Ġvog": 31273, "ĠÑĢаÑģÑĤ": 31274, "Ġenseñ": 31275, "Ġsellers": 31276, "Ġguten": 31277, "zd": 31278, "Cal": 31279, "Ġdescript": 31280, "Ġreconciliation": 31281, "zinho": 31282, "á¹ĩa": 31283, "ãģĺãĤĥãģĤ": 31284, "acyj": 31285, "ĠCOL": 31286, "saw": 31287, "ĠíĻķìĿ¸": 31288, "Ġvarit": 31289, "Ġpartnering": 31290, "Ġdetention": 31291, "Ġbombing": 31292, "clapping": 31293, "iencies": 31294, "ondu": 31295, "AME": 31296, "Ġê°ĻìĬµëĭĪëĭ¤": 31297, "cÃŃa": 31298, "ĠпоÑģÑĤо": 31299, "ĠASMR": 31300, "Ġhomepage": 31301, "Ġsiè": 31302, "antha": 31303, "ĠPoll": 31304, "Ġigen": 31305, "cych": 31306, "Ġê°ijìŀIJ기": 31307, "Ġconsiderably": 31308, "ä»ĸçļĦ": 31309, "ĠArist": 31310, "Ġwithstand": 31311, "Ġqualitative": 31312, "ĠKraft": 31313, "ĠÑįлекÑĤ": 31314, "ĠBead": 31315, "екÑĤив": 31316, "Ġcrushing": 31317, "ì³IJ": 31318, "Ġnavy": 31319, "ÙĪÚº": 31320, "sho": 31321, "Ġoak": 31322, "ippers": 31323, "Ġsoils": 31324, "Ġpigment": 31325, "Ġevitar": 31326, "ãĥĩ": 31327, "Ġfuse": 31328, "ĠDale": 31329, ":\"": 31330, "Ġcomplètement": 31331, "Ġkel": 31332, "à¹Ĩ": 31333, "Ġquatre": 31334, "ĠUM": 31335, "Ġë§IJë": 31336, "æł¹": 31337, "ÃŃr": 31338, "Ġleisure": 31339, "ĠHousing": 31340, "Ġfolds": 31341, "estion": 31342, "ARS": 31343, "Ġmash": 31344, "urpose": 31345, "Ġaccumulated": 31346, "ĠStuff": 31347, "èªŀ": 31348, "Ġtapes": 31349, "ĠÑģилÑĮно": 31350, "ĠLOVE": 31351, "Ġ1982": 31352, "Ġscars": 31353, "Ġcapitalist": 31354, "ĠNed": 31355, "Ġsoften": 31356, "Ġnotably": 31357, "Ġforcément": 31358, "ĠRaum": 31359, "ĠнеобÑħод": 31360, "Ġtrademark": 31361, "Ġfertig": 31362, "Ġ?!": 31363, "æĹł": 31364, "Ġreinforced": 31365, "Ġrecharge": 31366, "ĠPutting": 31367, "Ġvillains": 31368, "Ġhandic": 31369, "Ġadvertisement": 31370, "تÙĬ": 31371, "ĠÑģÑĥм": 31372, "ĠRiley": 31373, "×ķ×ij×": 31374, "京": 31375, "Os": 31376, "از": 31377, "Boy": 31378, "Ġsquish": 31379, "ocket": 31380, "Ġtestify": 31381, "æ¼Ķ": 31382, "Ġ׾×ŀ×": 31383, "ĠмаÑģÑģ": 31384, "manuel": 31385, "ĠArkansas": 31386, "iffe": 31387, "Ġanalysts": 31388, "ĠDeaf": 31389, "Ġjó": 31390, "Ġgroceries": 31391, "ĠWheel": 31392, "ĠÑĢиÑģ": 31393, "Ġcòn": 31394, "ĠCob": 31395, "Ġprisons": 31396, "ève": 31397, "ĠCabinet": 31398, "Ġposed": 31399, "Ġguerre": 31400, "ĠLloyd": 31401, "Ġclerk": 31402, "Ġcrises": 31403, "ĠSho": 31404, "ĠOre": 31405, "ĠFootball": 31406, "ĠAdvis": 31407, "ĠZheng": 31408, "èį": 31409, "ĠAMY": 31410, "Ġunfor": 31411, "Ġmonaster": 31412, "Ġcompile": 31413, "Ġimmortal": 31414, "atable": 31415, "Ġparano": 31416, "Ġtiver": 31417, "ĠSteph": 31418, "ĠFuÃŁ": 31419, "Ġdiscontin": 31420, "Ġripe": 31421, "Ġhacking": 31422, "Ġsiendo": 31423, "Ġseguro": 31424, "altres": 31425, "Ġanderes": 31426, "Ġ리ë": 31427, "Ġexports": 31428, "æŃ¥": 31429, "Ġtabii": 31430, "Ġ기ëĭ¤ë": 31431, "Ġbothering": 31432, "Ġpickle": 31433, "ĠBRIAN": 31434, "Ġaltar": 31435, "ĠпÑĢиб": 31436, "Ġtransferring": 31437, "ĠVors": 31438, "ĠÙĩÙĪ": 31439, "ĠZa": 31440, "ĠFrances": 31441, "Ġbrowse": 31442, "emit": 31443, "Ġchewing": 31444, "ĠFreddy": 31445, "Ġeditors": 31446, "älle": 31447, "ĠíĮĢ": 31448, "ĠSque": 31449, "ĠCultural": 31450, "awk": 31451, "ĠSache": 31452, "ĠCarbon": 31453, "ắt": 31454, "FL": 31455, "ĠNGO": 31456, "peÅĤ": 31457, "ĠSou": 31458, "Ġhvor": 31459, "unintelligible": 31460, "Ġë²ķ": 31461, "Ġ°": 31462, "iin": 31463, "Ġ×¢×Ŀ": 31464, "Ġderrière": 31465, "Ġczym": 31466, "ĠApost": 31467, "Ġregarder": 31468, "Ġagrade": 31469, "ĠCandy": 31470, "Ġmare": 31471, "Ġintroduces": 31472, "birds": 31473, "Ġuniquely": 31474, "Ġmuk": 31475, "Ġcooker": 31476, "Ġcrews": 31477, "Ġjeito": 31478, "ERT": 31479, "¶Ħë": 31480, "nisse": 31481, "Ġef": 31482, "Ġcarte": 31483, "ĠYak": 31484, "ĠPAT": 31485, "ино": 31486, "bokki": 31487, "Ġmates": 31488, "Ġdistint": 31489, "Ġì½Ķë¡ľëĤĺ": 31490, "Ġyıl": 31491, "Ġκάν": 31492, "Ġconfigurations": 31493, "enga": 31494, "recht": 31495, "Happy": 31496, "ãĤĦãģ£ãģ¦": 31497, "invest": 31498, "Ġreconstruct": 31499, "ĠÑįÑĤомÑĥ": 31500, "Ġmosque": 31501, "raum": 31502, "Ġvoyez": 31503, "ĠNBC": 31504, "ĠìŀIJìĭł": 31505, "Ġsturdy": 31506, "Ġкап": 31507, "Ġansch": 31508, "alid": 31509, "Ġmasih": 31510, "ĠREP": 31511, "Ġì½Ķë": 31512, "Ġdeduct": 31513, "Ġsalir": 31514, "wurf": 31515, "ilot": 31516, "ĠMutter": 31517, "olds": 31518, "ĠFEMA": 31519, "ĠBib": 31520, "Ġneighboring": 31521, "Ġbliss": 31522, "Ġíĺ¼": 31523, "лиÑģÑĮ": 31524, "ĠÑĤÑĢеб": 31525, "Ġå°±æĺ¯": 31526, "Ġgrenade": 31527, "Ġegal": 31528, "Ġfinely": 31529, "Ġpetals": 31530, "Ġkeer": 31531, "Ġchyba": 31532, "Ġskipping": 31533, "Ġthirteen": 31534, "Ġgravy": 31535, "ĠSAT": 31536, "61": 31537, "Ġног": 31538, "Ġmins": 31539, "ITE": 31540, "Ġsozial": 31541, "íķĺë©´ìĦľ": 31542, "ruktur": 31543, "Ġвозмож": 31544, "ĠопÑıÑĤÑĮ": 31545, "Ġarth": 31546, "ĠCuban": 31547, "Ġtreasures": 31548, "Ġfertilizer": 31549, "Ġawakening": 31550, "Ġë°±ìĭł": 31551, "Ġrall": 31552, "Ġdepict": 31553, "ĠPablo": 31554, "Ġnineteen": 31555, "Ġwatt": 31556, "Ġentirety": 31557, "KS": 31558, "ĠWoods": 31559, "Sch": 31560, "ĠÚ©ÙĪ": 31561, "ĠDry": 31562, "ãģŀ": 31563, "uve": 31564, "Ġreconstruction": 31565, "Ġanatomy": 31566, "Ī를": 31567, "Ġbaba": 31568, "Ġlistener": 31569, "Ġsharpen": 31570, "ĠPeru": 31571, "ĠвÑĭз": 31572, "Ġrecreation": 31573, "Ġinitiate": 31574, "Ġcalor": 31575, "ĠNaj": 31576, "gee": 31577, "ĠFeels": 31578, "ĠSnapchat": 31579, "ĠTet": 31580, "ĠNest": 31581, "ĠDaf": 31582, "ĠFinish": 31583, "ĠÑĤаким": 31584, "úc": 31585, "izens": 31586, "Ġspins": 31587, "Ġembry": 31588, "Ġpassages": 31589, "Ġcient": 31590, "Ġjustification": 31591, "ä»ĸ說": 31592, "Ġolmaz": 31593, "Ġflooded": 31594, "Ġemoji": 31595, "Ġembracing": 31596, "Ġdiscard": 31597, "ĠBasic": 31598, "agog": 31599, "ĠìľĦíķ´": 31600, "Ġasylum": 31601, "erin": 31602, "Ġfim": 31603, "Ġninja": 31604, "Ġautomate": 31605, "Ġallergic": 31606, "ÿÿÿÿ": 31607, "amam": 31608, "ĠмаÑĢ": 31609, "ĠOi": 31610, "äus": 31611, "Ġinduct": 31612, "ĠBEN": 31613, "ĠzÅĤ": 31614, "Ġkażdy": 31615, "ĠAMP": 31616, "nÄĽ": 31617, "Sure": 31618, "Ġquil": 31619, "Ġespec": 31620, "rok": 31621, "BSCRI": 31622, "Ġliebe": 31623, "pus": 31624, "achsen": 31625, "Ġcricket": 31626, "ëĬIJ": 31627, "ĠFrame": 31628, "ekkür": 31629, "arb": 31630, "ĠpÅĻ": 31631, "иÑģÑģ": 31632, "Ġzeggen": 31633, "Ġdoubles": 31634, "ĠDre": 31635, "test": 31636, "insp": 31637, "boys": 31638, "Ġmão": 31639, "ĠVerse": 31640, "Ġmuscular": 31641, "ĠMALE": 31642, "Ġdulu": 31643, "Ġoccasional": 31644, "Lo": 31645, "conomic": 31646, "Ġvak": 31647, "Ġremedy": 31648, "å¤ł": 31649, "ĠâĻªâĻªâĻª": 31650, "vem": 31651, "Ġönem": 31652, "ĠkarÅŁÄ±": 31653, "ĠSharp": 31654, "hur": 31655, "Ġë°©ë²ķ": 31656, "Ġgrandson": 31657, "Ġaktiv": 31658, "ĠThrones": 31659, "ĠìķĪìĹIJ": 31660, "Ġtots": 31661, "Ġsubd": 31662, "ĠPaula": 31663, "Ġgraves": 31664, "ĠBrent": 31665, "ĠникÑĤо": 31666, "Ġsöz": 31667, "Ġcrec": 31668, "ĠVladimir": 31669, "çĸ«": 31670, "Ġпой": 31671, "Ġ\"-": 31672, "Ġpsy": 31673, "atri": 31674, "idan": 31675, "Ġaún": 31676, "Ġstandardized": 31677, "ì¹ĺë": 31678, "ĠкÑĢов": 31679, "ĠZhu": 31680, "something": 31681, "Ġ750": 31682, "Ġmujeres": 31683, "Ġait": 31684, "éĹ´": 31685, "agu": 31686, "Ġcorrected": 31687, "ikka": 31688, "eled": 31689, "ĠCareer": 31690, "owym": 31691, "Ġroommate": 31692, "Ġdescendants": 31693, "ĠNapoleon": 31694, "ĠÐĶо": 31695, "íĸĪìĸ´ìļĶ": 31696, "Ġbunun": 31697, "ĠMicha": 31698, "ç·ļ": 31699, "Ġdescob": 31700, "PI": 31701, "Ġpalabra": 31702, "Ġtracked": 31703, "Ġdependence": 31704, "ĠBarack": 31705, "åģĩ": 31706, "Ġfertility": 31707, "ĠSouthwest": 31708, "Ġincomplete": 31709, "Ġcomunic": 31710, "Ġcompris": 31711, "ĠRestaur": 31712, "Ġacron": 31713, "κα": 31714, "Ġapprentices": 31715, "Ġmusst": 31716, "ĠAbr": 31717, "Ġpentru": 31718, "ĠConsort": 31719, "ĠAvec": 31720, "Ġdumplings": 31721, "LR": 31722, "Ġwszystkie": 31723, "Ġswamp": 31724, "нев": 31725, "uggle": 31726, "Ġwatercolor": 31727, "Ġproton": 31728, "ĠEspaña": 31729, "ocking": 31730, "овал": 31731, "Ġtakim": 31732, "Very": 31733, "Ġdementia": 31734, "ĠÅŁeyi": 31735, "Jac": 31736, "ĠMacBook": 31737, "ĠLiv": 31738, "fficients": 31739, "ĠHunt": 31740, "Ġoverlay": 31741, "æĦŁè¦º": 31742, "ĠSkype": 31743, "punkt": 31744, "Ġconfined": 31745, "ĠAdrian": 31746, "رÙĥ": 31747, "ĠJeep": 31748, "Ġenquanto": 31749, "Ġanest": 31750, "оÑĤвеÑĤ": 31751, "ĠменÑĮ": 31752, "Ġirrigation": 31753, "á»ijn": 31754, "Ġeighteen": 31755, "ĠPon": 31756, "Ġrescued": 31757, "Ġ1983": 31758, "rü": 31759, "jae": 31760, "ĠJeong": 31761, "Ġamazingly": 31762, "ĠFDP": 31763, "Ġbackstage": 31764, "cue": 31765, "ĠÏĥÏĦην": 31766, "ĠاÙĦص": 31767, "Ġlivestock": 31768, "ĠWarner": 31769, "Ġmajors": 31770, "ãĥģãĥ£": 31771, "Ġcooperative": 31772, "ĠBrady": 31773, "rained": 31774, "rieb": 31775, "Ġ×ij×ŀ×": 31776, "ĠдоволÑĮно": 31777, "ĠFE": 31778, "Ġleaked": 31779, "ĠMercury": 31780, "Ġpersuade": 31781, "Ġtransformer": 31782, "ĠNorweg": 31783, "ĠìŬ룬": 31784, "ĠzrobiÄĩ": 31785, "Ġcardiovascular": 31786, "ĠCrash": 31787, "Ġgossip": 31788, "аÑģÑĤÑĮ": 31789, "Ġ쪽": 31790, "Ġswept": 31791, "ĠHorn": 31792, "ĠAté": 31793, "Ġbukan": 31794, "ĠKaw": 31795, "KY": 31796, "ĠStories": 31797, "Gary": 31798, "Ġgardening": 31799, "ĠQuickly": 31800, "ĠFalcon": 31801, "Ġovat": 31802, "cı": 31803, "ĠComplet": 31804, "ĠDate": 31805, "ĠпÑĢим": 31806, "Ġläuft": 31807, "ĠAudrey": 31808, "ĠWent": 31809, "ĠpelÃŃcul": 31810, "Ġcarriage": 31811, "Ġunacceptable": 31812, "nymi": 31813, "ĠÑģлÑĭÑĪ": 31814, "Ġterre": 31815, "uellement": 31816, "EEEE": 31817, "Ġpharmac": 31818, "hões": 31819, "Ġzich": 31820, "Ġmigrate": 31821, "ĠFry": 31822, "ñana": 31823, "ĠMuito": 31824, "EOVER": 31825, "Ġfortress": 31826, "ĠCompan": 31827, "ĠJSON": 31828, "ordnung": 31829, "Ġwarto": 31830, "Ġungef": 31831, "ìħĶìĦľ": 31832, "ĠÑĢок": 31833, "Ġpaddle": 31834, "Jared": 31835, "Ġsubmitting": 31836, "Ġlatch": 31837, "Ġfug": 31838, "ĠкоÑģ": 31839, "ĠEf": 31840, "Ġlaunches": 31841, "Ġft": 31842, "otechn": 31843, "Ġtravelled": 31844, "اÙģ": 31845, "éģķ": 31846, "Ġproch": 31847, "Ġdedim": 31848, "83": 31849, "Ġrebound": 31850, "ĠLU": 31851, "path": 31852, "ĠÑģпÑĢав": 31853, "Ġöl": 31854, "ĠíĤ¤": 31855, "Ġprivat": 31856, "Ġtractor": 31857, "ĠAttention": 31858, "Ser": 31859, "Ġcoses": 31860, "ária": 31861, "pal": 31862, "ĠìĿĢ": 31863, "Ġsuccessor": 31864, "Ġconnectors": 31865, "ĠÑĥÑģÑĤанов": 31866, "Ġgenocide": 31867, "Ġsufficiently": 31868, "ĠAixò": 31869, "Ġstabilize": 31870, "Ġcongest": 31871, "Ġcarving": 31872, "Ġzost": 31873, "ĠбÑĭÑģÑĤÑĢо": 31874, "Ġshortest": 31875, "Ġlivel": 31876, "Ġ89": 31877, "éģĬ": 31878, "Ġerk": 31879, "Ġportraits": 31880, "à¥Ģ": 31881, "èĺ": 31882, "boat": 31883, "llah": 31884, "ANC": 31885, "Ġempirical": 31886, "ĠEcho": 31887, "ĠNederland": 31888, "è¿Ļä¹Ī": 31889, "Net": 31890, "Ġcuidado": 31891, "ĠRoma": 31892, "Ġcalf": 31893, "Ġgiants": 31894, "ĠExplorer": 31895, "ĠCollect": 31896, "alition": 31897, "ĠDestiny": 31898, "Ġausge": 31899, "ĠEdu": 31900, "ĠClo": 31901, "Ġearrings": 31902, "ĠTrack": 31903, "ĠROS": 31904, "ĠBelle": 31905, "çĻ¾": 31906, "Ġpueda": 31907, "Ġdaytime": 31908, "Ġsupplier": 31909, "ĠSV": 31910, "ĠExhale": 31911, "Ġgalera": 31912, "course": 31913, "Ġcentimeter": 31914, "ĠBast": 31915, "mud": 31916, "Ġsangat": 31917, "ĠPhysical": 31918, "Ġprivately": 31919, "Ġtrata": 31920, "lynn": 31921, "illi": 31922, "Ġë©ĶìĿ´íģ¬ìĹħ": 31923, "Ġcrystall": 31924, "Ġpods": 31925, "ản": 31926, "inator": 31927, "ĠRecords": 31928, "å®ĺ": 31929, "ÄŁimiz": 31930, "issement": 31931, "hare": 31932, "hadow": 31933, "ĠDK": 31934, "ĠìķĮê³ł": 31935, "Ġwyn": 31936, "Ġrequesting": 31937, "ĠDonna": 31938, "ĠìĹ´ìĭ¬íŀĪ": 31939, "inea": 31940, "Ġexert": 31941, "ĠDuncan": 31942, "ĠвеÑĩ": 31943, "ĠHah": 31944, "à¤Ĥ": 31945, "ĠLif": 31946, "ĠFinding": 31947, "ĠNov": 31948, "Ġзнак": 31949, "ĠоÑĦ": 31950, "ĠQuè": 31951, "Ġquarterback": 31952, "ĠÑĦак": 31953, "Ġbipartisan": 31954, "ÄŁin": 31955, "Ġnécess": 31956, "Ġreferendum": 31957, "Ġcompiler": 31958, "Ġprobabil": 31959, "еди": 31960, "Ġtrader": 31961, "æĺĵ": 31962, "ĠRum": 31963, "geme": 31964, "Ġdio": 31965, "ĠbÄĻdziemy": 31966, "ĠÏĢά": 31967, "꾸": 31968, "×ķ×ĺ": 31969, "Ġà¤ķ": 31970, "Ġблаг": 31971, "Ġscalp": 31972, "ĠPause": 31973, "Ġcaption": 31974, "Ġendanger": 31975, "Ġenlar": 31976, "Ġrotten": 31977, "ãĥĥãĥĪ": 31978, "Ġwah": 31979, "èĤī": 31980, "Ġdzi": 31981, "ĠInstall": 31982, "Ay": 31983, "Ġcrear": 31984, "енÑĤа": 31985, "Ġweighing": 31986, "Ġbutterflies": 31987, "ĠGast": 31988, "äºķ": 31989, "horn": 31990, "warz": 31991, "ICEOVER": 31992, "ĠнайÑĤи": 31993, "Ġcoefficients": 31994, "ç°¡åĸ®": 31995, "ĠSpencer": 31996, "ĠHigher": 31997, "Ġcowork": 31998, "å¨ĺ": 31999, "ĠкоÑĤоÑĢое": 32000, "Ġmonit": 32001, "Ġdysfunction": 32002, "ĠÑģÑĤанов": 32003, "Ġtournaments": 32004, "Ġoyster": 32005, "BN": 32006, "Ġtrud": 32007, "slow": 32008, "ĠPenny": 32009, "ĠOdys": 32010, "ær": 32011, "Ġfou": 32012, "Ġenjoyment": 32013, "аÑĤÑĭ": 32014, "ĠwyglÄħda": 32015, "алÑĮнаÑı": 32016, "ĠProtect": 32017, "Ġmoy": 32018, "Ġclaw": 32019, "Ġsuspicion": 32020, "Ġsacrificed": 32021, "Ġgosto": 32022, "Big": 32023, "Ġaggressively": 32024, "Ġvorne": 32025, "ãĥł": 32026, "Ġblamed": 32027, "ĠSehr": 32028, "פר": 32029, "cito": 32030, "Ġseals": 32031, "Ġmujer": 32032, "ĠWeird": 32033, "Ġforens": 32034, "Ġcontributes": 32035, "estra": 32036, "Ġpog": 32037, "LOL": 32038, "Ġhacerlo": 32039, "оÑĤÑĮ": 32040, "fiction": 32041, "79": 32042, "λο": 32043, "大æ¦Ĥ": 32044, "声": 32045, "ĠÑĤоб": 32046, "ĠGS": 32047, "ĠClara": 32048, "itez": 32049, "Ġadvocating": 32050, "ĠíĶĦë": 32051, "sung": 32052, "Ġvertices": 32053, "Ġnavigating": 32054, "Ġeuropé": 32055, "çļĨ": 32056, "Ġslowed": 32057, "Ġforeground": 32058, "ĠIndustrial": 32059, "Ġadore": 32060, "ìĭŃ": 32061, "Ġcréer": 32062, "æŀĹ": 32063, "chnitt": 32064, "Ġunaware": 32065, "Ġcurly": 32066, "entar": 32067, "Ġler": 32068, "Ġprohibited": 32069, "ĠHeroes": 32070, "ĠReed": 32071, "uca": 32072, "Ġsmok": 32073, "Ġkunna": 32074, "zeitig": 32075, "immen": 32076, "ĠLun": 32077, "ĠабÑģолÑİÑĤ": 32078, "Ġdegli": 32079, "Ġvillagers": 32080, "Ġpreset": 32081, "zept": 32082, "uds": 32083, "Ġemit": 32084, "ä½łè¦ģ": 32085, "Ġëī": 32086, "ëĬĶì§Ģ": 32087, "нако": 32088, "Ġosób": 32089, "Ġ1969": 32090, "ĠÐIJÑĢ": 32091, "Ġmanchmal": 32092, "ĠBrock": 32093, "Ġmantra": 32094, "ĠWIL": 32095, "bach": 32096, "inä": 32097, "elas": 32098, "keln": 32099, "Ġdisciple": 32100, "Ġqualc": 32101, "Ġdehyd": 32102, "ìĿ´ëĿ¼ëĬĶ": 32103, "Af": 32104, "ìĦ±ìĿ´": 32105, "Ryan": 32106, "Ġpuppet": 32107, "ĠдÑĢÑĥгие": 32108, "Ġrud": 32109, "Ġpending": 32110, "Plus": 32111, "ĠìķĬìĿĦ": 32112, "Ġbá»ĭ": 32113, "ĠSega": 32114, "çe": 32115, "Ġprogrammer": 32116, "bli": 32117, "Ġunl": 32118, "Ġenslaved": 32119, "Ġsociété": 32120, "Äģh": 32121, "Ġinheritance": 32122, "ĠBangl": 32123, "ermaid": 32124, "Ġpractitioner": 32125, "ĠStalin": 32126, "ĠUser": 32127, "cible": 32128, "Ġcardiac": 32129, "ĠKoreans": 32130, "Ġdumped": 32131, "Ġ×Ķ×Ļ×Ķ": 32132, "áis": 32133, "Ġhydraulic": 32134, "oubtedly": 32135, "ĠPit": 32136, "Ġpicnic": 32137, "Ġbehöver": 32138, "ĠÑģмог": 32139, "Ġbraking": 32140, "é»ij": 32141, "utar": 32142, "ĠìĦ¸ë": 32143, "ubl": 32144, "Ġüz": 32145, "Ġmajesty": 32146, "Ġbers": 32147, "utable": 32148, "Ġhotter": 32149, "çħ§": 32150, "ÛĮÙĨ": 32151, "Ġbiases": 32152, "Ġsubjected": 32153, "Ġnaughty": 32154, "Ġcircus": 32155, "ãģĹãģĭ": 32156, "ĠImmedi": 32157, "ĠStefan": 32158, "ĠTriple": 32159, "enk": 32160, "Ġwit": 32161, "Ġrecycle": 32162, "emie": 32163, "dated": 32164, "Ġunload": 32165, "Ġpopula": 32166, "chin": 32167, "Ġyields": 32168, "Ġenglish": 32169, "ĠBonnie": 32170, "Ġspiders": 32171, "Ãģ": 32172, "Ġerosion": 32173, "éĥ¨åĪĨ": 32174, "ĠNICK": 32175, "иÑıÑħ": 32176, "Ġimpart": 32177, "Ġкни": 32178, "Ġresolutions": 32179, "Ġlithium": 32180, "Ġconvergence": 32181, "ĠTara": 32182, "Ġдве": 32183, "ths": 32184, "ĠCindy": 32185, "æĪijè¦ģ": 32186, "幫": 32187, "ĠDIE": 32188, "Ġassurance": 32189, "ĠопиÑģ": 32190, "Ġbuckets": 32191, "Ġcues": 32192, "ĠQuiet": 32193, "Ġsimilarity": 32194, "Ġfoundational": 32195, "ĠMinist": 32196, "滿": 32197, "Ġpian": 32198, "Ġcentr": 32199, "Ġnumb": 32200, "Ġmonks": 32201, "ujourd": 32202, "enzie": 32203, "Ġskateboard": 32204, "Ġdlatego": 32205, "ĠÑģоÑĤ": 32206, "ĠAE": 32207, "Ġmasterpiece": 32208, "ĠSolomon": 32209, "ĠReddit": 32210, "Ġriot": 32211, "abl": 32212, "ĠJazz": 32213, "Ġelectromagnetic": 32214, "Ġinsecure": 32215, "ĠCompet": 32216, "geries": 32217, "обод": 32218, "ł×ķ": 32219, "ðŁĴ": 32220, "Ġsenators": 32221, "ĠBrisbane": 32222, "ĠAlb": 32223, "uttering": 32224, "ĠAllow": 32225, "zero": 32226, "Ġpai": 32227, "ĠÐIJлекÑģ": 32228, "ĠDisplay": 32229, "ĠBlade": 32230, "ĠApps": 32231, "Ġpä": 32232, "ĠдеÑģÑı": 32233, "Ġquella": 32234, "ĠGao": 32235, "еннÑĭÑħ": 32236, "Ġspoilers": 32237, "Ġgallons": 32238, "ĠÙĦÙĬ": 32239, "ĠZion": 32240, "æľīä¸Ģ": 32241, "onie": 32242, "ragt": 32243, "ĠChand": 32244, "Ġë³ij": 32245, "Ġblunt": 32246, "Ġusu": 32247, "ĠKad": 32248, "rakt": 32249, "Ġcinematic": 32250, "Ġammunition": 32251, "rene": 32252, "Ġfourteen": 32253, "ĠCarn": 32254, "crit": 32255, "Ġtenure": 32256, "vu": 32257, "Ġprincipalmente": 32258, "Ġalleen": 32259, "éĢĻä¸Ģ": 32260, "Ġkomplett": 32261, "Ġdüny": 32262, "James": 32263, "Ġreceptor": 32264, "Ġoneself": 32265, "guru": 32266, "Ġmerchant": 32267, "liness": 32268, "Ġoverlooked": 32269, "Ġharmonic": 32270, "éķ¿": 32271, "ieso": 32272, "×ķ×ŀ": 32273, "colm": 32274, "ĠпÑĢоекÑĤ": 32275, "ĠAda": 32276, "اس": 32277, "Tim": 32278, "Ġrecurring": 32279, "Ġproceeds": 32280, "ĠParticularly": 32281, "ĠDownload": 32282, "etrical": 32283, "Ġmatrices": 32284, "Ġproyecto": 32285, "ancies": 32286, "ĠUhm": 32287, "Ġcaves": 32288, "Ġìĸ´ëł¤": 32289, "ĠLeaf": 32290, "ĠобÑĭÑĩ": 32291, "ĠìĿ´ìľł": 32292, "Europe": 32293, "ĠtÄħ": 32294, "Ġpuls": 32295, "Ġtakiego": 32296, "ÐĿе": 32297, "GU": 32298, "Ġfors": 32299, "Ïģγ": 32300, "Ġfotos": 32301, "Ġ))": 32302, "Ġ멤ë": 32303, "Ġaquilo": 32304, "ĠKurd": 32305, "ï¸ı": 32306, "ptic": 32307, "ĠDort": 32308, "Ġmisery": 32309, "auso": 32310, "åĬŁ": 32311, "chuckling": 32312, "ĠRidge": 32313, "ĠíĸĪìĬµëĭĪëĭ¤": 32314, "Ġ***": 32315, "客": 32316, "ĠHmmm": 32317, "Ġgeographic": 32318, "Ġanys": 32319, "Ġtalvez": 32320, "Ġskelet": 32321, "Ġsignatures": 32322, "Ġliters": 32323, "IJë©´": 32324, "ĠÑģвоего": 32325, "Ġskiing": 32326, "ĠÐľÐ¾Ñģ": 32327, "Ġadopting": 32328, "Ġhaft": 32329, "Ġsymmetric": 32330, "ĠLiqu": 32331, "Ġthyroid": 32332, "Ġmisin": 32333, "lude": 32334, "Ġhull": 32335, "ĠXD": 32336, "ĠGust": 32337, "zeich": 32338, "Ġvibrations": 32339, "Ġesemp": 32340, "ĠвÑģÑİ": 32341, "ĠQuem": 32342, "Ġübrig": 32343, "ĠSke": 32344, "ĠLynch": 32345, "rooms": 32346, "artet": 32347, "fest": 32348, "Ġfrüher": 32349, "Ġlure": 32350, "ä¸į好æĦıæĢĿ": 32351, "ĠìķĮìķĦ": 32352, "ĠWIN": 32353, "ĠRYAN": 32354, "ĠкоÑĤоÑĢÑĥÑİ": 32355, "ĠKash": 32356, "Ġ×Ķ×ŀ": 32357, "Ġsafeg": 32358, "ĠHallelujah": 32359, "ĠдвÑĥÑħ": 32360, "Ġstaple": 32361, "Ġsediment": 32362, "ĠActs": 32363, "Ġblaming": 32364, "Ġmainland": 32365, "Ġsporting": 32366, "Ġdecorations": 32367, "Ġexecuting": 32368, "Ġparan": 32369, "ĠDollar": 32370, "Ġprojections": 32371, "Ġcommissioned": 32372, "Ġbour": 32373, "öm": 32374, "Ġsteamed": 32375, "ĠëŃĺ": 32376, "Ġpetrol": 32377, "Ġcelular": 32378, "帶": 32379, "ĠHungary": 32380, "Ġrented": 32381, "ĠваÑĢи": 32382, "bbie": 32383, "Ġsécur": 32384, "üll": 32385, "Ġswings": 32386, "between": 32387, "ĠиÑĤ": 32388, "estro": 32389, "Ġniemand": 32390, "ĠìĤ¼": 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"wald": 33262, "risk": 33263, "Ġhari": 33264, "ĠKens": 33265, "ĠIdol": 33266, "Ġвнимание": 33267, "Ġtodd": 33268, "Ġsmashed": 33269, "Ġinvari": 33270, "ĠконÑĤÑĢ": 33271, "Ġautistic": 33272, "ìŀ¥ëĭĺ": 33273, "Res": 33274, "дÑĭ": 33275, "chau": 33276, "Ġselv": 33277, "Ġhätten": 33278, "ि": 33279, "Ġexpects": 33280, "Ïģη": 33281, "Ġaçık": 33282, "ĠHTTP": 33283, "leÅŁ": 33284, "Ġsweeping": 33285, "ĠBeta": 33286, "Ġcounterparts": 33287, "abile": 33288, "ĠSims": 33289, "Cs": 33290, "Ġrepar": 33291, "squ": 33292, "Ġprovincial": 33293, "Ġshareholders": 33294, "Ġrunter": 33295, "Ġgedacht": 33296, "ĠTeen": 33297, "Ġgrands": 33298, "çĶ¢": 33299, "agles": 33300, "Ġrocky": 33301, "vens": 33302, "Ġrivals": 33303, "unal": 33304, "Ġreacts": 33305, "ë©": 33306, "Ġmercury": 33307, "ĠLuigi": 33308, "Ġог": 33309, "ĠJUST": 33310, "Ġlod": 33311, "Ġcortex": 33312, "wig": 33313, "Ġlakh": 33314, "ì¤ijìĹIJ": 33315, "ĠVic": 33316, "ĠMund": 33317, "Ġmapped": 33318, "ĠDell": 33319, "ĠDruck": 33320, "Ġlifes": 33321, "алÑĮное": 33322, "ividual": 33323, "adım": 33324, "Ġatrav": 33325, "ĠFlug": 33326, "ĠKlein": 33327, "ê±°ìķ¼": 33328, "หà¸Ļ": 33329, "Ġappli": 33330, "ா?": 33331, "üyorum": 33332, "ĠинÑĤеÑĢеÑģно": 33333, "Ġdisinfect": 33334, ">-": 33335, "Ġchampagne": 33336, "Ġkla": 33337, "opers": 33338, "Trans": 33339, "ĠDesert": 33340, "Ġcultivate": 33341, "ĠFucking": 33342, "idelity": 33343, "ĠÑĤан": 33344, "Ġincub": 33345, "Ġtemu": 33346, "Ġlearner": 33347, "founder": 33348, "ĠSyl": 33349, "ãĤĢ": 33350, "Ġfato": 33351, "zier": 33352, "ĠìĹĨìĿ´": 33353, "ĠìĪ¨": 33354, "Ġpsycho": 33355, "ĠÑĤелеÑĦ": 33356, "Ġregarde": 33357, "Ġrepresentations": 33358, "Ġlitigation": 33359, "Ġspann": 33360, "ults": 33361, "bior": 33362, "è¦ĭãģ¦": 33363, "ä¸įå¤ļ": 33364, "ĠSurvey": 33365, "ĠLEDs": 33366, "Ġträ": 33367, "Ġlên": 33368, "Ġantioxid": 33369, "еÑĢом": 33370, "Ġinduction": 33371, "Ġfooled": 33372, "ätzlich": 33373, "ĠговоÑĢÑıÑĤ": 33374, "ĠFact": 33375, "umbai": 33376, "Ġwiggle": 33377, "NOUN": 33378, "Ġdévelopp": 33379, "ĠClaro": 33380, "Ġì¸": 33381, "ë¬": 33382, "ãģªãĤĵãģł": 33383, "Ġaccumulate": 33384, "Ġmaintains": 33385, "ëĦ": 33386, "ĠFighter": 33387, "íĨł": 33388, "Ġmatin": 33389, "Ġcoupon": 33390, "Ġstunt": 33391, "Ġdebuted": 33392, "å¾ħãģ£ãģ¦": 33393, "Ġprag": 33394, "иваем": 33395, "73": 33396, "Ġexpres": 33397, "Ġìĺ¤ë¹ł": 33398, "ĠпеÑĢÑģон": 33399, "Ġcalculus": 33400, "Ġabrupt": 33401, "ĠInspector": 33402, "ourt": 33403, "æĸĻ": 33404, "źniej": 33405, "intense": 33406, "Ba": 33407, "Ġlounge": 33408, "Ġasthma": 33409, "ĠHiç": 33410, "ª»": 33411, "Ġeditorial": 33412, "Ġseize": 33413, "Ġkır": 33414, "Ġmouve": 33415, "Ġtierra": 33416, "Ġtestosterone": 33417, "Ġrh": 33418, "ĠKingston": 33419, "ELLE": 33420, "ĠRepresentative": 33421, "Ġ1974": 33422, "Ġiba": 33423, "Ts": 33424, "Ġsorta": 33425, "Ġ(?)": 33426, "ĠتÙĪ": 33427, "ĠëĤ´ëł¤": 33428, "Ġbekommt": 33429, "Ġspiritually": 33430, "Ġdistorted": 33431, "Mad": 33432, "Ġreim": 33433, "ánh": 33434, "ĠOttoman": 33435, "ĠRelig": 33436, "ĠEls": 33437, "Ġretained": 33438, "ĠLaughs": 33439, "æĢ»": 33440, "ĠSAS": 33441, "ĠколиÑĩеÑģÑĤво": 33442, "×ķתר": 33443, "Ġinnovate": 33444, "Ġkork": 33445, "ĠÑĢаÑģÑģказÑĭв": 33446, "ondere": 33447, "ivi": 33448, "aye": 33449, "ounty": 33450, "ĠполÑĥÑĩаеÑĤÑģÑı": 33451, "Ġbuns": 33452, "åħ«": 33453, "Ġyüzden": 33454, "Ġsurgeries": 33455, "Ø£ÙĨ": 33456, "Ġbankruptcy": 33457, "welt": 33458, "Ġsiamo": 33459, "Ġdarkest": 33460, "ĠHann": 33461, "gga": 33462, "Ġformas": 33463, "ĠDj": 33464, "named": 33465, "Ġshields": 33466, "ueller": 33467, "ĠFew": 33468, "Ġlace": 33469, "Ġfurious": 33470, "ĠYU": 33471, "Ġsocietal": 33472, "Ġjudgement": 33473, "ĠDos": 33474, "Ġjab": 33475, "laws": 33476, "Ġreinvent": 33477, "ĠKatherine": 33478, "ĠChoi": 33479, "adows": 33480, "Ġrans": 33481, "oden": 33482, "ĠMidwest": 33483, "nın": 33484, "Ġdeport": 33485, "ĠDip": 33486, "ç´ħ": 33487, "Ġatención": 33488, "ĠCourtney": 33489, "ividad": 33490, "ĠÚ©Ûģ": 33491, "Ġefficacy": 33492, "ĠBrooks": 33493, "Ġreferral": 33494, "ĠконÑĨ": 33495, "Ġmalicious": 33496, "Ġkir": 33497, "ĠGoddess": 33498, "Ġfunky": 33499, "Ġinterim": 33500, "ĠKörper": 33501, "Ġìĸ¼ë§": 33502, "kur": 33503, "Ġкли": 33504, "Ġtrucs": 33505, "gesetz": 33506, "Ġzug": 33507, "ĠGlück": 33508, "ĠMinute": 33509, "Ġprestigious": 33510, "Ġniez": 33511, "Ġconcentrations": 33512, "лаÑģÑĤи": 33513, "ĠSis": 33514, "ĠVitamin": 33515, "kov": 33516, "ĠPBS": 33517, "Ġнее": 33518, "Ġretailers": 33519, "Ġconventions": 33520, "ĠSamantha": 33521, "Ġproudly": 33522, "Jordan": 33523, "ĠJASON": 33524, "atk": 33525, "Ġtriste": 33526, "Ġstär": 33527, "Ġreiterate": 33528, "Ġposterior": 33529, "Ġ1973": 33530, "ĠPine": 33531, "ĠJuliet": 33532, "Ġpedir": 33533, "kil": 33534, "Ġoverlapping": 33535, "Ġexclude": 33536, "Ġeconóm": 33537, "Ġaccepts": 33538, "ĠSter": 33539, "決": 33540, "Ġìļ´ëıĻ": 33541, "estab": 33542, "Ġtug": 33543, "arg": 33544, "Ġlivro": 33545, "اص": 33546, "Ġseams": 33547, "Ġburaya": 33548, "Ġello": 33549, "ĠTM": 33550, "ĠPaw": 33551, "ĠIndex": 33552, "Exc": 33553, "Ġinspirational": 33554, "Ġdunk": 33555, "è°ģ": 33556, "akter": 33557, "Ġconditioner": 33558, "ĠSalut": 33559, "ÅĤec": 33560, "Ġìī½": 33561, "ĠÑĥзна": 33562, "ĠRomeo": 33563, "fruit": 33564, "ĠYO": 33565, "Ġchá»ī": 33566, "бÑĥ": 33567, "bons": 33568, "Ġreproductive": 33569, "Ġorada": 33570, "Ġíļ¨": 33571, "Ġtentar": 33572, "Ġmañana": 33573, "ãĤ¬": 33574, "Ġsolvent": 33575, "Jessica": 33576, "ĠLegal": 33577, "Ġtua": 33578, "Ġsic": 33579, "ĠEQ": 33580, "aukee": 33581, "ìĭľëĭ¤": 33582, "ĠÅŀu": 33583, "Ġadhere": 33584, "ĠTul": 33585, "Ġà®Ĩ": 33586, "Ġtextbooks": 33587, "ĠFifth": 33588, "Ġexperi": 33589, "Ġchic": 33590, "Ġheap": 33591, "inely": 33592, "atra": 33593, "Two": 33594, "Ġhelemaal": 33595, "Ġfren": 33596, "æݨ": 33597, "Ġbisher": 33598, "اش": 33599, "ĠìĦłìĥĿ": 33600, "ĠTages": 33601, "Ġsá»±": 33602, "Ġbullied": 33603, "ؤ": 33604, "Ġbenefited": 33605, "ĠPreviously": 33606, "ĠÑįÑĦÑĦ": 33607, "Ùį": 33608, "Ġsenate": 33609, "ĠMorm": 33610, "ijke": 33611, "ĠFlu": 33612, "Ġincorporating": 33613, "jack": 33614, "ĠпиÑĤ": 33615, "Ġimply": 33616, "Ġhacks": 33617, "ĠRICH": 33618, "ĠкваÑĢ": 33619, "ĠпÑĢекÑĢаÑģ": 33620, "Ġdependency": 33621, "Ġìļ©": 33622, "Ġì±ħ": 33623, "Ġwährend": 33624, "Ġsulla": 33625, "ĠPittsburgh": 33626, "Ġesempio": 33627, "¼ë¡ľ": 33628, "prot": 33629, "ĠRosen": 33630, "ĠIndependence": 33631, "Ġparsley": 33632, "iegen": 33633, "Ġhaw": 33634, "Ġaquell": 33635, "ĠCAP": 33636, "ĠÑĢабоÑĤаÑĤÑĮ": 33637, "ĠCliff": 33638, "ionar": 33639, "Ġsecuring": 33640, "æĪijåĢijçļĦ": 33641, "νε": 33642, "Ġutilis": 33643, "Ġcoule": 33644, "ĠPing": 33645, "Ġtrek": 33646, "Ġfak": 33647, "Ġenorme": 33648, "Ġìĭ«": 33649, "让": 33650, "Ġdoubling": 33651, "ĠнÑĢавиÑĤÑģÑı": 33652, "Ġhed": 33653, "hoven": 33654, "ĠStanding": 33655, "ĠmÃŃn": 33656, "ĠJimin": 33657, "Ġmonarch": 33658, "Ġcoke": 33659, "Ġmr": 33660, "Ġclic": 33661, "Ãį": 33662, "Ġimpeachment": 33663, "Ġdurability": 33664, "Ġvarios": 33665, "Ġcommercials": 33666, "Ġgreetings": 33667, "ĠRi": 33668, "ĠAppreci": 33669, "ìŀĪëĬĶ": 33670, "Ġrésult": 33671, "ért": 33672, "Ġsalute": 33673, "Ġpoderia": 33674, "Ġsunrise": 33675, "veck": 33676, "Ġreluctant": 33677, "Ġcommissioner": 33678, "念": 33679, "âte": 33680, "ĠKenny": 33681, "ĠSiri": 33682, "ãĥĥãĥĹ": 33683, "ĠëĬĺ": 33684, "ĠEE": 33685, "Ġunch": 33686, "кон": 33687, "ĠاÙĦØ¥": 33688, "Ġbelts": 33689, "Ġhass": 33690, "ĠмоÑı": 33691, "Ġdisplaced": 33692, "Ġabra": 33693, "ÎŃλ": 33694, "Ġscratches": 33695, "Ġcomet": 33696, "Ġauthorization": 33697, "ĠLLC": 33698, "Ġproduk": 33699, "Ġrehabilitation": 33700, "åŀ": 33701, "ÑĸÑĩ": 33702, "uding": 33703, "olit": 33704, "Ġ105": 33705, "Ġexpands": 33706, "Ġaltri": 33707, "ĠKomment": 33708, "Ġanf": 33709, "Pl": 