73 lines
2.0 KiB
Python
73 lines
2.0 KiB
Python
import argparse
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import concurrent.futures
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import warnings
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import numpy as np
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import torch
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from tqdm import tqdm
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import utils
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from config import config
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from common.stdout_wrapper import SAFE_STDOUT
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warnings.filterwarnings("ignore", category=UserWarning)
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from pyannote.audio import Inference, Model
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model = Model.from_pretrained("pyannote/wespeaker-voxceleb-resnet34-LM")
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inference = Inference(model, window="whole")
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device = torch.device(config.style_gen_config.device)
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inference.to(device)
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def save_style_vector(wav_path):
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try:
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style_vec = inference(wav_path)
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except Exception as e:
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print(f"\nIncluding any problems: {wav_path}")
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print(e)
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raise
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np.save(f"{wav_path}.npy", style_vec) # `test.wav` -> `test.wav.npy`
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return style_vec
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def save_average_style_vector(style_vectors, filename="style_vectors.npy"):
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average_vector = np.mean(style_vectors, axis=0)
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np.save(filename, average_vector)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"-c", "--config", type=str, default=config.style_gen_config.config_path
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)
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parser.add_argument(
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"--num_processes", type=int, default=config.style_gen_config.num_processes
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)
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args, _ = parser.parse_known_args()
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config_path = args.config
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num_processes = args.num_processes
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hps = utils.get_hparams_from_file(config_path)
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device = config.style_gen_config.device
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lines = []
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with open(hps.data.training_files, encoding="utf-8") as f:
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lines.extend(f.readlines())
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with open(hps.data.validation_files, encoding="utf-8") as f:
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lines.extend(f.readlines())
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wavnames = [line.split("|")[0] for line in lines]
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with concurrent.futures.ThreadPoolExecutor(max_workers=num_processes) as executor:
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list(
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tqdm(
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executor.map(save_style_vector, wavnames),
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total=len(wavnames),
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file=SAFE_STDOUT,
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)
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)
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print(f"Finished generating style vectors! total: {len(wavnames)} npy files.")
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