| 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 logging |
| logging.getLogger('numba').setLevel(logging.WARNING) |
|
|
| import parselmouth |
| import librosa |
| import numpy as np |
|
|
|
|
| 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 = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| 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 if torch.cuda.is_available() else None) |
| print("Loaded hubert.") |
|
|
| filenames = glob(f'{args.in_dir}/*/*.wav', recursive=True) |
| |
| for filename in tqdm(filenames): |
| process(filename) |
| |
