gwelliannau/improvements & Common Voice 8

.dockerignore

@@ -0,0 +1,2 @@
+models
+homedir

README.md

@@ -1,6 +1,8 @@
 
+
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5270295.svg)](https://doi.org/10.5281/zenodo.5270295)
 
+
 # docker-wav2vec2-xlsr-ft-cy
 
 [(click here to read the README in English)](README_en.md)

inference/.dockerignore

@@ -1,2 +1,3 @@
 models
-recordings
+recordings
+data

inference/.gitignore

@@ -1,2 +1,3 @@
+data
 models
-recordings
+recordings

inference/Makefile

@@ -1,32 +1,54 @@
 default: build
 
+$(eval DEVICE = cpu)
+#$(eval DEVICE = gpu)
 
 config:
+ # to use a local model, provide the full /models/.... path for WAV2VEC2_MODEL_NAME and 
+ # leave the MODEL_VERSION blank empty string.
  $(eval WAV2VEC2_MODEL_NAME = techiaith/wav2vec2-xlsr-ft-cy)
  $(eval MODEL_VERSION = 21.08)
+ mkdir -p ${PWD}/data/
 
 
 build: config
- docker build --rm -t techiaith/wav2vec2-xlsr-ft-cy \
+ docker build --rm -t techiaith/wav2vec2-xlsr-ft-cy-${USER} \
  --build-arg WAV2VEC2_MODEL_NAME=${WAV2VEC2_MODEL_NAME} \
  --build-arg MODEL_VERSION=${MODEL_VERSION} \
  .
 
+run: config run-${DEVICE}
 
-run: config
- mkdir -p ${PWD}/recordings/
- docker run --name techiaith-wav2vec2-xlsr-ft-cy \
+run-gpu:
+ docker run --gpus all --name techiaith-wav2vec2-xlsr-ft-cy-${USER} \
  --restart=always \
  -it \
  -v ${PWD}/models/:/models \
- -v ${PWD}/recordings/:/recordings \
- techiaith/wav2vec2-xlsr-ft-cy
+ -v ${PWD}/data/:/data \
+ techiaith/wav2vec2-xlsr-ft-cy-${USER}
 
+run-cpu:
+ docker run --name techiaith-wav2vec2-xlsr-ft-cy-${USER} \
+ --restart=always \
+ -it \
+ -v ${PWD}/models/:/models \
+ -v ${PWD}/data/:/data \
+ techiaith/wav2vec2-xlsr-ft-cy-${USER}
+
+
+fetch-test:
+ if [ ! -d "data/corpws-profi-adnabod-lleferydd" ]; then \
+ mkdir -p data; \
+ cd data && git clone -b fersiwn2 --single-branch https://git.techiaith.bangor.ac.uk/data-porth-technolegau-iaith/corpws-profi-adnabod-lleferydd.git; \
+ fi
 
 stop: config
- -docker stop techiaith-wav2vec2-xlsr-ft-cy
- -docker rm techiaith-wav2vec2-xlsr-ft-cy
+ -docker stop techiaith-wav2vec2-xlsr-ft-cy-${USER}
+ -docker rm techiaith-wav2vec2-xlsr-ft-cy-${USER}
 
 
 clean: config stop
- -docker rmi techiaith/wav2vec2-xlsr-ft-cy
+ -docker rmi techiaith/wav2vec2-xlsr-ft-cy-${USER}
+
+purge: clean
+ sudo rm -rf models

inference/python/models.py

@@ -1,11 +1,14 @@
 import os
+import yaml
 import tarfile
 import urllib.request
 from urllib.parse import urlparse
 
 from pathlib import Path
 from tqdm import tqdm
 
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+from ctcdecode import CTCBeamDecoder
 
 
 class DownloadProgressBar(tqdm):
@@ -14,40 +17,90 @@ def update_to(self, b=1, bsize=1, tsize=None):
  self.total = tsize
  self.update(b * bsize - self.n)
 
-
-def default_root_dir():
- return os.path.join("/", "models")
-
+
+def create(model_path, revision):
 
-def download_file(models_root_dir, model_name, version, file_name):
- # expecting model name as HuggingFace Model name e.g. techiaith/wav2vec2-xlsr-ft-cy
- # expecting file name within the HuggingFace model git repository e.g. kenlm.tar.gz
+ cache_dir=model_path
 
