Medix-AI/coqui-tts
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coqui-tts
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Update librispeech deepspeech recipe

This commit is contained in:
Eren Gölge 2021-10-01 13:34:00 +00:00
parent 3aaf6a28e9
commit 42f77e7185
1 changed files with 20 additions and 16 deletions
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36
recipes/librispeech/stt/deep_speech/train_deep_speech.py
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@ -13,21 +13,27 @@ output_path = os.path.dirname(os.path.abspath(__file__))
if not os.path.exists("/home/ubuntu/librispeech/LibriSpeech/train-clean-100"):
download_librispeech("/home/ubuntu/librispeech/", "train-clean-100")
if not os.path.exists("/home/ubuntu/librispeech/LibriSpeech/train-clean-360"):
download_librispeech("/home/ubuntu/librispeech/", "train-clean-360")
if not os.path.exists("/home/ubuntu/librispeech/LibriSpeech/train-other-500"):
download_librispeech("/home/ubuntu/librispeech/", "train-other-500")
if not os.path.exists("/home/ubuntu/librispeech/LibriSpeech/dev-clean"):
download_librispeech("/home/ubuntu/librispeech/", "dev-clean")
# train_dataset_config = BaseDatasetConfig(
# name="librispeech", meta_file_train=None, path="/home/ubuntu/librispeech/LibriSpeech/train-clean-100"
# )
train_dataset_config1 = BaseDatasetConfig(
name="librispeech", meta_file_train=None, path="/home/ubuntu/librispeech/LibriSpeech/train-clean-100"
)
# eval_dataset_config = BaseDatasetConfig(
# name="librispeech", meta_file_train=None, path="/home/ubuntu/librispeech/LibriSpeech/dev-clean"
# )
train_dataset_config2 = BaseDatasetConfig(
name="librispeech", meta_file_train=None, path="/home/ubuntu/librispeech/LibriSpeech/train-clean-360"
)
train_dataset_config = BaseDatasetConfig(
name="ljspeech",
meta_file_train="metadata.csv",
path="/home/ubuntu/ljspeech/LJSpeech-1.1/",
train_dataset_config3 = BaseDatasetConfig(
name="librispeech", meta_file_train=None, path="/home/ubuntu/librispeech/LibriSpeech/train-other-500"
)
eval_dataset_config = BaseDatasetConfig(
name="librispeech", meta_file_train=None, path="/home/ubuntu/librispeech/LibriSpeech/dev-clean"
)
@ -59,16 +65,16 @@ config = DeepSpeechConfig(
mixed_precision=True,
max_seq_len=500000,
output_path=output_path,
train_datasets=[train_dataset_config],
# eval_datasets=[eval_dataset_config],
train_datasets=[train_dataset_config1, train_dataset_config2, train_dataset_config3],
eval_datasets=[eval_dataset_config]
)
# init audio processor
ap = AudioProcessor(**config.audio.to_dict())
# load training samples
train_samples, eval_samples = load_stt_samples(train_dataset_config, eval_split=True)
# eval_samples, _ = load_stt_samples(eval_dataset_config, eval_split=False)
train_samples, _ = load_stt_samples(config.train_datasets, eval_split=False)
eval_samples, _ = load_stt_samples(config.eval_datasets, eval_split=False)
transcripts = [s["text"] for s in train_samples]
# init tokenizer
@ -81,13 +87,11 @@ config.vocabulary = tokenizer.vocab_dict
model = DeepSpeech(config)
# init training and kick it 🚀
# args, config, output_path, _, c_logger, tb_logger = init_training(TrainingArgs(), config)
trainer = Trainer(
TrainingArgs(),
config,
output_path,
model=model,
tokenizer=tokenizer,
train_samples=train_samples,
eval_samples=eval_samples,
cudnn_benchmark=False,