preprocess_hubert_f0.py

import os
import argparse

import torch
import json
from glob import glob

from pyworld import pyworld
from tqdm import tqdm
from scipy.io import wavfile

import utils
from mel_processing import mel_spectrogram_torch
#import h5py
import logging
logging.getLogger('numba').setLevel(logging.WARNING)

import parselmouth
import librosa
import numpy as np

import torch_directml
dml = torch_directml.device()

def get_f0(path,p_len=None, f0_up_key=0):
    x, _ = librosa.load(path, 32000)
    if p_len is None:
        p_len = x.shape[0]//320
    else:
        assert abs(p_len-x.shape[0]//320) < 3, (path, p_len, x.shape)
    time_step = 320 / 32000 * 1000
    f0_min = 50
    f0_max = 1100
    f0_mel_min = 1127 * np.log(1 + f0_min / 700)
    f0_mel_max = 1127 * np.log(1 + f0_max / 700)

    f0 = parselmouth.Sound(x, 32000).to_pitch_ac(
        time_step=time_step / 1000, voicing_threshold=0.6,
        pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']

    pad_size=(p_len - len(f0) + 1) // 2
    if(pad_size>0 or p_len - len(f0) - pad_size>0):
        f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')

    f0bak = f0.copy()
    f0 *= pow(2, f0_up_key / 12)
    f0_mel = 1127 * np.log(1 + f0 / 700)
    f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1
    f0_mel[f0_mel <= 1] = 1
    f0_mel[f0_mel > 255] = 255
    f0_coarse = np.rint(f0_mel).astype(np.int)
    return f0_coarse, f0bak

def resize2d(x, target_len):
    source = np.array(x)
    source[source<0.001] = np.nan
    target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source)
    res = np.nan_to_num(target)
    return res

def compute_f0(path, c_len):
    x, sr = librosa.load(path, sr=32000)
    f0, t = pyworld.dio(
        x.astype(np.double),
        fs=sr,
        f0_ceil=800,
        frame_period=1000 * 320 / sr,
    )
    f0 = pyworld.stonemask(x.astype(np.double), f0, t, 32000)
    for index, pitch in enumerate(f0):
        f0[index] = round(pitch, 1)
    assert abs(c_len - x.shape[0]//320) < 3, (c_len, f0.shape)

    return None, resize2d(f0, c_len)


def process(filename):
    print(filename)
    save_name = filename+".soft.pt"
    if not os.path.exists(save_name):
        devive = dml
        wav, _ = librosa.load(filename, sr=16000)
        wav = torch.from_numpy(wav).unsqueeze(0).to(devive)
        c = utils.get_hubert_content(hmodel, wav)
        torch.save(c.cpu(), save_name)
    else:
        c = torch.load(save_name)
    f0path = filename+".f0.npy"
    if not os.path.exists(f0path):
        cf0, f0 = compute_f0(filename, c.shape[-1] * 2)
        np.save(f0path, f0)



if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--in_dir", type=str, default="dataset/32k", help="path to input dir")
    args = parser.parse_args()

    print("Loading hubert for content...")
    hmodel = utils.get_hubert_model(0)
    print("Loaded hubert.")

    filenames = glob(f'{args.in_dir}/*/*.wav', recursive=True)#[:10]
    
    for filename in tqdm(filenames):
        process(filename)