33710, "ĠMana": 33711, "fed": 33712, "Ġbri": 33713, "Ġora": 33714, "Gs": 33715, "ĠGur": 33716, "uckland": 33717, "Ġjunction": 33718, "Ġironic": 33719, "ĠFeed": 33720, "Ġprakt": 33721, "ĠHammer": 33722, "ĮëıĦ": 33723, "ĠTracy": 33724, "çµ±": 33725, "ĠAside": 33726, "него": 33727, "ĠиÑģполÑĮзоваÑĤÑĮ": 33728, "Ġzaj": 33729, "Ġequitable": 33730, "Ġcurb": 33731, "ĠãģĵãĤĮ": 33732, "Ġderivatives": 33733, "Ġpuppies": 33734, "ĠKenneth": 33735, "ĠCompl": 33736, "igram": 33737, "ĠGarcia": 33738, ")\"": 33739, "ĠHarbor": 33740, "estial": 33741, "Ġä¾Ĩ": 33742, "Ġers": 33743, "æ¹": 33744, "Ġunwanted": 33745, "Ġbelang": 33746, "аго": 33747, "emb": 33748, "dos": 33749, "ĠìĻľë": 33750, "ĠBudget": 33751, "Ġbattling": 33752, "ØŃت": 33753, "kok": 33754, "наÑĩала": 33755, "Ġplag": 33756, "Ġcantidad": 33757, "Ġgrupos": 33758, "Ġplugins": 33759, "lerini": 33760, "ĠимееÑĤ": 33761, "Ġsozusagen": 33762, "olics": 33763, "Ġpueblo": 33764, "Ġreminis": 33765, "rän": 33766, "ĠMorrison": 33767, "Ġlinha": 33768, "Ġbreaths": 33769, "ĠTaste": 33770, "Ġenfrent": 33771, "ĠDocker": 33772, "Ġден": 33773, "Ġethnicity": 33774, "Ġwob": 33775, "Ġsuffers": 33776, "Ġtransitioning": 33777, "ĠRange": 33778, "ÄĻdzy": 33779, "ĠкаÑĤ": 33780, "Ġsyner": 33781, "Ġdonut": 33782, "Ġprobabilities": 33783, "ĠOmar": 33784, "Which": 33785, "uish": 33786, "isin": 33787, "Ġdemos": 33788, "ĠìłĢ기": 33789, "Ġëĺijê°Ļ": 33790, "Ġедин": 33791, "Ġcerve": 33792, "Ġjoka": 33793, "IAN": 33794, "Ġkilometer": 33795, "Ġhorizontally": 33796, "ĠBhag": 33797, "Ġ->": 33798, "ĠMonitor": 33799, "Ġknowledgeable": 33800, "Ġfav": 33801, "Ġpinned": 33802, "ĠeBay": 33803, "icker": 33804, "Ġìŀłê¹IJë§Į": 33805, "ĠXiaomi": 33806, "Ġcapit": 33807, "Ġnp": 33808, "Ġ1965": 33809, "hoe": 33810, "Ġnok": 33811, "ĠSage": 33812, "ĠнелÑĮзÑı": 33813, "ĠTow": 33814, "gam": 33815, "Ġdicen": 33816, "ĠSUBSCRIBE": 33817, "Ġreboot": 33818, "Ġpaj": 33819, "Ġë³´ìŬë": 33820, "Ġthicken": 33821, "ĠReality": 33822, "idän": 33823, "Na": 33824, "Ġê²ĥìĿĢ": 33825, "!!)": 33826, "Ġroutines": 33827, "Ġодного": 33828, "Ġexting": 33829, "Ġì¦Ŀ": 33830, "Ġsulfur": 33831, "Ġcarve": 33832, "Ġasteroid": 33833, "ĠWarrior": 33834, "Ġphotographers": 33835, "Ġpell": 33836, "Ġcrossover": 33837, "æĪijçŁ¥éģĵ": 33838, "Ġhacemos": 33839, "ĠNej": 33840, "Ġsettling": 33841, "Ġirm": 33842, "ĠBooks": 33843, "ientôt": 33844, "Ġespacio": 33845, "ĠScholars": 33846, "Ġdoomed": 33847, "ĠIRS": 33848, "wohl": 33849, "Ġsegue": 33850, "ĠëĪĦê°Ģ": 33851, "Ġpratic": 33852, "BT": 33853, "ĠConsidering": 33854, "ĠBuffalo": 33855, "Ġtrainings": 33856, "Ġgebru": 33857, "ĠGleich": 33858, "Ġpirates": 33859, "Ġenvelop": 33860, "Ġreopen": 33861, "imat": 33862, "Ġtee": 33863, "Ġsued": 33864, "feh": 33865, "Ġ×Ķק": 33866, "Ġdiets": 33867, "Ġjuntos": 33868, "asto": 33869, "Ġmisunderstood": 33870, "Ġruim": 33871, "Ġclassify": 33872, "ĠпÑĢодÑĥк": 33873, "Ġinse": 33874, "Ġillustrated": 33875, "Ġcorrosion": 33876, "Ġaccred": 33877, "ĠAuntie": 33878, "ĠпÑĢивеÑĤ": 33879, "ĠLIVE": 33880, "Ġrek": 33881, "Ġreceipt": 33882, "åĪ°åºķ": 33883, "ĠBarbie": 33884, "ĠSnake": 33885, "turn": 33886, "Jeff": 33887, "ãģĬãģĬ": 33888, "ķĦ": 33889, "VOICEOVER": 33890, "coll": 33891, "Ġrunners": 33892, "ìłľë": 33893, "osos": 33894, "moon": 33895, "Ġkeynote": 33896, "ĠInstit": 33897, "SPEAK": 33898, "Ġplugs": 33899, "Ġcurv": 33900, "ĠYuri": 33901, "ĠTheres": 33902, "ĠPs": 33903, "ĠμÏĢο": 33904, "Ġconverter": 33905, "Ġrefine": 33906, "Ġbadass": 33907, "Ġοι": 33908, "Ġregen": 33909, "azzi": 33910, "ÙĬÙģ": 33911, "Ġseized": 33912, "Ġiçer": 33913, "ilee": 33914, "Ġupstream": 33915, "Ġbuds": 33916, "Ġpim": 33917, "Ġíķĺ루": 33918, "Ġalluded": 33919, "Ġthemed": 33920, "Ġconsisting": 33921, "Ġbons": 33922, "unuz": 33923, "ĠпÑĢовод": 33924, "ĠLovely": 33925, "à¥ĭ": 33926, "Ġparach": 33927, "ĠStaats": 33928, "éļĬ": 33929, "Ġselective": 33930, "Ġfase": 33931, "ĠGeorget": 33932, "Ġcocaine": 33933, "Ġreproduction": 33934, "ĠLara": 33935, "ĠLD": 33936, "Ġgh": 33937, "Jon": 33938, "ĠlÃ¥": 33939, "ĠëijIJë": 33940, "Ġtyped": 33941, "ĠBana": 33942, "ëĵľë": 33943, "Ġsavory": 33944, "ĠZomb": 33945, "standen": 33946, "Ġpedestrian": 33947, "Ġdifférents": 33948, "Ġìĭ¸": 33949, "èī¯": 33950, "Ġcomplained": 33951, "ç¦ı": 33952, "ĠÐļÑĤо": 33953, "Ġ׾פ": 33954, "aliÅĽmy": 33955, "Ġmortar": 33956, "Ġverdict": 33957, "Ġsuficiente": 33958, "ĠMillion": 33959, "mittel": 33960, "inals": 33961, "ĠاÙĦØ®": 33962, "аÑİÑģÑĮ": 33963, "ĠmiÄĻdzy": 33964, "ĠOle": 33965, "Ġinvert": 33966, "czyÄĩ": 33967, "озможно": 33968, "starter": 33969, "Ġauditor": 33970, "ĠScout": 33971, "chien": 33972, "ĠSverige": 33973, "uffled": 33974, "Ġzehn": 33975, "ĠAuckland": 33976, "Ġargent": 33977, "Ġ1976": 33978, "ĠHoe": 33979, "Ġbothers": 33980, "Ġsocialist": 33981, "Ġpliers": 33982, "Ġemergen": 33983, "ĠXP": 33984, "еÑĢов": 33985, "More": 33986, "ĠLevi": 33987, "ĠAnders": 33988, "ibilidad": 33989, "ĠParents": 33990, "Ġinduced": 33991, "ìĸ´ì¤": 33992, "Ġbalances": 33993, "ĠвÑĭÑĪ": 33994, "Ġsubmarine": 33995, "Start": 33996, "Ġdries": 33997, "Ġvolver": 33998, "Ġticking": 33999, "cott": 34000, "Ġfaj": 34001, "prés": 34002, "ĠSabb": 34003, "ĠзаÑĩ": 34004, "ĠпокÑĥп": 34005, "Ġbaptized": 34006, "ĠBrilliant": 34007, "ĠÐijог": 34008, "Ġmots": 34009, "bits": 34010, "Ġlattice": 34011, "æĪijè·Łä½ł": 34012, "Ġcoriander": 34013, "Ġresidency": 34014, "ync": 34015, "Ġpierwszy": 34016, "ĠKnock": 34017, "ĠZap": 34018, "ĠÐķв": 34019, "견": 34020, "å°ıå¿ĥ": 34021, "Ġuneven": 34022, "ĠJas": 34023, "odor": 34024, "ç¿Ĵ": 34025, "74": 34026, "ĠSite": 34027, "Ġaconteceu": 34028, "ympt": 34029, "Ġtrilogy": 34030, "Ġlantern": 34031, "ĠZucker": 34032, "vari": 34033, "welling": 34034, "ĠPotato": 34035, "gomery": 34036, "Ġreacted": 34037, "ĠChron": 34038, "Ġjede": 34039, "beeld": 34040, "Ġtwent": 34041, "Ġlact": 34042, "æ¨Ĥ": 34043, "Ġrése": 34044, "Ġrelent": 34045, "Ġfurnace": 34046, "Ġwidget": 34047, "Ġearthquakes": 34048, "ĠAdjust": 34049, "ilit": 34050, "ĠØ£ÙĪ": 34051, "Ġhearings": 34052, "Ġdefendant": 34053, "irsiniz": 34054, "Ġbask": 34055, "cja": 34056, "ľ¨": 34057, "Ġrifles": 34058, "Ġinstal": 34059, "ĠForgive": 34060, "pical": 34061, "ĠÐŀÑĩенÑĮ": 34062, "Ġpetites": 34063, "Ġhp": 34064, "Ġrenowned": 34065, "ĠInn": 34066, "Ġ주ìĦ¸ìļĶ": 34067, "Ġemphasized": 34068, "éĹ®é¢ĺ": 34069, "ĠìŀĪì£ł": 34070, "Ġê²ĥìľ¼ë¡ľ": 34071, "ãĤĨ": 34072, "Åĵ": 34073, "gili": 34074, "Dave": 34075, "Ġexhausting": 34076, "ÅĤug": 34077, "Ġschema": 34078, "μά": 34079, "cycl": 34080, "Ġautant": 34081, "Ġparcel": 34082, "Ġmateria": 34083, "ĠBerry": 34084, "ĠÑģами": 34085, "Ġextracted": 34086, "ĠSaying": 34087, "ismatic": 34088, "ĠпопÑĢоб": 34089, "Ġneuron": 34090, "graph": 34091, "ľë©´": 34092, "Ġenclosure": 34093, "ĠJohann": 34094, "Ġaftermath": 34095, "ÑĤоб": 34096, "Ġuży": 34097, "Ġsamp": 34098, "360": 34099, "ĠMei": 34100, "Ġtaco": 34101, "Ġreceptors": 34102, "Ġpunches": 34103, "ĠHoje": 34104, "ĠÙĩÙĨا": 34105, "=\"#": 34106, "ĠAngular": 34107, "Ġmusique": 34108, "Ġrol": 34109, "Ġñ": 34110, "sterreich": 34111, "Ġclam": 34112, "ĠTreasury": 34113, "chemical": 34114, "Ġapar": 34115, "Ġappend": 34116, "Ġforbid": 34117, "ĠHamburg": 34118, "аков": 34119, "Ġê¸Ī": 34120, "ilda": 34121, "Ġpreparations": 34122, "ĠmogÄħ": 34123, "Ġcamino": 34124, "Eric": 34125, "ĠBlind": 34126, "èĪĩ": 34127, "å¹´çļĦ": 34128, "ĠDiscovery": 34129, "ì¸ł": 34130, "çĪ¶": 34131, "Ġinterpreter": 34132, "Ġbred": 34133, "ĠPsalm": 34134, "Ġdefended": 34135, "ìī¬": 34136, "ĠErfahr": 34137, "ĠPeach": 34138, "Ġmoons": 34139, "ĠOst": 34140, "Ġspécial": 34141, "Ġarriver": 34142, "ĠWis": 34143, "uci": 34144, "Ġrobotics": 34145, "IVE": 34146, "Ġsiege": 34147, "arla": 34148, "Ġseparates": 34149, "ĠTC": 34150, "íı°": 34151, "quisite": 34152, "Ġparentheses": 34153, "ике": 34154, "ç«Ļ": 34155, "Ġtrous": 34156, "建": 34157, "ĠÑģилÑĮ": 34158, "Ġbeers": 34159, "ĠплаÑĤ": 34160, "ãģĻãģĶãģĦ": 34161, "Ġsola": 34162, "Ġdès": 34163, "mingham": 34164, "ikte": 34165, "Ġoops": 34166, "Ġtwitch": 34167, "å°ĩ": 34168, "ÏĪ": 34169, "ĠShouldn": 34170, "uvre": 34171, "Ġleer": 34172, "criptions": 34173, "Ġeyeshadow": 34174, "ĠGuo": 34175, "ĠPowell": 34176, "Ġsupuesto": 34177, "Ġana": 34178, "rals": 34179, "ĠMontreal": 34180, "Ġsurfing": 34181, "ĠÐŁÐµÑĢв": 34182, "×ŀ×ķ": 34183, "Ġmilliseconds": 34184, "Ġsuburbs": 34185, "Ġplaneta": 34186, "ÑĥÑĪка": 34187, "hrlich": 34188, "ĠHY": 34189, "ĠسÛĴ": 34190, "ĠMM": 34191, "ĠEff": 34192, "åı¯æĦĽ": 34193, "ĠHS": 34194, "anson": 34195, "Ġì§ģìłij": 34196, "Ġsuo": 34197, "Ġdeploying": 34198, "Ġkunt": 34199, "tering": 34200, "Ġerect": 34201, "ìŀ¥ìĿ´": 34202, "ĠìĿĮìĭĿ": 34203, "Ġspecimen": 34204, "!...": 34205, "æĪij說": 34206, "Ġligne": 34207, "Ġkonst": 34208, "adequ": 34209, "Ġìĥģíĥľ": 34210, "Ġaccessed": 34211, "ĠPole": 34212, "kill": 34213, "Ġë²Ħë": 34214, "Ġauthenticity": 34215, "Ġappelle": 34216, "ulle": 34217, "Ġrevision": 34218, "Ġgoats": 34219, "гли": 34220, "Ġpau": 34221, "ĠRanger": 34222, "ĠImag": 34223, "author": 34224, "Ġeve": 34225, "ĠMessenger": 34226, "Ġnay": 34227, "Ġwholes": 34228, "ätte": 34229, "Ġonwards": 34230, "ĠDepois": 34231, "ĠíijľíĺĦ": 34232, "ĠSARS": 34233, "Ġwszystkich": 34234, "Ġdestru": 34235, "umbing": 34236, "Ġcompatibility": 34237, "Ġmisinformation": 34238, "odore": 34239, "ĠFavor": 34240, "eko": 34241, "ıĮ": 34242, "waukee": 34243, "ĠTeaching": 34244, "ĠKO": 34245, "Ġbetting": 34246, "Ġquests": 34247, "Ġvivre": 34248, "ĠмÑĥзÑĭ": 34249, "Ġsaga": 34250, "Ġswell": 34251, "Ġgehe": 34252, "æĢİ麼樣": 34253, "ĠоÑĢганиз": 34254, "Ġgide": 34255, "ĠGross": 34256, "Ġdalej": 34257, "Ġclaws": 34258, "á»Ļc": 34259, "Ġprejudice": 34260, "Ġinsign": 34261, "ihood": 34262, "Ġpled": 34263, "Ġdónde": 34264, "ĠPolitical": 34265, "Ġpremises": 34266, "undert": 34267, "عت": 34268, "onnen": 34269, "Ġespaço": 34270, "Ġfé": 34271, "ĠHarrison": 34272, "ĠCensus": 34273, "Ġcardio": 34274, "Ġdiy": 34275, "Ġmilieu": 34276, "Ġjournée": 34277, "ĠRelease": 34278, "NIE": 34279, "ĠMuk": 34280, "idée": 34281, "á»įi": 34282, "Ġiçinde": 34283, "ŀĻ": 34284, "Ġresonate": 34285, "Ġmoles": 34286, "ĠFlying": 34287, "ĠGloria": 34288, "ĠPastor": 34289, "ĠArena": 34290, "好ä¸į好": 34291, "NON": 34292, "олов": 34293, "ĠallÃŃ": 34294, "omat": 34295, "ìĸ´ëıĦ": 34296, "ĠcaracterÃŃst": 34297, "Ġdeclining": 34298, "ÑĸÑı": 34299, "anco": 34300, "ĠInform": 34301, "Ġbargain": 34302, "Ġbushes": 34303, "ĠNaturally": 34304, "Ġrechts": 34305, "ĠTensor": 34306, "ĠPatricia": 34307, "Ġprincipio": 34308, "ĠMumbai": 34309, "Ġwomb": 34310, "Ġnostra": 34311, "Ġdilemma": 34312, "Ġirgendwann": 34313, "Ġ1964": 34314, "ĠenergÃŃa": 34315, "ĠнаÑĢ": 34316, "Ġsegregation": 34317, "ĠAthlet": 34318, "Ġ»,": 34319, "Ġyeni": 34320, "ĠSeit": 34321, "Ġvenom": 34322, "Ġdakika": 34323, "ĠëıĮë": 34324, "ĠÃīl": 34325, "Ġfus": 34326, "ĠMog": 34327, "¦½ëĭĪëĭ¤": 34328, "Ġremar": 34329, "ĠTeddy": 34330, "Ġbreasts": 34331, "icans": 34332, "æĶ¶çľĭ": 34333, "kap": 34334, "ĠhÆ¡n": 34335, "ĠJP": 34336, "ãĥ³ãĤ¿": 34337, "Ġresurrect": 34338, "ĠìĿ¸ë": 34339, "herical": 34340, "Ġfotograf": 34341, "ĠJosé": 34342, "Ġlivelihood": 34343, "Ġbibli": 34344, "teri": 34345, "Ġvorstellen": 34346, "ĠAAA": 34347, "Ġassessing": 34348, "YA": 34349, "Ġsplend": 34350, "Ġexcav": 34351, "Ġbaptism": 34352, "yll": 34353, "wow": 34354, "Mac": 34355, "Ġplastics": 34356, "teokbokki": 34357, "Ġintéressant": 34358, "Ġcommanded": 34359, "Ġfamously": 34360, "ĠÐĺли": 34361, "ĠManuel": 34362, "Ġsouthwest": 34363, "Ġdeformation": 34364, "ÃŃculo": 34365, "ĠнаÑħодиÑĤÑģÑı": 34366, "ĠPatter": 34367, "degree": 34368, "ĠczÄĻsto": 34369, "\"-": 34370, "Ġìħĭ": 34371, "Ġmanger": 34372, "ĠTrustee": 34373, "Ģ리": 34374, "Ġpuntos": 34375, "ivable": 34376, "Ġvolatile": 34377, "ĠëĬIJ": 34378, "Ġinstability": 34379, "Ġciel": 34380, "ciÄħ": 34381, "Ġpurity": 34382, "ноÑģÑĤ": 34383, "Sil": 34384, "edar": 34385, "åĻ¨": 34386, "NOUNCER": 34387, "Ġspelled": 34388, "GER": 34389, "Ġsanctuary": 34390, "Ġaccelerating": 34391, "Ġscout": 34392, "ĠпÑĢев": 34393, "fahren": 34394, "ãģĵãģ¡ãĤī": 34395, "ĠëĤĺìĺ¨": 34396, "ĠpoczÄħt": 34397, "ĠMeu": 34398, "kaar": 34399, "³´ê³ł": 34400, "akra": 34401, "Down": 34402, "ĠÃĦr": 34403, "ĠElite": 34404, "Ġallons": 34405, "Ġmayonnaise": 34406, "ĠSustain": 34407, "prisingly": 34408, "Ġsupervis": 34409, "Ġê·¸ëłĩì£ł": 34410, "Ġunemployed": 34411, "Ġfreshly": 34412, "Ġ×ŀ×¢": 34413, "ĠDh": 34414, "Ġtackling": 34415, "Ġogr": 34416, "Ġì´Īë": 34417, "ãĤĪãĤį": 34418, "Ġloft": 34419, "arah": 34420, "ĠAirl": 34421, "ĠDir": 34422, "ĠÐľÐ¾Ð¶Ð½Ð¾": 34423, "Ġbooking": 34424, "ĠCRA": 34425, "Ġhttps": 34426, "Ġchoke": 34427, "Ġgown": 34428, "Ġnoite": 34429, "Ġzac": 34430, "istol": 34431, "Ġsecre": 34432, "Ġresembles": 34433, "Ġcuad": 34434, "ìĤ¬ê°Ģ": 34435, "show": 34436, "Ġblanc": 34437, "Ġagu": 34438, "ĠPrint": 34439, "asted": 34440, "ĠWeather": 34441, "ipl": 34442, "Ġobscure": 34443, "Ġconte": 34444, "oughs": 34445, ");": 34446, "ĠDame": 34447, "ä¸Ģ缴": 34448, "Ġclarification": 34449, "Ġintimacy": 34450, "Ġuphold": 34451, "ĠMirror": 34452, "Ġwagon": 34453, "xide": 34454, "Ġclog": 34455, "apper": 34456, "ĠImmediately": 34457, "úde": 34458, "Ġtouchdown": 34459, "Ġrooft": 34460, "аÑĪа": 34461, "Ġçıkt": 34462, "Ġlaisser": 34463, "ĠUnreal": 34464, "ensitive": 34465, "Ġ123": 34466, "Ġplaster": 34467, "Ġducks": 34468, "Ġetme": 34469, "Ġbishop": 34470, "brevi": 34471, "Ġbic": 34472, "ä¸ĭåİ»": 34473, "Ġruntime": 34474, "Ġambitions": 34475, "маÑĤ": 34476, "ĠWein": 34477, "ĠMari": 34478, "ĠíĬ¸ë": 34479, "Ġresolver": 34480, "ĠngÃły": 34481, "ĠRise": 34482, "ãĤĪãģĨãģ«": 34483, "ĠCrus": 34484, "Ġmerchandise": 34485, "Ġeli": 34486, "Ġstatewide": 34487, "Ġowl": 34488, "éģł": 34489, "æĶ¹": 34490, "Ġtwisting": 34491, "Ġcontaminated": 34492, "ĠCommerce": 34493, "hythm": 34494, "ĠÃĪ": 34495, "Ġìĭ¤ë": 34496, "Ġmusste": 34497, "uir": 34498, "Ġsums": 34499, "ĠSomewhere": 34500, "ãĥİ": 34501, "Ġkami": 34502, "Ġaired": 34503, "ĠANDREW": 34504, "Ġêº": 34505, "Ġviendo": 34506, "Ġantibody": 34507, "Ġabsolument": 34508, "Ġprotesters": 34509, "ĠQuébec": 34510, "stadt": 34511, "Shaun": 34512, "Ġchambers": 34513, "ĠWear": 34514, "ĠEffects": 34515, "Ġhazards": 34516, "Ġnei": 34517, "Ġcorazón": 34518, "Ġá¼": 34519, "ĠSG": 34520, "Ķ©": 34521, "ĠìĹŃìĭľ": 34522, "Ġcomfy": 34523, "ĠCody": 34524, "Ġpensando": 34525, "Ġganska": 34526, "ĠAcross": 34527, "öllig": 34528, "abyte": 34529, "Ġwedge": 34530, "Ġkalian": 34531, "Ġsigue": 34532, "endes": 34533, "ĠGroÃŁ": 34534, "Ġutiliser": 34535, "Ġflown": 34536, "аниÑİ": 34537, "Ġlevar": 34538, "restrial": 34539, "Ġillustrations": 34540, "Ġaslında": 34541, "BLEEP": 34542, "ĠдоÑģÑĤ": 34543, "Ġturret": 34544, "Ġsuitcase": 34545, "ziÄĻki": 34546, "Ġsketches": 34547, "Ġacred": 34548, "ĠRei": 34549, "Ġtsun": 34550, "ĠSag": 34551, "Ġthirds": 34552, "ĠKIRBY": 34553, "rai": 34554, "Ġhumanos": 34555, "Ġrecommends": 34556, "Ġextraordinarily": 34557, "Ġcommencement": 34558, "KN": 34559, "opez": 34560, "Ġ×ijש": 34561, "Ġlethal": 34562, "ĠEstamos": 34563, "Ġinspector": 34564, "ĠSeok": 34565, "eun": 34566, "Ġoffshore": 34567, "Ġgettin": 34568, "years": 34569, "ĠSilence": 34570, "ĠNatur": 34571, "upun": 34572, "Ġtrzy": 34573, "Ġnoget": 34574, "Ġhamburger": 34575, "ĠPraise": 34576, "énd": 34577, "Ġ1971": 34578, "ylie": 34579, "krit": 34580, "ĠìĥĿê°ģìĿ´": 34581, "çļ®": 34582, "Ġmomentos": 34583, "Ġesté": 34584, "Ġdissemin": 34585, "Ġgigs": 34586, "Ġdesaf": 34587, "Ġavis": 34588, "ĠZoo": 34589, "ĠìķĬìĿĢ": 34590, "häng": 34591, "åı¥": 34592, "hake": 34593, "ĠBism": 34594, "Ġrethink": 34595, "ĠMalcolm": 34596, "Ġidentifies": 34597, "lower": 34598, "ixel": 34599, "ĠtvÃ¥": 34600, "ked": 34601, "ierz": 34602, "Ġöffentlich": 34603, "Ġproclaim": 34604, "soon": 34605, "lol": 34606, "Ġloi": 34607, "Ġbitten": 34608, "rollo": 34609, "Ġsermon": 34610, "Ġesqu": 34611, "Ġjackets": 34612, "Ġgráfic": 34613, "ĠпоказÑĭв": 34614, "Ġcabeza": 34615, "chodzi": 34616, "Ġpelvis": 34617, "Ġnostalgia": 34618, "Ġbrew": 34619, "Ġshortcuts": 34620, "ĠAdemás": 34621, "Ġsuperficial": 34622, "åħ©åĢĭ": 34623, "Ġboca": 34624, "ĠæĪijæĺ¯": 34625, "imentos": 34626, "åĽłä¸º": 34627, "Ġsprouts": 34628, "é£Ľ": 34629, "ĠJonas": 34630, "ĠFlorence": 34631, "static": 34632, "daughter": 34633, "*)": 34634, "ÅĤby": 34635, "fashion": 34636, "ĠGinger": 34637, "Ġ매ë": 34638, "Ġhustle": 34639, "utos": 34640, "ĠÑĤÑıж": 34641, "ĠLös": 34642, "ש×Ļ×Ŀ": 34643, "anych": 34644, "tuber": 34645, "Ġtidy": 34646, "Ġfrontal": 34647, "Ġwhiskey": 34648, "Ġhumid": 34649, "ĠÎŁ": 34650, "Ġridge": 34651, "Ġmarin": 34652, "Ġbientôt": 34653, "ĠCarrie": 34654, "chw": 34655, "Ġtahun": 34656, "ĠErgeb": 34657, "FR": 34658, "Ġìłķë¶Ģ": 34659, "ĠSoldier": 34660, "Ġenlightenment": 34661, "Ġexamining": 34662, "ĠNotre": 34663, "Ġeram": 34664, "ĠSunny": 34665, "Ġlayered": 34666, "ĠDazu": 34667, "rades": 34668, "好åIJĥ": 34669, "ĠнаÑĪей": 34670, "Ġtimber": 34671, "Ġmanners": 34672, "ĠBirmingham": 34673, "Ġminiature": 34674, "ometers": 34675, "Ġfiller": 34676, "ĠRip": 34677, "ĠKomb": 34678, "owner": 34679, "ì¿": 34680, "idian": 34681, "Ġdemás": 34682, "ĠÙĪت": 34683, "Ġprecautions": 34684, "Ġgoverno": 34685, "zelf": 34686, "ĠComplete": 34687, "å¸ĥ": 34688, "ĠPhantom": 34689, "ãģ¾ãģļ": 34690, "Ġнез": 34691, "ĠкаÑĢÑĤ": 34692, "ĠAntwort": 34693, "ĠPfizer": 34694, "ĠFranco": 34695, "ĠwÅĤ": 34696, "Ġfrig": 34697, "esper": 34698, "Ġkale": 34699, "Ġfilmmaker": 34700, "Ġkurt": 34701, "Ġinvalid": 34702, "å±Ģ": 34703, "arella": 34704, "Äĥng": 34705, "ramento": 34706, "Ġnutritional": 34707, "Ġdictators": 34708, "Ġafin": 34709, "Ġfuzzy": 34710, "ĠGina": 34711, "ót": 34712, "ĠExtremadura": 34713, "Ġdemonstrations": 34714, "ĠMontgomery": 34715, "íķ´ìĦ¤": 34716, "ĠGandhi": 34717, "ãĥĿ": 34718, "ç½®": 34719, "Ġreunion": 34720, "ĠjakiÅĽ": 34721, "ĠZug": 34722, "OUGH": 34723, "lifting": 34724, "Ġà²": 34725, "á¹Ľá¹£": 34726, "eb": 34727, "ĠWOW": 34728, "ĠShiva": 34729, "ometry": 34730, "Ġwildly": 34731, "Ġtended": 34732, "Ġmegap": 34733, "ì²ĺ": 34734, "Ġnause": 34735, "Ġgerek": 34736, "ãĥĭ": 34737, "ĠMarcel": 34738, "Ġneste": 34739, "خر": 34740, "Ġfeh": 34741, "åĨħ": 34742, "suspenseful": 34743, "ĠWrestle": 34744, "ĠPalestinians": 34745, "ĠGORD": 34746, "iyet": 34747, "ĠÑĢади": 34748, "Ġversuchen": 34749, "Ġtransistor": 34750, "ĠÐŁÑĢоÑģÑĤо": 34751, "ĠпонÑĢав": 34752, "Ġrhyme": 34753, "ĠVermont": 34754, "platz": 34755, "è®°": 34756, "ĠÄ°ÅŁte": 34757, "ĠHag": 34758, "ĠÐĺм": 34759, "ĠÑĢаÑģÑģказ": 34760, 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34819, "jaz": 34820, "plain": 34821, "Ġwrinkles": 34822, "Ġreais": 34823, "Ġpaljon": 34824, "Ġempowerment": 34825, "Ġattendees": 34826, "ppa": 34827, "Ġneden": 34828, "онÑĭ": 34829, "Ġtimeframe": 34830, "ĠCherry": 34831, "Ġidée": 34832, "Ġgag": 34833, "Ġdonkey": 34834, "Ġông": 34835, "ĠHare": 34836, "éļĽ": 34837, "ĠKara": 34838, "Ġacompan": 34839, "places": 34840, "imientos": 34841, "ĠHamm": 34842, "би": 34843, "uben": 34844, "iliyor": 34845, "Ġthirst": 34846, "Ġkry": 34847, "ĠGeorgetown": 34848, "׳×Ķ": 34849, "Ġorch": 34850, "Ġheartbeat": 34851, "Ġtransformations": 34852, "estones": 34853, "ĠKH": 34854, "Ġcartoons": 34855, "Ġanci": 34856, "Ġworthless": 34857, "Ġtailored": 34858, "pu": 34859, "Americans": 34860, "Ġpiles": 34861, "ĠMonkey": 34862, "Ġbasin": 34863, "ĠTemper": 34864, "ĠPaint": 34865, "Ġpunching": 34866, "Ġbaik": 34867, "ĠOakland": 34868, "vre": 34869, "ÅŁallah": 34870, "ydd": 34871, "Ġcasually": 34872, "odu": 34873, "Ġcoded": 34874, "ĠNorwegian": 34875, "ĠVince": 34876, 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35107, "ĠÐļонеÑĩно": 35108, "ĠBryce": 35109, "ĠÑıкÑĸ": 35110, "Ġpearls": 35111, "ĠпонимаÑİ": 35112, "Ġadditions": 35113, "Ġasympt": 35114, "ĠменÑĮÑĪе": 35115, "Ġscans": 35116, "Child": 35117, "ĠHide": 35118, "кÑĥÑİ": 35119, "etas": 35120, "Ġdank": 35121, "Ġpleas": 35122, "Ġessays": 35123, "Ġjets": 35124, "åħĴ": 35125, "Ġвед": 35126, "Ġpositives": 35127, "hof": 35128, "-)": 35129, "zzo": 35130, "Ġstarters": 35131, "Ġsmiled": 35132, "Ġ1944": 35133, "quiera": 35134, "Ġrok": 35135, "Ġpuesto": 35136, "Nico": 35137, "Ġsimulations": 35138, "Ġà¶": 35139, "Ġintrigued": 35140, "ĠOverwatch": 35141, "åĸĤ": 35142, "sigh": 35143, "bai": 35144, "Ġë§IJê³ł": 35145, "idé": 35146, "Ġcrabs": 35147, "áºŃp": 35148, "ĠIraqi": 35149, "ìĿ´ë¥¼": 35150, "ÑĤÑı": 35151, "ĠSophia": 35152, "ĠDNS": 35153, "Ġönemli": 35154, "ĠLuo": 35155, "Ŀ¤": 35156, "ĠCounsel": 35157, "ligen": 35158, "анÑĮÑĪе": 35159, "Ġtrumpet": 35160, "Ġdapat": 35161, "ĠJM": 35162, "ĠEVERY": 35163, "Ġå°įä¸įå°į": 35164, "夢": 35165, "ĠLayer": 35166, "Ġcô": 35167, "нал": 35168, "ĠJoo": 35169, "ĠHack": 35170, "Ġsunt": 35171, "ĠLeonard": 35172, "ĠFirebase": 35173, "änger": 35174, "Ġexploding": 35175, "voy": 35176, "Ġì¦IJ": 35177, "ĠÑģеÑĢÑĮ": 35178, "Ġseverity": 35179, "Ġbestimm": 35180, "çµIJæŀľ": 35181, "Ġtiring": 35182, "Ġprocurement": 35183, "Ġdiplomacy": 35184, "Ġdecorative": 35185, "ĠÙĬا": 35186, "Ġpenetration": 35187, "Õ«": 35188, "Ġoutright": 35189, "ENE": 35190, "ĠUni": 35191, "odles": 35192, "Ġzeros": 35193, "Ġdelightful": 35194, "jm": 35195, "Ġdopo": 35196, "没äºĭ": 35197, "Ġpositivity": 35198, "ĠVISTA": 35199, "ĠResource": 35200, "íĥĢë": 35201, "ÑĪие": 35202, "Carl": 35203, "Ġpiping": 35204, "Ġchopping": 35205, "ĠGanze": 35206, "üss": 35207, "ĠAo": 35208, "Ġshattered": 35209, "ĠDetective": 35210, "Ġundoubtedly": 35211, "Ġhalluc": 35212, "Ġench": 35213, "ÑĭÑĩно": 35214, "ÑĥлÑıÑĢ": 35215, "isesti": 35216, "Ġpedals": 35217, "Ġdurum": 35218, "¤íĶ": 35219, "laimer": 35220, "Ġpropre": 35221, "Cu": 35222, "Ġtranslator": 35223, "ĠcaÅĤ": 35224, "Ġ그걸": 35225, "ĠcaÅĤy": 35226, "UA": 35227, "Ġrevised": 35228, "Ġподоб": 35229, "ĠArticle": 35230, "ĠHaiti": 35231, "ĠÃĵ": 35232, "ĠCtrl": 35233, "Ġrozm": 35234, "lait": 35235, "Ġletzte": 35236, "ispering": 35237, "display": 35238, "Ġaluminium": 35239, "Ġpalabras": 35240, "Ġconocer": 35241, "Ġzitten": 35242, "Ġdirig": 35243, "åıªæľī": 35244, "Ġbrainstorm": 35245, "Ġwifi": 35246, "ĠParticip": 35247, "Ġviewpoint": 35248, "ĠQuan": 35249, "Ġhierarch": 35250, "Welcome": 35251, "対": 35252, "Ġoffen": 35253, "ĠRecovery": 35254, "gano": 35255, "Would": 35256, "Ġrepro": 35257, "Ġperceptions": 35258, "Ġdemasi": 35259, "ĠBangladesh": 35260, "ĠIncredible": 35261, "Ġletzt": 35262, "Ġbehaving": 35263, "Ġastonishing": 35264, "ĠâĨ": 35265, "ĠëĤ¨ìŀIJ": 35266, "èµ°äºĨ": 35267, "ãĥĶ": 35268, "ĠGORDON": 35269, "CAR": 35270, "?!\"": 35271, "ĠPrest": 35272, "Ġë§ŀìķĦìļĶ": 35273, "Ġtand": 35274, "Ġlash": 35275, "çĬ": 35276, "ificant": 35277, "Ġintoler": 35278, "ĠгеÑĢо": 35279, "Ġteu": 35280, "aso": 35281, "ĠÑģовеÑĤ": 35282, "Ġtravelers": 35283, "ĠSynd": 35284, "ĠвеÑĢÑģ": 35285, "Fonda": 35286, "adı": 35287, "Ġtranscription": 35288, "Ġtitanium": 35289, "Ġtwists": 35290, "Ġgearbox": 35291, "ensation": 35292, "fat": 35293, "Coll": 35294, "ĠCommonwealth": 35295, "zon": 35296, "ĠPolizei": 35297, "ĠAPPLAUSE": 35298, "fry": 35299, "ĠJuda": 35300, "esteem": 35301, "Ġsock": 35302, "ĠJugend": 35303, "ĠкÑģÑĤаÑĤи": 35304, "ĠDro": 35305, "Ġprochaine": 35306, "ãĥ¼ãĥ«": 35307, "Ġliksom": 35308, "ĠEnergie": 35309, "ĠMarina": 35310, "Ġ230": 35311, "Ġê°ĢìĦľ": 35312, "umping": 35313, "Ġlone": 35314, "ç´ļ": 35315, "Ġfonts": 35316, "Ġbusinessman": 35317, "Ġply": 35318, "Ġdoe": 35319, "grid": 35320, "ĠMilwaukee": 35321, "ĠEden": 35322, "!\".": 35323, "ĠÛĮÛģ": 35324, "ogens": 35325, "Ġteaser": 35326, "Ġquién": 35327, "Ġincentiv": 35328, "govern": 35329, "Ġchildcare": 35330, "Ġsneakers": 35331, "Ġimprisoned": 35332, "®": 35333, "иÑĤеÑģÑĮ": 35334, "anbul": 35335, "Ġregain": 35336, "Ġtranquil": 35337, "Redner": 35338, "鼨": 35339, "IFA": 35340, "Ġideological": 35341, "ĠmayorÃŃa": 35342, "Ġbureau": 35343, "eterm": 35344, "ĠDID": 35345, "ìĬ·": 35346, "Ġwaving": 35347, "Ġbeb": 35348, "Ġár": 35349, "Ġкв": 35350, "Ġenvoy": 35351, "anut": 35352, "икÑĥ": 35353, "ĠEnvironment": 35354, "ĠAssass": 35355, "ãĤĵãģ§": 35356, "ĠBread": 35357, "ĠТÑĥÑĤ": 35358, "Ġstaircase": 35359, "ĠDisease": 35360, "Ġaucun": 35361, "ĠëĭĪ": 35362, "Ġconfrontation": 35363, "Ġ1941": 35364, "Ġirony": 35365, "Ġworsh": 35366, "ãĤĮãĤĭ": 35367, "Ġfick": 35368, "ĠNaomi": 35369, "Ġbackside": 35370, "ieux": 35371, "Kap": 35372, "Ġvedere": 35373, "Ġlengthy": 35374, "Ġbreaker": 35375, "ĠRolle": 35376, "Ġpredator": 35377, "Ġnossos": 35378, "Ġadvertise": 35379, "è³ĩ": 35380, "ÑĢоде": 35381, "Rednerwechsel": 35382, "reten": 35383, "Ġcollectors": 35384, "ıģımız": 35385, "Ġtrig": 35386, "Ġaxes": 35387, "inters": 35388, "Ġpenalties": 35389, "ĠOsman": 35390, "ĠJenna": 35391, "Ġflakes": 35392, "Ġtrainers": 35393, "Ġstunned": 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35627, "Ġcasualties": 35628, "ĠANNOUNCER": 35629, "ä»İ": 35630, "Ġtocar": 35631, "Ġbacterial": 35632, "Ho": 35633, "Ġstreak": 35634, "ĠJENN": 35635, "Ġplast": 35636, "Ñģлед": 35637, "Ġreapp": 35638, "Ġpaycheck": 35639, "Ġminers": 35640, "habt": 35641, "ĠJap": 35642, "нÑĥÑĤ": 35643, "Ġredemption": 35644, "Ġquir": 35645, "hnlich": 35646, "Ġaccumulation": 35647, "Ġshove": 35648, "Ġadrenaline": 35649, "Make": 35650, "ĠHern": 35651, "ossing": 35652, "ĠVil": 35653, "ubby": 35654, "hertz": 35655, "breaks": 35656, "Ġspur": 35657, "ĠDaha": 35658, "USTIN": 35659, "Ġcontinuer": 35660, "ĠSaul": 35661, "ãģ®ãģ¯": 35662, "ĠíıŃ": 35663, "ĠëIJĺë©´": 35664, "Ġë§IJìĶĢ": 35665, "Ġож": 35666, "Ġsuspects": 35667, "Ġlaquelle": 35668, "ĠMuchas": 35669, "Ġvöllig": 35670, "ulen": 35671, "Ġimpres": 35672, "Ġlobb": 35673, "enee": 35674, "Ġнаж": 35675, "Ta": 35676, "Ġréalité": 35677, "ĠRex": 35678, "Ġharvesting": 35679, "Ġestr": 35680, "æ¶": 35681, "ospace": 35682, "OSS": 35683, "Ġdisturbance": 35684, "assic": 35685, "ĠIsab": 35686, "Ġdécouv": 35687, "ĠHampshire": 35688, "Ġornament": 35689, "Ġluôn": 35690, "ĠUW": 35691, "ĠjÄħ": 35692, "éĤ£ä¹Ī": 35693, "Ġrespecto": 35694, "Ġcomunidad": 35695, "Ġcomigo": 35696, "agna": 35697, "Ġintrinsic": 35698, "ĠAlumni": 35699, "Ġsesleri": 35700, "Ġestimation": 35701, "âĢĶâĢĶ": 35702, "Ġproduit": 35703, "ãĢĤãĢį": 35704, "ĠвÑĢ": 35705, "Ġwhirl": 35706, "Ġacces": 35707, "çu": 35708, "Ġvariability": 35709, "Ġvodka": 35710, "itsu": 35711, "Ġinternships": 35712, "Ġallocate": 35713, "RR": 35714, "íĽĪ": 35715, "Ġinstructional": 35716, "tant": 35717, "Ġà®ħத": 35718, "Ġinvites": 35719, "Ġhak": 35720, "Ġscares": 35721, "Ġeclipse": 35722, "пов": 35723, "колÑĮ": 35724, "ativas": 35725, "Ġstabbed": 35726, "ĠDOM": 35727, "ä¸įåĪ°": 35728, "roots": 35729, "ĠPicture": 35730, "íĺ¼": 35731, "ĠCHA": 35732, "iec": 35733, "ıı": 35734, "hanol": 35735, "Ġmisunderstand": 35736, "Ray": 35737, "Ġroadmap": 35738, "ocumented": 35739, "izione": 35740, "ĠOlive": 35741, "rift": 35742, 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"pur": 35801, "Ġzat": 35802, "Ġobsess": 35803, "Ġmotivo": 35804, "ĠKub": 35805, "ĠDrug": 35806, "Ant": 35807, "ĠPlayers": 35808, "ĠHumans": 35809, "Ġmelee": 35810, "ĠWildlife": 35811, "ĠVP": 35812, "Ġvolcanic": 35813, "Ġcomin": 35814, "ĠGuang": 35815, "ĠÏĦιÏĤ": 35816, "ĠоÑģобенно": 35817, "ĠSize": 35818, "Listen": 35819, "ĠAaa": 35820, "appro": 35821, "Ġbarbar": 35822, "ĠParkinson": 35823, "нÑıÑĤÑĮ": 35824, "åį°": 35825, "Ġunderestimate": 35826, "Ġsubstitution": 35827, "Ġcosmetic": 35828, "ä¸ĭ次": 35829, "Ġwillen": 35830, "Ġbeide": 35831, "anni": 35832, "Ġconditioned": 35833, "ĠDebbie": 35834, "Ġisto": 35835, "ĠEdwards": 35836, "ìĽĮìļĶ": 35837, "ĠÑĤов": 35838, "Ġabbrevi": 35839, "ĠMün": 35840, "ĠPrinc": 35841, "ĠLiang": 35842, "Ġstink": 35843, "Ġradioactive": 35844, "ãģĨãĤı": 35845, "Ġacontec": 35846, "Ġuncon": 35847, "ĠTurbo": 35848, "ãģIJ": 35849, "Ġkisses": 35850, "æĺ¯ä»Ģ麼": 35851, "еÑĤÑĢов": 35852, "Ġfrontier": 35853, "ĠSpy": 35854, "ĠBelarus": 35855, "ĠCBS": 35856, "á»Ĺ": 35857, 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"ortex": 36143, "ĠìłĪë": 36144, "ĠØ¥ÙĨ": 36145, "Ġdisclose": 36146, "ĠElijah": 36147, "ĠλÎŃ": 36148, "ĠQuer": 36149, "بÙĦ": 36150, "ãĤ¡": 36151, "Tell": 36152, "arle": 36153, "ÑĸÑĢ": 36154, "Ġaugmented": 36155, "Ġë¹ĦìĬ·": 36156, "Ġandroid": 36157, "त": 36158, "arma": 36159, "Ġszer": 36160, "geord": 36161, "Ġgeek": 36162, "Ġyeux": 36163, "Ġpong": 36164, "ĠãģĿãģĨ": 36165, "Ġtortured": 36166, "ĠBath": 36167, "zig": 36168, "asonable": 36169, "Ġnets": 36170, "Ġbaru": 36171, "ĠFlat": 36172, "ĠVater": 36173, "ĠTerror": 36174, "ĠAvo": 36175, "Ġceremonies": 36176, "roe": 36177, "Ùģس": 36178, "Ops": 36179, "Ġhyvin": 36180, "Ġapresent": 36181, "olor": 36182, "ĠигÑĢÑĭ": 36183, "orton": 36184, "Ġê·¸ëŀ¬": 36185, "Ġlookin": 36186, "ĠTY": 36187, "ĠMint": 36188, "Add": 36189, "Ġmite": 36190, "ĠSmoke": 36191, "Ġnota": 36192, "Ġmoss": 36193, "ĠAbend": 36194, "Ġ컨": 36195, "Ġexaggerated": 36196, "fires": 36197, "Ġredist": 36198, "ffiti": 36199, "Ġopenness": 36200, "ê°IJìĿ´": 36201, "endeu": 36202, "енной": 36203, "Watch": 36204, "Ġavatar": 36205, "ĠPey": 36206, "urun": 36207, "Ġsenza": 36208, "Ġì§ĢìĹŃ": 36209, "ĠNatomiast": 36210, "Ġemergence": 36211, "rays": 36212, "Ġcrafted": 36213, "gary": 36214, "ãģłãģij": 36215, "üng": 36216, "-\"": 36217, "Ġhacked": 36218, "Ġstray": 36219, "encie": 36220, "emo": 36221, "Ġcomen": 36222, "ĠKız": 36223, "ĠJasmine": 36224, "ĠHindi": 36225, "manas": 36226, "Ġinfinitely": 36227, "emon": 36228, "ìĿ¸ëį°ìļĶ": 36229, "jak": 36230, "Ġroaring": 36231, "érique": 36232, "sweise": 36233, "ĠRolex": 36234, "åł±å°İ": 36235, "ĠStuart": 36236, "bnb": 36237, "Ġdiagnose": 36238, "Ġcoherent": 36239, "ĠMJ": 36240, "æºĸåĤĻ": 36241, "Ġpike": 36242, "lav": 36243, "Ġorchestral": 36244, "аÑģÑĤи": 36245, "Ġterminar": 36246, "Ġgatherings": 36247, "Ġcompliant": 36248, "Ġupgrading": 36249, "Ġregulator": 36250, "Ġlanç": 36251, "éĢ£": 36252, "Ġmerchants": 36253, "tawa": 36254, "Ġmonitored": 36255, "Ġrendre": 36256, "两": 36257, "Ġunterwegs": 36258, "anguard": 36259, "gard": 36260, "ĠBelow": 36261, "duino": 36262, "ĠЦе": 36263, "Ġimpedance": 36264, "ìľ¡": 36265, "份": 36266, "Ġaktuell": 36267, "ĠVatic": 36268, "åŃ©": 36269, "Ġstewards": 36270, "Ġbrightest": 36271, "Ġkenn": 36272, "Ġkau": 36273, "ĠMatrix": 36274, "ĠBark": 36275, "ĠðŁij": 36276, "Ġtaper": 36277, "Ġcasino": 36278, "ר×Ķ": 36279, "ysical": 36280, "Ġbuilders": 36281, "ĠczÅĤowie": 36282, "ĠNepal": 36283, "Ġ!