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