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import math
import multiprocessing
import os
import argparse
from random import shuffle
import paddle
from glob import glob
from tqdm import tqdm
import utils
import logging
logging.getLogger('numba').setLevel(logging.WARNING)
import librosa
import numpy as np
hps = utils.get_hparams_from_file("configs/config.json")
sampling_rate = hps.data.sampling_rate
hop_length = hps.data.hop_length
def process_one(filename, hmodel):
# print(filename)
wav, sr = librosa.load(filename, sr=sampling_rate)
soft_path = filename + ".soft.pdtensor"
if not os.path.exists(soft_path):
devive = "cuda" if paddle.device.is_compiled_with_cuda() else "cpu"
wav16k = librosa.resample(wav, orig_sr=sampling_rate, target_sr=16000)
wav16k = paddle.to_tensor(wav16k).cpu() if devive=='cpu' else paddle.to_tensor(wav16k).cuda()
c:paddle.Tensor = utils.get_hubert_content(hmodel, wav_16k_tensor=wav16k)
paddle.save(c.cpu(), soft_path)
f0_path = filename + ".f0.npy"
if not os.path.exists(f0_path):
f0 = utils.compute_f0_dio(wav, sampling_rate=sampling_rate, hop_length=hop_length)
np.save(f0_path, f0)
def process_batch(filenames):
print("正在加载内容的HuBERT……")
device = "cuda" if paddle.device.is_compiled_with_cuda() else "cpu"
hmodel = utils.get_hubert_model()
print("HuBERT已被装载。")
for filename in tqdm(filenames):
process_one(filename, hmodel)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--in_dir", type=str, default="dataset/44k", help="path to input dir")
args = parser.parse_args()
filenames = glob(f'{args.in_dir}/*/*.wav', recursive=True) # [:10]
shuffle(filenames)
multiprocessing.set_start_method('spawn',force=True)
num_processes = 1
chunk_size = int(math.ceil(len(filenames) / num_processes))
chunks = [filenames[i:i + chunk_size] for i in range(0, len(filenames), chunk_size)]
print([len(c) for c in chunks])
processes = [multiprocessing.Process(target=process_batch, args=(chunk,)) for chunk in chunks]
for p in processes:
p.start()
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