mirror of https://github.com/coqui-ai/TTS.git
85 lines
3.1 KiB
Python
85 lines
3.1 KiB
Python
import argparse
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import os
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from argparse import RawTextHelpFormatter
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import torch
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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_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 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 speaker_encoder_config.json dataset_config.json
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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("config_path", type=str, help="Path to model config file.")
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parser.add_argument("config_dataset_path", type=str, help="Path to dataset config file.")
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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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args = parser.parse_args()
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use_cuda = torch.cuda.is_available() and not args.disable_cuda
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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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if meta_data_eval is None:
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wav_files = meta_data_train
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else:
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wav_files = 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, wav_file in enumerate(tqdm(wav_files)):
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if isinstance(wav_file, dict):
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class_name = wav_file[class_name_key]
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wav_file = wav_file["audio_file"]
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else:
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class_name = None
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wav_file_name = os.path.basename(wav_file)
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if args.old_file is not None and wav_file_name 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(wav_file_name)
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else:
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# extract the embedding
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embedd = encoder_manager.compute_embedding_from_clip(wav_file)
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# create speaker_mapping if target dataset is defined
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speaker_mapping[wav_file_name] = {}
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speaker_mapping[wav_file_name]["name"] = class_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 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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else:
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mapping_file_path = args.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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