Spaces:
Running
Running
| 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() | |