\"": 36284, "Ġterme": 36285, "Ġinnych": 36286, "Ġmaths": 36287, "Ġdrafted": 36288, "ĠBalk": 36289, "Ġhesitant": 36290, "Ġvoltar": 36291, "Ġrevive": 36292, "ĠÑĦилÑĮма": 36293, "Ġassassin": 36294, "ĠSolutions": 36295, "Ġduel": 36296, "Ġbearings": 36297, "à¸Ħะ": 36298, "Ġrookie": 36299, "ikat": 36300, "Ġbiscuits": 36301, "Ġcords": 36302, "ÑĥваÑĤи": 36303, "ARIN": 36304, "Ġprogressing": 36305, "ĠGir": 36306, "Ġpenetrate": 36307, "ĠStorage": 36308, "eight": 36309, "ĠÑĤÑĢÑĥ": 36310, "ĠdonÃŃt": 36311, "Ġsizin": 36312, "Ġoutdated": 36313, "ĠнаÑĪи": 36314, "Ġaffir": 36315, "Ġspoons": 36316, "Ġoni": 36317, "Ġflank": 36318, "ĠGol": 36319, "hã": 36320, "Ġpéri": 36321, "Ġhonorable": 36322, "ĠBreathe": 36323, "scenes": 36324, "Ġobviamente": 36325, "икÑģ": 36326, "Ġש×ŀ×": 36327, "Ġsmoothie": 36328, "ŀĪë": 36329, "Ġdime": 36330, "ĠíĸĪìĸ´ìļĶ": 36331, "Ġappel": 36332, "ĠCatholics": 36333, "Ġsingles": 36334, "Ġlaten": 36335, "Ġçünkü": 36336, "ĠVader": 36337, "æıĽ": 36338, "Ġvardı": 36339, "ĠIstanbul": 36340, "gré": 36341, "ĠElsa": 36342, "ël": 36343, "Ġinvece": 36344, "Ġcrane": 36345, "Ġobe": 36346, "ĠShark": 36347, "Ġsmack": 36348, "Ġrestoring": 36349, ".\\": 36350, "Ġë¹łë": 36351, "Ġfaded": 36352, "umbers": 36353, "Singing": 36354, "Ġdepressing": 36355, "thest": 36356, "ĠWahr": 36357, "Ġmultitude": 36358, "ÑĢавÑģÑĤвÑĥйÑĤе": 36359, "rijk": 36360, "eka": 36361, "Ġcompletes": 36362, "ĠWells": 36363, "Ġroy": 36364, "ĠPray": 36365, "ĠKalau": 36366, "izin": 36367, "iaÅĤem": 36368, "Ġlocom": 36369, "ĠNashville": 36370, "ĠPentagon": 36371, "미": 36372, "ĠNEW": 36373, "ÄħÄĩ": 36374, "ÃŃss": 36375, "Ġmarrying": 36376, "Ġfeud": 36377, "íĻķ": 36378, "æĢ¥": 36379, ")!": 36380, "ĠOperations": 36381, "ÑĥÑĶ": 36382, "Ġmoje": 36383, "Ġinstructed": 36384, "ĠëĪĦ구": 36385, "Ġ×Ķ×Ĵ": 36386, "ĠпомоÑīÑĮÑİ": 36387, "Ġsabia": 36388, "ìķĺìĸ´ìļĶ": 36389, "plane": 36390, "pri": 36391, "ĠполноÑģÑĤÑĮÑİ": 36392, "ĠKitty": 36393, "Ġpróprio": 36394, "edere": 36395, "Ġinteresante": 36396, "Ġде": 36397, "Ġcondensed": 36398, "Ġavent": 36399, "TOR": 36400, "Ġgreasy": 36401, "ARK": 36402, "orta": 36403, "AJ": 36404, "Ġdisreg": 36405, "Ġcorrections": 36406, "Ġstero": 36407, "Ġinfluenza": 36408, "Ġdesses": 36409, "Ġballots": 36410, "Ġmeget": 36411, "Ġmafia": 36412, "Ġböl": 36413, "nost": 36414, "ĠÑģÑĤаÑĤÑĮ": 36415, "Ġresponder": 36416, "Ġhinten": 36417, "grav": 36418, "à¸Ńะ": 36419, "ynchron": 36420, "Ġviens": 36421, "Ġsamo": 36422, "Ġdt": 36423, "pannt": 36424, "ĠÅĽwiat": 36425, "ĠзапиÑģ": 36426, "Ġmerged": 36427, "Ġkep": 36428, "Ġmisleading": 36429, "Ġdigamos": 36430, "Ġammon": 36431, "è¾Ľ": 36432, "chet": 36433, "Ġê°Ģìł¸": 36434, "Ġuni": 36435, "ĠëIJĺëĬĶëį°": 36436, "ĠнапÑĢав": 36437, "ĠкоÑĤоÑĢого": 36438, "Ġanimate": 36439, "×ķ×IJ×": 36440, "еÑĢв": 36441, "Ġminced": 36442, "Ġkaum": 36443, "ãģĤãģģ": 36444, "ÏĢε": 36445, "лег": 36446, "existing": 36447, "Ġplataform": 36448, "ĠKRIS": 36449, "ìĽł": 36450, "ĠFamilien": 36451, "ĠLibya": 36452, "Ġbiodiversity": 36453, "Ġidiots": 36454, "irdi": 36455, "Ġszyb": 36456, "ĠRolling": 36457, "ücht": 36458, "ĠÑĥдив": 36459, "ÑģÑĥд": 36460, "Ġrealizar": 36461, "Ġcanned": 36462, "ĠÑĢан": 36463, "Ġmetabolic": 36464, "ĠBeef": 36465, "Ġkilka": 36466, "лÑİÑģ": 36467, "Ġregistry": 36468, "моÑĤÑĢиÑĤе": 36469, "Ġvielä": 36470, "Ġodc": 36471, "Ġcondemned": 36472, "æ©ĭ": 36473, "fal": 36474, "ĠDil": 36475, "woÅĽci": 36476, "Aw": 36477, "Ġstatistically": 36478, "Ġsogen": 36479, "ĠBETH": 36480, "Ġshaving": 36481, "幸": 36482, "ocal": 36483, "ĠFunny": 36484, "Ġpeacefully": 36485, "Ġaddictive": 36486, "ĠInsert": 36487, "lauf": 36488, "Ġexperiencia": 36489, "é¦ĸåħĪ": 36490, "иÑĤелÑı": 36491, "ÃŃgen": 36492, "ágina": 36493, "Ġabdomen": 36494, "íķľëĭ¤": 36495, "icus": 36496, "imana": 36497, "ìį¨": 36498, "arching": 36499, "Ġkonkret": 36500, "ìķĺë": 36501, "ека": 36502, "oufl": 36503, "ivel": 36504, "Ġnude": 36505, "ètres": 36506, "Ġmonsieur": 36507, "Ġclash": 36508, "Ġtherapists": 36509, "Ġcubed": 36510, "Ġretrouver": 36511, "Ġwaveform": 36512, "Ġpotem": 36513, "ĠFormer": 36514, "isión": 36515, "åºľ": 36516, "Ġ×IJ×Ŀ": 36517, "undos": 36518, "ĠMeinung": 36519, "صÙĦ": 36520, "ĠJude": 36521, "ĠnÃ¥r": 36522, "ĠLeonardo": 36523, "ĠCristo": 36524, "ĠGOT": 36525, "ÑģÑĤÑĢÑĥк": 36526, "LAN": 36527, "ĠgÃ¥ng": 36528, "Ġdéb": 36529, "ĠFrankfurt": 36530, "Ġcrappy": 36531, "Ġlil": 36532, "année": 36533, "ĠмеÑģÑĤе": 36534, "RET": 36535, "ĠNer": 36536, "ĠCOSTA": 36537, "Ġjedem": 36538, "Ġcurtains": 36539, "Ġiterations": 36540, "Ġunav": 36541, "Ġplaque": 36542, "orum": 36543, "Ġζ": 36544, "Ġnúmeros": 36545, "Ġdesap": 36546, "²½": 36547, "Ġcompiled": 36548, "Ġrefle": 36549, "Ġrankings": 36550, "Ġrepaired": 36551, "ĠÐĿапÑĢ": 36552, "Ġdownloads": 36553, "Ġarmour": 36554, "Ġ×Ļ×ķתר": 36555, "Ġlongevity": 36556, "ĠTONER": 36557, "ĠкомменÑĤаÑĢ": 36558, "Ġczego": 36559, "Ġnotify": 36560, "Ġairports": 36561, "Ġenduring": 36562, "lette": 36563, "Ġapparat": 36564, "Ġhabil": 36565, "á»ĩc": 36566, "nad": 36567, "ICO": 36568, "ĠBrah": 36569, "Ġsegún": 36570, "Ġgovernors": 36571, "kaha": 36572, "ĠSchluss": 36573, "Ġodpowied": 36574, "irting": 36575, "Ġrempl": 36576, "ĠAboriginal": 36577, "identally": 36578, "Ġenhancing": 36579, "licting": 36580, "ĠHawaiian": 36581, "Ġstriving": 36582, "ĠNiet": 36583, "Ġznaczy": 36584, "Ġobedience": 36585, "ĠnÃ¥got": 36586, "Ġexpired": 36587, "Ġ1918": 36588, "presented": 36589, "Ġprowad": 36590, "ĠTerr": 36591, "ĠPrinceton": 36592, "Ġmorgen": 36593, "Ġattracting": 36594, "ĠSigma": 36595, "igner": 36596, "ĠRechts": 36597, "ĠPeki": 36598, "Ġmethy": 36599, "Ġhamm": 36600, "Ġdireito": 36601, "Ġdelegation": 36602, "иваÑİÑĤ": 36603, "Ġgin": 36604, "Young": 36605, "Ġdependencies": 36606, "ĠBradley": 36607, "buds": 36608, "Ġfis": 36609, "Ġpytanie": 36610, "Ġinterconnected": 36611, "Ġembaixo": 36612, "ĠSas": 36613, "Ġruh": 36614, "ĠSicht": 36615, "Sur": 36616, "Ġsuperb": 36617, "ĠSabbath": 36618, "ĠDanger": 36619, "kol": 36620, "Ġhou": 36621, "supp": 36622, "ĠNacional": 36623, "Ġsuccession": 36624, "Ġvá": 36625, "ĠMaÃŁnahmen": 36626, "ĠJessie": 36627, "ĠIdaho": 36628, "forest": 36629, "ħĺ": 36630, "Ġ×ŀ×ĵ": 36631, "ĠØ£ÙĬ": 36632, "Ġsweetheart": 36633, "Ġneatly": 36634, "ĠEvangel": 36635, "곡": 36636, "ĠSuite": 36637, "ública": 36638, "ĠÑĥли": 36639, "ĠAnnouncer": 36640, "ligh": 36641, "Ġsensations": 36642, "Ġshelters": 36643, "Ġhart": 36644, "Ġsqueezing": 36645, "ĠRivers": 36646, "ĠCooking": 36647, "ì±ħ": 36648, "personal": 36649, "Ġmanos": 36650, "ÑijÑĤÑģÑı": 36651, "wij": 36652, "Ġgogg": 36653, "ĠMilli": 36654, "ĠFP": 36655, "ünst": 36656, "ĠLS": 36657, "Ġspraying": 36658, "Ġfaux": 36659, "Ġautograph": 36660, "ologic": 36661, "Ġtorment": 36662, "Ġencrypted": 36663, "á»ħ": 36664, "Ġestre": 36665, "ç¹¼": 36666, "à±": 36667, "Ġstumbled": 36668, "Ġaider": 36669, "Ġsaben": 36670, "xter": 36671, "ĠCities": 36672, "ĠTürk": 36673, "ëĭ¥": 36674, "chine": 36675, "Ġtopping": 36676, "Ġpoisoned": 36677, "ĠRomania": 36678, "×ĵ×Ļ": 36679, "Ģë¡ľ": 36680, "ĠпоÑĢÑıд": 36681, "Ġchirping": 36682, "ĠìĻĦë": 36683, "×ij×¢": 36684, "Ġcuanto": 36685, "Ġdonating": 36686, "ĠRegent": 36687, "ĠBeruf": 36688, "Ġdistracting": 36689, "Ġstamina": 36690, "ĠDarren": 36691, "Ġì¶ķ": 36692, "lists": 36693, "dal": 36694, "chuss": 36695, "Ġeconomist": 36696, "ãģĪãĥ¼": 36697, "orgt": 36698, "Ġistiyorum": 36699, "è¿Ľ": 36700, "ĠSurprise": 36701, "ĠHao": 36702, "Ġìµľê³ł": 36703, "ĠGW": 36704, "ĠInner": 36705, "Ġquieren": 36706, "Ġminded": 36707, "Ġsupercomputer": 36708, "Ġdiagrams": 36709, "íĬľë": 36710, "ê²łìĸ´": 36711, "ĠобÑĬÑıÑģ": 36712, "Ġestaban": 36713, "Ġdestroys": 36714, "ĠBreaking": 36715, "ĠkarÄ±ÅŁ": 36716, "Ġrebuilding": 36717, "ľëĮĢ": 36718, "ливо": 36719, "ĠSauce": 36720, "ĠFusion": 36721, "×ķ×ŀ×": 36722, "ĠQuinn": 36723, "Ġgauche": 36724, "ĠÙĪØ£": 36725, "ĠÈ": 36726, "çĵľ": 36727, "Ġtechno": 36728, "Ġdispatch": 36729, "ĠaÅŁk": 36730, "Ġeinzel": 36731, "ĠGmail": 36732, "çŀ": 36733, "Ġê°ľìĿ¸": 36734, "ĠÑģемÑĮ": 36735, "Ġjourneys": 36736, "Ġiht": 36737, "Ġfibre": 36738, "Ġdramas": 36739, "ouched": 36740, "Ġrename": 36741, "ĠопеÑĢ": 36742, "Ġpoo": 36743, "ĠDru": 36744, "ĠиÑĤог": 36745, "Ġzast": 36746, "Ġcoz": 36747, "Ġzucch": 36748, "Ġobtaining": 36749, "Ġcommute": 36750, "Ġsubmer": 36751, "ĠVish": 36752, "ĠRabb": 36753, "ogg": 36754, "Ġhut": 36755, "íĸĪìĸ´": 36756, "æ¯Ķå¦Ĥ": 36757, "eremi": 36758, "Ġμα": 36759, "Ġdiskut": 36760, "ĠбÑĥк": 36761, "Ġimpaired": 36762, "depend": 36763, "ĠÙĪا": 36764, "ĠÑĢÑĥк": 36765, "ĠбаÑĢ": 36766, "Ġoxidation": 36767, "Ġsituação": 36768, "ÉĻn": 36769, "ução": 36770, "Ġsagte": 36771, "ĠSER": 36772, "ĠCake": 36773, "Ġturmeric": 36774, "ĠKak": 36775, "bung": 36776, "ĠKá¹Ľá¹£á¹ĩa": 36777, "Ġpoisoning": 36778, "Ġslipping": 36779, "ĠSays": 36780, "å°±åı¯ä»¥": 36781, "òng": 36782, "çŁ³": 36783, "«": 36784, "ĠClaudia": 36785, "ĠCharacter": 36786, "ниÑĨ": 36787, "coat": 36788, "Ġprogressed": 36789, "ĠFergus": 36790, "Ġìĺ¤ëĬ": 36791, "Ġoat": 36792, "ordable": 36793, "ĠLey": 36794, "ĠHeraus": 36795, "Ġresultados": 36796, "ĠKayla": 36797, "Ġriff": 36798, "Ġchegou": 36799, "Ġxi": 36800, "Ġspacious": 36801, "Ġrecognised": 36802, "Ġech": 36803, "ĠTie": 36804, "Ġlauncher": 36805, "Jim": 36806, "Ġsuppression": 36807, "ĠImpossible": 36808, "Ġguitars": 36809, "ĠFourier": 36810, "иÑĩеÑģкий": 36811, "ĠTherap": 36812, "ĠKaf": 36813, "centered": 36814, "ĠÑģооÑĤвеÑĤ": 36815, "Ġklim": 36816, "Ġcarbohydrates": 36817, "ignant": 36818, "ĠAstron": 36819, "Ġemple": 36820, "Ġdrastic": 36821, "ĠмиÑĢе": 36822, "вин": 36823, "uw": 36824, "Ġprettier": 36825, "Ġdonuts": 36826, "ĠAthena": 36827, "Ġdissert": 36828, "Ġplante": 36829, "Ġuranium": 36830, "ìĿĮë": 36831, "aré": 36832, "Ġrzecz": 36833, "Ġdisplaying": 36834, "æĪ²": 36835, "Ġsarc": 36836, "rão": 36837, "Ġtampoco": 36838, "Ġphilosophers": 36839, "ĠRecht": 36840, "æĵļ": 36841, "Ġcomentarios": 36842, "yse": 36843, "Ġìľ¤": 36844, "Ġmise": 36845, "ĠGin": 36846, "Ġном": 36847, "ĠFROM": 36848, "liner": 36849, "atif": 36850, "ĠspoÅĤec": 36851, "xa": 36852, "ĠÑĤÑĢÑĥд": 36853, "Ġwag": 36854, "기ìĹIJ": 36855, "ĠMG": 36856, "Ġoffspring": 36857, "ĠUnderstanding": 36858, "åıªæĺ¯": 36859, "ORA": 36860, "Ġwhirring": 36861, "Ġsurrend": 36862, "Ġpoker": 36863, "Ġmonuments": 36864, "ĠâĻ©": 36865, "Ġorganised": 36866, "ĠSozial": 36867, "ĠFactory": 36868, "Ñħа": 36869, "Ġresemble": 36870, "зд": 36871, "Ġexplosions": 36872, "Ġpayroll": 36873, "Ġomn": 36874, "ĠJorge": 36875, "ιÏĥ": 36876, "Ġfracture": 36877, "Ġpersecution": 36878, "Ġdemais": 36879, "ECH": 36880, ",)": 36881, "Ġcriar": 36882, "ĠJOSH": 36883, "Ġdemographics": 36884, "Ġ1600": 36885, "Ġcurrencies": 36886, "ĠTips": 36887, "ĠéĢĻåĢĭ": 36888, "ĠRefer": 36889, 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37751, "wright": 37752, "Ġpourra": 37753, "êter": 37754, "Ġcamel": 37755, "áŀ": 37756, "Ġrapidement": 37757, "Ġmej": 37758, "Ġstiffness": 37759, "ADAS": 37760, "Ġdiffers": 37761, "Ġalot": 37762, "ĠSig": 37763, "ÑıÑĤелÑĮ": 37764, "Ġabstraction": 37765, "åľĺ": 37766, "Ġkeiner": 37767, "grupp": 37768, "ĠSherlock": 37769, "íĺĶ": 37770, "Ġcite": 37771, "Ġoverflow": 37772, "Ġtại": 37773, "úcar": 37774, "bula": 37775, "Ġconjunto": 37776, "ĠCI": 37777, "Ġmoderator": 37778, "Ġindirectly": 37779, "Ġalleine": 37780, "âĤ": 37781, "ÑĪиб": 37782, "Ġбаб": 37783, "Ġdanach": 37784, "Ġ1939": 37785, "Ġpromet": 37786, "Ġdestinations": 37787, "ĠIllust": 37788, "ικÏĮ": 37789, "Ġsabes": 37790, "Ġheh": 37791, "ĠGesetzent": 37792, "ĠMiz": 37793, "енко": 37794, "ĠMys": 37795, "Ь": 37796, "ĠJudaism": 37797, "Ġmustache": 37798, "Ġstimmt": 37799, "ĠGaza": 37800, "Ġvolte": 37801, "Ġnuo": 37802, "Ġmón": 37803, "ĠComput": 37804, "ูà¹Ī": 37805, "ĠRadi": 37806, "Ġexceptionally": 37807, "Ġassumes": 37808, "éĸĭå¿ĥ": 37809, "ãģĪãģ°": 37810, "inform": 37811, "Ġshrine": 37812, "æĵĬ": 37813, "Ġimplication": 37814, "ĠFitz": 37815, "æ²ĴéĹľä¿Ĥ": 37816, "!.": 37817, "Ġlt": 37818, "Ġalloy": 37819, "Ġethic": 37820, "Ġmonastery": 37821, "ìĭľì£ł": 37822, "icação": 37823, "Ġcoordinating": 37824, "ĠMoto": 37825, "Ġoverlook": 37826, "Ġchois": 37827, "Ġantibiotic": 37828, "ĠMinne": 37829, "ĠBJ": 37830, "ĠApa": 37831, "orian": 37832, "Ġspilled": 37833, "Jam": 37834, "Ġhusbands": 37835, "Ġcreations": 37836, "Ġañ": 37837, "üssel": 37838, "ĠìĿ´ìļ©": 37839, "Ġanalyse": 37840, "rose": 37841, "Ġpunched": 37842, "Ġpresque": 37843, "Ġastronomy": 37844, "Ġschwierig": 37845, "ĠEbola": 37846, "Ġcis": 37847, "Ġacet": 37848, "ĠFX": 37849, "endre": 37850, "ĠìĿĮìķħ": 37851, "Ġwebpage": 37852, "Ġfreaked": 37853, "Ġlatte": 37854, "Ġì¿ł": 37855, "Ġ머ë": 37856, "Never": 37857, "Gra": 37858, "íĻĶ를": 37859, "eyed": 37860, "Ġë°ľëĿ¼": 37861, "Ġespera": 37862, "Ġaparece": 37863, "ração": 37864, "Ġdisruptive": 37865, "ĠJoint": 37866, "urous": 37867, "reas": 37868, "ĠquerÃŃa": 37869, "Ġdistributions": 37870, "Ġexponent": 37871, "ì¹ĺ를": 37872, "Ġdl": 37873, "zhou": 37874, "ĠHearing": 37875, "å·®ä¸įå¤ļ": 37876, "ĠCraw": 37877, "Ġfloats": 37878, "ounced": 37879, "Lab": 37880, "World": 37881, "Ġburdens": 37882, "Ġauthoritarian": 37883, "ĠBolt": 37884, "ĠоднÑĥ": 37885, "Ġpigeon": 37886, "Ġdistractions": 37887, "ĠHerausforder": 37888, "Ġzest": 37889, "esc": 37890, "Ġshakes": 37891, "atas": 37892, "ĠÙħØ´": 37893, "holes": 37894, "Ġthinkers": 37895, "alta": 37896, "Ġarche": 37897, "ĠSuk": 37898, "anha": 37899, "Ġtempting": 37900, "Ġyoutuber": 37901, "Ġvì": 37902, "ĠdziaÅĤa": 37903, "ĠVatican": 37904, "Park": 37905, "Ġsupers": 37906, "ĠNikki": 37907, "ëĬIJë": 37908, "orang": 37909, "ramient": 37910, "鬼": 37911, "Ġê°ĸê³ł": 37912, "Ġdesserts": 37913, "Ġavere": 37914, "ĠGregory": 37915, "Ġëĵ¤ìĸ´ìĺ": 37916, "Ġcosting": 37917, "ĠClinic": 37918, "Ġrebels": 37919, "ĠMob": 37920, "Ġbunlar": 37921, "ĠYours": 37922, "ertime": 37923, "Ġretali": 37924, "mara": 37925, "atus": 37926, "alles": 37927, "ĠдÑĢ": 37928, "ĠдиÑģ": 37929, "Ġdiscounts": 37930, "ĠGUY": 37931, "Ġкакое": 37932, "ĠExperiment": 37933, "rement": 37934, "ĠXiang": 37935, "Ġbate": 37936, "WE": 37937, "Ġspecialize": 37938, "Ġdeity": 37939, "ĠLoki": 37940, "mag": 37941, "ĠNit": 37942, "West": 37943, "Ġmaternal": 37944, "Ġquis": 37945, "åŁºæľ¬": 37946, "broken": 37947, "Ġlasers": 37948, "Ġhakk": 37949, "ĠAngels": 37950, "Ġmastery": 37951, "antis": 37952, "Tiffany": 37953, "eee": 37954, "çij": 37955, "orem": 37956, "Ġinacc": 37957, "Ġjurisdictions": 37958, "ĠKardash": 37959, "æľº": 37960, "Il": 37961, "ĠSinn": 37962, "åĭķçĶ»": 37963, "Ġathletics": 37964, "cÄĻ": 37965, "Ġloosely": 37966, "Ġdieta": 37967, "Ag": 37968, "Ġ??": 37969, "ĠëĮĢíijľ": 37970, "Ġsuperv": 37971, "Ġnutrit": 37972, "Ġdrifting": 37973, "ĠìĦłìĥĿëĭĺ": 37974, "ĠпонÑıл": 37975, "ĠVictory": 37976, "ÙĦØ©": 37977, "×ķ׳×Ķ": 37978, "ĠпиÑĪ": 37979, "Ġshaved": 37980, "Ġmesure": 37981, "onden": 37982, "Ùĥر": 37983, "Ġexile": 37984, "ĠDesde": 37985, "ĠPinterest": 37986, "Ġattachments": 37987, "Ġhombres": 37988, "Ġfines": 37989, "ĠìĦ¸ìĥģ": 37990, "Ġsleeps": 37991, "ĠTaco": 37992, "ĠIRA": 37993, "rios": 37994, "Ġoll": 37995, "etes": 37996, "Ġunut": 37997, "fashioned": 37998, "Ġtreball": 37999, "ĠNearly": 38000, "ĠÑĢеалÑĮно": 38001, "Ġchil": 38002, "éĢ±": 38003, "ÄŁa": 38004, "ĠMEL": 38005, "roscop": 38006, "ĠCG": 38007, "Ġvenge": 38008, "Ġdishwasher": 38009, "algic": 38010, "Ġmodifier": 38011, "Ġembassy": 38012, "timer": 38013, "emics": 38014, "Ġintricate": 38015, "Ġevet": 38016, "ĠëĮĢë°ķ": 38017, "Ġisot": 38018, "ĠнаÑĥÑĩ": 38019, "ĠQuiz": 38020, "reso": 38021, "δÏİ": 38022, "Ġyelled": 38023, "Ġfeder": 38024, "ELLER": 38025, "Ġexceeded": 38026, "onas": 38027, "icano": 38028, "ĠживоÑĤ": 38029, "ĠMao": 38030, "ĠKazuto": 38031, "Ġãħĭãħĭãħĭãħĭ": 38032, "Ġfrontline": 38033, "ĠHungarian": 38034, "Ġüberall": 38035, "awat": 38036, "Ġgrips": 38037, "ições": 38038, "arnya": 38039, "ĠÍ¡": 38040, "Ġseid": 38041, "Ġanak": 38042, "Ġacabou": 38043, "íķij": 38044, "Ġnotorious": 38045, "ĠGodzilla": 38046, "Ġovercoming": 38047, "ĠPend": 38048, "Ġolabilir": 38049, "ülme": 38050, "Ġerhalten": 38051, "ãĤīãģĦ": 38052, "ê·¹": 38053, "ĠMeter": 38054, "Ġstaan": 38055, "Ol": 38056, "Ġchats": 38057, "ĠBuenos": 38058, "ÃŃve": 38059, "aluable": 38060, "Ġstrategically": 38061, "Ġcomprised": 38062, "ĠпеÑĢÑģонаж": 38063, "Ġwann": 38064, "ĠCen": 38065, "ниÑĤе": 38066, "Łģ": 38067, "ĠÑĤобой": 38068, "iad": 38069, "ĠkardeÅŁim": 38070, "ĠCongressman": 38071, "reaming": 38072, "homme": 38073, "Ġcommunaut": 38074, "Ġalcoholic": 38075, "Ġpickled": 38076, "Ġacord": 38077, "position": 38078, "egól": 38079, "Ġtroubling": 38080, "ĠMarcheg": 38081, "Ġzumindest": 38082, "Ġseamlessly": 38083, "Ġolun": 38084, "ĠTVs": 38085, "ĠпÑĢакÑĤиÑĩеÑģки": 38086, "Ġbackend": 38087, "ãģĵãĤĵãģ«ãģ¡ãģ¯": 38088, "idable": 38089, "Ġgadget": 38090, "Ġfaço": 38091, "ĠMarchegiani": 38092, "Ġë°¤": 38093, "Ġaccidental": 38094, "ĠLP": 38095, "Ġeldest": 38096, "ĠAdmiral": 38097, "ĠnÄĥm": 38098, "lever": 38099, "Ġpastel": 38100, "Ġfondo": 38101, "Connie": 38102, "Ġtercer": 38103, "Ġpact": 38104, "ĠMonte": 38105, "Ġmeats": 38106, "ĠSMS": 38107, "ĠAustralians": 38108, "ç¼": 38109, "Rhett": 38110, "Ġexactement": 38111, "Ġë¹¼": 38112, "ĠMOD": 38113, "ç¡": 38114, "ĠRapt": 38115, "ĠNoch": 38116, "Ġabort": 38117, "ĠNaval": 38118, "ĠFuji": 38119, "INTER": 38120, "ĠновÑĭй": 38121, "Ġmiejsce": 38122, "ĠICU": 38123, "ĠGraduate": 38124, "ĠGlen": 38125, "ardi": 38126, "ĠÈĺ": 38127, "Ġsolder": 38128, "Ġprofessions": 38129, "Ġorthog": 38130, "omn": 38131, "introdu": 38132, "ĠDenise": 38133, "ìŀIJ를": 38134, "Ġcorrespondence": 38135, "AMA": 38136, "Ġinflict": 38137, "Ġfand": 38138, "ĠGü": 38139, "ĠÑĩеÑĤ": 38140, "Ġtraced": 38141, "Ġpatents": 38142, "Ġambush": 38143, "Ġlotta": 38144, "ffer": 38145, "ĠWagner": 38146, "Ġimperson": 38147, "Ġextrêmement": 38148, "ÙĤت": 38149, "conduct": 38150, "Att": 38151, "ĠMueller": 38152, "ĠAlicia": 38153, "Ġcyc": 38154, "Ġhacker": 38155, "Ġtys": 38156, "Ġhail": 38157, "ĠзаÑıв": 38158, "Ġpasso": 38159, "Ġì¶Ķê°Ģ": 38160, "ĠÎĪ": 38161, "Ġpackaged": 38162, "ĠCynthia": 38163, "heet": 38164, "ä¸ŃåĽ½": 38165, "ĠNissan": 38166, "ĠQuesto": 38167, "é¨": 38168, "did": 38169, "Ġμια": 38170, "ĠEllis": 38171, "ĠAnalysis": 38172, "cemos": 38173, "Ġaseg": 38174, "ĠMyster": 38175, "ĠCao": 38176, "Ġtuv": 38177, "ĠIndustry": 38178, "ì£¼ê³ł": 38179, "otal": 38180, "Ġpequeño": 38181, "bras": 38182, "Ġcomprehend": 38183, "ĠSimpson": 38184, "ÑģÑĤвие": 38185, "ocracy": 38186, "иÑĩеÑģки": 38187, "ĠMush": 38188, "ĠLaurie": 38189, "Ġtriangular": 38190, "ĠPresents": 38191, "ĠKunden": 38192, "ç´¹": 38193, "æѦ": 38194, "ĠIss": 38195, "ĠDeck": 38196, "á»ĥn": 38197, "ĠDarkness": 38198, "Ġinflammatory": 38199, "eremiah": 38200, "Ġwarmed": 38201, "veyard": 38202, "ĠMemory": 38203, "etty": 38204, "Ġtaxpayers": 38205, "à¸ĵ": 38206, "Ø¡": 38207, "Ġpractise": 38208, "ëĭ¬ë": 38209, "Ġdrilled": 38210, "mÃ¼ÅŁ": 38211, "logo": 38212, "ĠFach": 38213, "¤ë¡ľ": 38214, "Ġübrigens": 38215, "Ġkonnten": 38216, "Ġnormalmente": 38217, "Ġargues": 38218, "ilingual": 38219, "°ë¥¼": 38220, "egal": 38221, "Ġtravaill": 38222, "ovy": 38223, "аÑĤо": 38224, "Ġruth": 38225, "ĠLights": 38226, "Ġconsisted": 38227, "×ijר×Ļ×Ŀ": 38228, "Ġstereotype": 38229, "Ġpayer": 38230, "ĠRee": 38231, "ĠAirbnb": 38232, "Ġdrowned": 38233, "ĠZoe": 38234, "Ġcanopy": 38235, "Ġbarr": 38236, "ĠноÑĩ": 38237, "Ġpagan": 38238, "Ġjars": 38239, "Ġrê": 38240, "erver": 38241, "æĪ¿": 38242, "ieben": 38243, "Ġespect": 38244, "ĠFi": 38245, "Ġunwilling": 38246, "Ġtechnician": 38247, "ặt": 38248, "member": 38249, "ĠCanal": 38250, "سÙħ": 38251, "Ġlieber": 38252, "Ġinference": 38253, "Ġhonoring": 38254, "åijµ": 38255, "ĠCampaign": 38256, "Ġlineage": 38257, "ĠStress": 38258, "Ġvictories": 38259, "Ġdeja": 38260, "×£": 38261, "êtes": 38262, "blick": 38263, "Ġменее": 38264, "oths": 38265, "ĠCouple": 38266, "Jason": 38267, "ĠNicolas": 38268, "екÑģ": 38269, "lib": 38270, "Ġherramient": 38271, "Ġ×IJ×ķ×ŀר": 38272, "Ġвидим": 38273, "millimeter": 38274, "Ġsilhouette": 38275, "Ġdriveway": 38276, "Ġcherish": 38277, "ãħłãħł": 38278, "Ġransom": 38279, "Ġinterdisciplinary": 38280, "ĠPortal": 38281, "Ġtrag": 38282, "thood": 38283, "Ġtedious": 38284, "Ġglossy": 38285, "Ġprépar": 38286, "ĠCay": 38287, "ĠTook": 38288, "ĠBottom": 38289, "Ġzig": 38290, "å«": 38291, "åį±": 38292, "represented": 38293, "à¹Ģลย": 38294, "Ġdesarrollo": 38295, "ìĦľë": 38296, "Ġviscos": 38297, "Ġmilligram": 38298, "ĠGund": 38299, "Ġferment": 38300, "drum": 38301, "Ġdrawers": 38302, "Laugh": 38303, "Ġpelos": 38304, "Ġpavement": 38305, "Ġmemoir": 38306, "avait": 38307, "Ġ2050": 38308, "¤ë¥¼": 38309, "Ġrazón": 38310, "Ġflourish": 38311, "Ġstern": 38312, "ä¸Ī": 38313, "ĠChung": 38314, "Ġserpent": 38315, "ĠGentlemen": 38316, "羣çļĦå¾Ī": 38317, "kook": 38318, "Ġlut": 38319, "importe": 38320, "parent": 38321, "Ġwsz": 38322, "Ġscree": 38323, "ĠMitarbeiter": 38324, "å·´": 38325, "mut": 38326, "Ġìĸĺ기를": 38327, "Ġsemble": 38328, "ĠOW": 38329, "Ġinvestigator": 38330, "ĠCheryl": 38331, "ĠGerald": 38332, "Ġprere": 38333, "Ġcompares": 38334, "nyt": 38335, "Ġdiferença": 38336, "?