mirror of https://github.com/coqui-ai/TTS.git
Add YourTTS VCTK recipe (#2198)
* Add YourTTS VCTK recipe * Fix lint * Add compute_embeddings and resample_files functions to be able to reuse it * Add automatic download and speaker embedding computation for YourTTS VCTK recipe * Add parameter for eval metadata file on compute embeddings function
This commit is contained in:
Edresson Casanova
GitHub
parent
3b8b105b0d
commit
3b1a28fa95
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@ -11,121 +11,162 @@ from TTS.tts.datasets import load_tts_samples
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from TTS.tts.utils.managers import save_file
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from TTS.tts.utils.speakers import SpeakerManager
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parser = argparse.ArgumentParser(
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description="""Compute embedding vectors for each audio file in a dataset and store them keyed by `{dataset_name}#{file_path}` in a .pth file\n\n"""
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"""
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Example runs:
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python TTS/bin/compute_embeddings.py --model_path speaker_encoder_model.pth --config_path speaker_encoder_config.json --config_dataset_path dataset_config.json
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python TTS/bin/compute_embeddings.py --model_path speaker_encoder_model.pth --config_path speaker_encoder_config.json --fomatter vctk --dataset_path /path/to/vctk/dataset --dataset_name my_vctk --metafile /path/to/vctk/metafile.csv
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""",
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formatter_class=RawTextHelpFormatter,
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)
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parser.add_argument(
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"--model_path",
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type=str,
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help="Path to model checkpoint file. It defaults to the released speaker encoder.",
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default="https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/model_se.pth.tar",
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)
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parser.add_argument(
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"--config_path",
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type=str,
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help="Path to model config file. It defaults to the released speaker encoder config.",
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default="https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/config_se.json",
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)
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parser.add_argument(
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"--config_dataset_path",
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type=str,
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help="Path to dataset config file. You either need to provide this or `formatter_name`, `dataset_name` and `dataset_path` arguments.",
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default=None,
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)
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parser.add_argument("--output_path", type=str, help="Path for output `pth` or `json` file.", default="speakers.pth")
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parser.add_argument("--old_file", type=str, help="Previous embedding file to only compute new audios.", default=None)
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parser.add_argument("--disable_cuda", type=bool, help="Flag to disable cuda.", default=False)
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parser.add_argument("--no_eval", type=bool, help="Do not compute eval?. Default False", default=False)
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parser.add_argument(
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"--formatter_name",
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type=str,
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help="Name of the formatter to use. You either need to provide this or `config_dataset_path`",
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default=None,
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)
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parser.add_argument(
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"--dataset_name",
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type=str,
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help="Name of the dataset to use. You either need to provide this or `config_dataset_path`",
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default=None,
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)
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parser.add_argument(
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"--dataset_path",
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type=str,
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help="Path to the dataset. You either need to provide this or `config_dataset_path`",
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default=None,
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)
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parser.add_argument(
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"--metafile",
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type=str,
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help="Path to the meta file. If not set, dataset formatter uses the default metafile if it is defined in the formatter. You either need to provide this or `config_dataset_path`",
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default=None,
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)
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args = parser.parse_args()
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def compute_embeddings(
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model_path,
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config_path,
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output_path,
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old_spakers_file=None,
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config_dataset_path=None,
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formatter_name=None,
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dataset_name=None,
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dataset_path=None,
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meta_file_train=None,
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meta_file_val=None,
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disable_cuda=False,
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no_eval=False,
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):
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use_cuda = torch.cuda.is_available() and not disable_cuda
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use_cuda = torch.cuda.is_available() and not args.disable_cuda
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if args.config_dataset_path is not None:
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c_dataset = load_config(args.config_dataset_path)
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meta_data_train, meta_data_eval = load_tts_samples(c_dataset.datasets, eval_split=not args.no_eval)
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else:
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c_dataset = BaseDatasetConfig()
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c_dataset.formatter = args.formatter_name
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c_dataset.dataset_name = args.dataset_name
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c_dataset.path = args.dataset_path
