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