-": 38337, "Ġquá": 38338, "ר×Ļ": 38339, "Sen": 38340, "Ġheps": 38341, "Ġgratuit": 38342, "Ġconsort": 38343, "ĠSTOP": 38344, "ĠProtestant": 38345, "Ġelectrode": 38346, "âĹ": 38347, "Ġsecurely": 38348, "иÑĩеÑģкой": 38349, "Ġtää": 38350, "Ġregisters": 38351, "ĠHeavenly": 38352, "ogly": 38353, "issä": 38354, "ĠPhysics": 38355, "ĠMerkel": 38356, "Ġrév": 38357, "éĻ¢": 38358, "Ġerased": 38359, "ĠSacramento": 38360, "Ġcoffin": 38361, "Ġexacer": 38362, "Ġlanz": 38363, "Ġpoets": 38364, "ulif": 38365, "Ġì¹ĺë": 38366, "ĠNerd": 38367, "ĠNCT": 38368, "ĠHour": 38369, "nehmer": 38370, "ŀĺëıĦ": 38371, "ĠPrinci": 38372, "Sw": 38373, "mies": 38374, "armed": 38375, "ĠBeatles": 38376, "Ġpropagation": 38377, "Ġexchanged": 38378, "Ġcumulative": 38379, "Ġì§ijìĹIJ": 38380, "Ġdefeating": 38381, "æĬ±": 38382, "bels": 38383, "Ġwes": 38384, "ĠOdyssey": 38385, "ä½łæĥ³": 38386, "avior": 38387, "ĠìľĦìĹIJ": 38388, "Ġbrit": 38389, "Ġhijo": 38390, "DAY": 38391, "ĠاÙĦتÙĬ": 38392, "ĠСеÑĢг": 38393, "Ñĥка": 38394, "edsiÄĻ": 38395, "Ġimpos": 38396, "Ġellas": 38397, "Ġfirearms": 38398, "ĠNR": 38399, "Ġ×ij×IJ": 38400, "ĠÐŁÐ¾ÐºÐ°": 38401, "awi": 38402, "ĠìĦ±ê³µ": 38403, "Ġpupils": 38404, "ĠTack": 38405, "Ġfrase": 38406, "ĠShip": 38407, "Ġstad": 38408, "举": 38409, "ĠGreater": 38410, "unun": 38411, "immung": 38412, "grown": 38413, "ĠNXT": 38414, "ĠAmericas": 38415, "fox": 38416, "Ġmanten": 38417, "éłIJåĤĻ": 38418, "ĠÑģок": 38419, "Ġrikt": 38420, "lectric": 38421, "deep": 38422, "ĠзнаеÑĪÑĮ": 38423, "Ġbenut": 38424, "ĠInfrast": 38425, "ĠEmir": 38426, "ĠоÑĤпÑĢав": 38427, "ĠKimchi": 38428, "ĠFinnish": 38429, "´ìłģ": 38430, "inaire": 38431, "Ġoike": 38432, "æ¸ħæ¥ļ": 38433, "Ġhostage": 38434, "ĠButton": 38435, "ÙĤÙĬ": 38436, "eking": 38437, "ĠKazakh": 38438, "Ġcomforting": 38439, "Ġsog": 38440, "Ġgreeted": 38441, "guitar": 38442, "payer": 38443, "Ġrelational": 38444, "Ġconstruir": 38445, "çī¹åĪ¥": 38446, "opian": 38447, "ĠVolume": 38448, "ieth": 38449, "ÑģÑĤвом": 38450, "urrection": 38451, "liÅĽmy": 38452, "Ġhemisphere": 38453, "ĠBean": 38454, "IGN": 38455, "Ġkötü": 38456, "ĠFallout": 38457, "Ġbrace": 38458, "ç¹¼çºĮ": 38459, "ÏĢά": 38460, "ĠHAS": 38461, "Ġgé": 38462, "Ġcharacterize": 38463, "ặc": 38464, "ĠMilky": 38465, "Ġtumors": 38466, "Ġnuit": 38467, "ĠGaz": 38468, "ĠìŀĪëĭ¤ëĬĶ": 38469, "ĠгаÑĢ": 38470, "essment": 38471, "ĠAbe": 38472, "Ġë½ij": 38473, "ĠEinsatz": 38474, "JIN": 38475, "jä": 38476, "Cry": 38477, "ĠPromised": 38478, "ĠÑģеÑĢд": 38479, "okus": 38480, "Ġscalable": 38481, "ĠпоÑģмоÑĤÑĢеÑĤÑĮ": 38482, "ücklich": 38483, "Ġrealism": 38484, "Ġmayo": 38485, "Ġjuvenile": 38486, "Ġheadlights": 38487, "ĠgörÃ¼ÅŁ": 38488, "ĠReform": 38489, "Ġhalves": 38490, "czne": 38491, "Ġbreakup": 38492, "żej": 38493, "Ġrätt": 38494, "Day": 38495, "ĠìĿ¼ë³¸": 38496, "Ġmuerte": 38497, "Ġtunes": 38498, "ĠSmile": 38499, 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38784, "ĠAndreas": 38785, "edd": 38786, "ĠìĹ´ë": 38787, "ĠAirlines": 38788, "ãģĵãĤį": 38789, "Ġvalores": 38790, "ê·ľ": 38791, "Hy": 38792, "ĠзадаÑĩ": 38793, "ĠKendall": 38794, "ĠÑħаÑĢ": 38795, "ĠVamp": 38796, "Ġpython": 38797, "Ġmanageable": 38798, "ĠGente": 38799, "oise": 38800, "iciary": 38801, "Ġimposs": 38802, "ĠBunny": 38803, "iesta": 38804, "Andrew": 38805, "Ġsert": 38806, "ĠCec": 38807, "zzarella": 38808, "Ġautomobile": 38809, "ĠTiere": 38810, "allows": 38811, "åĨĨ": 38812, "Ġë°Ģ": 38813, "ĠScorp": 38814, "ĠJelly": 38815, "agara": 38816, "ĠStretch": 38817, "Ġredef": 38818, "Ġexacerb": 38819, "ĠSHA": 38820, "éf": 38821, "orsa": 38822, "Ġflawed": 38823, "ĠNoel": 38824, "?!?": 38825, "Ġprocent": 38826, "Ġmenstru": 38827, "ĠпÑĢоÑĩ": 38828, "Ġinfants": 38829, "ðŁİµ": 38830, "pause": 38831, "ĠRacing": 38832, "Ġ1948": 38833, "Ġsuperintendent": 38834, "idores": 38835, "idy": 38836, "brahim": 38837, "Ġunlucky": 38838, "Ġperk": 38839, "anci": 38840, "Ġë§ĮëĤĺ": 38841, "ĠÐľÐ¾Ñģкв": 38842, 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38900, "Ġconstrained": 38901, "Ġdifferentiation": 38902, "ĠQuebec": 38903, "ĠÛģÛĮÚº": 38904, "Ġpública": 38905, "itel": 38906, "Ġaccommodations": 38907, "ĠGrü": 38908, "íľ": 38909, "Ġpickles": 38910, "иÑĩеÑģкиÑħ": 38911, "Ġcommissions": 38912, "ĠBaek": 38913, "ĠçocuÄŁ": 38914, "ĠMedium": 38915, "Ġperiodically": 38916, "Ġwonderfully": 38917, "Ġstaffing": 38918, "ìĽIJë": 38919, "rire": 38920, "fle": 38921, "ĠMcL": 38922, "ĠÑĤеп": 38923, "ĠпеÑĢек": 38924, "нолог": 38925, "Ġíģ¬ê²Į": 38926, "çĻ¼çı¾": 38927, "Ġprosperous": 38928, "ĠSpiritual": 38929, "ĠChick": 38930, "DIA": 38931, "ĠÐŁÑĢивеÑĤ": 38932, "ĠperÃŃ": 38933, "ÑĮÑİÑĤ": 38934, "Ġconsultants": 38935, "ĠEarl": 38936, "ä»Ĭå¹´": 38937, "Ġruining": 38938, "оÑĢе": 38939, "Ġpenser": 38940, "Ġtakiej": 38941, "Ġstrengthened": 38942, "ĠLiquid": 38943, "онеÑĨ": 38944, "аваÑĤÑĮ": 38945, "Ġcamer": 38946, "Ġdisagreement": 38947, "Ġbathing": 38948, "ĠYosh": 38949, "aal": 38950, "prechen": 38951, "RISADAS": 38952, "Ġsuperstar": 38953, "æģŃ": 38954, "лÑıÑĤÑĮ": 38955, "Ġnib": 38956, "ĠTherm": 38957, "ĠDANIEL": 38958, "Ġpaw": 38959, "Ġliquids": 38960, "Ġcapacit": 38961, "arken": 38962, "Ġvagina": 38963, "Ġmashed": 38964, "Ġemerges": 38965, "yscy": 38966, "Ġunrelated": 38967, "ĠGuild": 38968, "Ġinverted": 38969, "itives": 38970, "Tra": 38971, "Ġbegr": 38972, "Ġalte": 38973, "ì§ķ": 38974, "ãĤģãģ¦": 38975, "ĠÑĢазÑĢабоÑĤ": 38976, "finder": 38977, "Ġдалее": 38978, "ĠблагодаÑĢ": 38979, "walker": 38980, "Ġcrater": 38981, "assadors": 38982, "rences": 38983, "inski": 38984, "ĠKIM": 38985, "ĠElliot": 38986, "2017": 38987, "ĠSr": 38988, "inka": 38989, "anov": 38990, "Ġìŀĺ못": 38991, "Ġproprietary": 38992, "displaystyle": 38993, "ĠÑģим": 38994, "Ġизб": 38995, "ĠPanel": 38996, "Ġinstincts": 38997, "ĠCommunications": 38998, "麻": 38999, "midt": 39000, "Ġë§Įëĵ¤ìĸ´": 39001, "ĠÑģлова": 39002, "ĠGilbert": 39003, "缮åīį": 39004, "Так": 39005, "voorbeeld": 39006, "еÑİÑģÑĮ": 39007, "aryn": 39008, "quez": 39009, "Ġdart": 39010, "ÑĸÑĪ": 39011, "ĠHut": 39012, "Sal": 39013, "Ġsoutheast": 39014, "Ġpesticides": 39015, "Ġhelicopters": 39016, "Ġendured": 39017, "iada": 39018, "Ġbrewing": 39019, "ìŬë": 39020, "ĠÑģвобод": 39021, "ĠSaints": 39022, "ĠFrançais": 39023, "ĠEconomics": 39024, "Ġdisloc": 39025, "ophobia": 39026, "Camer": 39027, "Ġnegotiated": 39028, "ĠÑģÑĤали": 39029, "ìĬ¤íģ": 39030, "ogie": 39031, "Ġtsunami": 39032, "Ġpeeled": 39033, "Ġmotivations": 39034, "è¨Ń": 39035, "ostat": 39036, "flan": 39037, "ĠDAC": 39038, "Ġkav": 39039, "'RE": 39040, "ĠPearson": 39041, "bbe": 39042, "czenie": 39043, "Ġatenção": 39044, "íĨµëł¹": 39045, "ãģ£ãģ¡": 39046, "ĠÑĥдаÑĢ": 39047, "Ġintroductory": 39048, "ĠIci": 39049, "ëĮĢë": 39050, "akat": 39051, "Ġtrench": 39052, "Ġproceeded": 39053, "ĠCoin": 39054, "Ġderecho": 39055, "ĠRede": 39056, "æ¯Ľ": 39057, "аннÑĭй": 39058, "Ġincarcerated": 39059, "ĠRichmond": 39060, "Rock": 39061, "ĠPav": 39062, "ĠKarma": 39063, "uges": 39064, "Ġconteú": 39065, "ë¹Ħ": 39066, "Ġê·¸ë§Į": 39067, "ĠGone": 39068, "ĠwspóÅĤ": 39069, "ĠRahmen": 39070, "unken": 39071, "Ġì¤ijìļĶíķľ": 39072, "Ġib": 39073, "Ġattaching": 39074, "Hay": 39075, "Ġsuka": 39076, "ìį¹": 39077, "Ġpivotal": 39078, "ĠRespect": 39079, "ÃŃda": 39080, "IB": 39081, "ĠVerantwort": 39082, "wiet": 39083, "Ġforensic": 39084, "ÑĢиÑģÑĤ": 39085, "ĠпÑĢинÑĨипе": 39086, "Ġmarkings": 39087, "Ġkettle": 39088, "ĠOpera": 39089, "ĠDoctors": 39090, "Ġshredded": 39091, "Ġrecuer": 39092, "Ġvigil": 39093, "ĠFail": 39094, "Ġentrev": 39095, "ĠдÑĥÑĪ": 39096, "Ġoutbreaks": 39097, "èµ°åIJ§": 39098, "ĠÏĢο": 39099, "Ġrogue": 39100, "angled": 39101, "Ġyearly": 39102, "ĠCreed": 39103, "Ġwam": 39104, "Ġlotus": 39105, "ê³¼ë": 39106, "ãĢģãĢģ": 39107, "ĠSpit": 39108, "ĠItu": 39109, "Ġstrains": 39110, "Ġstamped": 39111, "Ġplaint": 39112, "Ġpotion": 39113, "Ġconsolidation": 39114, "è©ķ": 39115, "оÑĩкÑĥ": 39116, "Ġvlogging": 39117, "Ġslate": 39118, "ĠAuft": 39119, "ĠIncor": 39120, "ừng": 39121, "§IJ": 39122, "enh": 39123, "ĠheiÃŁ": 39124, "Ġdomest": 39125, "ĠStrom": 39126, "åį³": 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39186, "Ġcouncils": 39187, "ĠGriffin": 39188, "ĠAhmed": 39189, "Ġclich": 39190, "ĠOL": 39191, "wl": 39192, "ĠPilot": 39193, "å®®": 39194, "Ġacronym": 39195, "Ġgels": 39196, "Ġelectroly": 39197, "èĵ": 39198, "Ġмной": 39199, "Ġepisod": 39200, "ĠDieses": 39201, "ĠATP": 39202, "Ġediyorum": 39203, "Ġexpresses": 39204, "Ġexhibits": 39205, "Comm": 39206, "ĠкÑĢÑĥп": 39207, "Ġmatar": 39208, "Ġ2025": 39209, "ĠArtem": 39210, "vasive": 39211, "rÃł": 39212, "ĠbeÅŁ": 39213, "é»ĥ": 39214, "Ġlizard": 39215, "Ġfille": 39216, "Ġì§Ī문": 39217, "ĠмоÑī": 39218, "Ġtür": 39219, "Ġculprit": 39220, "Ġwoven": 39221, "ĠANY": 39222, "nim": 39223, "Ġtay": 39224, "Ġpromin": 39225, "Ġacompa": 39226, "Ġidé": 39227, "Ġboiler": 39228, "ĠThemen": 39229, "Ġavenue": 39230, "ĠMud": 39231, "ĠновÑĭе": 39232, "Ġwitnessing": 39233, "Ġlance": 39234, "ĠCHAN": 39235, "ĠBever": 39236, "تÙħ": 39237, "Ġchemotherapy": 39238, "King": 39239, "ĠbÄĻdÄĻ": 39240, "Ġatual": 39241, "Ġtive": 39242, "Ġtalkin": 39243, "Ġquedar": 39244, "ieÃŁ": 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"ĠScore": 47901, "ĠSavannah": 47902, "Ġgarde": 47903, "ĠNOR": 47904, "å°įåIJ§": 47905, "Ġscheint": 47906, "ĠpóÅĤ": 47907, "Ġcorri": 47908, "Ġbrute": 47909, "ĠÅĤad": 47910, "ä»ĸ们": 47911, "Ġsucceeding": 47912, "Ġbicycles": 47913, "Non": 47914, "Ġseekers": 47915, "Ġunconditional": 47916, "Ġrhymes": 47917, "ĠGarage": 47918, "Ġinvoice": 47919, "Ġcanvi": 47920, "neck": 47921, "Ġcustomizable": 47922, "iritual": 47923, "Queen": 47924, "íķĺìĭľëĬĶ": 47925, "Ġpowerless": 47926, "Ġcsak": 47927, "ä¸įä¼ļ": 47928, "isoft": 47929, "ĠìłķíĻķ": 47930, "Ġnhân": 47931, "ĠMAND": 47932, "ĠHaf": 47933, "Ġrevolves": 47934, "ä¹Łåı¯ä»¥": 47935, "ovan": 47936, "aroo": 47937, "ĠGrind": 47938, "éĽª": 47939, "Ġindispensable": 47940, "Ġconsulted": 47941, "ĠClinical": 47942, "Acc": 47943, "Ġolhos": 47944, "Ġmonter": 47945, "ĠHana": 47946, "etah": 47947, "Ġvaan": 47948, "Ġtigers": 47949, "Ġcaucus": 47950, "ðŁĺĤ": 47951, "³´ìŀIJ": 47952, "powers": 47953, "iums": 47954, "ĠíĨłë": 47955, "Ġtradicional": 47956, 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48070, "apons": 48071, "ĠUFC": 48072, "ĠSole": 48073, "Ġjoystick": 48074, "ĠOlivier": 48075, "warming": 48076, "Ġsyllabus": 48077, "ĠобÑīе": 48078, "Ġhiá»ĩn": 48079, "Ġfesta": 48080, "Ġcradle": 48081, "ĠZac": 48082, "Ġremembrance": 48083, "Ġê°ĻìķĦìĦľ": 48084, "ĠpiÄĻk": 48085, "Ġcoexist": 48086, "ĠVII": 48087, "Ġáreas": 48088, "Ġuważ": 48089, "Ġobservers": 48090, "Ġmänniskor": 48091, "coon": 48092, "ĠDAM": 48093, "Ġnaszym": 48094, "Ġalligator": 48095, "ĠFreeze": 48096, "ĠEstate": 48097, "ĠÑĤÑĢади": 48098, "Ġundercover": 48099, "Ġnies": 48100, "ĠFehler": 48101, "plin": 48102, "ĠKabul": 48103, "ilate": 48104, "Ġê³łìĸij": 48105, "Ġmop": 48106, "ìĦ¼": 48107, "Ġanderer": 48108, "ĠKELL": 48109, "оки": 48110, "ĠжеÑģÑĤ": 48111, "Ġgrazing": 48112, "ĠdaÃŃ": 48113, "Ġcapitalize": 48114, "Ġapex": 48115, "Ġnurturing": 48116, "Ġcortar": 48117, "Ġcontrac": 48118, "ımızı": 48119, "Ġtandem": 48120, "éĥ½æľī": 48121, "gement": 48122, "ĠÑģиÑģÑĤема": 48123, "Ġmanque": 48124, "iajÄħ": 48125, "WOR": 48126, 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"Ġneurological": 48185, "Ġmotivational": 48186, "ĠMcMahon": 48187, "ĠFinished": 48188, "Ġë³´ìĿ´": 48189, "ĠFields": 48190, "Ġadolescents": 48191, "ĠTisch": 48192, "ĠNeben": 48193, "ĠFlowers": 48194, "ĠEnerg": 48195, "Ġdiret": 48196, "ĠThi": 48197, "ĠPicas": 48198, "æĥľ": 48199, "æĢİä¹Īæł·": 48200, "Ġavete": 48201, "ĠFors": 48202, "ĠChapel": 48203, "Não": 48204, "Et": 48205, "ĠÑģодеÑĢж": 48206, "reno": 48207, "Ġsven": 48208, "ĠdostÄĻp": 48209, "nee": 48210, "ĠSnapdragon": 48211, "ĠIDs": 48212, "ìķĺëĬĶëį°": 48213, "ר×ļ": 48214, "Ġsunflower": 48215, "Ġperpetual": 48216, "ç³ĸ": 48217, "Ġknights": 48218, "Ġgird": 48219, "ĠTold": 48220, "Ġvolcanoes": 48221, "Ġadversary": 48222, "ĠEconomy": 48223, "Ġextrapol": 48224, "Ġbluetooth": 48225, "Ġzooming": 48226, "Ġskys": 48227, "Ġgenial": 48228, "ÃŃculos": 48229, "ambre": 48230, "ĠмеÑĢ": 48231, "Ġteeny": 48232, "Ġstressing": 48233, "ìķĮ": 48234, "ONY": 48235, "Ġtranslucent": 48236, "Ġrounding": 48237, "Ġgrues": 48238, "×Ļ׳×Ķ": 48239, "après": 48240, "Ġprueba": 48241, "Ġpolygon": 48242, "Ġblueberry": 48243, "ĠProgramm": 48244, "Ġtrenches": 48245, "Ġsebagai": 48246, "Ġpalate": 48247, "Ġlaude": 48248, "Ġbehaved": 48249, "Ġlongitudinal": 48250, "ĠModule": 48251, "Ġadmir": 48252, "λι": 48253, "Greg": 48254, "Ġwyst": 48255, "Ġpropagate": 48256, "Ġmolds": 48257, "ĠTub": 48258, "ĠLoud": 48259, "usto": 48260, "Ġunstoppable": 48261, "Ġreinforcing": 48262, "éĿŀ常çļĦ": 48263, "ĠпÑĢоблема": 48264, "Ġpotencial": 48265, "Ġhemp": 48266, "ìŀĶ": 48267, "य": 48268, "Ġoptic": 48269, "Ġerfolgreich": 48270, "ÑģÑĭ": 48271, "олÑĮÑĪе": 48272, "urst": 48273, "ĠPois": 48274, "Ġrespondents": 48275, "Ġnehme": 48276, "ĠExternal": 48277, "olate": 48278, "Hyun": 48279, "Ġquartz": 48280, "Ġmathematician": 48281, "Ġbásicamente": 48282, "Ġail": 48283, "ìłľë¥¼": 48284, "attutto": 48285, "Ġnooit": 48286, "Ġafflict": 48287, "ĠOlga": 48288, "èŃ·": 48289, "ĠнаÑĤ": 48290, "Ġdites": 48291, "Ġrealidade": 48292, "Ġkän": 48293, "Ġuniqueness": 48294, "Ġpadres": 48295, "Ġsubsidi": 48296, "Ġpigeons": 48297, "βα": 48298, "stad": 48299, "Ġderen": 48300, "ĠСлед": 48301, "doo": 48302, "ĠопиÑģании": 48303, "Ġamber": 48304, "Ġgoosebumps": 48305, "ĠfrÃ¥gor": 48306, "ĠVital": 48307, "ĠIsraelites": 48308, "wasser": 48309, "Isn": 48310, "Ġcommits": 48311, "ĠSTEVEN": 48312, "ĠBevölker": 48313, "uitive": 48314, "Ġlegen": 48315, "Ġbruk": 48316, "иÑĢован": 48317, "ynen": 48318, "helm": 48319, "Ġgenerational": 48320, "ĠLändern": 48321, "οιÏĢÏĮν": 48322, "uzu": 48323, "Ġcaller": 48324, "онÑĮ": 48325, "ümü": 48326, "Ġbesar": 48327, "Ġplats": 48328, "Ġmigrated": 48329, "Ġjap": 48330, "ĠWAR": 48331, "Ġdissect": 48332, "ĠZusch": 48333, "ĠZeiten": 48334, "ĠLions": 48335, "ĠDF": 48336, "âĶ": 48337, "кив": 48338, "Ġpedestrians": 48339, "ĠMarilyn": 48340, "dock": 48341, "Ġyht": 48342, "Ġreincarn": 48343, "ĠSono": 48344, "ĠGrowth": 48345, "ÑĥÑģов": 48346, "Ġdungeons": 48347, "Ġbagus": 48348, "kich": 48349, "ĠÑĥкÑĢаÑĹ": 48350, "éĨ«": 48351, "ĠKeller": 48352, "chemistry": 48353, "Japanese": 48354, "Ġwillst": 48355, "Ġdecomposition": 48356, "ĠÑģÑĤен": 48357, "Ġrevived": 48358, "íķĻêµIJ": 48359, "ĠÅĵ": 48360, "ä½IJ": 48361, "ìĭ¸": 48362, "ippy": 48363, "Ġhourly": 48364, "jän": 48365, "ĠWorkshop": 48366, "Ŀ¼ìĦľ": 48367, "Ġcuarto": 48368, "Ġpatrim": 48369, "ĠBurch": 48370, "ĠìŀĪ기": 48371, "Ġhepat": 48372, "ĠhÃłng": 48373, "ĠëĮĢíķ´": 48374, "ĠваÑĪи": 48375, "Ġrework": 48376, "Ġparse": 48377, "Ġçıktı": 48378, "ĠSax": 48379, "ĠMongo": 48380, "ĠAaah": 48381, "ramble": 48382, "DJ": 48383, "Ġstabilized": 48384, "ĠSpeech": 48385, "Books": 48386, "Ġhurdles": 48387, "ĠWO": 48388, "ĠLamborg": 48389, "Ġ1933": 48390, "Ġvorbere": 48391, "Ġclinically": 48392, "Ġbreathtaking": 48393, "ĠGateway": 48394, "пеÑĢвÑĭÑħ": 48395, "uters": 48396, "Ġë¹µ": 48397, "Ġyeter": 48398, "Ġpulley": 48399, "Ġmuffin": 48400, "ĠPrefer": 48401, "ĠPence": 48402, "Ġinformação": 48403, "ìĬ¤íĬ¸ë": 48404, "ãĤ¸ãĥ£": 48405, "ĠTurtle": 48406, "ĠRegina": 48407, "ĠLoad": 48408, "does": 48409, "panze": 48410, "¸Ķ": 48411, "Ġmina": 48412, "ĠLatinos": 48413, "ammers": 48414, "ĠTort": 48415, "ĠBeyonce": 48416, "имоÑģÑĤи": 48417, "ĠвопÑĢоÑģÑĭ": 48418, "Ġbulun": 48419, "èĢĮå·²": 48420, "inek": 48421, "bereich": 48422, "Ġpasture": 48423, "ĠOA": 48424, "ĠMelt": 48425, "ĠEtt": 48426, "ĠDY": 48427, "Ġobwohl": 48428, "Ġleagues": 48429, "ÑĤеÑģÑĮ": 48430, "ĠкÑĥÑģ": 48431, "Ġvors": 48432, "Ġtopp": 48433, "ographical": 48434, "asst": 48435, "Ġlindo": 48436, "Ġë°ĿíĺĶ": 48437, "Ġréfl": 48438, "Ġclimbs": 48439, "Ġvarsa": 48440, "Ġmethyl": 48441, "ĠKarere": 48442, "Æ°á»Ł": 48443, "Rad": 48444, "Ġpreparedness": 48445, "онÑĩ": 48446, "ĠOD": 48447, "ĠCGI": 48448, "Ġम": 48449, "Ġspeechless": 48450, "Ġlasci": 48451, "Ġbolag": 48452, "ĠÑħоÑĩеÑĤÑģÑı": 48453, "Ġgrieving": 48454, "ĠJohannes": 48455, "ĠCarroll": 48456, "adaki": 48457, "Ī¬ë": 48458, "ĠsÅĤu": 48459, "Ġinnerhalb": 48460, "Ġgymnastics": 48461, "пÑĢи": 48462, "ifiques": 48463, "Ġkarate": 48464, "Ġdomu": 48465, "ãģĿãĤĮãģ§": 48466, "OTHER": 48467, "Ġdemandé": 48468, "Ġbooklet": 48469, "ĠKyoto": 48470, "Ġwoh": 48471, "ĠMarÃŃa": 48472, "violent": 48473, "JE": 48474, "Ġlóg": 48475, "Ġbrutally": 48476, "cot": 48477, "ĠÙħÛĮ": 48478, "ĠWarsz": 48479, "å®Ī": 48480, "wol": 48481, "Ġmikä": 48482, "ĠPronounce": 48483, "ĠBrendan": 48484, "Ġroup": 48485, "Ġitaliano": 48486, "å¦ĤæѤ": 48487, "ĠкомпÑĮÑİÑĤ": 48488, "Ġurging": 48489, "edes": 48490, "Ġcarbono": 48491, "ĠRichardson": 48492, "ĠÐĿаÑĩ": 48493, "ĠTrainer": 48494, "ĠCrimea": 48495, "Ġdiapers": 48496, "Ġcovet": 48497, "ĠMahar": 48498, "ĠHutch": 48499, "ĠAusw": 48500, "berty": 48501, "Ġindifferent": 48502, "кÑĢеÑĤ": 48503, "uldade": 48504, "Ġharms": 48505, "¢ÙĨ": 48506, "lesia": 48507, "Ġgio": 48508, "ĠMistress": 48509, "ĠKnox": 48510, "ĠFREE": 48511, "Ġ루ë": 48512, "ĠнаÑĪа": 48513, "Ġinvincible": 48514, "Ġmaiden": 48515, "ĠJeez": 48516, "Ġbreve": 48517, "pole": 48518, "Ġcriticisms": 48519, "ĠRusia": 48520, "म": 48521, "phin": 48522, "ĠCompare": 48523, "ĠBON": 48524, "Ġsneaking": 48525, "ĠRails": 48526, "ĠGeral": 48527, "Ġ1953": 48528, "Hola": 48529, "ĠопÑĭÑĤ": 48530, "Ġrainforest": 48531, "Ġbelum": 48532, "ĠObi": 48533, "ĠISS": 48534, "ãĤĮãģªãģĦ": 48535, "ĠСв": 48536, "Ġblond": 48537, "Ġwzgl": 48538, "ĠpowiedziaÅĤ": 48539, "Ġchoking": 48540, "ĠSongs": 48541, "ĠBiraz": 48542, "Ġyells": 48543, "Ġstylist": 48544, "ÏĮÏĦε": 48545, "Ġschreiben": 48546, "ĠJaw": 48547, "ĠEleven": 48548, "ĠRif": 48549, "/.": 48550, "Ġìĺ¤ëŀľë§Į": 48551, "Ġtreaties": 48552, "uffed": 48553, "ĠâĪĴ": 48554, "Ġroofs": 48555, "à¹Ģส": 48556, "Ġë»": 48557, "Ġsparkle": 48558, "ĠKiev": 48559, "ĠArgu": 48560, "erecht": 48561, "ĠÐĿадо": 48562, "ĠFIL": 48563, "Ġmolta": 48564, "ĠDevi": 48565, "Ġcampe": 48566, "Ġbenevol": 48567, "ĠTough": 48568, "Ġmoim": 48569, "Ġevacuate": 48570, "Ġerrado": 48571, "å©Ĩ": 48572, "ÑĢÑĥго": 48573, "Ġíİĺ": 48574, "ĠÎĵια": 48575, "Ġweaken": 48576, "Ġilluminated": 48577, "Ġsiglo": 48578, "ĠVacc": 48579, "ией": 48580, "alis": 48581, "ĠÑĥÑģÑĤÑĢой": 48582, "Ġdona": 48583, "ÅĤos": 48584, "üman": 48585, "Ġproducción": 48586, "Ġclot": 48587, "ĠMango": 48588, "Ġuneasy": 48589, "Ġshuts": 48590, "ĠExamples": 48591, "vell": 48592, "ebe": 48593, "Ġpromptly": 48594, "ĠTeles": 48595, "ĠпÑĢоÑĪл": 48596, "Ġpuerta": 48597, "Ġüberzeug": 48598, "Ġcoch": 48599, "social": 48600, "ĠBenson": 48601, "ĠMeth": 48602, "ĠExped": 48603, "Ġsupplemental": 48604, "Ġconceive": 48605, "Ġ×ĺ×ķ×ij": 48606, "Ġcaptivity": 48607, "ıĻìķĪ": 48608, "ĠÑħÑĥд": 48609, "forming": 48610, "Ġuploads": 48611, "Ġturbulence": 48612, "joint": 48613, "Ġsatisfactory": 48614, "ĠAnime": 48615, "Ġwashes": 48616, "Ġliberals": 48617, "ĠSunshine": 48618, "ĠREAL": 48619, "ublik": 48620, "binary": 48621, "Tony": 48622, "Ġpolarized": 48623, "Ġenriched": 48624, "taking": 48625, "ĠëģĿëĤĺ": 48626, "Ġpleasures": 48627, "Ġextermin": 48628, "inese": 48629, "atl": 48630, "vär": 48631, "аÑĢÑĭ": 48632, "ĠmyÅĽ": 48633, "narrator": 48634, "Ġодном": 48635, "ĠnajwiÄĻ": 48636, "Ġmobilize": 48637, "Ġmillor": 48638, "Ġata": 48639, "æ··": 48640, "ĠpolÃŃtico": 48641, "Ġplead": 48642, "Ġpainters": 48643, "ĠSow": 48644, "оÑĦ": 48645, "ĠìĺĽëĤł": 48646, "ĠÑĩÑĤоб": 48647, "Ġsabor": 48648, "ĠUndert": 48649, "ĠJERRY": 48650, "Å¡ÃŃ": 48651, "Ġë°ĸìĹIJ": 48652, "Ġprécéd": 48653, "Ġannotation": 48654, "ĠInaudible": 48655, "Ġtextured": 48656, "Ġfisherman": 48657, "vordan": 48658, "icherung": 48659, "ĠìłģìĿ´": 48660, "Ġgezeigt": 48661, "Ġmandates": 48662, "Ġbeak": 48663, "ĠTWO": 48664, "ĠAkbar": 48665, "ilian": 48666, "Ġtiếp": 48667, "Ġsuperiority": 48668, "inku": 48669, "Ġlys": 48670, "ĠFCC": 48671, "ĠCPA": 48672, "ustering": 48673, "nicos": 48674, "anja": 48675, "Ġchills": 48676, "ĠCage": 48677, "Ġsealing": 48678, "Ġsaç": 48679, "Ġdedans": 48680, "ĠAlger": 48681, "Ġspezie": 48682, "Ġcoloss": 48683, "ıyı": 48684, "clockwise": 48685, "Ġexactamente": 48686, "Ġiemand": 48687, "amı": 48688, "Ġmandar": 48689, "raj": 48690, "faced": 48691, "agua": 48692, "Ġê¹Ķë": 48693, "Ġinsbesondere": 48694, "Ġdrizzle": 48695, "Ġdiminish": 48696, "ĠYoda": 48697, "AI": 48698, "Ġbilmiyorum": 48699, "ĠMMA": 48700, "ategory": 48701, "ĠпеÑĢеп": 48702, "Ġparticipar": 48703, "Ġnormalized": 48704, "Ġcomplexities": 48705, "æ´²": 48706, "æݧ": 48707, "аÑĢов": 48708, "mist": 48709, "icha": 48710, "Group": 48711, "Ġresiliency": 48712, "Ġnogle": 48713, "ĠCNC": 48714, "prü": 48715, "Ġphysicists": 48716, "нок": 48717, "LI": 48718, "Ġstuffs": 48719, "Ġsistemas": 48720, "Ġinterfering": 48721, "ĠMarvin": 48722, "ército": 48723, "ĠìĹĨê³ł": 48724, "Ġsonic": 48725, "Ġequiv": 48726, "Ġabord": 48727, "ĠRamen": 48728, "Ġ09": 48729, "medim": 48730, "atiques": 48731, "ĠделаÑİÑĤ": 48732, "Ġunanimously": 48733, "Ġskirts": 48734, "ĠíĬ¹ë³Ħ": 48735, "ĠPrix": 48736, "kami": 48737, "Ġfruition": 48738, "Ġbirthdays": 48739, "иком": 48740, "Ġinaugural": 48741, "Ġcorrelate": 48742, "ĠTory": 48743, "ĠëĤĺìģ": 48744, "Ġdew": 48745, "ĠPrecis": 48746, "ihi": 48747, "Ġë¬¸ìłľê°Ģ": 48748, "Ġciting": 48749, "ĠLana": 48750, "ĠKag": 48751, "Ġplaythrough": 48752, "ĠProtocol": 48753, "frist": 48754, "hovah": 48755, "Ġmerciful": 48756, "Ġbilingual": 48757, "ĠGuitar": 48758, "rh": 48759, "Ġglamorous": 48760, "ĠVikings": 48761, "ĠOoooh": 48762, "íķĺëĬĶëį°": 48763, "ĠUganda": 48764, "Ġcollapses": 48765, "entry": 48766, "Ġantioxidants": 48767, "ëĤĺë": 48768, "ÑĪаÑı": 48769, "Ġtrivia": 48770, "Ġgäller": 48771, "Ġfungi": 48772, "Ġmilks": 48773, "Ġdicht": 48774, "μη": 48775, "poke": 48776, "ĠвÑĭпÑĥÑģк": 48777, "Ġfeeder": 48778, "ĠAlcohol": 48779, "hower": 48780, "Ġdeserving": 48781, "ĠRebel": 48782, "iosis": 48783, "Ġ103": 48784, "Ġhandout": 48785, "Ġenm": 48786, "Ġlandlords": 48787, "Ġgeology": 48788, "rils": 48789, "Ġcobra": 48790, "ĠVold": 48791, "ĠPanch": 48792, "ĠGREG": 48793, "Ġpross": 48794, "Ġbracelets": 48795, "ĠVega": 48796, "Ġrozum": 48797, "款": 48798, "азд": 48799, "ĠLynd": 48800, "ĠHonors": 48801, "Ġsurrendered": 48802, "Ġlibrarians": 48803, "125": 48804, "ĠÑģиг": 48805, "Ġuniformly": 48806, "ĠEagles": 48807, "ìķĻ": 48808, "иÑĤан": 48809, "andid": 48810, "ĠìłĪëĮĢ": 48811, "Ġض": 48812, "Ġarrests": 48813, "ĠCSV": 48814, "ĠAzerbaijan": 48815, "ortic": 48816, "ĠDX": 48817, "ĠAdventures": 48818, "Ġabus": 48819, "ĠFau": 48820, "Ġschlimm": 48821, "Ġrattling": 48822, "Ġconsumes": 48823, "ĠTolkien": 48824, "Ġresurrected": 48825, "ĠXY": 48826, "íĬ¸ê°Ģ": 48827, "ĠвÑĭÑģÑĤÑĥп": 48828, "ĠAngie": 48829, "żenia": 48830, "Mic": 48831, "ĠSheila": 48832, "achtet": 48833, "Ġoverst": 48834, "Ġlâ": 48835, "Ġineffective": 48836, "æĿ¡": 48837, "æĢİä¹ĪäºĨ": 48838, "å¿Ļ": 48839, "Ġwichtiger": 48840, "Ġvino": 48841, "Ġpum": 48842, "Ġangled": 48843, "ĠPione": 48844, "ĠMỹ": 48845, "ãģĿãĤĮãģ¯": 48846, "woÅĽÄĩ": 48847, "draw": 48848, "ัà¹Ī": 48849, "markets": 48850, "Ġcafes": 48851, "ĠCem": 48852, "âĿ¤": 48853, "ĠSuit": 48854, "MK": 48855, "Ġemphasizes": 48856, "Ġtortilla": 48857, "Ġmejorar": 48858, "ĠSurviv": 48859, "casting": 48860, "Ġeducación": 48861, "ĠGum": 48862, "uely": 48863, "ĠìĹ¬ê¸°ëĬĶ": 48864, "Ġstretchy": 48865, "ença": 48866, "Ġwithhold": 48867, "Ġexiting": 48868, "Ġenthalpy": 48869, "ĠTransit": 48870, "ılmÄ±ÅŁ": 48871, "alies": 48872, "Ġsalvar": 48873, "Ġleaned": 48874, "ĠgroÃŁes": 48875, "Ġfitt": 48876, "аки": 48877, "Sarah": 48878, "Ġhostel": 48879, "Ġfingerna": 48880, "ĠnadziejÄĻ": 48881, "wives": 48882, "Rec": 48883, "Ġspool": 48884, "аÑĤов": 48885, "ĠEnemy": 48886, "Ġfury": 48887, "Ġdetta": 48888, "ĠFay": 48889, "éļ¨": 48890, "ÑıÑİÑĤ": 48891, "Ġaproximadamente": 48892, "Ġsilos": 48893, "Ġmagist": 48894, "Ġcree": 48895, "ĠKrank": 48896, "ĠDOWN": 48897, "Ġstartled": 48898, "Ġreborn": 48899, "ĠUmwelt": 48900, "ĠSuzanne": 48901, "ниÑĨÑĭ": 48902, "outez": 48903, "ĠJAC": 48904, "yards": 48905, "radas": 48906, "rau": 48907, "ipts": 48908, "hail": 48909, "Ġparagraphs": 48910, "Ġmeglio": 48911, "Ġisolating": 48912, "Ġaceite": 48913, "ĠHarsh": 48914, "Ġcyst": 48915, "ĠBlockchain": 48916, "ĠÑħоÑĢоÑĪий": 48917, "Ġvirtuous": 48918, "Ġinvestigación": 48919, "Ġdevoir": 48920, "Ġmasturb": 48921, "ĠSale": 48922, "ÙĬرة": 48923, "ĠΧ": 48924, "ĠStraÃŁen": 48925, "Ġdikk": 48926, "Ġafore": 48927, "ĠJungkook": 48928, "Ġchociaż": 48929, "ĠDebatte": 48930, "Ġweirdly": 48931, "Ġviaje": 48932, "regist": 48933, "Help": 48934, "Ġkinderen": 48935, "Ġformulated": 48936, "Ġenfim": 48937, "ĠTowards": 48938, "коÑĹ": 48939, "ivering": 48940, "ĠдеÑĤи": 48941, "charger": 48942, "Ġpurl": 48943, "Ġacademically": 48944, "ĠNurse": 48945, "Ġdeleting": 48946, "ayo": 48947, "Ġrefusal": 48948, "Ġdepicts": 48949, "ĠDracula": 48950, "Ġtoasted": 48951, "ĠZombie": 48952, "ĠSuperior": 48953, "ĠBold": 48954, "Ġquizzes": 48955, "Ġgle": 48956, "450": 48957, "Ġcomeço": 48958, "ynn": 48959, "Ġverst": 48960, "ĠOlaf": 48961, "Ġpomoc": 48962, "ĠSask": 48963, "ëĺ": 48964, "ĠTCP": 48965, "ĠProperty": 48966, "íķĺì£ł": 48967, "à¸ľà¸¡": 48968, "boom": 48969, "aros": 48970, "ĠÑĢоÑģÑģий": 48971, "ĠбÑĭваеÑĤ": 48972, "åĩºåİ»": 48973, "ĠìĿ´ìķ¼ê¸°ë¥¼": 48974, "Ġcombien": 48975, "vacc": 48976, "Ġebenfalls": 48977, "para": 48978, "Ġзм": 48979, "Ġdesperation": 48980, "ordre": 48981, "Ġש׾×Ļ": 48982, "Ġgenerously": 48983, "ĠÐŀк": 48984, "Ġorbiting": 48985, ">"] + translate: int = self.special_tokens["<|translate|>"] + transcribe: int = self.special_tokens["<|transcribe|>"] + + langs = tuple(LANGUAGES.keys()) + sot_sequence = [sot] + if self.language is not None: + sot_sequence.append(sot + 1 + langs.index(self.language)) + if self.task is not None: + task_token: int = transcribe if self.task == "transcribe" else translate + sot_sequence.append(task_token) + + self.sot_sequence = tuple(sot_sequence) def encode(self, text, **kwargs): - return self.tokenizer.encode(text, **kwargs) + return self.encoding.encode(text, **kwargs) - def decode( - self, token_ids: Union[int, List[int], np.ndarray, torch.Tensor], **kwargs - ): - return self.tokenizer.decode(token_ids, **kwargs) + def decode(self, token_ids: List[int], **kwargs) -> str: + token_ids = [t for t in token_ids if t < self.timestamp_begin] + return self.encoding.decode(token_ids, **kwargs) - def decode_with_timestamps(self, tokens) -> str: + def decode_with_timestamps(self, token_ids: List[int], **kwargs) -> str: """ - Timestamp tokens are above the special tokens' id range and are ignored by `decode()`. + Timestamp tokens are above other special tokens' id range and are ignored by `decode()`. This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>". """ - outputs = [[]] - for token in tokens: - if token >= self.timestamp_begin: - timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>" - outputs.append(timestamp) - outputs.append([]) - else: - outputs[-1].append(token) - return "".join( - [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs] - ) + return self.encoding.decode(token_ids, **kwargs) @cached_property def eot(self) -> int: - return self.tokenizer.eos_token_id + return self.encoding.eot_token @cached_property def transcribe(self) -> int: - return self._get_single_token_id("<|transcribe|>") + return self.special_tokens["<|transcribe|>"] @cached_property def translate(self) -> int: - return self._get_single_token_id("<|translate|>") + return self.special_tokens["<|translate|>"] @cached_property def sot(self) -> int: - return self._get_single_token_id("<|startoftranscript|>") + return self.special_tokens["<|startoftranscript|>"] @cached_property def sot_lm(self) -> int: - return self._get_single_token_id("<|startoflm|>") + return self.special_tokens["<|startoflm|>"] @cached_property def sot_prev(self) -> int: - return self._get_single_token_id("<|startofprev|>") + return self.special_tokens["<|startofprev|>"] @cached_property def no_speech(self) -> int: - return self._get_single_token_id("<|nospeech|>") + return self.special_tokens["<|nospeech|>"] @cached_property def no_timestamps(self) -> int: - return self._get_single_token_id("<|notimestamps|>") + return self.special_tokens["<|notimestamps|>"] @cached_property def timestamp_begin(self) -> int: - return self.tokenizer.all_special_ids[-1] + 1 + return self.special_tokens["<|0.00|>"] @cached_property def language_token(self) -> int: @@ -202,25 +212,15 @@ def language_token(self) -> int: if self.language is None: raise ValueError("This tokenizer does not have language token configured") - additional_tokens = dict( - zip( - self.tokenizer.additional_special_tokens, - self.tokenizer.additional_special_tokens_ids, - ) - ) - candidate = f"<|{self.language}|>" - if candidate in additional_tokens: - return additional_tokens[candidate] + if token := self.special_tokens.get(f"<|{self.language}|>", None): + return token raise KeyError(f"Language {self.language} not found in tokenizer.") @cached_property def all_language_tokens(self) -> Tuple[int]: result = [] - for token, token_id in zip( - self.tokenizer.additional_special_tokens, - self.tokenizer.additional_special_tokens_ids, - ): + for token, token_id in self.special_tokens.items(): if token.strip("<|>") in LANGUAGES: result.append(token_id) return tuple(result) @@ -258,22 +258,17 @@ def non_speech_tokens(self) -> Tuple[int]: assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous) # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word - result = {self.tokenizer.encode(" -")[0], self.tokenizer.encode(" '")[0]} + result = {self.encoding.encode(" -")[0], self.encoding.encode(" '")[0]} for symbol in symbols + list(miscellaneous): for tokens in [ - self.tokenizer.encode(symbol), - self.tokenizer.encode(" " + symbol), + self.encoding.encode(symbol), + self.encoding.encode(" " + symbol), ]: if len(tokens) == 1 or symbol in miscellaneous: result.add(tokens[0]) return tuple(sorted(result)) - def _get_single_token_id(self, text) -> int: - tokens = self.tokenizer.encode(text) - assert len(tokens) == 1, f"{text} is not encoded as a single token" - return tokens[0] - def split_to_word_tokens(self, tokens: List[int]): if self.language in {"zh", "ja", "th", "lo", "my"}: # These languages don't typically use spaces, so it is difficult to split words @@ -318,12 +313,17 @@ def split_tokens_on_spaces(self, tokens: List[int]): @lru_cache(maxsize=None) -def build_tokenizer(name: str = "gpt2"): - os.environ["TOKENIZERS_PARALLELISM"] = "false" - path = os.path.join(os.path.dirname(__file__), "assets", name) - tokenizer = GPT2TokenizerFast.from_pretrained(path) +def get_encoding(name: str = "gpt2"): + vocab_path = os.path.join(os.path.dirname(__file__), "assets", f"{name}.tiktoken") + ranks = { + base64.b64decode(token): int(rank) + for token, rank in (line.split() for line in open(vocab_path) if line) + } + n_vocab = len(ranks) + special_tokens = {} specials = [ + "<|endoftext|>", "<|startoftranscript|>", *[f"<|{lang}|>" for lang in LANGUAGES.keys()], "<|translate|>", @@ -332,18 +332,28 @@ def build_tokenizer(name: str = "gpt2"): "<|startofprev|>", "<|nospeech|>", "<|notimestamps|>", + *[f"<|{i * 0.02:.2f}|>" for i in range(1501)], ] - tokenizer.add_special_tokens(dict(additional_special_tokens=specials)) - return tokenizer + for token in specials: + special_tokens[token] = n_vocab + n_vocab += 1 + + return tiktoken.Encoding( + name=os.path.basename(vocab_path), + explicit_n_vocab=n_vocab, + pat_str=gpt2()["pat_str"], + mergeable_ranks=ranks, + special_tokens=special_tokens, + ) @lru_cache(maxsize=None) def get_tokenizer( multilingual: bool, *, - task: Optional[str] = None, # Literal["transcribe", "translate", None] language: Optional[str] = None, + task: Optional[str] = None, # Literal["transcribe", "translate", None] ) -> Tokenizer: if language is not None: language = language.lower() @@ -354,27 +364,14 @@ def get_tokenizer( raise ValueError(f"Unsupported language: {language}") if multilingual: - tokenizer_name = "multilingual" - task = task or "transcribe" + encoding_name = "multilingual" language = language or "en" + task = task or "transcribe" else: - tokenizer_name = "gpt2" - task = None + encoding_name = "gpt2" language = None + task = None - tokenizer = build_tokenizer(name=tokenizer_name) - all_special_ids: List[int] = tokenizer.all_special_ids - sot: int = all_special_ids[1] - translate: int = all_special_ids[-6] - transcribe: int = all_special_ids[-5] + encoding = get_encoding(name=encoding_name) - langs = tuple(LANGUAGES.keys()) - sot_sequence = [sot] - if language is not None: - sot_sequence.append(sot + 1 + langs.index(language)) - if task is not None: - sot_sequence.append(transcribe if task == "transcribe" else translate) - - return Tokenizer( - tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence) - ) + return Tokenizer(encoding=encoding, language=language, task=task) From 671ac5a4ceb8980403554da94feb995161fb5fef Mon Sep 17 00:00:00 2001 From: Guillaume Klein Date: Tue, 14 Mar 2023 00:34:09 +0100 Subject: [PATCH 30/69] Fix alignment between the segments and the list of words (#1087) * Fix alignment between the segments and the list of words * Ensure the word index does not overflow --- whisper/timing.py | 52 +++++++++++++++++++++++++++-------------------- 1 file changed, 30 insertions(+), 22 deletions(-) diff --git a/whisper/timing.py b/whisper/timing.py index 7bc2b9a61..1f8f4cf8c 100644 --- a/whisper/timing.py +++ b/whisper/timing.py @@ -1,3 +1,4 @@ +import itertools import subprocess import warnings from dataclasses import dataclass @@ -290,34 +291,41 @@ def add_word_timestamps( if len(segments) == 0: return - text_tokens = [t for s in segments for t in s["tokens"] if t < tokenizer.eot] + text_tokens_per_segment = [ + [token for token in segment["tokens"] if token < tokenizer.eot] + for segment in segments + ] + + text_tokens = list(itertools.chain.from_iterable(text_tokens_per_segment)) alignment = find_alignment(model, tokenizer, text_tokens, mel, num_frames, **kwargs) merge_punctuations(alignment, prepend_punctuations, append_punctuations) time_offset = segments[0]["seek"] * HOP_LENGTH / SAMPLE_RATE - segment_lengths = [len(s["tokens"]) for s in segments] - token_sources = np.repeat(np.arange(len(segments)), segment_lengths) - - for segment in segments: - segment["words"] = [] - - word_boundaries = np.pad(np.cumsum([len(w.tokens) for w in alignment]), (1, 0)) - for i, timing in enumerate(alignment): - if timing.word: - segment = segments[token_sources[word_boundaries[i]]] - start = round(time_offset + timing.start, 2) - end = round(time_offset + timing.end, 2) - segment["words"].append( - dict( - word=timing.word, - start=start, - end=end, - probability=timing.probability, + word_index = 0 + + for segment, text_tokens in zip(segments, text_tokens_per_segment): + saved_tokens = 0 + words = [] + + while word_index < len(alignment) and saved_tokens < len(text_tokens): + timing = alignment[word_index] + + if timing.word: + words.append( + dict( + word=timing.word, + start=round(time_offset + timing.start, 2), + end=round(time_offset + timing.end, 2), + probability=timing.probability, + ) ) - ) - for segment in segments: - if len(words := segment["words"]) > 0: + saved_tokens += len(timing.tokens) + word_index += 1 + + if len(words) > 0: # adjust the segment-level timestamps based on the word-level timestamps segment["start"] = words[0]["start"] segment["end"] = words[-1]["end"] + + segment["words"] = words From ba88b8e1b38fb6098bfe1e06b09533592e776067 Mon Sep 17 00:00:00 2001 From: Akash Mahajan Date: Tue, 14 Mar 2023 00:07:09 -0700 Subject: [PATCH 31/69] fix github language stats getting dominated by jupyter notebook (#1076) Co-authored-by: Akash Mahajan Co-authored-by: Jong Wook Kim --- .gitattributes | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 .gitattributes diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 000000000..b2d461810 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,3 @@ +# Override jupyter in Github language stats for more accurate estimate of repo code languages +# reference: https://github.com/github/linguist/blob/master/docs/overrides.md#generated-code +*.ipynb linguist-generated From 5f9ac653b7f0fa772af9aa8e2cec89726b526c3e Mon Sep 17 00:00:00 2001 From: Guillaume Klein Date: Tue, 14 Mar 2023 17:32:41 +0100 Subject: [PATCH 32/69] Fix truncated words list when the replacement character is decoded (#1089) --- tests/test_tokenizer.py | 10 ++++++++++ whisper/tokenizer.py | 12 +++++++++++- 2 files changed, 21 insertions(+), 1 deletion(-) diff --git a/tests/test_tokenizer.py b/tests/test_tokenizer.py index f4cd80b9f..09d0351e1 100644 --- a/tests/test_tokenizer.py +++ b/tests/test_tokenizer.py @@ -12,3 +12,13 @@ def test_tokenizer(): assert gpt2_tokenizer.decode(gpt2_tokens) == text assert multilingual_tokenizer.decode(multilingual_tokens) == text assert len(gpt2_tokens) > len(multilingual_tokens) + + +def test_split_on_unicode(): + multilingual_tokenizer = get_tokenizer(multilingual=True) + + tokens = [8404, 871, 287, 6, 246, 526, 3210, 20378] + words, word_tokens = multilingual_tokenizer.split_tokens_on_unicode(tokens) + + assert words == [" elle", " est", " l", "'", "�", "é", "rit", "oire"] + assert word_tokens == [[8404], [871], [287], [6], [246], [526], [3210], [20378]] diff --git a/whisper/tokenizer.py b/whisper/tokenizer.py index dfa9f71eb..236f65e00 100644 --- a/whisper/tokenizer.py +++ b/whisper/tokenizer.py @@ -279,17 +279,27 @@ def split_to_word_tokens(self, tokens: List[int]): return self.split_tokens_on_spaces(tokens) def split_tokens_on_unicode(self, tokens: List[int]): + decoded_full = self.decode_with_timestamps(tokens) + replacement_char = "\ufffd" + words = [] word_tokens = [] current_tokens = [] + unicode_offset = 0 for token in tokens: current_tokens.append(token) decoded = self.decode_with_timestamps(current_tokens) - if "\ufffd" not in decoded: + + if ( + replacement_char not in decoded + or decoded_full[unicode_offset + decoded.index(replacement_char)] + == replacement_char + ): words.append(decoded) word_tokens.append(current_tokens) current_tokens = [] + unicode_offset += len(decoded) return words, word_tokens From 79c43e48592bd5cd9d893236a617d2ddeee8d878 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Tue, 14 Mar 2023 15:47:58 -0400 Subject: [PATCH 33/69] abort find_alignment on empty input (#1090) --- whisper/timing.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/whisper/timing.py b/whisper/timing.py index 1f8f4cf8c..f5d07e5d1 100644 --- a/whisper/timing.py +++ b/whisper/timing.py @@ -170,6 +170,9 @@ def find_alignment( medfilt_width: int = 7, qk_scale: float = 1.0, ) -> List[WordTiming]: + if len(text_tokens) == 0: + return [] + tokens = torch.tensor( [ *tokenizer.sot_sequence, From 6dea21fd7f7253bfe450f1e2512a0fe47ee2d258 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Wed, 15 Mar 2023 00:39:05 -0700 Subject: [PATCH 34/69] Release 20230314 --- CHANGELOG.md | 8 ++++++++ whisper/version.py | 2 +- 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 51697a0f6..a77d966f9 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,13 @@ # CHANGELOG +## [v20230314](https://github.com/openai/whisper/releases/tag/v20230314) + +* abort find_alignment on empty input ([#1090](https://github.com/openai/whisper/pull/1090)) +* Fix truncated words list when the replacement character is decoded ([#1089](https://github.com/openai/whisper/pull/1089)) +* fix github language stats getting dominated by jupyter notebook ([#1076](https://github.com/openai/whisper/pull/1076)) +* Fix alignment between the segments and the list of words ([#1087](https://github.com/openai/whisper/pull/1087)) +* Use tiktoken ([#1044](https://github.com/openai/whisper/pull/1044)) + ## [v20230308](https://github.com/openai/whisper/releases/tag/v20230308) * kwargs in decode() for convenience ([#1061](https://github.com/openai/whisper/pull/1061)) diff --git a/whisper/version.py b/whisper/version.py index a0e37f977..572259a53 100644 --- a/whisper/version.py +++ b/whisper/version.py @@ -1 +1 @@ -__version__ = "20230308" +__version__ = "20230314" From b5851c6c40e753606765ac45b85b298e3ae9e00d Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Wed, 29 Mar 2023 16:12:36 -0400 Subject: [PATCH 35/69] Update tokenizer.py (#1163) --- whisper/tokenizer.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/whisper/tokenizer.py b/whisper/tokenizer.py index 236f65e00..4030e15aa 100644 --- a/whisper/tokenizer.py +++ b/whisper/tokenizer.py @@ -6,7 +6,6 @@ from typing import Dict, List, Optional, Tuple import tiktoken -from tiktoken_ext.openai_public import gpt2 LANGUAGES = { "en": "english", @@ -352,7 +351,7 @@ def get_encoding(name: str = "gpt2"): return tiktoken.Encoding( name=os.path.basename(vocab_path), explicit_n_vocab=n_vocab, - pat_str=gpt2()["pat_str"], + pat_str=r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""", mergeable_ranks=ranks, special_tokens=special_tokens, ) From a151816b6b123824c1630e9aed48b04fb90cf4cd Mon Sep 17 00:00:00 2001 From: "K.B.Dharun Krishna" Date: Tue, 11 Apr 2023 02:24:09 +0530 Subject: [PATCH 36/69] python-publish.yml: bump actions version to fix node warning (#1211) --- .github/workflows/python-publish.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml index 116c85937..4b91a2ac0 100644 --- a/.github/workflows/python-publish.yml +++ b/.github/workflows/python-publish.yml @@ -8,14 +8,14 @@ jobs: deploy: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: actions-ecosystem/action-regex-match@v2 id: regex-match with: text: ${{ github.event.head_commit.message }} regex: '^Release ([^ ]+)' - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: '3.8' - name: Install dependencies From 255887f219e6b632bc1a6aac1caf28eecfca1bac Mon Sep 17 00:00:00 2001 From: ryanheise Date: Tue, 11 Apr 2023 10:23:53 +1000 Subject: [PATCH 37/69] Squash long words at window and sentence boundaries. (#1114) * Squash long words at window and sentence boundaries. * Formatting requirements. * Fix squashing logic to point to correct words. --------- Co-authored-by: Jong Wook Kim --- whisper/timing.py | 32 +++++++++++++++++++++++++------- 1 file changed, 25 insertions(+), 7 deletions(-) diff --git a/whisper/timing.py b/whisper/timing.py index f5d07e5d1..055ccc0ae 100644 --- a/whisper/timing.py +++ b/whisper/timing.py @@ -225,17 +225,26 @@ def find_alignment( for i, j in zip(word_boundaries[:-1], word_boundaries[1:]) ] - # hack: ensure the first and second word is not longer than twice the median word duration. + # hack: truncate long words at the start of a window and the start of a sentence. # a better segmentation algorithm based on VAD should be able to replace this. word_durations = end_times - start_times word_durations = word_durations[word_durations.nonzero()] if len(word_durations) > 0: median_duration = np.median(word_durations) max_duration = median_duration * 2 - if len(word_durations) >= 2 and word_durations[1] > max_duration: - boundary = max(end_times[2] / 2, end_times[2] - max_duration) - end_times[0] = start_times[1] = boundary - if len(word_durations) >= 1 and end_times[0] - start_times[0] > max_duration: + sentence_end_marks = ".。!！?？" + # ensure words at sentence boundaries are not longer than twice the median word duration. + for i in range(1, len(start_times)): + if end_times[i] - start_times[i] > max_duration: + if words[i] in sentence_end_marks: + end_times[i] = start_times[i] + max_duration + elif words[i - 1] in sentence_end_marks: + start_times[i] = end_times[i] - max_duration + # ensure the first and second word is not longer than twice the median word duration. + if len(start_times) > 0 and end_times[0] - start_times[0] > max_duration: + if len(start_times) > 1 and end_times[1] - start_times[1] > max_duration: + boundary = max(end_times[1] / 2, end_times[1] - max_duration) + end_times[0] = start_times[1] = boundary start_times[0] = max(0, end_times[0] - max_duration) return [ @@ -327,8 +336,17 @@ def add_word_timestamps( word_index += 1 if len(words) > 0: - # adjust the segment-level timestamps based on the word-level timestamps segment["start"] = words[0]["start"] - segment["end"] = words[-1]["end"] + # hack: prefer the segment-level end timestamp if the last word is too long. + # a better segmentation algorithm based on VAD should be able to replace this. + if ( + segment["end"] > words[-1]["start"] + and segment["end"] + 0.5 < words[-1]["end"] + ): + # adjust the word-level timestamps based on the segment-level timestamps + words[-1]["end"] = segment["end"] + else: + # adjust the segment-level timestamps based on the word-level timestamps + segment["end"] = words[-1]["end"] segment["words"] = words From 43940fc9780cb91c4f94899755b4648e19d7b977 Mon Sep 17 00:00:00 2001 From: ryanheise Date: Tue, 11 Apr 2023 10:28:35 +1000 Subject: [PATCH 38/69] Implement max line width and max line count, and make word highlighting optional (#1184) * Add highlight_words, max_line_width, max_line_count * Refactor subtitle generator --------- Co-authored-by: Jong Wook Kim --- whisper/transcribe.py | 13 +++- whisper/utils.py | 135 +++++++++++++++++++++++++++++------------- 2 files changed, 106 insertions(+), 42 deletions(-) diff --git a/whisper/transcribe.py b/whisper/transcribe.py index ed6d8205d..84feb12a9 100644 --- a/whisper/transcribe.py +++ b/whisper/transcribe.py @@ -401,6 +401,9 @@ def cli(): parser.add_argument("--word_timestamps", type=str2bool, default=False, help="(experimental) extract word-level timestamps and refine the results based on them") parser.add_argument("--prepend_punctuations", type=str, default="\"\'“¿([{-", help="if word_timestamps is True, merge these punctuation symbols with the next word") parser.add_argument("--append_punctuations", type=str, default="\"\'.。,，!！?？:：”)]}、", help="if word_timestamps is True, merge these punctuation symbols with the previous word") + parser.add_argument("--highlight_words", type=str2bool, default=False, help="(requires --word_timestamps True) underline each word as it is spoken in srt and vtt") + parser.add_argument("--max_line_width", type=optional_int, default=None, help="(requires --word_timestamps True) the maximum number of characters in a line before breaking the line") + parser.add_argument("--max_line_count", type=optional_int, default=None, help="(requires --word_timestamps True) the maximum number of lines in a segment") parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS") # fmt: on @@ -433,9 +436,17 @@ def cli(): model = load_model(model_name, device=device, download_root=model_dir) writer = get_writer(output_format, output_dir) + word_options = ["highlight_words", "max_line_count", "max_line_width"] + if not args["word_timestamps"]: + for option in word_options: + if args[option]: + parser.error(f"--{option} requires --word_timestamps True") + if args["max_line_count"] and not args["max_line_width"]: + warnings.warn("--max_line_count has no effect without --max_line_width") + writer_args = {arg: args.pop(arg) for arg in word_options} for audio_path in args.pop("audio"): result = transcribe(model, audio_path, temperature=temperature, **args) - writer(result, audio_path) + writer(result, audio_path, writer_args) if __name__ == "__main__": diff --git a/whisper/utils.py b/whisper/utils.py index 490bdd19f..ba5a10c41 100644 --- a/whisper/utils.py +++ b/whisper/utils.py @@ -1,8 +1,9 @@ import json import os +import re import sys import zlib -from typing import Callable, TextIO +from typing import Callable, Optional, TextIO system_encoding = sys.getdefaultencoding() @@ -73,7 +74,7 @@ class ResultWriter: def __init__(self, output_dir: str): self.output_dir = output_dir - def __call__(self, result: dict, audio_path: str): + def __call__(self, result: dict, audio_path: str, options: dict): audio_basename = os.path.basename(audio_path) audio_basename = os.path.splitext(audio_basename)[0] output_path = os.path.join( @@ -81,16 +82,16 @@ def __call__(self, result: dict, audio_path: str): ) with open(output_path, "w", encoding="utf-8") as f: - self.write_result(result, file=f) + self.write_result(result, file=f, options=options) - def write_result(self, result: dict, file: TextIO): + def write_result(self, result: dict, file: TextIO, options: dict): raise NotImplementedError class WriteTXT(ResultWriter): extension: str = "txt" - def write_result(self, result: dict, file: TextIO): + def write_result(self, result: dict, file: TextIO, options: dict): for segment in result["segments"]: print(segment["text"].strip(), file=file, flush=True) @@ -99,33 +100,81 @@ class SubtitlesWriter(ResultWriter): always_include_hours: bool decimal_marker: str - def iterate_result(self, result: dict): - for segment in result["segments"]: - segment_start = self.format_timestamp(segment["start"]) - segment_end = self.format_timestamp(segment["end"]) - segment_text = segment["text"].strip().replace("-->", "->") - - if word_timings := segment.get("words", None): - all_words = [timing["word"] for timing in word_timings] - all_words[0] = all_words[0].strip() # remove the leading space, if any - last = segment_start - for i, this_word in enumerate(word_timings): - start = self.format_timestamp(this_word["start"]) - end = self.format_timestamp(this_word["end"]) - if last != start: - yield last, start, segment_text - - yield start, end, "".join( - [ - f"{word}" if j == i else word - for j, word in enumerate(all_words) - ] - ) - last = end - - if last != segment_end: - yield last, segment_end, segment_text - else: + def iterate_result(self, result: dict, options: dict): + raw_max_line_width: Optional[int] = options["max_line_width"] + max_line_count: Optional[int] = options["max_line_count"] + highlight_words: bool = options["highlight_words"] + max_line_width = 1000 if raw_max_line_width is None else raw_max_line_width + preserve_segments = max_line_count is None or raw_max_line_width is None + + def iterate_subtitles(): + line_len = 0 + line_count = 1 + # the next subtitle to yield (a list of word timings with whitespace) + subtitle: list[dict] = [] + last = result["segments"][0]["words"][0]["start"] + for segment in result["segments"]: + for i, original_timing in enumerate(segment["words"]): + timing = original_timing.copy() + long_pause = not preserve_segments and timing["start"] - last > 3.0 + has_room = line_len + len(timing["word"]) <= max_line_width + seg_break = i == 0 and len(subtitle) > 0 and preserve_segments + if line_len > 0 and has_room and not long_pause and not seg_break: + # line continuation + line_len += len(timing["word"]) + else: + # new line + timing["word"] = timing["word"].strip() + if ( + len(subtitle) > 0 + and max_line_count is not None + and (long_pause or line_count >= max_line_count) + or seg_break + ): + # subtitle break + yield subtitle + subtitle = [] + line_count = 1 + elif line_len > 0: + # line break + line_count += 1 + timing["word"] = "\n" + timing["word"] + line_len = len(timing["word"].strip()) + subtitle.append(timing) + last = timing["start"] + if len(subtitle) > 0: + yield subtitle + + if "words" in result["segments"][0]: + for subtitle in iterate_subtitles(): + subtitle_start = self.format_timestamp(subtitle[0]["start"]) + subtitle_end = self.format_timestamp(subtitle[-1]["end"]) + subtitle_text = "".join([word["word"] for word in subtitle]) + if highlight_words: + last = subtitle_start + all_words = [timing["word"] for timing in subtitle] + for i, this_word in enumerate(subtitle): + start = self.format_timestamp(this_word["start"]) + end = self.format_timestamp(this_word["end"]) + if last != start: + yield last, start, subtitle_text + + yield start, end, "".join( + [ + re.sub(r"^(\s*)(.*)$", r"\1\2", word) + if j == i + else word + for j, word in enumerate(all_words) + ] + ) + last = end + else: + yield subtitle_start, subtitle_end, subtitle_text + else: + for segment in result["segments"]: + segment_start = self.format_timestamp(segment["start"]) + segment_end = self.format_timestamp(segment["end"]) + segment_text = segment["text"].strip().replace("-->", "->") yield segment_start, segment_end, segment_text def format_timestamp(self, seconds: float): @@ -141,9 +190,9 @@ class WriteVTT(SubtitlesWriter): always_include_hours: bool = False decimal_marker: str = "." - def write_result(self, result: dict, file: TextIO): + def write_result(self, result: dict, file: TextIO, options: dict): print("WEBVTT\n", file=file) - for start, end, text in self.iterate_result(result): + for start, end, text in self.iterate_result(result, options): print(f"{start} --> {end}\n{text}\n", file=file, flush=True) @@ -152,8 +201,10 @@ class WriteSRT(SubtitlesWriter): always_include_hours: bool = True decimal_marker: str = "," - def write_result(self, result: dict, file: TextIO): - for i, (start, end, text) in enumerate(self.iterate_result(result), start=1): + def write_result(self, result: dict, file: TextIO, options: dict): + for i, (start, end, text) in enumerate( + self.iterate_result(result, options), start=1 + ): print(f"{i}\n{start} --> {end}\n{text}\n", file=file, flush=True) @@ -169,7 +220,7 @@ class WriteTSV(ResultWriter): extension: str = "tsv" - def write_result(self, result: dict, file: TextIO): + def write_result(self, result: dict, file: TextIO, options: dict): print("start", "end", "text", sep="\t", file=file) for segment in result["segments"]: print(round(1000 * segment["start"]), file=file, end="\t") @@ -180,11 +231,13 @@ def write_result(self, result: dict, file: TextIO): class WriteJSON(ResultWriter): extension: str = "json" - def write_result(self, result: dict, file: TextIO): + def write_result(self, result: dict, file: TextIO, options: dict): json.dump(result, file) -def get_writer(output_format: str, output_dir: str) -> Callable[[dict, TextIO], None]: +def get_writer( + output_format: str, output_dir: str +) -> Callable[[dict, TextIO, dict], None]: writers = { "txt": WriteTXT, "vtt": WriteVTT, @@ -196,9 +249,9 @@ def get_writer(output_format: str, output_dir: str) -> Callable[[dict, TextIO], if output_format == "all": all_writers = [writer(output_dir) for writer in writers.values()] - def write_all(result: dict, file: TextIO): + def write_all(result: dict, file: TextIO, options: dict): for writer in all_writers: - writer(result, file) + writer(result, file, options) return write_all From 76c901ab8d4558992c44138479c4d69eb52fadcb Mon Sep 17 00:00:00 2001 From: Arseniy Bushyn Date: Tue, 11 Apr 2023 03:39:17 +0300 Subject: [PATCH 39/69] Update README.md to reference tiktoken (#1105) Co-authored-by: Jong Wook Kim --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index eba82ce22..64e2d84c1 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ A Transformer sequence-to-sequence model is trained on various speech processing ## Setup -We used Python 3.9.9 and [PyTorch](https://pytorch.org/) 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.8-3.10 and recent PyTorch versions. The codebase also depends on a few Python packages, most notably [HuggingFace Transformers](https://huggingface.co/docs/transformers/index) for their fast tokenizer implementation and [ffmpeg-python](https://github.com/kkroening/ffmpeg-python) for reading audio files. You can download and install (or update to) the latest release of Whisper with the following command: +We used Python 3.9.9 and [PyTorch](https://pytorch.org/) 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.8-3.10 and recent PyTorch versions. The codebase also depends on a few Python packages, most notably [OpenAI's tiktoken](https://github.com/openai/tiktoken) for their fast tokenizer implementation and [ffmpeg-python](https://github.com/kkroening/ffmpeg-python) for reading audio files. You can download and install (or update to) the latest release of Whisper with the following command: pip install -U openai-whisper @@ -48,7 +48,7 @@ choco install ffmpeg scoop install ffmpeg ``` -You may need [`rust`](http://rust-lang.org) installed as well, in case [tokenizers](https://pypi.org/project/tokenizers/) does not provide a pre-built wheel for your platform. If you see installation errors during the `pip install` command above, please follow the [Getting started page](https://www.rust-lang.org/learn/get-started) to install Rust development environment. Additionally, you may need to configure the `PATH` environment variable, e.g. `export PATH="$HOME/.cargo/bin:$PATH"`. If the installation fails with `No module named 'setuptools_rust'`, you need to install `setuptools_rust`, e.g. by running: +You may need [`rust`](http://rust-lang.org) installed as well, in case [tiktoken](https://github.com/openai/tiktoken) does not provide a pre-built wheel for your platform. If you see installation errors during the `pip install` command above, please follow the [Getting started page](https://www.rust-lang.org/learn/get-started) to install Rust development environment. Additionally, you may need to configure the `PATH` environment variable, e.g. `export PATH="$HOME/.cargo/bin:$PATH"`. If the installation fails with `No module named 'setuptools_rust'`, you need to install `setuptools_rust`, e.g. by running: ```bash pip install setuptools-rust From b0022b3283232b2b9f19262360cd80ec9975aeb4 Mon Sep 17 00:00:00 2001 From: "Fernando O. Gallego" Date: Wed, 12 Apr 2023 00:06:03 +0200 Subject: [PATCH 40/69] Update decoding.py (#1155) * Update decoding.py Following the suggestions of @Jeronymous in https://github.com/openai/whisper/pull/914 and https://github.com/openai/whisper/discussions/924, it solves the problem of endless loop. * Removed blank line and whitespaces in empty lines. * Suggested changes according to the linter --------- Co-authored-by: Jong Wook Kim --- whisper/decoding.