so-vits-svc/preprocess_hubert_f0.py

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 json

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
speech_encoder = hps["model"]["speech_encoder"]

def process_one(filename, hmodel,f0p):
    # 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 = 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

        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,f0p):
    print("Loading hubert for content...")
    device = "cuda" if torch.cuda.is_available() else "cpu"
    hmodel = utils.get_speech_encoder(speech_encoder,device=device)
    print("Loaded hubert.")
    for filename in tqdm(filenames):
        process_one(filename, hmodel,f0p)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--in_dir", type=str, default="dataset/44k", help="path to input dir"
    )
    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( 
        '--ues_diff',action='store_true', help='Whether to use the diffusion model'
    )
    args = parser.parse_args()
    f0p = args.f0_predictor
    print(speech_encoder)
    print(f0p)
    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,f0p)) for chunk in chunks
    ]
    for p in processes:
        p.start()