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import math
import multiprocessing
import os
import argparse
from random import shuffle
import torch
from glob import glob
from tqdm import tqdm
from modules.mel_processing import spectrogram_torch
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.pt"
if not os.path.exists(soft_path):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
wav16k = librosa.resample(wav, orig_sr=sampling_rate, target_sr=16000)
wav16k = torch.from_numpy(wav16k).to(device)
c = utils.get_hubert_content(hmodel, wav_16k_tensor=wav16k)
torch.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)
spec_path = filename.replace(".wav", ".spec.pt")
if not os.path.exists(spec_path):
# Process spectrogram
# The following code can't be replaced by torch.FloatTensor(wav)
# because load_wav_to_torch return a tensor that need to be normalized
audio, sr = utils.load_wav_to_torch(filename)
if sr != hps.data.sampling_rate:
raise ValueError(
"{} SR doesn't match target {} SR".format(
sr, hps.data.sampling_rate
)
)
audio_norm = audio / hps.data.max_wav_value
audio_norm = audio_norm.unsqueeze(0)
spec = spectrogram_torch(
audio_norm,
hps.data.filter_length,
hps.data.sampling_rate,
hps.data.hop_length,
hps.data.win_length,
center=False,
)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_path)
def process_batch(filenames):
print("Loading hubert for content...")
device = "cuda" if torch.cuda.is_available() else "cpu"
hmodel = utils.get_hubert_model().to(device)
print("Loaded hubert.")
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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