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/whisper/decoding.py b/whisper/decoding.py index 81cd8452b..2592ba9b6 100644 --- a/whisper/decoding.py +++ b/whisper/decoding.py @@ -471,6 +471,13 @@ def apply(self, logits: Tensor, tokens: Tensor): # timestamps shouldn't decrease; forbid timestamp tokens smaller than the last logits[k, self.tokenizer.timestamp_begin : timestamps[-1]] = -np.inf + # to force that timestamps are strictly increasing + if last_was_timestamp and not penultimate_was_timestamp: + timestamp_last = timestamps[-1] + else: + timestamp_last = timestamps[-1] + 1 + logits[k, self.tokenizer.timestamp_begin : timestamp_last] = -np.inf + if tokens.shape[1] == self.sample_begin: # suppress generating non-timestamp tokens at the beginning logits[:, : self.tokenizer.timestamp_begin] = -np.inf From c09a7ae299c4c34c5839a76380ae407e7d785914 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Tue, 11 Apr 2023 18:13:13 -0400 Subject: [PATCH 41/69] Update decoding.py (#1219) --- whisper/decoding.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/whisper/decoding.py b/whisper/decoding.py index 2592ba9b6..457ee7ccb 100644 --- a/whisper/decoding.py +++ b/whisper/decoding.py @@ -469,9 +469,7 @@ def apply(self, logits: Tensor, tokens: Tensor): ] if timestamps.numel() > 0: # timestamps shouldn't decrease; forbid timestamp tokens smaller than the last - logits[k, self.tokenizer.timestamp_begin : timestamps[-1]] = -np.inf - - # to force that timestamps are strictly increasing + # also force each segment to have a nonzero length, to prevent infinite looping if last_was_timestamp and not penultimate_was_timestamp: timestamp_last = timestamps[-1] else: From e69930cb9c92c6d821225ccf9cc2fb0029f07635 Mon Sep 17 00:00:00 2001 From: Johnny Date: Thu, 4 May 2023 19:42:09 +0200 Subject: [PATCH 42/69] Python 3.11 (#1171) * python 3.11 * python 3.11 * fix * fix * fix * revert changes * Update requirements.txt * Trying pip3 install instead * Excluding cp39 - torch 1.10.2 * Removing 1.10.2 from test --------- Co-authored-by: Jong Wook Kim --- .github/workflows/test.yml | 13 +++++++------ README.md | 4 +++- requirements.txt | 2 +- 3 files changed, 11 insertions(+), 8 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index bca49b575..3796a3973 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -11,15 +11,16 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ['3.8', '3.9', '3.10'] - pytorch-version: [1.10.2, 1.13.1] + python-version: ['3.8', '3.9', '3.10', '3.11'] + pytorch-version: [1.13.1, 2.0.0] exclude: - - python-version: '3.10' - pytorch-version: 1.10.2 + - python-version: '3.11' + pytorch-version: 1.13.1 steps: - uses: conda-incubator/setup-miniconda@v2 - - run: conda install -n test ffmpeg python=${{ matrix.python-version }} pytorch=${{ matrix.pytorch-version }} cpuonly -c pytorch - - uses: actions/checkout@v2 + - run: conda install -n test ffmpeg python=${{ matrix.python-version }} + - run: pip3 install torch==${{ matrix.pytorch-version }}+cpu --index-url https://download.pytorch.org/whl/cpu + - uses: actions/checkout@v3 - run: echo "$CONDA/envs/test/bin" >> $GITHUB_PATH - run: pip install .["dev"] - run: black --check --diff -t py38 --include '(\.pyi?)$' . diff --git a/README.md b/README.md index 64e2d84c1..648d0c180 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,9 @@ A Transformer sequence-to-sequence model is trained on various speech processing ## Setup -We used Python 3.9.9 and [PyTorch](https://pytorch.org/) 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.8-3.10 and recent PyTorch versions. The codebase also depends on a few Python packages, most notably [OpenAI's tiktoken](https://github.com/openai/tiktoken) for their fast tokenizer implementation and [ffmpeg-python](https://github.com/kkroening/ffmpeg-python) for reading audio files. You can download and install (or update to) the latest release of Whisper with the following command: + +We used Python 3.9.9 and [PyTorch](https://pytorch.org/) 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.8-3.11 and recent PyTorch versions. The codebase also depends on a few Python packages, most notably [OpenAI's tiktoken](https://github.com/openai/tiktoken) for their fast tokenizer implementation and [ffmpeg-python](https://github.com/kkroening/ffmpeg-python) for reading audio files. You can download and install (or update to) the latest release of Whisper with the following command: + pip install -U openai-whisper diff --git a/requirements.txt b/requirements.txt index 09ad0e340..995977a55 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,5 +3,5 @@ numpy torch tqdm more-itertools -tiktoken==0.3.1 +tiktoken==0.3.3 ffmpeg-python==0.2.0 From 8035e9ef4890a06824a9a56a54d0feea9588f23b Mon Sep 17 00:00:00 2001 From: petterreinholdtsen Date: Thu, 4 May 2023 19:53:59 +0200 Subject: [PATCH 43/69] Drop ffmpeg-python dependency and call ffmpeg directly. (#1242) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Drop ffmpeg-python dependency and call ffmpeg directly. The last ffmpeg-python module release was in 2019[1], upstream seem to be unavailable[2] and the project development seem to have stagnated[3]. As the features it provide is trivial to replace using the Python native subprocess module, drop the dependency. [1] [2] [3] * Rewrote to use subprocess.run() instead of subprocess.Popen(). * formatting changes * formatting update * isort fix * Error checking * isort 🤦🏻 * flake8 fix * minor spelling changes --------- Co-authored-by: Jong Wook Kim --- README.md | 4 +--- requirements.txt | 1 - whisper/audio.py | 28 +++++++++++++++++++--------- 3 files changed, 20 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index 648d0c180..b4d3998c4 100644 --- a/README.md +++ b/README.md @@ -17,9 +17,7 @@ A Transformer sequence-to-sequence model is trained on various speech processing ## Setup - -We used Python 3.9.9 and [PyTorch](https://pytorch.org/) 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.8-3.11 and recent PyTorch versions. The codebase also depends on a few Python packages, most notably [OpenAI's tiktoken](https://github.com/openai/tiktoken) for their fast tokenizer implementation and [ffmpeg-python](https://github.com/kkroening/ffmpeg-python) for reading audio files. You can download and install (or update to) the latest release of Whisper with the following command: - +We used Python 3.9.9 and [PyTorch](https://pytorch.org/) 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.8-3.11 and recent PyTorch versions. The codebase also depends on a few Python packages, most notably [OpenAI's tiktoken](https://github.com/openai/tiktoken) for their fast tokenizer implementation. You can download and install (or update to) the latest release of Whisper with the following command: pip install -U openai-whisper diff --git a/requirements.txt b/requirements.txt index 995977a55..3c11ac32c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,4 +4,3 @@ torch tqdm more-itertools tiktoken==0.3.3 -ffmpeg-python==0.2.0 diff --git a/whisper/audio.py b/whisper/audio.py index 513ab7c9d..4f5b6e073 100644 --- a/whisper/audio.py +++ b/whisper/audio.py @@ -1,8 +1,8 @@ import os from functools import lru_cache +from subprocess import CalledProcessError, run from typing import Optional, Union -import ffmpeg import numpy as np import torch import torch.nn.functional as F @@ -39,15 +39,25 @@ def load_audio(file: str, sr: int = SAMPLE_RATE): ------- A NumPy array containing the audio waveform, in float32 dtype. """ + + # This launches a subprocess to decode audio while down-mixing + # and resampling as necessary. Requires the ffmpeg CLI in PATH. + # fmt: off + cmd = [ + "ffmpeg", + "-nostdin", + "-threads", "0", + "-i", file, + "-f", "s16le", + "-ac", "1", + "-acodec", "pcm_s16le", + "-ar", str(sr), + "-" + ] + # fmt: on try: - # This launches a subprocess to decode audio while down-mixing and resampling as necessary. - # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed. - out, _ = ( - ffmpeg.input(file, threads=0) - .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr) - .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True) - ) - except ffmpeg.Error as e: + out = run(cmd, capture_output=True, check=True).stdout + except CalledProcessError as e: raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0 From 55237228425e39828bbb964fd7bf774c9962eb67 Mon Sep 17 00:00:00 2001 From: petterreinholdtsen Date: Thu, 4 May 2023 19:58:56 +0200 Subject: [PATCH 44/69] Dropped unused execute bit from mel_filters.npz. (#1254) --- whisper/assets/mel_filters.npz | Bin 1 file changed, 0 insertions(+), 0 deletions(-) mode change 100755 => 100644 whisper/assets/mel_filters.npz diff --git a/whisper/assets/mel_filters.npz b/whisper/assets/mel_filters.npz old mode 100755 new mode 100644 From e334ff141d5444fbf6904edaaf408e5b0b416fe8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20BOYER?= Date: Fri, 5 May 2023 02:02:36 +0200 Subject: [PATCH 45/69] Avoid computing higher temperatures on no_speech segments (#1279) * Avoid computing higher temperatures on no_speech In decode_with_fallback, we compute higher temperatures in the case where compression_ratio is too high or avg_logprob is too low. But as the computation of no_speech_prob doens't depend on sampling, we can avoid computing higher temperatures if we detect in the first one that the no_speech condition is fulfilled * Update transcribe.py --------- Co-authored-by: Jong Wook Kim --- whisper/transcribe.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/whisper/transcribe.py b/whisper/transcribe.py index 84feb12a9..cba59ecfa 100644 --- a/whisper/transcribe.py +++ b/whisper/transcribe.py @@ -174,7 +174,11 @@ def decode_with_fallback(segment: torch.Tensor) -> DecodingResult: and decode_result.avg_logprob < logprob_threshold ): needs_fallback = True # average log probability is too low - + if ( + no_speech_threshold is not None + and decode_result.no_speech_prob > no_speech_threshold + ): + needs_fallback = False # silence if not needs_fallback: break From b1c0815c7959fed6e9e1840799c0a7eae8db095c Mon Sep 17 00:00:00 2001 From: Brett Balquist <113616657+brett-b112@users.noreply.github.com> Date: Fri, 5 May 2023 01:47:45 -0500 Subject: [PATCH 46/69] Updated README.md to provide more insight on BLEU and specific appendices (#1236) * Updated README.md to provide more insight on BLEU and specific appendices in the research paper * Update README.md --------- Co-authored-by: Jong Wook Kim --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b4d3998c4..20532574c 100644 --- a/README.md +++ b/README.md @@ -70,7 +70,7 @@ There are five model sizes, four with English-only versions, offering speed and The `.en` models for English-only applications tend to perform better, especially for the `tiny.en` and `base.en` models. We observed that the difference becomes less significant for the `small.en` and `medium.en` models. -Whisper's performance varies widely depending on the language. The figure below shows a WER (Word Error Rate) breakdown by languages of the Fleurs dataset using the `large-v2` model. More WER and BLEU scores corresponding to the other models and datasets can be found in Appendix D in [the paper](https://arxiv.org/abs/2212.04356). The smaller, the better. +Whisper's performance varies widely depending on the language. The figure below shows a WER (Word Error Rate) breakdown by languages of the Fleurs dataset using the `large-v2` model (The smaller the numbers, the better the performance). Additional WER scores corresponding to the other models and datasets can be found in Appendix D.1, D.2, and D.4. Meanwhile, more BLEU (Bilingual Evaluation Understudy) scores can be found in Appendix D.3. Both are found in [the paper](https://arxiv.org/abs/2212.04356). ![WER breakdown by language](https://raw.githubusercontent.com/openai/whisper/main/language-breakdown.svg) From 7ca9fbea86987a11266c0a6205bc284c089e5fbc Mon Sep 17 00:00:00 2001 From: Paul Willot Date: Fri, 5 May 2023 08:48:06 +0200 Subject: [PATCH 47/69] Fix numba depreceation notice (#1233) From numba 0.57 raise a warning if `nopython` is not supplied: https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit --- whisper/timing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/whisper/timing.py b/whisper/timing.py index 055ccc0ae..1a73eaafc 100644 --- a/whisper/timing.py +++ b/whisper/timing.py @@ -54,7 +54,7 @@ def median_filter(x: torch.Tensor, filter_width: int): return result -@numba.jit +@numba.jit(nopython=True) def backtrace(trace: np.ndarray): i = trace.shape[0] - 1 j = trace.shape[1] - 1 From 248b6cb124225dd263bb9bd32d060b6517e067f8 Mon Sep 17 00:00:00 2001 From: Valentin Berkes <16121857+funboarder13920@users.noreply.github.com> Date: Fri, 5 May 2023 09:31:35 +0200 Subject: [PATCH 48/69] fix condition_on_previous_text (#1224) prompt_reset_since is set before all_tokens is extended hence does not have the expected effect. --- whisper/transcribe.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/whisper/transcribe.py b/whisper/transcribe.py index cba59ecfa..ff73a5530 100644 --- a/whisper/transcribe.py +++ b/whisper/transcribe.py @@ -312,10 +312,6 @@ def new_segment( ) seek += segment_size - if not condition_on_previous_text or result.temperature > 0.5: - # do not feed the prompt tokens if a high temperature was used - prompt_reset_since = len(all_tokens) - if word_timestamps: add_word_timestamps( segments=current_segments, @@ -361,6 +357,10 @@ def new_segment( [token for segment in current_segments for token in segment["tokens"]] ) + if not condition_on_previous_text or result.temperature > 0.5: + # do not feed the prompt tokens if a high temperature was used + prompt_reset_since = len(all_tokens) + # update progress bar pbar.update(min(content_frames, seek) - previous_seek) From f572f2161ba831bae131364c3bffdead7af6d210 Mon Sep 17 00:00:00 2001 From: ryanheise Date: Fri, 30 Jun 2023 09:51:24 +1000 Subject: [PATCH 49/69] Improve timestamp heuristics. (#1461) * Improve timestamp heuristics. * Track pauses with last_speech_timestamp --- whisper/timing.py | 84 ++++++++++++++++++++++++++++--------------- whisper/transcribe.py | 4 +++ 2 files changed, 60 insertions(+), 28 deletions(-) diff --git a/whisper/timing.py b/whisper/timing.py index 1a73eaafc..207d877d4 100644 --- a/whisper/timing.py +++ b/whisper/timing.py @@ -225,28 +225,6 @@ def find_alignment( for i, j in zip(word_boundaries[:-1], word_boundaries[1:]) ] - # hack: truncate long words at the start of a window and the start of a sentence. - # a better segmentation algorithm based on VAD should be able to replace this. - word_durations = end_times - start_times - word_durations = word_durations[word_durations.nonzero()] - if len(word_durations) > 0: - median_duration = np.median(word_durations) - max_duration = median_duration * 2 - sentence_end_marks = ".。!！?？" - # ensure words at sentence boundaries are not longer than twice the median word duration. - for i in range(1, len(start_times)): - if end_times[i] - start_times[i] > max_duration: - if words[i] in sentence_end_marks: - end_times[i] = start_times[i] + max_duration - elif words[i - 1] in sentence_end_marks: - start_times[i] = end_times[i] - max_duration - # ensure the first and second word is not longer than twice the median word duration. - if len(start_times) > 0 and end_times[0] - start_times[0] > max_duration: - if len(start_times) > 1 and end_times[1] - start_times[1] > max_duration: - boundary = max(end_times[1] / 2, end_times[1] - max_duration) - end_times[0] = start_times[1] = boundary - start_times[0] = max(0, end_times[0] - max_duration) - return [ WordTiming(word, tokens, start, end, probability) for word, tokens, start, end, probability in zip( @@ -298,6 +276,7 @@ def add_word_timestamps( num_frames: int, prepend_punctuations: str = "\"'“¿([{-", append_punctuations: str = "\"'.。,，!！?？:：”)]}、", + last_speech_timestamp: float, **kwargs, ): if len(segments) == 0: @@ -310,6 +289,25 @@ def add_word_timestamps( text_tokens = list(itertools.chain.from_iterable(text_tokens_per_segment)) alignment = find_alignment(model, tokenizer, text_tokens, mel, num_frames, **kwargs) + word_durations = np.array([t.end - t.start for t in alignment]) + word_durations = word_durations[word_durations.nonzero()] + median_duration = np.median(word_durations) if len(word_durations) > 0 else 0.0 + max_duration = median_duration * 2 + + # hack: truncate long words at sentence boundaries. + # a better segmentation algorithm based on VAD should be able to replace this. + if len(word_durations) > 0: + median_duration = np.median(word_durations) + max_duration = median_duration * 2 + sentence_end_marks = ".。!！?？" + # ensure words at sentence boundaries are not longer than twice the median word duration. + for i in range(1, len(alignment)): + if alignment[i].end - alignment[i].start > max_duration: + if alignment[i].word in sentence_end_marks: + alignment[i].end = alignment[i].start + max_duration + elif alignment[i - 1].word in sentence_end_marks: + alignment[i].start = alignment[i].end - max_duration + merge_punctuations(alignment, prepend_punctuations, append_punctuations) time_offset = segments[0]["seek"] * HOP_LENGTH / SAMPLE_RATE @@ -335,18 +333,48 @@ def add_word_timestamps( saved_tokens += len(timing.tokens) word_index += 1 + # hack: truncate long words at segment boundaries. + # a better segmentation algorithm based on VAD should be able to replace this. if len(words) > 0: - segment["start"] = words[0]["start"] - # hack: prefer the segment-level end timestamp if the last word is too long. - # a better segmentation algorithm based on VAD should be able to replace this. + # ensure the first and second word after a pause is not longer than + # twice the median word duration. + if words[0]["end"] - last_speech_timestamp > median_duration * 4 and ( + words[0]["end"] - words[0]["start"] > max_duration + or ( + len(words) > 1 + and words[1]["end"] - words[0]["start"] > max_duration * 2 + ) + ): + if ( + len(words) > 1 + and words[1]["end"] - words[1]["start"] > max_duration + ): + boundary = max(words[1]["end"] / 2, words[1]["end"] - max_duration) + words[0]["end"] = words[1]["start"] = boundary + words[0]["start"] = max(0, words[0]["end"] - max_duration) + + # prefer the segment-level start timestamp if the first word is too long. + if ( + segment["start"] < words[0]["end"] + and segment["start"] - 0.5 > words[0]["start"] + ): + words[0]["start"] = max( + 0, min(words[0]["end"] - median_duration, segment["start"]) + ) + else: + segment["start"] = words[0]["start"] + + # prefer the segment-level end timestamp if the last word is too long. if ( segment["end"] > words[-1]["start"] and segment["end"] + 0.5 < words[-1]["end"] ): - # adjust the word-level timestamps based on the segment-level timestamps - words[-1]["end"] = segment["end"] + words[-1]["end"] = max( + words[-1]["start"] + median_duration, segment["end"] + ) else: - # adjust the segment-level timestamps based on the word-level timestamps segment["end"] = words[-1]["end"] + last_speech_timestamp = segment["end"] + segment["words"] = words diff --git a/whisper/transcribe.py b/whisper/transcribe.py index ff73a5530..6e43a22fa 100644 --- a/whisper/transcribe.py +++ b/whisper/transcribe.py @@ -222,6 +222,7 @@ def new_segment( with tqdm.tqdm( total=content_frames, unit="frames", disable=verbose is not False ) as pbar: + last_speech_timestamp = 0.0 while seek < content_frames: time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE) mel_segment = mel[:, seek : seek + N_FRAMES] @@ -321,10 +322,13 @@ def new_segment( num_frames=segment_size, prepend_punctuations=prepend_punctuations, append_punctuations=append_punctuations, + last_speech_timestamp=last_speech_timestamp, ) word_end_timestamps = [ w["end"] for s in current_segments for w in s["words"] ] + if len(word_end_timestamps) > 0: + last_speech_timestamp = word_end_timestamps[-1] if not single_timestamp_ending and len(word_end_timestamps) > 0: seek_shift = round( (word_end_timestamps[-1] - time_offset) * FRAMES_PER_SECOND From b91c907694f96a3fb9da03d4bbdc83fbcd3a40a4 Mon Sep 17 00:00:00 2001 From: WangChou Lu Date: Fri, 7 Jul 2023 03:48:08 +0800 Subject: [PATCH 50/69] Avoid rearranging all caches (#1483) * avoid rearranging all kv_caches * avoid calculating the same kv_cache from cross attn * Update decoding.py * linter fix --------- Co-authored-by: Jong Wook Kim --- whisper/decoding.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/whisper/decoding.py b/whisper/decoding.py index 457ee7ccb..ecd98a455 100644 --- a/whisper/decoding.py +++ b/whisper/decoding.py @@ -146,6 +146,10 @@ def __init__(self, model: "Whisper", initial_token_length: int): self.kv_cache = {} self.hooks = [] + key_modules = [block.attn.key for block in self.model.decoder.blocks] + value_modules = [block.attn.value for block in self.model.decoder.blocks] + self.kv_modules = key_modules + value_modules + def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor: if not self.kv_cache: self.kv_cache, self.hooks = self.model.install_kv_cache_hooks() @@ -164,9 +168,10 @@ def cleanup_caching(self): self.hooks = [] def rearrange_kv_cache(self, source_indices): - for module, tensor in self.kv_cache.items(): - # update the key/value cache to contain the selected sequences - self.kv_cache[module] = tensor[source_indices].detach() + if source_indices != list(range(len(source_indices))): + for module in self.kv_modules: + # update the key/value cache to contain the selected sequences + self.kv_cache[module] = self.kv_cache[module][source_indices].detach() class SequenceRanker: @@ -668,7 +673,6 @@ def _detect_language(self, audio_features: Tensor, tokens: Tensor): return languages, lang_probs def _main_loop(self, audio_features: Tensor, tokens: Tensor): - assert audio_features.shape[0] == tokens.shape[0] n_batch = tokens.shape[0] sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device) no_speech_probs = [np.nan] * n_batch @@ -721,8 +725,7 @@ def run(self, mel: Tensor) -> List[DecodingResult]: ) ] - # repeat the audio & text tensors by the group size, for beam search or best-of-n sampling - audio_features = audio_features.repeat_interleave(self.n_group, dim=0) + # repeat text tensors by the group size, for beam search or best-of-n sampling tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device) # call the main sampling loop From e8622f9afc4eba139bf796c210f5c01081000472 Mon Sep 17 00:00:00 2001 From: taylorchu Date: Mon, 7 Aug 2023 14:48:56 -0700 Subject: [PATCH 51/69] word timing tweaks (#1559) * word timing tweaks * comment on eot * clearer comments --- whisper/timing.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/whisper/timing.py b/whisper/timing.py index 207d877d4..56e84d432 100644 --- a/whisper/timing.py +++ b/whisper/timing.py @@ -214,6 +214,13 @@ def find_alignment( text_indices, time_indices = dtw(-matrix) words, word_tokens = tokenizer.split_to_word_tokens(text_tokens + [tokenizer.eot]) + if len(word_tokens) <= 1: + # return on eot only + # >>> np.pad([], (1, 0)) + # array([0.]) + # This results in crashes when we lookup jump_times with float, like + # IndexError: arrays used as indices must be of integer (or boolean) type + return [] word_boundaries = np.pad(np.cumsum([len(t) for t in word_tokens[:-1]]), (1, 0)) jumps = np.pad(np.diff(text_indices), (1, 0), constant_values=1).astype(bool) @@ -297,8 +304,6 @@ def add_word_timestamps( # hack: truncate long words at sentence boundaries. # a better segmentation algorithm based on VAD should be able to replace this. if len(word_durations) > 0: - median_duration = np.median(word_durations) - max_duration = median_duration * 2 sentence_end_marks = ".。!！?？" # ensure words at sentence boundaries are not longer than twice the median word duration. for i in range(1, len(alignment)): From 29b7df62317180a18531e2acbc9c4f52fbf8f639 Mon Sep 17 00:00:00 2001 From: Nino Risteski <95188570+NinoRisteski@users.noreply.github.com> Date: Tue, 19 Sep 2023 00:59:49 +0200 Subject: [PATCH 52/69] Update model-card.md (#1643) fixed a few typos --- model-card.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/model-card.md b/model-card.md index 2ed85cf24..b5a571a3d 100644 --- a/model-card.md +++ b/model-card.md @@ -37,7 +37,7 @@ Sequence-to-sequence ASR (automatic speech recognition) and speech translation m ### Evaluated Use -The primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research. +The primary intended users of these models are AI researchers studying the robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research. The models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them. @@ -53,17 +53,17 @@ As discussed in [the accompanying paper](https://arxiv.org/abs/2212.04356), we s ## Performance and Limitations -Our studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level. +Our studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, and technical language, as well as zero-shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level. However, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself. -Our models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in [the paper accompanying this release](https://arxiv.org/abs/2212.04356). +Our models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include a higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in [the paper accompanying this release](https://arxiv.org/abs/2212.04356). -In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in [the paper](https://arxiv.org/abs/2212.04356). It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages. +In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis of these limitations is provided in [the paper](https://arxiv.org/abs/2212.04356). It is likely that this behavior and hallucinations may be worse in lower-resource and/or lower-discoverability languages. ## Broader Implications We anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications. -There are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects. +There are also potential dual-use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects. From 21010ef454fb25954b0914785180311fb077add9 Mon Sep 17 00:00:00 2001 From: sqhao Date: Tue, 19 Sep 2023 07:09:59 +0800 Subject: [PATCH 53/69] fix doc of TextDecoder (#1526) Signed-off-by: haoshengqiang Co-authored-by: haoshengqiang --- whisper/model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/whisper/model.py b/whisper/model.py index 3457fcfc6..69130022a 100644 --- a/whisper/model.py +++ b/whisper/model.py @@ -197,7 +197,7 @@ def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None): """ x : torch.LongTensor, shape = (batch_size, <= n_ctx) the text tokens - xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx) + xa : torch.Tensor, shape = (batch_size, n_audio_ctx, n_audio_state) the encoded audio features to be attended on """ offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0 From 8b330df0961ff64499609d70f1ba7c6a8a2f9b1a Mon Sep 17 00:00:00 2001 From: Arthur Kim Date: Tue, 19 Sep 2023 08:15:33 +0900 Subject: [PATCH 54/69] Add .pre-commit-config.yaml (#1528) * Add .pre-commit-config.yaml Co-authored-by: arthur * flake8 E741 --------- Co-authored-by: Jong Wook Kim --- .github/workflows/test.yml | 33 ++++++++++++++++++++++++++++++--- .pre-commit-config.yaml | 28 ++++++++++++++++++++++++++++ whisper/timing.py | 2 +- whisper/tokenizer.py | 2 +- 4 files changed, 60 insertions(+), 5 deletions(-) create mode 100644 .pre-commit-config.yaml diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 3796a3973..0f9cfabe4 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -6,8 +6,38 @@ on: pull_request: branches: - main + jobs: + pre-commit: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - name: Fetch base branch + run: git fetch origin ${{ github.base_ref }} + - uses: actions/setup-python@v4 + with: + python-version: "3.8" + architecture: x64 + - name: Get pip cache dir + id: pip-cache + run: | + echo "dir=$(pip cache dir)" >> $GITHUB_OUTPUT + - name: pip/pre-commit cache + uses: actions/cache@v3 + with: + path: | + ${{ steps.pip-cache.outputs.dir }} + ~/.cache/pre-commit + key: ${{ runner.os }}-pip-pre-commit-${{ hashFiles('**/.pre-commit-config.yaml') }} + restore-keys: | + ${{ runner.os }}-pip-pre-commit + - name: pre-commit + run: | + pip install -U pre-commit + pre-commit install --install-hooks + pre-commit run --from-ref=origin/${{ github.base_ref }} --to-ref=HEAD whisper-test: + needs: pre-commit runs-on: ubuntu-latest strategy: matrix: @@ -23,7 +53,4 @@ jobs: - uses: actions/checkout@v3 - run: echo "$CONDA/envs/test/bin" >> $GITHUB_PATH - run: pip install .