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coqui-tts
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Add M-AI-labs preprocessor and set config for en_UK

This commit is contained in:
Eren Golge 2019-01-14 17:29:14 +01:00
parent f4fa155cd3
commit 7af1aeaf7a
2 changed files with 27 additions and 5 deletions
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10
config.json
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@ -43,13 +43,13 @@
"tb_model_param_stats": true, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
"run_eval": true,
"data_path": "../../Data/LJSpeech-1.1/", // DATASET-RELATED: can overwritten from command argument
"meta_file_train": "transcript_train.txt", // DATASET-RELATED: metafile for training dataloader.
"meta_file_val": "transcript_val.txt", // DATASET-RELATED: metafile for evaluation dataloader.
"dataset": "tweb", // DATASET-RELATED: one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py
"data_path": "/home/erogol/Data/en_UK/by_book/female/elizabeth_klett/", // DATASET-RELATED: can overwritten from command argument
"meta_file_train": "jane_eyre/metadata_train.csv, wives_and_daughters/metadata_train.csv", // DATASET-RELATED: metafile for training dataloader.
"meta_file_val": "jane_eyre/metadata_val.csv, wives_and_daughters/metadata_val.csv", // DATASET-RELATED: metafile for evaluation dataloader.
"dataset": "mailabs", // DATASET-RELATED: one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py
"min_seq_len": 0, // DATASET-RELATED: minimum text length to use in training
"max_seq_len": 300, // DATASET-RELATED: maximum text length
"output_path": "/media/erogol/data_ssd/Data/models/tweb_models/", // DATASET-RELATED: output path for all training outputs.
"output_path": "/media/erogol/data_ssd/Data/models/en_UK/", // DATASET-RELATED: output path for all training outputs.
"num_loader_workers": 8, // number of training data loader processes. Don't set it too big. 4-8 are good values.
"num_val_loader_workers": 4 // number of evaluation data loader processes.
}

22
datasets/preprocess.py
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@ -42,6 +42,28 @@ def tweb(root_path, meta_file):
# return {'text': texts, 'wavs': wavs}
def mailabs(root_path, meta_files):
"""Normalizes M-AI-Labs meta data files to TTS format"""
folders = [os.path.dirname(f.strip()) for f in meta_files.split(",")]
meta_files = [f.strip() for f in meta_files.split(",")]
items = []
for idx, meta_file in enumerate(meta_files):
print(" | > {}".format(meta_file))
folder = folders[idx]
txt_file = os.path.join(root_path, meta_file)
with open(txt_file, 'r') as ttf:
for line in ttf:
cols = line.split('|')
wav_file = os.path.join(root_path, folder, 'wavs', cols[0]+'.wav')
if os.path.isfile(wav_file):
text = cols[1]
items.append([text, wav_file])
else:
continue
random.shuffle(items)
return items
def ljspeech(root_path, meta_file):
"""Normalizes the Nancy meta data file to TTS format"""
txt_file = os.path.join(root_path, meta_file)