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c_dataset.meta_file_train = args.metafile if args.metafile else None
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meta_data_train, meta_data_eval = load_tts_samples(c_dataset, eval_split=not args.no_eval)
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if meta_data_eval is None:
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samples = meta_data_train
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else:
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samples = meta_data_train + meta_data_eval
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encoder_manager = SpeakerManager(
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encoder_model_path=args.model_path,
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encoder_config_path=args.config_path,
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d_vectors_file_path=args.old_file,
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use_cuda=use_cuda,
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)
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class_name_key = encoder_manager.encoder_config.class_name_key
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# compute speaker embeddings
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speaker_mapping = {}
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for idx, fields in enumerate(tqdm(samples)):
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class_name = fields[class_name_key]
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audio_file = fields["audio_file"]
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embedding_key = fields["audio_unique_name"]
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root_path = fields["root_path"]
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if args.old_file is not None and embedding_key in encoder_manager.clip_ids:
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# get the embedding from the old file
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embedd = encoder_manager.get_embedding_by_clip(embedding_key)
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if config_dataset_path is not None:
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c_dataset = load_config(config_dataset_path)
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meta_data_train, meta_data_eval = load_tts_samples(c_dataset.datasets, eval_split=not no_eval)
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else:
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# extract the embedding
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embedd = encoder_manager.compute_embedding_from_clip(audio_file)
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c_dataset = BaseDatasetConfig()
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c_dataset.formatter = formatter_name
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c_dataset.dataset_name = dataset_name
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c_dataset.path = dataset_path
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if meta_file_train is not None:
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c_dataset.meta_file_train = meta_file_train
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if meta_file_val is not None:
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c_dataset.meta_file_val = meta_file_val
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meta_data_train, meta_data_eval = load_tts_samples(c_dataset, eval_split=not no_eval)
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# create speaker_mapping if target dataset is defined
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speaker_mapping[embedding_key] = {}
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speaker_mapping[embedding_key]["name"] = class_name
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speaker_mapping[embedding_key]["embedding"] = embedd
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if speaker_mapping:
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# save speaker_mapping if target dataset is defined
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if os.path.isdir(args.output_path):
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mapping_file_path = os.path.join(args.output_path, "speakers.pth")
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if meta_data_eval is None:
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samples = meta_data_train
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else:
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mapping_file_path = args.output_path
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samples = meta_data_train + meta_data_eval
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if os.path.dirname(mapping_file_path) != "":
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os.makedirs(os.path.dirname(mapping_file_path), exist_ok=True)
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encoder_manager = SpeakerManager(
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encoder_model_path=model_path,
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encoder_config_path=config_path,
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d_vectors_file_path=old_spakers_file,
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use_cuda=use_cuda,
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)
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save_file(speaker_mapping, mapping_file_path)
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print("Speaker embeddings saved at:", mapping_file_path)
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class_name_key = encoder_manager.encoder_config.class_name_key
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# compute speaker embeddings
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speaker_mapping = {}
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for fields in tqdm(samples):
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class_name = fields[class_name_key]
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audio_file = fields["audio_file"]
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embedding_key = fields["audio_unique_name"]
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if old_spakers_file is not None and embedding_key in encoder_manager.clip_ids:
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# get the embedding from the old file
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embedd = encoder_manager.get_embedding_by_clip(embedding_key)
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else:
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# extract the embedding
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embedd = encoder_manager.compute_embedding_from_clip(audio_file)
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# create speaker_mapping if target dataset is defined
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speaker_mapping[embedding_key] = {}
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speaker_mapping[embedding_key]["name"] = class_name
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speaker_mapping[embedding_key]["embedding"] = embedd
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if speaker_mapping:
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# save speaker_mapping if target dataset is defined
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if os.path.isdir(output_path):
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mapping_file_path = os.path.join(output_path, "speakers.pth")
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else:
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mapping_file_path = output_path
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if os.path.dirname(mapping_file_path) != "":
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os.makedirs(os.path.dirname(mapping_file_path), exist_ok=True)
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save_file(speaker_mapping, mapping_file_path)