["dev"] - - run: black --check --diff -t py38 --include '(\.pyi?)$' . - - run: isort --check --diff . - - run: flake8 --ignore E203,W503,W504,E501,E731,E741 . - run: pytest --durations=0 -vv -k 'not test_transcribe or test_transcribe[tiny] or test_transcribe[tiny.en]' -m 'not requires_cuda' diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 000000000..3f5a74b6d --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,28 @@ +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.0.1 + hooks: + - id: check-json + - id: end-of-file-fixer + types: [file, python] + - id: trailing-whitespace + types: [file, python] + - id: mixed-line-ending + - id: check-added-large-files + args: [--maxkb=4096] + - repo: https://github.com/psf/black + rev: 23.7.0 + hooks: + - id: black + - repo: https://github.com/pycqa/isort + rev: 5.12.0 + hooks: + - id: isort + name: isort (python) + args: ["--profile", "black", "-l", "88", "--trailing-comma", "--multi-line", "3"] + - repo: https://github.com/pycqa/flake8.git + rev: 6.0.0 + hooks: + - id: flake8 + types: [python] + args: ["--max-line-length", "88", "--ignore", "E203,E501,W503,W504"] diff --git a/whisper/timing.py b/whisper/timing.py index 56e84d432..befcf464e 100644 --- a/whisper/timing.py +++ b/whisper/timing.py @@ -202,7 +202,7 @@ def find_alignment( hook.remove() # heads * tokens * frames - weights = torch.stack([QKs[l][h] for l, h in model.alignment_heads.indices().T]) + weights = torch.stack([QKs[_l][_h] for _l, _h in model.alignment_heads.indices().T]) weights = weights[:, :, : num_frames // 2] weights = (weights * qk_scale).softmax(dim=-1) std, mean = torch.std_mean(weights, dim=-2, keepdim=True, unbiased=False) diff --git a/whisper/tokenizer.py b/whisper/tokenizer.py index 4030e15aa..3b2399184 100644 --- a/whisper/tokenizer.py +++ b/whisper/tokenizer.py @@ -226,7 +226,7 @@ def all_language_tokens(self) -> Tuple[int]: @cached_property def all_language_codes(self) -> Tuple[str]: - return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens) + return tuple(self.decode([_l]).strip("<|>") for _l in self.all_language_tokens) @cached_property def sot_sequence_including_notimestamps(self) -> Tuple[int]: From 5f957da5ca9ab45a4ac149047e6a8004bc9d25bc Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Mon, 18 Sep 2023 16:38:17 -0700 Subject: [PATCH 55/69] Update test.yml --- .github/workflows/test.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 0f9cfabe4..dffc17c61 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -35,7 +35,7 @@ jobs: run: | pip install -U pre-commit pre-commit install --install-hooks - pre-commit run --from-ref=origin/${{ github.base_ref }} --to-ref=HEAD + pre-commit run --all-files whisper-test: needs: pre-commit runs-on: ubuntu-latest From 0a60fcaa9b86748389a656aa013c416030287d47 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Mon, 18 Sep 2023 17:13:19 -0700 Subject: [PATCH 56/69] Release 20230918 --- CHANGELOG.md | 23 +++++++++++++++++++++++ whisper/version.py | 2 +- 2 files changed, 24 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index a77d966f9..50c053687 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,28 @@ # CHANGELOG +## [v20230918](https://github.com/openai/whisper/releases/tag/v20230918) + +* Add .pre-commit-config.yaml ([#1528](https://github.com/openai/whisper/pull/1528)) +* fix doc of TextDecoder ([#1526](https://github.com/openai/whisper/pull/1526)) +* Update model-card.md ([#1643](https://github.com/openai/whisper/pull/1643)) +* word timing tweaks ([#1559](https://github.com/openai/whisper/pull/1559)) +* Avoid rearranging all caches ([#1483](https://github.com/openai/whisper/pull/1483)) +* Improve timestamp heuristics. ([#1461](https://github.com/openai/whisper/pull/1461)) +* fix condition_on_previous_text ([#1224](https://github.com/openai/whisper/pull/1224)) +* Fix numba depreceation notice ([#1233](https://github.com/openai/whisper/pull/1233)) +* Updated README.md to provide more insight on BLEU and specific appendices ([#1236](https://github.com/openai/whisper/pull/1236)) +* Avoid computing higher temperatures on no_speech segments ([#1279](https://github.com/openai/whisper/pull/1279)) +* Dropped unused execute bit from mel_filters.npz. ([#1254](https://github.com/openai/whisper/pull/1254)) +* Drop ffmpeg-python dependency and call ffmpeg directly. ([#1242](https://github.com/openai/whisper/pull/1242)) +* Python 3.11 ([#1171](https://github.com/openai/whisper/pull/1171)) +* Update decoding.py ([#1219](https://github.com/openai/whisper/pull/1219)) +* Update decoding.py ([#1155](https://github.com/openai/whisper/pull/1155)) +* Update README.md to reference tiktoken ([#1105](https://github.com/openai/whisper/pull/1105)) +* Implement max line width and max line count, and make word highlighting optional ([#1184](https://github.com/openai/whisper/pull/1184)) +* Squash long words at window and sentence boundaries. ([#1114](https://github.com/openai/whisper/pull/1114)) +* python-publish.yml: bump actions version to fix node warning ([#1211](https://github.com/openai/whisper/pull/1211)) +* Update tokenizer.py ([#1163](https://github.com/openai/whisper/pull/1163)) + ## [v20230314](https://github.com/openai/whisper/releases/tag/v20230314) * abort find_alignment on empty input ([#1090](https://github.com/openai/whisper/pull/1090)) diff --git a/whisper/version.py b/whisper/version.py index 572259a53..c43bf6f78 100644 --- a/whisper/version.py +++ b/whisper/version.py @@ -1 +1 @@ -__version__ = "20230314" +__version__ = "20230918" From b38a1f20f4b23f3f3099af2c3e0ca95627276ddf Mon Sep 17 00:00:00 2001 From: Jordi Mas Date: Tue, 10 Oct 2023 19:01:01 +0200 Subject: [PATCH 57/69] Fix exception when an audio file with no speech is provided (#1396) Co-authored-by: Jong Wook Kim --- whisper/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/whisper/utils.py b/whisper/utils.py index ba5a10c41..22260d0d9 100644 --- a/whisper/utils.py +++ b/whisper/utils.py @@ -145,7 +145,7 @@ def iterate_subtitles(): if len(subtitle) > 0: yield subtitle - if "words" in result["segments"][0]: + if len(result["segments"]) > 0 and "words" in result["segments"][0]: for subtitle in iterate_subtitles(): subtitle_start = self.format_timestamp(subtitle[0]["start"]) subtitle_end = self.format_timestamp(subtitle[-1]["end"]) From 6ed314fe4109eed40b79500eaf465935905e1aa7 Mon Sep 17 00:00:00 2001 From: amosal Date: Mon, 6 Nov 2023 10:49:33 +0100 Subject: [PATCH 58/69] Add new option to generate subtitles by a specific number of words (#1729) * ADD parser for new argument --max_words_count * ADD max_words_count in words_options ADD warning for max_line_width compatibility * ADD logic for max_words_count * rename to max_words_per_line * make them kwargs * allow specifying file path by --model * black formatting --------- Co-authored-by: Jong Wook Kim --- whisper/transcribe.py | 21 ++++++- whisper/utils.py | 132 ++++++++++++++++++++++++++++-------------- 2 files changed, 106 insertions(+), 47 deletions(-) diff --git a/whisper/transcribe.py b/whisper/transcribe.py index 6e43a22fa..509e322e3 100644 --- a/whisper/transcribe.py +++ b/whisper/transcribe.py @@ -378,10 +378,17 @@ def new_segment( def cli(): from . import available_models + def valid_model_name(name): + if name in available_models() or os.path.exists(name): + return name + raise ValueError( + f"model should be one of {available_models()} or path to a model checkpoint" + ) + # fmt: off parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe") - parser.add_argument("--model", default="small", choices=available_models(), help="name of the Whisper model to use") + parser.add_argument("--model", default="small", type=valid_model_name, help="name of the Whisper model to use") parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default") parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference") parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs") @@ -412,6 +419,7 @@ def cli(): parser.add_argument("--highlight_words", type=str2bool, default=False, help="(requires --word_timestamps True) underline each word as it is spoken in srt and vtt") parser.add_argument("--max_line_width", type=optional_int, default=None, help="(requires --word_timestamps True) the maximum number of characters in a line before breaking the line") parser.add_argument("--max_line_count", type=optional_int, default=None, help="(requires --word_timestamps True) the maximum number of lines in a segment") + parser.add_argument("--max_words_per_line", type=optional_int, default=None, help="(requires --word_timestamps True, no effect with --max_line_width) the maximum number of words in a segment") parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS") # fmt: on @@ -444,17 +452,24 @@ def cli(): model = load_model(model_name, device=device, download_root=model_dir) writer = get_writer(output_format, output_dir) - word_options = ["highlight_words", "max_line_count", "max_line_width"] + word_options = [ + "highlight_words", + "max_line_count", + "max_line_width", + "max_words_per_line", + ] if not args["word_timestamps"]: for option in word_options: if args[option]: parser.error(f"--{option} requires --word_timestamps True") if args["max_line_count"] and not args["max_line_width"]: warnings.warn("--max_line_count has no effect without --max_line_width") + if args["max_words_per_line"] and args["max_line_width"]: + warnings.warn("--max_words_per_line has no effect with --max_line_width") writer_args = {arg: args.pop(arg) for arg in word_options} for audio_path in args.pop("audio"): result = transcribe(model, audio_path, temperature=temperature, **args) - writer(result, audio_path, writer_args) + writer(result, audio_path, **writer_args) if __name__ == "__main__": diff --git a/whisper/utils.py b/whisper/utils.py index 22260d0d9..7a172c401 100644 --- a/whisper/utils.py +++ b/whisper/utils.py @@ -74,7 +74,9 @@ class ResultWriter: def __init__(self, output_dir: str): self.output_dir = output_dir - def __call__(self, result: dict, audio_path: str, options: dict): + def __call__( + self, result: dict, audio_path: str, options: Optional[dict] = None, **kwargs + ): audio_basename = os.path.basename(audio_path) audio_basename = os.path.splitext(audio_basename)[0] output_path = os.path.join( @@ -82,16 +84,20 @@ def __call__(self, result: dict, audio_path: str, options: dict): ) with open(output_path, "w", encoding="utf-8") as f: - self.write_result(result, file=f, options=options) + self.write_result(result, file=f, options=options, **kwargs) - def write_result(self, result: dict, file: TextIO, options: dict): + def write_result( + self, result: dict, file: TextIO, options: Optional[dict] = None, **kwargs + ): raise NotImplementedError class WriteTXT(ResultWriter): extension: str = "txt" - def write_result(self, result: dict, file: TextIO, options: dict): + def write_result( + self, result: dict, file: TextIO, options: Optional[dict] = None, **kwargs + ): for segment in result["segments"]: print(segment["text"].strip(), file=file, flush=True) @@ -100,12 +106,24 @@ class SubtitlesWriter(ResultWriter): always_include_hours: bool decimal_marker: str - def iterate_result(self, result: dict, options: dict): - raw_max_line_width: Optional[int] = options["max_line_width"] - max_line_count: Optional[int] = options["max_line_count"] - highlight_words: bool = options["highlight_words"] - max_line_width = 1000 if raw_max_line_width is None else raw_max_line_width - preserve_segments = max_line_count is None or raw_max_line_width is None + def iterate_result( + self, + result: dict, + options: Optional[dict] = None, + *, + max_line_width: Optional[int] = None, + max_line_count: Optional[int] = None, + highlight_words: bool = False, + max_words_per_line: Optional[int] = None, + ): + options = options or {} + max_line_width = max_line_width or options.get("max_line_width") + max_line_count = max_line_count or options.get("max_line_count") + highlight_words = highlight_words or options.get("highlight_words", False) + max_words_per_line = max_words_per_line or options.get("max_words_per_line") + preserve_segments = max_line_count is None or max_line_width is None + max_line_width = max_line_width or 1000 + max_words_per_line = max_words_per_line or 1000 def iterate_subtitles(): line_len = 0 @@ -114,34 +132,50 @@ def iterate_subtitles(): subtitle: list[dict] = [] last = result["segments"][0]["words"][0]["start"] for segment in result["segments"]: - for i, original_timing in enumerate(segment["words"]): - timing = original_timing.copy() - long_pause = not preserve_segments and timing["start"] - last > 3.0 - has_room = line_len + len(timing["word"]) <= max_line_width - seg_break = i == 0 and len(subtitle) > 0 and preserve_segments - if line_len > 0 and has_room and not long_pause and not seg_break: - # line continuation - line_len += len(timing["word"]) - else: - # new line - timing["word"] = timing["word"].strip() + chunk_index = 0 + words_count = max_words_per_line + while chunk_index < len(segment["words"]): + remaining_words = len(segment["words"]) - chunk_index + if max_words_per_line > len(segment["words"]) - chunk_index: + words_count = remaining_words + for i, original_timing in enumerate( + segment["words"][chunk_index : chunk_index + words_count] + ): + timing = original_timing.copy() + long_pause = ( + not preserve_segments and timing["start"] - last > 3.0 + ) + has_room = line_len + len(timing["word"]) <= max_line_width + seg_break = i == 0 and len(subtitle) > 0 and preserve_segments if ( - len(subtitle) > 0 - and max_line_count is not None - and (long_pause or line_count >= max_line_count) - or seg_break + line_len > 0 + and has_room + and not long_pause + and not seg_break ): - # subtitle break - yield subtitle - subtitle = [] - line_count = 1 - elif line_len > 0: - # line break - line_count += 1 - timing["word"] = "\n" + timing["word"] - line_len = len(timing["word"].strip()) - subtitle.append(timing) - last = timing["start"] + # line continuation + line_len += len(timing["word"]) + else: + # new line + timing["word"] = timing["word"].strip() + if ( + len(subtitle) > 0 + and max_line_count is not None + and (long_pause or line_count >= max_line_count) + or seg_break + ): + # subtitle break + yield subtitle + subtitle = [] + line_count = 1 + elif line_len > 0: + # line break + line_count += 1 + timing["word"] = "\n" + timing["word"] + line_len = len(timing["word"].strip()) + subtitle.append(timing) + last = timing["start"] + chunk_index += max_words_per_line if len(subtitle) > 0: yield subtitle @@ -190,9 +224,11 @@ class WriteVTT(SubtitlesWriter): always_include_hours: bool = False decimal_marker: str = "." - def write_result(self, result: dict, file: TextIO, options: dict): + def write_result( + self, result: dict, file: TextIO, options: Optional[dict] = None, **kwargs + ): print("WEBVTT\n", file=file) - for start, end, text in self.iterate_result(result, options): + for start, end, text in self.iterate_result(result, options, **kwargs): print(f"{start} --> {end}\n{text}\n", file=file, flush=True) @@ -201,9 +237,11 @@ class WriteSRT(SubtitlesWriter): always_include_hours: bool = True decimal_marker: str = "," - def write_result(self, result: dict, file: TextIO, options: dict): + def write_result( + self, result: dict, file: TextIO, options: Optional[dict] = None, **kwargs + ): for i, (start, end, text) in enumerate( - self.iterate_result(result, options), start=1 + self.iterate_result(result, options, **kwargs), start=1 ): print(f"{i}\n{start} --> {end}\n{text}\n", file=file, flush=True) @@ -220,7 +258,9 @@ class WriteTSV(ResultWriter): extension: str = "tsv" - def write_result(self, result: dict, file: TextIO, options: dict): + def write_result( + self, result: dict, file: TextIO, options: Optional[dict] = None, **kwargs + ): print("start", "end", "text", sep="\t", file=file) for segment in result["segments"]: print(round(1000 * segment["start"]), file=file, end="\t") @@ -231,7 +271,9 @@ def write_result(self, result: dict, file: TextIO, options: dict): class WriteJSON(ResultWriter): extension: str = "json" - def write_result(self, result: dict, file: TextIO, options: dict): + def write_result( + self, result: dict, file: TextIO, options: Optional[dict] = None, **kwargs + ): json.dump(result, file) @@ -249,9 +291,11 @@ def get_writer( if output_format == "all": all_writers = [writer(output_dir) for writer in writers.values()] - def write_all(result: dict, file: TextIO, options: dict): + def write_all( + result: dict, file: TextIO, options: Optional[dict] = None, **kwargs + ): for writer in all_writers: - writer(result, file, options) + writer(result, file, options, **kwargs) return write_all From b7d277acd59c19edab3c75b8bf362ddd27fddcc7 Mon Sep 17 00:00:00 2001 From: Marco Zucconelli <16992603+zuccon@users.noreply.github.com> Date: Mon, 6 Nov 2023 11:06:19 +0100 Subject: [PATCH 59/69] handling transcribe exceptions. (#1682) * handling transcribe() exceptions. * printing stacktrace --------- Co-authored-by: invalid Co-authored-by: Jong Wook Kim Co-authored-by: Jong Wook Kim --- whisper/transcribe.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/whisper/transcribe.py b/whisper/transcribe.py index 509e322e3..d5b3d4336 100644 --- a/whisper/transcribe.py +++ b/whisper/transcribe.py @@ -1,5 +1,6 @@ import argparse import os +import traceback import warnings from typing import TYPE_CHECKING, Optional, Tuple, Union @@ -468,8 +469,12 @@ def valid_model_name(name): warnings.warn("--max_words_per_line has no effect with --max_line_width") writer_args = {arg: args.pop(arg) for arg in word_options} for audio_path in args.pop("audio"): - result = transcribe(model, audio_path, temperature=temperature, **args) - writer(result, audio_path, **writer_args) + try: + result = transcribe(model, audio_path, temperature=temperature, **args) + writer(result, audio_path, **writer_args) + except Exception as e: + traceback.print_exc() + print(f"Skipping {audio_path} due to {type(e).__name__}: {str(e)}") if __name__ == "__main__": From 7dfcd56304a7025f949a3d69bd38eb0916622453 Mon Sep 17 00:00:00 2001 From: Mohamad Zamini <32536264+mzamini92@users.noreply.github.com> Date: Mon, 6 Nov 2023 03:28:51 -0700 Subject: [PATCH 60/69] allow_pickle=False while loading of mel matrix IN audio.py (#1511) * Update audio.py The `mel_filters` function is using a `np.load` function to load a pre-computed mel filterbank matrix. This function is not thread-safe, which means that if it is called from multiple threads at the same time, it may corrupt the data. To fix this, you can use the `torch.load` function instead. This function is thread-safe, so it will not corrupt the data if it is called from multiple threads at the same time. * Update audio.py updated the docstring * allow_pickle=False * newline --------- Co-authored-by: Jong Wook Kim Co-authored-by: Jong Wook Kim --- whisper/audio.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/whisper/audio.py b/whisper/audio.py index 4f5b6e073..f959e1c76 100644 --- a/whisper/audio.py +++ b/whisper/audio.py @@ -101,9 +101,9 @@ def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor: ) """ assert n_mels == 80, f"Unsupported n_mels: {n_mels}" - with np.load( - os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz") - ) as f: + + filters_path = os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz") + with np.load(filters_path, allow_pickle=False) as f: return torch.from_numpy(f[f"mel_{n_mels}"]).to(device) From b9f17e1f2da9fe735910c94593dba8d7c25f2a4a Mon Sep 17 00:00:00 2001 From: Philippe Hebert Date: Mon, 6 Nov 2023 05:43:07 -0500 Subject: [PATCH 61/69] docs: Disambiguation of the term "relative speed" in the README (#1751) * docs: defines relative speed in README * combined paragraphs --------- Co-authored-by: Jong Wook Kim --- README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/README.md b/README.md index 20532574c..3dc26c682 100644 --- a/README.md +++ b/README.md @@ -57,8 +57,7 @@ pip install setuptools-rust ## Available models and languages -There are five model sizes, four with English-only versions, offering speed and accuracy tradeoffs. Below are the names of the available models and their approximate memory requirements and relative speed. - +There are five model sizes, four with English-only versions, offering speed and accuracy tradeoffs. Below are the names of the available models and their approximate memory requirements and inference speed relative to the large model; actual speed may vary depending on many factors including the available hardware. | Size | Parameters | English-only model | Multilingual model | Required VRAM | Relative speed | |:------:|:----------:|:------------------:|:------------------:|:-------------:|:--------------:| From 746aaaeafa4ae746f97284ff43e3abe9af835b1d Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Mon, 6 Nov 2023 03:05:21 -0800 Subject: [PATCH 62/69] remove tiktoken pin (#1759) --- requirements.txt | 2 +- tests/test_tokenizer.py | 14 ++++++++++++-- 2 files changed, 13 insertions(+), 3 deletions(-) diff --git a/requirements.txt b/requirements.txt index 3c11ac32c..a03dae853 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,4 +3,4 @@ numpy torch tqdm more-itertools -tiktoken==0.3.3 +tiktoken diff --git a/tests/test_tokenizer.py b/tests/test_tokenizer.py index 09d0351e1..be424e5fe 100644 --- a/tests/test_tokenizer.py +++ b/tests/test_tokenizer.py @@ -1,7 +1,17 @@ +import pytest + from whisper.tokenizer import get_tokenizer -def test_tokenizer(): +@pytest.mark.parametrize("multilingual", [True, False]) +def test_tokenizer(multilingual): + tokenizer = get_tokenizer(multilingual=False) + assert tokenizer.sot in tokenizer.sot_sequence + assert len(tokenizer.all_language_codes) == len(tokenizer.all_language_tokens) + assert all(c < tokenizer.timestamp_begin for c in tokenizer.all_language_tokens) + + +def test_multilingual_tokenizer(): gpt2_tokenizer = get_tokenizer(multilingual=False) multilingual_tokenizer = get_tokenizer(multilingual=True) @@ -20,5 +30,5 @@ def test_split_on_unicode(): tokens = [8404, 871, 287, 6, 246, 526, 3210, 20378] words, word_tokens = multilingual_tokenizer.split_tokens_on_unicode(tokens) - assert words == [" elle", " est", " l", "'", "�", "é", "rit", "oire"] + assert words == [" elle", " est", " l", "'", "\ufffd", "é", "rit", "oire"] assert word_tokens == [[8404], [871], [287], [6], [246], [526], [3210], [20378]] From f6f01c561c45ad6ab421405e18ae22fd0c698e92 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Mon, 6 Nov 2023 03:08:56 -0800 Subject: [PATCH 63/69] Release 20231105 --- CHANGELOG.md | 9 +++++++++ whisper/version.py | 2 +- 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 50c053687..ea73cf108 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,14 @@ # CHANGELOG +## [v20231105](https://github.com/openai/whisper/releases/tag/v20231105) + +* remove tiktoken pin ([#1759](https://github.com/openai/whisper/pull/1759)) +* docs: Disambiguation of the term "relative speed" in the README ([#1751](https://github.com/openai/whisper/pull/1751)) +* allow_pickle=False while loading of mel matrix IN audio.py ([#1511](https://github.com/openai/whisper/pull/1511)) +* handling transcribe exceptions. ([#1682](https://github.com/openai/whisper/pull/1682)) +* Add new option to generate subtitles by a specific number of words ([#1729](https://github.com/openai/whisper/pull/1729)) +* Fix exception when an audio file with no speech is provided ([#1396](https://github.com/openai/whisper/pull/1396)) + ## [v20230918](https://github.com/openai/whisper/releases/tag/v20230918) * Add .pre-commit-config.yaml ([#1528](https://github.com/openai/whisper/pull/1528)) diff --git a/whisper/version.py b/whisper/version.py index c43bf6f78..cb8531531 100644 --- a/whisper/version.py +++ b/whisper/version.py @@ -1 +1 @@ -__version__ = "20230918" +__version__ = "20231105" From c5d42560760a05584c1c79546a098287e5a771eb Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Mon, 6 Nov 2023 10:10:30 -0800 Subject: [PATCH 64/69] large-v3 (#1761) * mel_filters() loads 128 mel bins * can load 100-language models * large-v3 checkpoint and evals * add mandarin alias * remove unused path * flake8 fix * formatting fix --- README.md | 4 +- language-breakdown.svg | 9302 ++++++++++++++++++++++++-------- model-card.md | 4 +- tests/test_transcribe.py | 2 +- whisper/__init__.py | 6 +- whisper/assets/mel_filters.npz | Bin 2048 -> 4271 bytes whisper/audio.py | 8 +- whisper/decoding.py | 9 +- whisper/model.py | 9 +- whisper/tokenizer.py | 27 +- whisper/transcribe.py | 9 +- 11 files changed, 7145 insertions(+), 2235 deletions(-) diff --git a/README.md b/README.md index 3dc26c682..afca9c971 100644 --- a/README.md +++ b/README.md @@ -69,9 +69,9 @@ There are five model sizes, four with English-only versions, offering speed and The `.en` models for English-only applications tend to perform better, especially for the `tiny.en` and `base.en` models. We observed that the difference becomes less significant for the `small.en` and `medium.en` models. -Whisper's performance varies widely depending on the language. The figure below shows a WER (Word Error Rate) breakdown by languages of the Fleurs dataset using the `large-v2` model (The smaller the numbers, the better the performance). Additional