- model_file = os.path.join(models_root_dir, file_name)
-
- if Path(model_file).is_file():
- print ("Model file {} already downloaded.".format(model_file))
+ # initialize acoustic model...
+ #
+ if Path(model_path).is_dir():
+ # from a local directory containing our own trained model
+ print("Initiaising wav2vec2 model from local directory: %s" % model_path)
+ processor = Wav2Vec2Processor.from_pretrained(model_path)
+ model = Wav2Vec2ForCTC.from_pretrained(model_path)
  else:
- print ("Downloading {} version {}".format(file_name, version))
- Path(models_root_dir).mkdir(parents=True, exist_ok=True)
-
- file_url = os.path.join("https://huggingface.co", model_name, "resolve", version, file_name)
- download_and_extract(file_url, model_file)
+ # from the HuggingFace models repository. 
+ print("Initialising wav2vec2 model \"%s\" from HuggingFace model repository" % model_path)
+ cache_dir = os.path.join('/models', model_path)
+ processor = Wav2Vec2Processor.from_pretrained(model_path, cache_dir=cache_dir, revision=revision)
+ model = Wav2Vec2ForCTC.from_pretrained(model_path, cache_dir=cache_dir, revision=revision)
+
+ # initialize language model...
+ #
+ targz_file_path=os.path.join(cache_dir, "kenlm.tar.gz")
+ if not Path(targz_file_path).is_file():
+ print ("Downloading kenlm language model version {}".format(revision))
+ try:
+ file_url = os.path.join("https://huggingface.co", model_path, "resolve", revision, 'kenlm.tar.gz')
+ download(file_url, os.path.join(cache_dir, targz_file_path))
+ except Exception as e:
+ print (e)
+
+ if not Path(os.path.join(cache_dir, "config_ctc.yaml")).is_file():
+ if Path(targz_file_path).is_file():
+ extract(targz_file_path)
 
- return models_root_dir
+ if Path(os.path.join(cache_dir, "config_ctc.yaml")).is_file():
+ with open(os.path.join(cache_dir, "config_ctc.yaml"), 'r') as config_file:
+ ctc_lm_params=yaml.load(config_file, Loader=yaml.FullLoader)
+
+ #
+ vocab=processor.tokenizer.convert_ids_to_tokens(range(0, processor.tokenizer.vocab_size))
+ space_ix = vocab.index('|')
+ vocab[space_ix]=' '
 
+ ctcdecoder = CTCBeamDecoder(vocab, 
+ model_path='', 
+ alpha=0,
+ beta=0,
+ cutoff_top_n=40,
+ cutoff_prob=1.0,
+ beam_width=100,
+ num_processes=4,
+ blank_id=processor.tokenizer.pad_token_id,
+ log_probs_input=True
+ )
 
-def download_and_extract(file_url, output_file_path):
+ kenlm_ctcdecoder=None
+ if Path(os.path.join(cache_dir, "lm.binary")).is_file(): 
+ if ctc_lm_params:
+ print ("Initializing KenLM language model...")
+ kenlm_ctcdecoder = CTCBeamDecoder(vocab,
+ model_path=os.path.join(cache_dir, "lm.binary"),
+ alpha=ctc_lm_params['alpha'],
+ beta=ctc_lm_params['beta'],
+ cutoff_top_n=40,
+ cutoff_prob=1.0,
+ beam_width=100,
+ num_processes=4,
+ blank_id=processor.tokenizer.pad_token_id,
+ log_probs_input=True
+ )
 
- #
+ return processor, model, vocab, ctcdecoder, kenlm_ctcdecoder
+
+
+def download(file_url, output_file_path):
  with DownloadProgressBar(unit='B', unit_scale=True, miniters=1, desc=file_url.split('/')[-1]) as t:
  urllib.request.urlretrieve(file_url, filename=output_file_path, reporthook=t.update_to)
 
+def extract(targz_file_path):
  # extract.
- if output_file_path.endswith(".tar.gz"):
+ if targz_file_path.endswith(".tar.gz"):
  print ("Extracting...")
- model_dir = Path(output_file_path).parent.absolute()
- tar = tarfile.open(output_file_path, "r:gz")
+ model_dir = Path(targz_file_path).parent.absolute()
+ tar = tarfile.open(targz_file_path, "r:gz")
  tar.extractall(model_dir)
  tar.close()
 

inference/python/requirements.txt

@@ -3,11 +3,16 @@ numpy
 wave
 jiwer
 webrtcvad
-transformers
+transformers==4.9.2
 tqdm==4.61.0
 pyyaml==5.4.1
 torchaudio==0.7.2
 torch==1.7.1
+datasets
 librosa
 srt
-praatio<5
+praatio<5
+pydub
+pandas
+python_speech_features
+scipy