drop python 3.7 support (#889)

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

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

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)