WER scores corresponding to the other models and datasets can be found in Appendix D.1, D.2, and D.4. Meanwhile, more BLEU (Bilingual Evaluation Understudy) scores can be found in Appendix D.3. Both are found in [the paper](https://arxiv.org/abs/2212.04356). +Whisper's performance varies widely depending on the language. The figure below shows a performance breakdown of `large-v3` and `large-v2` models by language, using WERs (word error rates) or CER (character error rates, shown in *Italic*) evaluated on the Common Voice 15 and Fleurs datasets. Additional WER/CER metrics corresponding to the other models and datasets can be found in Appendix D.1, D.2, and D.4 of [the paper](https://arxiv.org/abs/2212.04356), as well as the BLEU (Bilingual Evaluation Understudy) scores for translation in Appendix D.3. -![WER breakdown by language](https://raw.githubusercontent.com/openai/whisper/main/language-breakdown.svg) +![WER breakdown by language](https://github.com/openai/whisper/assets/266841/f4619d66-1058-4005-8f67-a9d811b77c62) diff --git a/language-breakdown.svg b/language-breakdown.svg index 49a0653eb..616fd57ea 100644 --- a/language-breakdown.svg +++ b/language-breakdown.svg @@ -1,16 +1,16 @@ - + - 2022-12-03T03:56:51.812586 + 2023-11-06T12:34:22.337927 image/svg+xml - Matplotlib v3.5.1, https://matplotlib.org/ + Matplotlib v3.7.3, https://matplotlib.org/ @@ -21,498 +21,42 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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- + + + - + - + - - - + + + - + - + - + - + - - - + + + - + - + - - - + + + - + - + - - - + + + - + - + - - - + + + - + - + - - - + + + - + - + - - - + + + - - - - - - - - - - + - + - - - + + + - + - + - - - + + + - + - + - - - + + + - + - - - + + + - + - + - - - + + + - + - - - - - + + + + + - + - - - - - + + + + + - + - - - + + + - + - + - + - - - + + + - + - + - - - + + + - + - + - - - + + + - + - + - - - + + + - + - + - - - - - + + + + + - - - - - + + + + + - + - - - - - + + + + + - - - - - - - - - - - - + + + - + - + - - - + + + - + - + - - - - - + + + + + - + - - + + + + + diff --git a/model-card.md b/model-card.md index b5a571a3d..3c041a1c0 100644 --- a/model-card.md +++ b/model-card.md @@ -17,12 +17,12 @@ The Whisper models are trained for speech recognition and translation tasks, cap | medium | 769 M | ✓ | ✓ | | large | 1550 M | | ✓ | -In December 2022, we [released an improved large model named `large-v2`](https://github.com/openai/whisper/discussions/661). +In December 2022, we [released an improved large model named `large-v2`](https://github.com/openai/whisper/discussions/661), and `large-v3` in November 2023. ### Release date -September 2022 (original series) and December 2022 (`large-v2`) +September 2022 (original series), December 2022 (`large-v2`), and November 2023 (`large-v3`) ### Model type diff --git a/tests/test_transcribe.py b/tests/test_transcribe.py index e4f8fd0f7..599221af5 100644 --- a/tests/test_transcribe.py +++ b/tests/test_transcribe.py @@ -25,7 +25,7 @@ def test_transcribe(model_name: str): assert "your country" in transcription assert "do for you" in transcription - tokenizer = get_tokenizer(model.is_multilingual) + tokenizer = get_tokenizer(model.is_multilingual, num_languages=model.num_languages) all_tokens = [t for s in result["segments"] for t in s["tokens"]] assert tokenizer.decode(all_tokens) == result["text"] assert tokenizer.decode_with_timestamps(all_tokens).startswith("<|0.00|>") diff --git a/whisper/__init__.py b/whisper/__init__.py index 379133b6a..d7fbba36f 100644 --- a/whisper/__init__.py +++ b/whisper/__init__.py @@ -25,7 +25,8 @@ "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt", "large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt", "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt", - "large": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt", + "large-v3": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt", + "large": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt", } # base85-encoded (n_layers, n_heads) boolean arrays indicating the cross-attention heads that are @@ -41,7 +42,8 @@ "medium": b"ABzY8B0Jh+0{>%R7}kK1fFL7w6%<-Pf*t^=N)Qr&0RR9", "large-v1": b"ABzY8r9j$a0{>%R7#4sLmoOs{s)o3~84-RPdcFk!JR%R7=D0pU<_bnWW*tkYAhobTNnu$jnkEkXqp)j;w1Tzk)UH3X%SZd&fFZ2fC2yj", - "large": b"ABzY8zd+h!0{>%R7=D0pU<_bnWW*tkYAhobTNnu$jnkEkXqp)j;w1Tzk)UH3X%SZd&fFZ2fC2yj", + "large-v3": b"ABzY8gWO1E0{>%R7(9S+Kn!D~%ngiGaR?*L!iJG9p-nab0JQ=-{D1-g00", + "large": b"ABzY8gWO1E0{>%R7(9S+Kn!D~%ngiGaR?*L!iJG9p-nab0JQ=-{D1-g00", } diff --git a/whisper/assets/mel_filters.npz b/whisper/assets/mel_filters.npz index 1a7839244dfb6b1cc02e4f3cfe12e4817a073bc7..28ea26909dbdfd608aef67afc4d74d7961ae4bb6 100644 GIT binary patch literal 4271 zcmZ`-cQjmYw;lx1g6JcN7QKe3LG%_Oh!VX=^k~teM-XGQ(Mu4$_Y%?jkm$lFBkB+( z3yfKIgF zxGiAhze`A@t->QRNVV!%P+W=o}VHkB) 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zW08H)1YX~66@fiwao!NULHf-Qhlc(ls!|>C$JYxGbvYR>IkC8z>4_1w77)u1i9f9j z`CNeL4f{Gch-9M@k!5I!Hqk!FGgytIaq%$Boq$1n;E@2nbt*r`=q!rl13cUd$J{Y87V=(HkNh28)}K{}#t{ zO^{$W0@G9G!_p=W1sPhnz2}4?|5n%-mo0`R_GCLmuf?k_JEupD6BXen#Q{rGEUni- zRs_QRLNpSBred8Kj14BOV0(PO@G2Z8qBE?yvk^6#w5os1IriR2;hB&t{^i{gLvO2a zEW{VCFC+2v_%sYZH3ZAfm>?{%MBE*q6PH4F@fjP@YrCGCi6>#nqQ^gOi)}ewvH$%K za5}>u<@W>e;MRwDRo)Z*w*M-|uFDWr2E)bAG40q7Q6trCi*+K4_lddg^BO torch.Tensor: +def mel_filters(device, n_mels: int) -> torch.Tensor: """ load the mel filterbank matrix for projecting STFT into a Mel spectrogram. Allows decoupling librosa dependency; saved using: @@ -98,9 +97,10 @@ def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor: np.savez_compressed( "mel_filters.npz", mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80), + mel_128=librosa.filters.mel(sr=16000, n_fft=400, n_mels=128), ) """ - assert n_mels == 80, f"Unsupported n_mels: {n_mels}" + assert n_mels in {80, 128}, f"Unsupported n_mels: {n_mels}" filters_path = os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz") with np.load(filters_path, allow_pickle=False) as f: @@ -109,7 +109,7 @@ def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor: def log_mel_spectrogram( audio: Union[str, np.ndarray, torch.Tensor], - n_mels: int = N_MELS, + n_mels: int = 80, padding: int = 0, device: Optional[Union[str, torch.device]] = None, ): diff --git a/whisper/decoding.py b/whisper/decoding.py index ecd98a455..49485d009 100644 --- a/whisper/decoding.py +++ b/whisper/decoding.py @@ -32,7 +32,9 @@ def detect_language( list of dictionaries containing the probability distribution over all languages. """ if tokenizer is None: - tokenizer = get_tokenizer(model.is_multilingual) + tokenizer = get_tokenizer( + model.is_multilingual, num_languages=model.num_languages + ) if ( tokenizer.language is None or tokenizer.language_token not in tokenizer.sot_sequence @@ -514,7 +516,10 @@ def __init__(self, model: "Whisper", options: DecodingOptions): language = options.language or "en" tokenizer = get_tokenizer( - model.is_multilingual, language=language, task=options.task + model.is_multilingual, + num_languages=model.num_languages, + language=language, + task=options.task, ) self.tokenizer: Tokenizer = tokenizer self.options: DecodingOptions = self._verify_options(options) diff --git a/whisper/model.py b/whisper/model.py index 69130022a..a67828397 100644 --- a/whisper/model.py +++ b/whisper/model.py @@ -236,7 +236,8 @@ def __init__(self, dims: ModelDimensions): self.dims.n_text_head, self.dims.n_text_layer, ) - # use the last half layers for alignment by default; see `set_alignment_heads()` below + # use the last half among the decoder layers for time alignment by default; + # to use a specific set of heads, see `set_alignment_heads()` below. all_heads = torch.zeros( self.dims.n_text_layer, self.dims.n_text_head, dtype=torch.bool ) @@ -269,7 +270,11 @@ def device(self): @property def is_multilingual(self): - return self.dims.n_vocab == 51865 + return self.dims.n_vocab >= 51865 + + @property + def num_languages(self): + return self.dims.n_vocab - 51765 - int(self.is_multilingual) def install_kv_cache_hooks(self, cache: Optional[dict] = None): """ diff --git a/whisper/tokenizer.py b/whisper/tokenizer.py index 3b2399184..2af837570 100644 --- a/whisper/tokenizer.py +++ b/whisper/tokenizer.py @@ -107,6 +107,7 @@ "ba": "bashkir", "jw": "javanese", "su": "sundanese", + "yue": "cantonese", } # language code lookup by name, with a few language aliases @@ -123,6 +124,7 @@ "moldovan": "ro", "sinhalese": "si", "castilian": "es", + "mandarin": "zh", } @@ -131,6 +133,7 @@ class Tokenizer: """A thin wrapper around `tiktoken` providing quick access to special tokens""" encoding: tiktoken.Encoding + num_languages: int language: Optional[str] = None task: Optional[str] = None sot_sequence: Tuple[int] = () @@ -145,7 +148,7 @@ def __post_init__(self): translate: int = self.special_tokens["<|translate|>"] transcribe: int = self.special_tokens["<|transcribe|>"] - langs = tuple(LANGUAGES.keys()) + langs = tuple(LANGUAGES.keys())[: self.num_languages] sot_sequence = [sot] if self.language is not None: sot_sequence.append(sot + 1 + langs.index(self.language)) @@ -211,10 +214,13 @@ def language_token(self) -> int: if self.language is None: raise ValueError("This tokenizer does not have language token configured") - if token := self.special_tokens.get(f"<|{self.language}|>", None): + return self.to_language_token(self.language) + + def to_language_token(self, language): + if token := self.special_tokens.get(f"<|{language}|>", None): return token - raise KeyError(f"Language {self.language} not found in tokenizer.") + raise KeyError(f"Language {language} not found in tokenizer.") @cached_property def all_language_tokens(self) -> Tuple[int]: @@ -222,7 +228,7 @@ def all_language_tokens(self) -> Tuple[int]: for token, token_id in self.special_tokens.items(): if token.strip("<|>") in LANGUAGES: result.append(token_id) - return tuple(result) + return tuple(result)[: self.num_languages] @cached_property def all_language_codes(self) -> Tuple[str]: @@ -269,7 +275,7 @@ def non_speech_tokens(self) -> Tuple[int]: return tuple(sorted(result)) def split_to_word_tokens(self, tokens: List[int]): - if self.language in {"zh", "ja", "th", "lo", "my"}: + if self.language in {"zh", "ja", "th", "lo", "my", "yue"}: # These languages don't typically use spaces, so it is difficult to split words # without morpheme analysis. Here, we instead split words at any # position where the tokens are decoded as valid unicode points @@ -322,7 +328,7 @@ def split_tokens_on_spaces(self, tokens: List[int]): @lru_cache(maxsize=None) -def get_encoding(name: str = "gpt2"): +def get_encoding(name: str = "gpt2", num_languages: int = 99): vocab_path = os.path.join(os.path.dirname(__file__), "assets", f"{name}.tiktoken") ranks = { base64.b64decode(token): int(rank) @@ -334,7 +340,7 @@ def get_encoding(name: str = "gpt2"): specials = [ "<|endoftext|>", "<|startoftranscript|>", - *[f"<|{lang}|>" for lang in LANGUAGES.keys()], + *[f"<|{lang}|>" for lang in list(LANGUAGES.keys())[:num_languages]], "<|translate|>", "<|transcribe|>", "<|startoflm|>", @@ -361,6 +367,7 @@ def get_encoding(name: str = "gpt2"): def get_tokenizer( multilingual: bool, *, + num_languages: int = 99, language: Optional[str] = None, task: Optional[str] = None, # Literal["transcribe", "translate", None] ) -> Tokenizer: @@ -381,6 +388,8 @@ def get_tokenizer( language = None task = None - encoding = get_encoding(name=encoding_name) + encoding = get_encoding(name=encoding_name, num_languages=num_languages) - return Tokenizer(encoding=encoding, language=language, task=task) + return Tokenizer( + encoding=encoding, num_languages=num_languages, language=language, task=task + ) diff --git a/whisper/transcribe.py b/whisper/transcribe.py index d5b3d4336..e80bede1d 100644 --- a/whisper/transcribe.py +++ b/whisper/transcribe.py @@ -119,7 +119,7 @@ def transcribe( decode_options["fp16"] = False # Pad 30-seconds of silence to the input audio, for slicing - mel = log_mel_spectrogram(audio, padding=N_SAMPLES) + mel = log_mel_spectrogram(audio, model.dims.n_mels, padding=N_SAMPLES) content_frames = mel.shape[-1] - N_FRAMES if decode_options.get("language", None) is None: @@ -140,7 +140,12 @@ def transcribe( language: str = decode_options["language"] task: str = decode_options.get("task", "transcribe") - tokenizer = get_tokenizer(model.is_multilingual, language=language, task=task) + tokenizer = get_tokenizer( + model.is_multilingual, + num_languages=model.num_languages, + language=language, + task=task, + ) if word_timestamps and task == "translate": warnings.warn("Word-level timestamps on translations may not be reliable.") From fcfeaf1b61994c071bba62da47d7846933576ac9 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Mon, 6 Nov 2023 10:14:04 -0800 Subject: [PATCH 65/69] Release 20231106 --- CHANGELOG.md | 4 ++++ whisper/version.py | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index ea73cf108..cb0908a88 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,9 @@ # CHANGELOG +## [v20231106](https://github.com/openai/whisper/releases/tag/v20231106) + +* large-v3 ([#1761](https://github.com/openai/whisper/pull/1761)) + ## [v20231105](https://github.com/openai/whisper/releases/tag/v20231105) * remove tiktoken pin ([#1759](https://github.com/openai/whisper/pull/1759)) diff --git a/whisper/version.py b/whisper/version.py index cb8531531..4aaccff96 100644 --- a/whisper/version.py +++ b/whisper/version.py @@ -1 +1 @@ -__version__ = "20231105" +__version__ = "20231106" From 1cea4357687b676b293cb5473e1ade25f5b1cef7 Mon Sep 17 00:00:00 2001 From: Eugene Indenbom <45334274+eindenbom@users.noreply.github.com> Date: Mon, 13 Nov 2023 19:43:42 +0200 Subject: [PATCH 66/69] Relax triton requirements for compatibility with pytorch 2.1 and newer (#1802) --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 1161d815a..ae8589e1d 100644 --- a/setup.py +++ b/setup.py @@ -13,7 +13,7 @@ def read_version(fname="whisper/version.py"): requirements = [] if sys.platform.startswith("linux") and platform.machine() == "x86_64": - requirements.append("triton==2.0.0") + requirements.append("triton>=2.0.0,<3") setup( name="openai-whisper", From e58f28804528831904c3b6f2c0e473f346223433 Mon Sep 17 00:00:00 2001 From: Jong Wook Kim Date: Fri, 17 Nov 2023 11:59:28 -0800 Subject: [PATCH 67/69] Release 20231117 --- CHANGELOG.md | 4 ++++ whisper/version.py | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index cb0908a88..58955410f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,9 @@ # CHANGELOG +## [v20231117](https://github.com/openai/whisper/releases/tag/v20231117) + +* Relax triton requirements for compatibility with pytorch 2.1 and newer ([#1802](https://github.com/openai/whisper/pull/1802)) + ## [v20231106](https://github.com/openai/whisper/releases/tag/v20231106) * large-v3 ([#1761](https://github.com/openai/whisper/pull/1761)) diff --git a/whisper/version.py b/whisper/version.py index 4aaccff96..c96dd9ce4 100644 --- a/whisper/version.py +++ b/whisper/version.py @@ -1 +1 @@ -__version__ = "20231106" +__version__ = "20231117" From 8bc8860694949db53c42ba47ddc23786c2e02a8b Mon Sep 17 00:00:00 2001 From: Bob Lin Date: Mon, 11 Dec 2023 23:39:08 +0800 Subject: [PATCH 68/69] Fix triton env marker (#1887) --- requirements.txt | 1 + setup.py | 7 +++---- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/requirements.txt b/requirements.txt index a03dae853..62f5f9d7c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,3 +4,4 @@ torch tqdm more-itertools tiktoken +triton>=2.0.0,<3;platform_machine=="x86_64" and sys_platform=="linux" or sys_platform=="linux2" diff --git a/setup.py b/setup.py index ae8589e1d..183b52755 100644 --- a/setup.py +++ b/setup.py @@ -1,6 +1,6 @@ -import os import platform import sys +from pathlib import Path import pkg_resources from setuptools import find_packages, setup @@ -28,11 +28,10 @@ def read_version(fname="whisper/version.py"): url="https://github.com/openai/whisper", license="MIT", packages=find_packages(exclude=["tests*"]), - install_requires=requirements - + [ + install_requires=[ str(r) for r in pkg_resources.parse_requirements( - open(os.path.join(os.path.dirname(__file__), "requirements.txt")) + Path(__file__).with_name("requirements.txt").open() ) ], entry_points={ From ba3f3cd54b0e5b8ce1ab3de13e32122d0d5f98ab Mon Sep 17 00:00:00 2001 From: ryanheise Date: Tue, 19 Dec 2023 07:11:16 +1100 Subject: [PATCH 69/69] Skip silence around hallucinations (#1838) * Add clip_timestamps option * Add hallucination_silence_threshold option * Fix typing for python < 3.9 --------- Co-authored-by: Jong Wook Kim --- whisper/timing.py | 1 + whisper/transcribe.py | 151 +++++++++++++++++++++++++++++++++++++----- whisper/utils.py | 20 +++++- 3 files changed, 153 insertions(+), 19 deletions(-) diff --git a/whisper/timing.py b/whisper/timing.py index befcf464e..b695ead0a 100644 --- a/whisper/timing.py +++ b/whisper/timing.py @@ -299,6 +299,7 @@ def add_word_timestamps( word_durations = np.array([t.end - t.start for t in alignment]) word_durations = word_durations[word_durations.nonzero()] median_duration = np.median(word_durations) if len(word_durations) > 0 else 0.0 + median_duration = min(0.7, float(median_duration)) max_duration = median_duration * 2 # hack: truncate long words at sentence boundaries. diff --git a/whisper/transcribe.py b/whisper/transcribe.py index e80bede1d..1c075a201 100644 --- a/whisper/transcribe.py +++ b/whisper/transcribe.py @@ -2,7 +2,7 @@ import os import traceback import warnings -from typing import TYPE_CHECKING, Optional, Tuple, Union +from typing import TYPE_CHECKING, List, Optional, Tuple, Union import numpy as np import torch @@ -23,6 +23,7 @@ from .utils import ( exact_div, format_timestamp, + get_end, get_writer, make_safe, optional_float, @@ -48,6 +49,8 @@ def transcribe( word_timestamps: bool = False, prepend_punctuations: str = "\"'“¿([{-", append_punctuations: str = "\"'.。,，!！?？:：”)]}、", + clip_timestamps: Union[str, List[float]] = "0", + hallucination_silence_threshold: Optional[float] = None, **decode_options, ): """ @@ -102,6 +105,14 @@ def transcribe( decode_options: dict Keyword arguments to construct `DecodingOptions` instances + clip_timestamps: Union[str, List[float]] + Comma-separated list start,end,start,end,... timestamps (in seconds) of clips to process. + The last end timestamp defaults to the end of the file. + + hallucination_silence_threshold: Optional[float] + When word_timestamps is True, skip silent periods longer than this threshold (in seconds) + when a possible hallucination is detected + Returns ------- A dictionary containing the resulting text ("text") and segment-level details ("segments"), and @@ -121,6 +132,7 @@ def transcribe( # Pad 30-seconds of silence to the input audio, for slicing mel = log_mel_spectrogram(audio, model.dims.n_mels, padding=N_SAMPLES) content_frames = mel.shape[-1] - N_FRAMES + content_duration = float(content_frames * HOP_LENGTH / SAMPLE_RATE) if decode_options.get("language", None) is None: if not model.is_multilingual: @@ -147,6 +159,19 @@ def transcribe( task=task, ) + if isinstance(clip_timestamps, str): + clip_timestamps = [ + float(ts) for ts in (clip_timestamps.split(",") if clip_timestamps else []) + ] + seek_points: List[int] = [round(ts * FRAMES_PER_SECOND) for ts in clip_timestamps] + if len(seek_points) == 0: + seek_points.append(0) + if len(seek_points) % 2 == 1: + seek_points.append(content_frames) + seek_clips: List[Tuple[int, int]] = list(zip(seek_points[::2], seek_points[1::2])) + + punctuation = "\"'“¿([{-\"'.。,，!！?？:：”)]}、" + if word_timestamps and task == "translate": warnings.warn("Word-level timestamps on translations may not be reliable.") @@ -190,7 +215,8 @@ def decode_with_fallback(segment: torch.Tensor) -> DecodingResult: return decode_result - seek = 0 + clip_idx = 0 + seek = seek_clips[clip_idx][0] input_stride = exact_div( N_FRAMES, model.dims.n_audio_ctx ) # mel frames per output token: 2 @@ -229,10 +255,23 @@ def new_segment( total=content_frames, unit="frames", disable=verbose is not False ) as pbar: last_speech_timestamp = 0.0 - while seek < content_frames: + # NOTE: This loop is obscurely flattened to make the diff readable. + # A later commit should turn this into a simpler nested loop. + # for seek_clip_start, seek_clip_end in seek_clips: + # while seek < seek_clip_end + while clip_idx < len(seek_clips): + seek_clip_start, seek_clip_end = seek_clips[clip_idx] + if seek < seek_clip_start: + seek = seek_clip_start + if seek >= seek_clip_end: + clip_idx += 1 + if clip_idx < len(seek_clips): + seek = seek_clips[clip_idx][0] + continue time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE) - mel_segment = mel[:, seek : seek + N_FRAMES] - segment_size = min(N_FRAMES, content_frames - seek) + window_end_time = float((seek + N_FRAMES) * HOP_LENGTH / SAMPLE_RATE) + segment_size = min(N_FRAMES, content_frames - seek, seek_clip_end - seek) + mel_segment = mel[:, seek : seek + segment_size] segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE mel_segment = pad_or_trim(mel_segment, N_FRAMES).to(model.device).to(dtype) @@ -257,6 +296,30 @@ def new_segment( previous_seek = seek current_segments = [] + # anomalous words are very long/short/improbable + def word_anomaly_score(word: dict) -> float: + probability = word.get("probability", 0.0) + duration = word["end"] - word["start"] + score = 0.0 + if probability < 0.15: + score += 1.0 + if duration < 0.133: + score += (0.133 - duration) * 15 + if duration > 2.0: + score += duration - 2.0 + return score + + def is_segment_anomaly(segment: Optional[dict]) -> bool: + if segment is None or not segment["words"]: + return False + words = [w for w in segment["words"] if w["word"] not in punctuation] + words = words[:8] + score = sum(word_anomaly_score(w) for w in words) + return score >= 3 or score + 0.01 >= len(words) + + def next_words_segment(segments: List[dict]) -> Optional[dict]: + return next((s for s in segments if s["words"]), None) + timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin) single_timestamp_ending = timestamp_tokens[-2:].tolist() == [False, True] @@ -330,17 +393,71 @@ def new_segment( append_punctuations=append_punctuations, last_speech_timestamp=last_speech_timestamp, ) - word_end_timestamps = [ - w["end"] for s in current_segments for w in s["words"] - ] - if len(word_end_timestamps) > 0: - last_speech_timestamp = word_end_timestamps[-1] - if not single_timestamp_ending and len(word_end_timestamps) > 0: - seek_shift = round( - (word_end_timestamps[-1] - time_offset) * FRAMES_PER_SECOND - ) - if seek_shift > 0: - seek = previous_seek + seek_shift + + if not single_timestamp_ending: + last_word_end = get_end(current_segments) + if last_word_end is not None and last_word_end > time_offset: + seek = round(last_word_end * FRAMES_PER_SECOND) + + # skip silence before possible hallucinations + if hallucination_silence_threshold is not None: + threshold = hallucination_silence_threshold + if not single_timestamp_ending: + last_word_end = get_end(current_segments) + if last_word_end is not None and last_word_end > time_offset: + remaining_duration = window_end_time - last_word_end + if remaining_duration > threshold: + seek = round(last_word_end * FRAMES_PER_SECOND) + else: + seek = previous_seek + segment_size + + # if first segment might be a hallucination, skip leading silence + first_segment = next_words_segment(current_segments) + if first_segment is not None and is_segment_anomaly(first_segment): + gap = first_segment["start"] - time_offset + if gap > threshold: + seek = previous_seek + round(gap * FRAMES_PER_SECOND) + continue + + # skip silence before any possible hallucination that is surrounded + # by silence or more hallucinations + hal_last_end = last_speech_timestamp + for si in range(len(current_segments)): + segment = current_segments[si] + if not segment["words"]: + continue + if is_segment_anomaly(segment): + next_segment = next_words_segment( + current_segments[si + 1 :] + ) + if next_segment is not None: + hal_next_start = next_segment["words"][0]["start"] + else: + hal_next_start = time_offset + segment_duration + silence_before = ( + segment["start"] - hal_last_end > threshold + or segment["start"] < threshold + or segment["start"] - time_offset < 2.0 + ) + silence_after = ( + hal_next_start - segment["end"] > threshold + or is_segment_anomaly(next_segment) + or window_end_time - segment["end"] < 2.0 + ) + if silence_before and silence_after: + seek = round( + max(time_offset + 1, segment["start"]) + * FRAMES_PER_SECOND + ) + if content_duration - segment["end"] < threshold: + seek = content_frames + current_segments[si:] = [] + break + hal_last_end = segment["end"] + + last_word_end = get_end(current_segments) + if last_word_end is not None: + last_speech_timestamp = last_word_end if verbose: for segment in current_segments: @@ -427,6 +544,8 @@ def valid_model_name(name): parser.add_argument("--max_line_count", type=optional_int, default=None, help="(requires --word_timestamps True) the maximum number of lines in a segment") parser.add_argument("--max_words_per_line", type=optional_int, default=None, help="(requires --word_timestamps True, no effect with --max_line_width) the maximum number of words in a segment") parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS") + parser.add_argument("--clip_timestamps", type=str, default="0", help="comma-separated list start,end,start,end,... timestamps (in seconds) of clips to process, where the last end timestamp defaults to the end of the file") + parser.add_argument("--hallucination_silence_threshold", type=optional_float, help="(requires --word_timestamps True) skip silent periods longer than this threshold (in seconds) when a possible hallucination is detected") # fmt: on args = parser.parse_args().__dict__ diff --git a/whisper/utils.py b/whisper/utils.py index 7a172c401..9b9b13862 100644 --- a/whisper/utils.py +++ b/whisper/utils.py @@ -3,7 +3,7 @@ import re import sys import zlib -from typing import Callable, Optional, TextIO +from typing import Callable, List, Optional, TextIO system_encoding = sys.getdefaultencoding() @@ -68,6 +68,20 @@ def format_timestamp( ) +def get_start(segments: List[dict]) -> Optional[float]: + return next( + (w["start"] for s in segments for w in s["words"]), + segments[0]["start"] if segments else None, + ) + + +def get_end(segments: List[dict]) -> Optional[float]: + return next( + (w["end"] for s in reversed(segments) for w in reversed(s["words"])), + segments[-1]["end"] if segments else None, + ) + + class ResultWriter: extension: str @@ -129,8 +143,8 @@ def iterate_subtitles(): line_len = 0 line_count = 1 # the next subtitle to yield (a list of word timings with whitespace) - subtitle: list[dict] = [] - last = result["segments"][0]["words"][0]["start"] + subtitle: List[dict] = [] + last: float = get_start(result["segments"]) or 0.0 for segment in result["segments"]: chunk_index = 0 words_count = max_words_per_line