| from multiprocessing import Process |
| import os |
| import numpy as np |
|
|
| import librosa |
| from librosa.core import load |
| from librosa.filters import mel as librosa_mel_fn |
| mel_basis = librosa_mel_fn(sr=24000, n_fft=1024, n_mels=100, fmin=0, fmax=12000) |
|
|
| from tqdm import tqdm |
| import pandas as pd |
| import pyworld as pw |
|
|
|
|
| def get_world_mel(wav_path, sr=24000): |
| wav, _ = librosa.load(wav_path, sr=sr) |
| wav = (wav * 32767).astype(np.int16) |
| wav = (wav / 32767).astype(np.float64) |
| |
| wav = wav[:(wav.shape[0] // 256) * 256] |
|
|
| _f0, t = pw.dio(wav, sr, frame_period=256/sr*1000) |
| f0 = pw.stonemask(wav, _f0, t, sr) |
| sp = pw.cheaptrick(wav, f0, t, sr) |
| ap = pw.d4c(wav, f0, t, sr) |
| wav_hat = pw.synthesize(f0 * 0, sp, ap, sr, frame_period=256/sr*1000) |
|
|
| |
| wav_hat = wav_hat[:len(wav)] |
| |
| assert len(wav_hat) == len(wav) |
| wav = wav_hat.astype(np.float32) |
| wav = np.pad(wav, 384, mode='reflect') |
| stft = librosa.core.stft(wav, n_fft=1024, hop_length=256, win_length=1024, window='hann', center=False) |
| stftm = np.sqrt(np.real(stft) ** 2 + np.imag(stft) ** 2 + (1e-9)) |
| mel_spectrogram = np.matmul(mel_basis, stftm) |
| log_mel_spectrogram = np.log(np.clip(mel_spectrogram, a_min=1e-5, a_max=None)) |
|
|
| return log_mel_spectrogram, f0 |
|
|
|
|
| def chunks(arr, m): |
| result = [[] for i in range(m)] |
| for i in range(len(arr)): |
| result[i%m].append(arr[i]) |
| return result |
|
|
|
|
| def extract_pw(subset, save_f0=False): |
| meta = pd.read_csv('../raw_data/meta_fix.csv') |
| meta = meta[meta['subset'] == 'train'] |
|
|
| for i in tqdm(subset): |
| line = meta.iloc[i] |
| audio_dir = '../raw_data/' + line['folder'] + line['subfolder'] |
| f = line['file_name'] |
|
|
| mel_dir = audio_dir.replace('vocal', 'world').replace('raw_data/', '24k_data/') |
| f0_dir = audio_dir.replace('vocal', 'f0').replace('raw_data/', '24k_f0/') |
|
|
| if os.path.exists(os.path.join(mel_dir, f+'.npy')) is False: |
| mel = get_world_mel(os.path.join(audio_dir, f)) |
|
|
| if os.path.exists(mel_dir) is False: |
| os.makedirs(mel_dir) |
| np.save(os.path.join(mel_dir, f+'.npy'), mel) |
|
|
| if save_f0 is True: |
| if os.path.exists(f0_dir) is False: |
| os.makedirs(f0_dir) |
| np.save(os.path.join(f0_dir, f + '.npy'), f0) |
|
|
|
|
| if __name__ == '__main__': |
| cores = 8 |
| meta = pd.read_csv('../raw_data/meta_fix.csv') |
| meta = meta[meta['subset'] == 'train'] |
|
|
| idx_list = [i for i in range(len(meta))] |
|
|
| subsets = chunks(idx_list, cores) |
|
|
| for subset in subsets: |
| t = Process(target=extract_pw, args=(subset,)) |
| t.start() |
