mirror of https://github.com/coqui-ai/TTS.git
Add compute_embeddings and resample_files functions to be able to reuse it
This commit is contained in:
Edresson Casanova
parent
5d925eaa4d
commit
0bf274688f
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@ -11,121 +11,151 @@ 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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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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c_dataset.meta_file_train = meta_file_train if meta_file_train else None
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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 --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(
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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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"--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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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.metafile,
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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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