mirror of https://github.com/coqui-ai/TTS.git
76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
import argparse
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import os
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from argparse import RawTextHelpFormatter
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from tqdm import tqdm
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from TTS.config import load_config
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from TTS.tts.datasets import load_meta_data
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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 wav file in a dataset.\n\n"""
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"""
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Example runs:
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python TTS/bin/compute_embeddings.py speaker_encoder_model.pth.tar speaker_encoder_config.json dataset_config.json embeddings_output_path/
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""",
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formatter_class=RawTextHelpFormatter,
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)
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parser.add_argument("model_path", type=str, help="Path to model checkpoint file.")
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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.",
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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.",
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)
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parser.add_argument("output_path", type=str, help="path for output speakers.json and/or speakers.npy.")
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parser.add_argument("--use_cuda", type=bool, help="flag to set cuda.", default=True)
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parser.add_argument("--eval", type=bool, help="compute eval.", default=True)
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args = parser.parse_args()
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c_dataset = load_config(args.config_dataset_path)
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meta_data_train, meta_data_eval = load_meta_data(c_dataset.datasets, eval_split=args.eval)
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wav_files = meta_data_train + meta_data_eval
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speaker_manager = SpeakerManager(
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encoder_model_path=args.model_path, encoder_config_path=args.config_path, use_cuda=args.use_cuda
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)
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# compute speaker embeddings
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speaker_mapping = {}
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for idx, wav_file in enumerate(tqdm(wav_files)):
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if isinstance(wav_file, list):
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speaker_name = wav_file[2]
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wav_file = wav_file[1]
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else:
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speaker_name = None
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# extract the embedding
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embedd = speaker_manager.compute_d_vector_from_clip(wav_file)
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# create speaker_mapping if target dataset is defined
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wav_file_name = os.path.basename(wav_file)
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speaker_mapping[wav_file_name] = {}
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speaker_mapping[wav_file_name]["name"] = speaker_name
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speaker_mapping[wav_file_name]["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 ".json" not in args.output_path:
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mapping_file_path = os.path.join(args.output_path, "speakers.json")
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else:
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mapping_file_path = args.output_path
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os.makedirs(os.path.dirname(mapping_file_path), exist_ok=True)
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# pylint: disable=W0212
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speaker_manager._save_json(mapping_file_path, speaker_mapping)
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print("Speaker embeddings saved at:", mapping_file_path)
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