so-vits-svc/preprocess_hubert_f0.py

160 lines
5.9 KiB
Python

import math
import multiprocessing
import os
import argparse
from random import shuffle
import random
import torch
from glob import glob
from tqdm import tqdm
from modules.mel_processing import spectrogram_torch
import json
import utils
import logging
logging.getLogger("numba").setLevel(logging.WARNING)
logging.getLogger("matplotlib").setLevel(logging.WARNING)
import diffusion.logger.utils as du
from diffusion.vocoder import Vocoder
import librosa
import numpy as np
hps = utils.get_hparams_from_file("configs/config.json")
dconfig = du.load_config("configs/diffusion.yaml")
sampling_rate = hps.data.sampling_rate
hop_length = hps.data.hop_length
speech_encoder = hps["model"]["speech_encoder"]
def process_one(filename, hmodel,f0p,diff=False,mel_extractor=None):
# print(filename)
wav, sr = librosa.load(filename, sr=sampling_rate)
audio_norm = torch.FloatTensor(wav)
audio_norm = audio_norm.unsqueeze(0)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
soft_path = filename + ".soft.pt"
if not os.path.exists(soft_path):
wav16k = librosa.resample(wav, orig_sr=sampling_rate, target_sr=16000)
wav16k = torch.from_numpy(wav16k).to(device)
c = hmodel.encoder(wav16k)
torch.save(c.cpu(), soft_path)
f0_path = filename + ".f0.npy"
if not os.path.exists(f0_path):
f0_predictor = utils.get_f0_predictor(f0p,sampling_rate=sampling_rate, hop_length=hop_length,device=None,threshold=0.05)
f0,uv = f0_predictor.compute_f0_uv(
wav
)
np.save(f0_path, np.asanyarray((f0,uv),dtype=object))
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
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
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)
if diff or hps.model.vol_embedding:
volume_path = filename + ".vol.npy"
volume_extractor = utils.Volume_Extractor(hop_length)
if not os.path.exists(volume_path):
volume = volume_extractor.extract(audio_norm)
np.save(volume_path, volume.to('cpu').numpy())
if diff:
mel_path = filename + ".mel.npy"
if not os.path.exists(mel_path) and mel_extractor is not None:
mel_t = mel_extractor.extract(audio_norm.to(device), sampling_rate)
mel = mel_t.squeeze().to('cpu').numpy()
np.save(mel_path, mel)
aug_mel_path = filename + ".aug_mel.npy"
aug_vol_path = filename + ".aug_vol.npy"
max_amp = float(torch.max(torch.abs(audio_norm))) + 1e-5
max_shift = min(1, np.log10(1/max_amp))
log10_vol_shift = random.uniform(-1, max_shift)
keyshift = random.uniform(-5, 5)
if mel_extractor is not None:
aug_mel_t = mel_extractor.extract(audio_norm * (10 ** log10_vol_shift), sampling_rate, keyshift = keyshift)
aug_mel = aug_mel_t.squeeze().to('cpu').numpy()
aug_vol = volume_extractor.extract(audio_norm * (10 ** log10_vol_shift))
if not os.path.exists(aug_mel_path):
np.save(aug_mel_path,np.asanyarray((aug_mel,keyshift),dtype=object))
if not os.path.exists(aug_vol_path):
np.save(aug_vol_path,aug_vol.to('cpu').numpy())
def process_batch(filenames,f0p,diff=False,mel_extractor=None):
print("Loading speech encoder for content...")
device = "cuda" if torch.cuda.is_available() else "cpu"
hmodel = utils.get_speech_encoder(speech_encoder,device=device)
print("Loaded speech encoder.")
for filename in tqdm(filenames):
process_one(filename, hmodel,f0p,diff,mel_extractor)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--in_dir", type=str, default="dataset/44k", help="path to input dir"
)
parser.add_argument(
'--use_diff',action='store_true', help='Whether to use the diffusion model'
)
parser.add_argument(
'--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)'
)
parser.add_argument(
'--num_processes', type=int, default=1, help='You are advised to set the number of processes to the same as the number of CPU cores'
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
args = parser.parse_args()
f0p = args.f0_predictor
print(speech_encoder)
print(f0p)
if args.use_diff:
print("use_diff")
print("Loading Mel Extractor...")
mel_extractor = Vocoder(dconfig.vocoder.type, dconfig.vocoder.ckpt, device = device)
print("Loaded Mel Extractor.")
else:
mel_extractor = None
filenames = glob(f"{args.in_dir}/*/*.wav", recursive=True) # [:10]
shuffle(filenames)
multiprocessing.set_start_method("spawn", force=True)
num_processes = args.num_processes
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,f0p,args.use_diff,mel_extractor)) for chunk in chunks
]
for p in processes:
p.start()