109 lines
3.5 KiB
Python
109 lines
3.5 KiB
Python
import math
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import multiprocessing
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import os
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import argparse
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from random import shuffle
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import torch
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from glob import glob
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from tqdm import tqdm
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from modules.mel_processing import spectrogram_torch
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import json
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import utils
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import logging
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logging.getLogger("numba").setLevel(logging.WARNING)
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import librosa
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import numpy as np
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hps = utils.get_hparams_from_file("configs/config.json")
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sampling_rate = hps.data.sampling_rate
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hop_length = hps.data.hop_length
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speech_encoder = hps["model"]["speech_encoder"]
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def process_one(filename, hmodel,f0p):
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# print(filename)
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wav, sr = librosa.load(filename, sr=sampling_rate)
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soft_path = filename + ".soft.pt"
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if not os.path.exists(soft_path):
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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wav16k = librosa.resample(wav, orig_sr=sampling_rate, target_sr=16000)
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wav16k = torch.from_numpy(wav16k).to(device)
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c = hmodel.encoder(wav16k)
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torch.save(c.cpu(), soft_path)
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f0_path = filename + ".f0.npy"
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if not os.path.exists(f0_path):
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f0_predictor = utils.get_f0_predictor(f0p,sampling_rate=sampling_rate, hop_length=hop_length,device=None,threshold=0.05)
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f0,uv = f0_predictor.compute_f0_uv(
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wav
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)
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np.save(f0_path, np.asanyarray((f0,uv),dtype=object))
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spec_path = filename.replace(".wav", ".spec.pt")
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if not os.path.exists(spec_path):
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# Process spectrogram
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# The following code can't be replaced by torch.FloatTensor(wav)
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# because load_wav_to_torch return a tensor that need to be normalized
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audio, sr = utils.load_wav_to_torch(filename)
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if sr != hps.data.sampling_rate:
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raise ValueError(
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"{} SR doesn't match target {} SR".format(
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sr, hps.data.sampling_rate
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)
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)
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audio_norm = audio / hps.data.max_wav_value
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audio_norm = audio_norm.unsqueeze(0)
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spec = spectrogram_torch(
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audio_norm,
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hps.data.filter_length,
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hps.data.sampling_rate,
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hps.data.hop_length,
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hps.data.win_length,
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center=False,
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)
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spec = torch.squeeze(spec, 0)
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torch.save(spec, spec_path)
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def process_batch(filenames,f0p):
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print("Loading hubert for content...")
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device = "cuda" if torch.cuda.is_available() else "cpu"
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hmodel = utils.get_speech_encoder(speech_encoder,device=device)
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print("Loaded hubert.")
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for filename in tqdm(filenames):
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process_one(filename, hmodel,f0p)
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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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"--in_dir", type=str, default="dataset/44k", help="path to input dir"
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)
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parser.add_argument(
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'--f0_predictor', type=str, default="dio", help='Select F0 predictor, can select crepe,pm,dio,harvest, default pm(note: crepe is original F0 using mean filter)'
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)
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args = parser.parse_args()
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f0p = args.f0_predictor
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print(speech_encoder)
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print(f0p)
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filenames = glob(f"{args.in_dir}/*/*.wav", recursive=True) # [:10]
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shuffle(filenames)
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multiprocessing.set_start_method("spawn", force=True)
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num_processes = 1
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chunk_size = int(math.ceil(len(filenames) / num_processes))
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chunks = [
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filenames[i : i + chunk_size] for i in range(0, len(filenames), chunk_size)
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]
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print([len(c) for c in chunks])
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processes = [
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multiprocessing.Process(target=process_batch, args=(chunk,f0p)) for chunk in chunks
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]
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for p in processes:
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p.start()
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