mirror of https://github.com/coqui-ai/TTS.git
94 lines
3.1 KiB
Python
94 lines
3.1 KiB
Python
import argparse
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import os
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import torch
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import numpy as np
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from tqdm import tqdm
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from TTS.config import load_config
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from TTS.config import BaseDatasetConfig, load_config
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from TTS.speaker_encoder.utils.generic_utils import setup_model
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from TTS.tts.datasets.preprocess import load_meta_data
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from TTS.tts.utils.speakers import SpeakerManager
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from TTS.utils.audio import AudioProcessor
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parser = argparse.ArgumentParser(
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description='Compute embedding vectors for each wav file in a dataset.'
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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("--save_npy", type=bool, help="flag to set cuda.", default=False)
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args = parser.parse_args()
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c = load_config(args.config_path)
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c_dataset = load_config(args.config_dataset_path)
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ap = AudioProcessor(**c["audio"])
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train_files, dev_files = load_meta_data(c_dataset.datasets, eval_split=True, ignore_generated_eval=True)
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wav_files = train_files + dev_files
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# define Encoder model
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model = setup_model(c)
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model.load_state_dict(torch.load(args.model_path)["model"])
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model.eval()
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if args.use_cuda:
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model.cuda()
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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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mel_spec = ap.melspectrogram(ap.load_wav(wav_file, sr=ap.sample_rate)).T
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mel_spec = torch.FloatTensor(mel_spec[None, :, :])
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if args.use_cuda:
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mel_spec = mel_spec.cuda()
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embedd = model.compute_embedding(mel_spec)
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embedd = embedd.detach().cpu().numpy()
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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.flatten().tolist()
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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 and '.npy' not in args.output_path:
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mapping_file_path = os.path.join(args.output_path, "speakers.json")
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mapping_npy_file_path = os.path.join(args.output_path, "speakers.npy")
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else:
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mapping_file_path = args.output_path.replace(".npy", ".json")
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mapping_npy_file_path = mapping_file_path.replace(".json", ".npy")
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os.makedirs(os.path.dirname(mapping_file_path), exist_ok=True)
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if args.save_npy:
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np.save(mapping_npy_file_path, speaker_mapping)
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print("Speaker embeddings saved at:", mapping_npy_file_path)
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speaker_manager = SpeakerManager()
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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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