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print("Speaker embeddings saved at:", mapping_file_path)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="""Compute embedding vectors for each audio file in a dataset and store them keyed by `{dataset_name}#{file_path}` in a .pth file\n\n"""
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"""
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Example runs:
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python TTS/bin/compute_embeddings.py --model_path speaker_encoder_model.pth --config_path speaker_encoder_config.json --config_dataset_path dataset_config.json
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python TTS/bin/compute_embeddings.py --model_path speaker_encoder_model.pth --config_path speaker_encoder_config.json --formatter_name coqui --dataset_path /path/to/vctk/dataset --dataset_name my_vctk --meta_file_train /path/to/vctk/metafile_train.csv --meta_file_val /path/to/vctk/metafile_eval.csv
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""",
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formatter_class=RawTextHelpFormatter,
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)
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parser.add_argument(
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"--model_path",
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type=str,
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help="Path to model checkpoint file. It defaults to the released speaker encoder.",
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default="https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/model_se.pth.tar",
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)
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parser.add_argument(
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"--config_path",
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type=str,
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help="Path to model config file. It defaults to the released speaker encoder config.",
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default="https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/config_se.json",
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)
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parser.add_argument(
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"--config_dataset_path",
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type=str,
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help="Path to dataset config file. You either need to provide this or `formatter_name`, `dataset_name` and `dataset_path` arguments.",
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default=None,
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)
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parser.add_argument("--output_path", type=str, help="Path for output `pth` or `json` file.", default="speakers.pth")
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parser.add_argument(
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"--old_file", type=str, help="Previous embedding file to only compute new audios.", default=None
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)
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parser.add_argument("--disable_cuda", type=bool, help="Flag to disable cuda.", default=False)
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parser.add_argument("--no_eval", type=bool, help="Do not compute eval?. Default False", default=False)
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parser.add_argument(
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"--formatter_name",
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type=str,
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help="Name of the formatter to use. You either need to provide this or `config_dataset_path`",
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default=None,
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)
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parser.add_argument(
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"--dataset_name",
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type=str,
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help="Name of the dataset to use. You either need to provide this or `config_dataset_path`",
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default=None,
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)
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parser.add_argument(
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"--dataset_path",
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type=str,
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help="Path to the dataset. You either need to provide this or `config_dataset_path`",
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default=None,
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)
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parser.add_argument(
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"--meta_file_train",
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type=str,
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help="Path to the train meta file. If not set, dataset formatter uses the default metafile if it is defined in the formatter. You either need to provide this or `config_dataset_path`",
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default=None,
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)
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parser.add_argument(
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"--meta_file_val",
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type=str,
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help="Path to the evaluation meta file. If not set, dataset formatter uses the default metafile if it is defined in the formatter. You either need to provide this or `config_dataset_path`",
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default=None,
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)
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args = parser.parse_args()
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compute_embeddings(
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args.model_path,
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args.config_path,
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args.output_path,
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old_spakers_file=args.old_file,
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config_dataset_path=args.config_dataset_path,
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formatter_name=args.formatter_name,
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dataset_name=args.dataset_name,
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dataset_path=args.dataset_path,
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meta_file_train=args.meta_file_train,
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meta_file_val=args.meta_file_val,
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disable_cuda=args.disable_cuda,
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no_eval=args.no_eval,
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)
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@ -16,6 +16,24 @@ def resample_file(func_args):
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sf.write(filename, y, sr)
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def resample_files(input_dir, output_sr, output_dir=None, file_ext="wav", n_jobs=10):
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if output_dir:
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print("Recursively copying the input folder...")
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copy_tree(input_dir, output_dir)
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input_dir = output_dir
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print("Resampling the audio files...")
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audio_files = glob.glob(os.path.join(input_dir, f"**/*.{file_ext}"), recursive=True)
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print(f"Found {len(audio_files)} files...")
