From 0bf274688ff1eafa59610a54975cd4c996e4f515 Mon Sep 17 00:00:00 2001 From: Edresson Casanova Date: Fri, 9 Dec 2022 18:06:01 -0300 Subject: [PATCH] Add compute_embeddings and resample_files functions to be able to reuse it --- TTS/bin/compute_embeddings.py | 252 +++++++++++++++++++--------------- TTS/bin/resample.py | 34 +++-- 2 files changed, 160 insertions(+), 126 deletions(-) diff --git a/TTS/bin/compute_embeddings.py b/TTS/bin/compute_embeddings.py index 3650ce32..ace6deef 100644 --- a/TTS/bin/compute_embeddings.py +++ b/TTS/bin/compute_embeddings.py @@ -11,121 +11,151 @@ from TTS.tts.datasets import load_tts_samples from TTS.tts.utils.managers import save_file from TTS.tts.utils.speakers import SpeakerManager -parser = argparse.ArgumentParser( - 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""" - """ - Example runs: - python TTS/bin/compute_embeddings.py --model_path speaker_encoder_model.pth --config_path speaker_encoder_config.json --config_dataset_path dataset_config.json - 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 - """, - formatter_class=RawTextHelpFormatter, -) -parser.add_argument( - "--model_path", - type=str, - help="Path to model checkpoint file. It defaults to the released speaker encoder.", - default="https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/model_se.pth.tar", -) -parser.add_argument( - "--config_path", - type=str, - help="Path to model config file. It defaults to the released speaker encoder config.", - default="https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/config_se.json", -) -parser.add_argument( - "--config_dataset_path", - type=str, - help="Path to dataset config file. You either need to provide this or `formatter_name`, `dataset_name` and `dataset_path` arguments.", - default=None, -) -parser.add_argument("--output_path", type=str, help="Path for output `pth` or `json` file.", default="speakers.pth") -parser.add_argument("--old_file", type=str, help="Previous embedding file to only compute new audios.", default=None) -parser.add_argument("--disable_cuda", type=bool, help="Flag to disable cuda.", default=False) -parser.add_argument("--no_eval", type=bool, help="Do not compute eval?. Default False", default=False) -parser.add_argument( - "--formatter_name", - type=str, - help="Name of the formatter to use. You either need to provide this or `config_dataset_path`", - default=None, -) -parser.add_argument( - "--dataset_name", - type=str, - help="Name of the dataset to use. You either need to provide this or `config_dataset_path`", - default=None, -) -parser.add_argument( - "--dataset_path", - type=str, - help="Path to the dataset. You either need to provide this or `config_dataset_path`", - default=None, -) -parser.add_argument( - "--metafile", - type=str, - 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`", - default=None, -) -args = parser.parse_args() +def compute_embeddings( + model_path, + config_path, + output_path, + old_spakers_file=None, + config_dataset_path=None, + formatter_name=None, + dataset_name=None, + dataset_path=None, + meta_file_train=None, + disable_cuda=False, + no_eval=False, +): + use_cuda = torch.cuda.is_available() and not disable_cuda -use_cuda = torch.cuda.is_available() and not args.disable_cuda - -if args.config_dataset_path is not None: - c_dataset = load_config(args.config_dataset_path) - meta_data_train, meta_data_eval = load_tts_samples(c_dataset.datasets, eval_split=not args.no_eval) -else: - c_dataset = BaseDatasetConfig() - c_dataset.formatter = args.formatter_name - c_dataset.dataset_name = args.dataset_name - c_dataset.path = args.dataset_path - c_dataset.meta_file_train = args.metafile if args.metafile else None - meta_data_train, meta_data_eval = load_tts_samples(c_dataset, eval_split=not args.no_eval) - - -if meta_data_eval is None: - samples = meta_data_train -else: - samples = meta_data_train + meta_data_eval - -encoder_manager = SpeakerManager( - encoder_model_path=args.model_path, - encoder_config_path=args.config_path, - d_vectors_file_path=args.old_file, - use_cuda=use_cuda, -) - -class_name_key = encoder_manager.encoder_config.class_name_key - -# compute speaker embeddings -speaker_mapping = {} -for idx, fields in enumerate(tqdm(samples)): - class_name = fields[class_name_key] - audio_file = fields["audio_file"] - embedding_key = fields["audio_unique_name"] - root_path = fields["root_path"] - - if args.old_file is not None and embedding_key in encoder_manager.clip_ids: - # get the embedding from the old file - embedd = encoder_manager.get_embedding_by_clip(embedding_key) + if config_dataset_path is not None: + c_dataset = load_config(config_dataset_path) + meta_data_train, meta_data_eval = load_tts_samples(c_dataset.datasets, eval_split=not no_eval) else: - # extract the embedding - embedd = encoder_manager.compute_embedding_from_clip(audio_file) + c_dataset = BaseDatasetConfig() + c_dataset.formatter = formatter_name + c_dataset.dataset_name = dataset_name + c_dataset.path = dataset_path + c_dataset.meta_file_train = meta_file_train if meta_file_train else None + meta_data_train, meta_data_eval = load_tts_samples(c_dataset, eval_split=not no_eval) - # create speaker_mapping if target dataset is defined - speaker_mapping[embedding_key] = {} - speaker_mapping[embedding_key]["name"] = class_name - speaker_mapping[embedding_key]["embedding"] = embedd - -if speaker_mapping: - # save speaker_mapping if target dataset is defined - if os.path.isdir(args.output_path): - mapping_file_path = os.path.join(args.output_path, "speakers.pth") + if meta_data_eval is None: + samples = meta_data_train else: - mapping_file_path = args.output_path + samples = meta_data_train + meta_data_eval - if os.path.dirname(mapping_file_path) != "": - os.makedirs(os.path.dirname(mapping_file_path), exist_ok=True) + encoder_manager = SpeakerManager( + encoder_model_path=model_path, + encoder_config_path=config_path, + d_vectors_file_path=old_spakers_file, + use_cuda=use_cuda, + ) - save_file(speaker_mapping, mapping_file_path) - print("Speaker embeddings saved at:", mapping_file_path) + class_name_key = encoder_manager.encoder_config.class_name_key + + # compute speaker embeddings + speaker_mapping = {} + for fields in tqdm(samples): + class_name = fields[class_name_key] + audio_file = fields["audio_file"] + embedding_key = fields["audio_unique_name"] + + if old_spakers_file is not None and embedding_key in encoder_manager.clip_ids: + # get the embedding from the old file + embedd = encoder_manager.get_embedding_by_clip(embedding_key) + else: + # extract the embedding + embedd = encoder_manager.compute_embedding_from_clip(audio_file) + + # create speaker_mapping if target dataset is defined + speaker_mapping[embedding_key] = {} + speaker_mapping[embedding_key]["name"] = class_name + speaker_mapping[embedding_key]["embedding"] = embedd + + if speaker_mapping: + # save speaker_mapping if target dataset is defined + if os.path.isdir(output_path): + mapping_file_path = os.path.join(output_path, "speakers.pth") + else: + mapping_file_path = output_path + + if os.path.dirname(mapping_file_path) != "": + os.makedirs(os.path.dirname(mapping_file_path), exist_ok=True) + + save_file(speaker_mapping, mapping_file_path) + print("Speaker embeddings saved at:", mapping_file_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + 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""" + """ + Example runs: + python TTS/bin/compute_embeddings.py --model_path speaker_encoder_model.pth --config_path speaker_encoder_config.json --config_dataset_path dataset_config.json + + 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 + """, + formatter_class=RawTextHelpFormatter, + ) + parser.add_argument( + "--model_path", + type=str, + help="Path to model checkpoint file. It defaults to the released speaker encoder.", + default="https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/model_se.pth.tar", + ) + parser.add_argument( + "--config_path", + type=str, + help="Path to model config file. It defaults to the released speaker encoder config.", + default="https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/config_se.json", + ) + parser.add_argument( + "--config_dataset_path", + type=str, + help="Path to dataset config file. You either need to provide this or `formatter_name`, `dataset_name` and `dataset_path` arguments.", + default=None, + ) + parser.add_argument("--output_path", type=str, help="Path for output `pth` or `json` file.", default="speakers.pth") + parser.add_argument( + "--old_file", type=str, help="Previous embedding file to only compute new audios.", default=None + ) + parser.add_argument("--disable_cuda", type=bool, help="Flag to disable cuda.", default=False) + parser.add_argument("--no_eval", type=bool, help="Do not compute eval?. Default False", default=False) + parser.add_argument( + "--formatter_name", + type=str, + help="Name of the formatter to use. You either need to provide this or `config_dataset_path`", + default=None, + ) + parser.add_argument( + "--dataset_name", + type=str, + help="Name of the dataset to use. You either need to provide this or `config_dataset_path`", + default=None, + ) + parser.add_argument( + "--dataset_path", + type=str, + help="Path to the dataset. You either need to provide this or `config_dataset_path`", + default=None, + ) + parser.add_argument( + "--metafile", + type=str, + 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`", + default=None, + ) + args = parser.parse_args() + + compute_embeddings( + args.model_path, + args.config_path, + args.output_path, + old_spakers_file=args.old_file, + config_dataset_path=args.config_dataset_path, + formatter_name=args.formatter_name, + dataset_name=args.dataset_name, + dataset_path=args.dataset_path, + meta_file_train=args.metafile, + disable_cuda=args.disable_cuda, + no_eval=args.no_eval, + ) diff --git a/TTS/bin/resample.py b/TTS/bin/resample.py index c9f1166a..ec96dcc0 100644 --- a/TTS/bin/resample.py +++ b/TTS/bin/resample.py @@ -16,6 +16,24 @@ def resample_file(func_args): sf.write(filename, y, sr) +def resample_files(input_dir, output_sr, output_dir=None, file_ext="wav", n_jobs=10): + if output_dir: + print("Recursively copying the input folder...") + copy_tree(input_dir, output_dir) + input_dir = output_dir + + print("Resampling the audio files...") + audio_files = glob.glob(os.path.join(input_dir, f"**/*.{file_ext}"), recursive=True) + print(f"Found {len(audio_files)} files...") + audio_files = list(zip(audio_files, len(audio_files) * [output_sr])) + with Pool(processes=n_jobs) as p: + with tqdm(total=len(audio_files)) as pbar: + for _, _ in enumerate(p.imap_unordered(resample_file, audio_files)): + pbar.update() + + print("Done !") + + if __name__ == "__main__": parser = argparse.ArgumentParser( @@ -70,18 +88,4 @@ if __name__ == "__main__": args = parser.parse_args() - if args.output_dir: - print("Recursively copying the input folder...") - copy_tree(args.input_dir, args.output_dir) - args.input_dir = args.output_dir - - print("Resampling the audio files...") - audio_files = glob.glob(os.path.join(args.input_dir, f"**/*.{args.file_ext}"), recursive=True) - print(f"Found {len(audio_files)} files...") - audio_files = list(zip(audio_files, len(audio_files) * [args.output_sr])) - with Pool(processes=args.n_jobs) as p: - with tqdm(total=len(audio_files)) as pbar: - for i, _ in enumerate(p.imap_unordered(resample_file, audio_files)): - pbar.update() - - print("Done !") + resample_files(args.input_dir, args.output_sr, args.output_dir, args.file_ext, args.n_jobs)