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)