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audio_files = list(zip(audio_files, len(audio_files) * [output_sr]))
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with Pool(processes=n_jobs) as p:
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with tqdm(total=len(audio_files)) as pbar:
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for _, _ in enumerate(p.imap_unordered(resample_file, audio_files)):
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pbar.update()
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print("Done !")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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@ -70,18 +88,4 @@ if __name__ == "__main__":
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args = parser.parse_args()
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if args.output_dir:
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print("Recursively copying the input folder...")
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copy_tree(args.input_dir, args.output_dir)
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args.input_dir = args.output_dir
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print("Resampling the audio files...")
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audio_files = glob.glob(os.path.join(args.input_dir, f"**/*.{args.file_ext}"), recursive=True)
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print(f"Found {len(audio_files)} files...")
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audio_files = list(zip(audio_files, len(audio_files) * [args.output_sr]))
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with Pool(processes=args.n_jobs) as p:
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with tqdm(total=len(audio_files)) as pbar:
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for i, _ in enumerate(p.imap_unordered(resample_file, audio_files)):
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pbar.update()
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print("Done !")
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resample_files(args.input_dir, args.output_sr, args.output_dir, args.file_ext, args.n_jobs)
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@ -0,0 +1,222 @@
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import os
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import torch
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from trainer import Trainer, TrainerArgs
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from TTS.bin.compute_embeddings import compute_embeddings
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from TTS.bin.resample import resample_files
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from TTS.config.shared_configs import BaseDatasetConfig
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from TTS.tts.configs.vits_config import VitsConfig
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from TTS.tts.datasets import load_tts_samples
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from TTS.tts.models.vits import Vits, VitsArgs, VitsAudioConfig
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from TTS.utils.downloaders import download_vctk
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torch.set_num_threads(24)
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# pylint: disable=W0105
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"""
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This recipe replicates the first experiment proposed in the YourTTS paper (https://arxiv.org/abs/2112.02418).
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YourTTS model is based on the VITS model however it uses external speaker embeddings extracted from a pre-trained speaker encoder and has small architecture changes.
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In addition, YourTTS can be trained in multilingual data, however, this recipe replicates the single language training using the VCTK dataset.
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If you are interested in multilingual training, we have commented on parameters on the VitsArgs class instance that should be enabled for multilingual training.
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In addition, you will need to add the extra datasets following the VCTK as an example.
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"""
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CURRENT_PATH = os.path.dirname(os.path.abspath(__file__))
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# Name of the run for the Trainer
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RUN_NAME = "YourTTS-EN-VCTK"
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# Path where you want to save the models outputs (configs, checkpoints and tensorboard logs)
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OUT_PATH = os.path.dirname(os.path.abspath(__file__)) # "/raid/coqui/Checkpoints/original-YourTTS/"
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# If you want to do transfer learning and speedup your training you can set here the path to the original YourTTS model
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RESTORE_PATH = None # "/root/.local/share/tts/tts_models--multilingual--multi-dataset--your_tts/model_file.pth"
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# This paramter is usefull to debug, it skips the training epochs and just do the evaluation and produce the test sentences
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SKIP_TRAIN_EPOCH = False
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# Set here the batch size to be used in training and evaluation
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BATCH_SIZE = 32
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# Training Sampling rate and the target sampling rate for resampling the downloaded dataset (Note: If you change this you might need to redownload the dataset !!)
