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- 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
- def stft(y):
- return librosa.stft(
- y=y,
- n_fft=1280,
- hop_length=320,
- win_length=1280,
- )
-
- def energy(y):
- # Extract energy
- S = librosa.magphase(stft(y))[0]
- e = np.sqrt(np.sum(S ** 2, axis=0)) # np.linalg.norm(S, axis=0)
- return e.squeeze() # (Number of frames) => (654,)
-
- def get_energy(path, p_len=None):
- wav, sr = librosa.load(path, 48000)
- e = energy(wav)
- if p_len is None:
- p_len = wav.shape[0] // 320
- assert e.shape[0] -p_len <2 ,(e.shape[0] ,p_len)
- e = e[: p_len]
- return e
-
-
-
- def get_f0(path,p_len=None, f0_up_key=0):
- x, _ = librosa.load(path, 48000)
- 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 / 48000 * 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, 48000).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=48000)
- 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, 48000)
- 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] * 3)
- np.save(f0path, f0)
-
-
-
- if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--in_dir", type=str, default="dataset/48k", 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)#[:10]
-
- for filename in tqdm(filenames):
- process(filename)
-
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