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# Note: If you add new datasets, please make sure that the dataset sampling rate and this parameter are matching, otherwise resample your audios
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SAMPLE_RATE = 16000
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# Max audio length in seconds to be used in training (every audio bigger than it will be ignored)
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MAX_AUDIO_LEN_IN_SECONDS = 10
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### Download VCTK dataset
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VCTK_DOWNLOAD_PATH = os.path.join(CURRENT_PATH, "VCTK")
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# Define the number of threads used during the audio resampling
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NUM_RESAMPLE_THREADS = 10
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# Check if VCTK dataset is not already downloaded, if not download it
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if not os.path.exists(VCTK_DOWNLOAD_PATH):
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print(">>> Downloading VCTK dataset:")
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download_vctk(VCTK_DOWNLOAD_PATH)
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resample_files(VCTK_DOWNLOAD_PATH, SAMPLE_RATE, file_ext="flac", n_jobs=NUM_RESAMPLE_THREADS)
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# init configs
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vctk_config = BaseDatasetConfig(
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formatter="vctk", dataset_name="vctk", meta_file_train="", meta_file_val="", path=VCTK_DOWNLOAD_PATH, language="en"
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)
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# Add here all datasets configs, in our case we just want to train with the VCTK dataset then we need to add just VCTK. Note: If you want to added new datasets just added they here and it will automatically compute the speaker embeddings (d-vectors) for this new dataset :)
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DATASETS_CONFIG_LIST = [vctk_config]
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### Extract speaker embeddings
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SPEAKER_ENCODER_CHECKPOINT_PATH = (
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"https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/model_se.pth.tar"
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)
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SPEAKER_ENCODER_CONFIG_PATH = "https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/config_se.json"
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|
||||
D_VECTOR_FILES = [] # List of speaker embeddings/d-vectors to be used during the training
|
||||
|
||||
# Iterates all the dataset configs checking if the speakers embeddings are already computated, if not compute it
|
||||
for dataset_conf in DATASETS_CONFIG_LIST:
|
||||
# Check if the embeddings weren't already computed, if not compute it
|
||||
embeddings_file = os.path.join(dataset_conf.path, "speakers.pth")
|
||||
if not os.path.isfile(embeddings_file):
|
||||
print(f">>> Computing the speaker embeddings for the {dataset_conf.dataset_name} dataset")
|
||||
compute_embeddings(
|
||||
SPEAKER_ENCODER_CHECKPOINT_PATH,
|
||||
SPEAKER_ENCODER_CONFIG_PATH,
|
||||
embeddings_file,
|
||||
old_spakers_file=None,
|
||||
config_dataset_path=None,
|
||||
formatter_name=dataset_conf.formatter,
|
||||
dataset_name=dataset_conf.dataset_name,
|
||||
dataset_path=dataset_conf.path,
|
||||
meta_file_train=dataset_conf.meta_file_train,
|
||||
meta_file_val=dataset_conf.meta_file_val,
|
||||
disable_cuda=False,
|
||||
no_eval=False,
|
||||
)
|
||||
D_VECTOR_FILES.append(embeddings_file)
|
||||
|
||||
|
||||
# Audio config used in training.
|
||||
audio_config = VitsAudioConfig(
|
||||
sample_rate=SAMPLE_RATE,
|
||||
hop_length=256,
|
||||
win_length=1024,
|
||||
fft_size=1024,
|
||||
mel_fmin=0.0,
|
||||
mel_fmax=None,
|
||||
num_mels=80,
|
||||
)
|
||||
|
||||
# Init VITSArgs setting the arguments that is needed for the YourTTS model
|
||||
model_args = VitsArgs(
|
||||
d_vector_file=D_VECTOR_FILES,
|
||||
use_d_vector_file=True,
|
||||
d_vector_dim=512,
|
||||
num_layers_text_encoder=10,
|
||||
resblock_type_decoder="2", # On the paper, we accidentally trained the YourTTS using ResNet blocks type 2, if you like you can use the ResNet blocks type 1 like the VITS model
|
||||
# Usefull parameters to enable the Speaker Consistency Loss (SCL) discribed in the paper
|
||||
# use_speaker_encoder_as_loss=True,
|
||||
# speaker_encoder_model_path=SPEAKER_ENCODER_CHECKPOINT_PATH,
|
||||
# speaker_encoder_config_path=SPEAKER_ENCODER_CONFIG_PATH,
|
||||
# Usefull parameters to the enable multilingual training
|
||||
# use_language_embedding=True,
|
||||
# embedded_language_dim=4,
|
||||
)
|
||||
|
||||
# General training config, here you can change the batch size and others usefull parameters
|
||||
config = VitsConfig(
|
||||
output_path=OUT_PATH,
|
||||
model_args=model_args,
|
||||
run_name=RUN_NAME,
|
||||
project_name="YourTTS",
|
||||
run_description="""
|
||||
- Original YourTTS trained using VCTK dataset
|
||||
""",
|
||||
dashboard_logger="tensorboard",
|
||||
logger_uri=None,
|
||||
audio=audio_config,
|
||||
batch_size=BATCH_SIZE,
|
||||
batch_group_size=48,
|
||||
eval_batch_size=BATCH_SIZE,
|
||||
num_loader_workers=8,
|
||||
eval_split_max_size=256,
|
||||
print_step=50,
|
||||
plot_step=100,
|
||||
log_model_step=1000,
|
||||
save_step=5000,
|
||||
save_n_checkpoints=2,
|
||||
save_checkpoints=True,
|
||||
target_loss="loss_1",
|
||||
print_eval=False,
|
||||
use_phonemes=False,
|
||||
phonemizer="espeak",
|
||||
phoneme_language="en",
|
||||
compute_input_seq_cache=True,
|
||||
add_blank=True,
|
||||
text_cleaner="english_cleaners",
|
||||
phoneme_cache_path=None,
|
||||
precompute_num_workers=12,
|
||||
start_by_longest=True,
|
||||
datasets=DATASETS_CONFIG_LIST,
|
||||
cudnn_benchmark=False,
|
||||
max_audio_len=SAMPLE_RATE * MAX_AUDIO_LEN_IN_SECONDS,
|
||||
mixed_precision=False,
|
||||
test_sentences=[
|
||||
[
|
||||
"It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
|
||||
"VCTK_p277",
|
||||
None,
|
||||
"en",
|
||||
],
|
||||
[
|
||||
"Be a voice, not an echo.",
|
||||
"VCTK_p239",
|
||||
None,
|
||||
"en",
|
||||
],
|
||||
[
|
||||
"I'm sorry Dave. I'm afraid I can't do that.",
|
||||
"VCTK_p258",
|
||||
None,
|
||||
"en",
|
||||
],
|
||||
[
|
||||
"This cake is great. It's so delicious and moist.",
|
||||
"VCTK_p244",
|
||||
None,
|
||||
"en",
|
||||
],
|
||||
[
|
||||
"Prior to November 22, 1963.",
|
||||
"VCTK_p305",
|
||||
None,
|
||||
"en",
|
||||
],
|
||||
],
|
||||
# Enable the weighted sampler
|
||||
use_weighted_sampler=True,
|
||||
# Ensures that all speakers are seen in the training batch equally no matter how many samples each speaker has
|
||||
weighted_sampler_attrs={"speaker_name": 1.0},
|
||||
# It defines the Speaker Consistency Loss (SCL) α to 9 like the paper
|
||||
speaker_encoder_loss_alpha=9.0,
|
||||
)
|
||||
|
||||
# Load all the datasets samples and split traning and evaluation sets
|
||||
train_samples, eval_samples = load_tts_samples(
|
||||
config.datasets,
|
||||
eval_split=True,
|
||||
eval_split_max_size=config.eval_split_max_size,
|
||||
eval_split_size=config.eval_split_size,
|
||||
)
|
||||
|
||||
# Init the model
|
||||
model = Vits.init_from_config(config)
|
||||
|
||||
# Init the trainer and 🚀
|
||||
trainer = Trainer(
|
||||
TrainerArgs(restore_path=RESTORE_PATH, skip_train_epoch=SKIP_TRAIN_EPOCH),
|
||||
config,
|
||||
output_path=OUT_PATH,
|
||||
model=model,
|
||||
train_samples=train_samples,
|
||||
eval_samples=eval_samples,
|
||||
)
|
||||
trainer.fit()
|
||||
Loading…
Reference in New Issue