101 lines
3.1 KiB
Python
101 lines
3.1 KiB
Python
import librosa
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import numpy as np
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import wave
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import soundfile as sf
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def librosa_pad_lr(x, fsize, fshift, pad_sides=1):
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'''compute right padding (final frame) or both sides padding (first and final frames)
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'''
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assert pad_sides in (1, 2)
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# return int(fsize // 2)
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pad = (x.shape[0] // fshift + 1) * fshift - x.shape[0]
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if pad_sides == 1:
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return 0, pad
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else:
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return pad // 2, pad // 2 + pad % 2
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def amp_to_db(x):
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return 20 * np.log10(np.maximum(1e-5, x))
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def db_to_amp(x):
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return 10.0 ** (x * 0.05)
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def normalize(S, min_level_db):
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return (S - min_level_db) / -min_level_db
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def denormalize(D, min_level_db):
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return (D * -min_level_db) + min_level_db
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def librosa_wav2spec(wav_path,
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fft_size=1024,
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hop_size=256,
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win_length=1024,
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window="hann",
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num_mels=80,
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fmin=80,
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fmax=-1,
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eps=1e-6,
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sample_rate=22050,
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loud_norm=False,
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trim_long_sil=False):
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import pyloudnorm as pyln
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if isinstance(wav_path, str):
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if trim_long_sil:
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from .vad import trim_long_silences
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wav, _, _ = trim_long_silences(wav_path, sample_rate)
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else:
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wav, _ = librosa.core.load(wav_path, sr=sample_rate)
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else:
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wav = wav_path
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wav_orig = np.copy(wav)
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if loud_norm:
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meter = pyln.Meter(sample_rate) # create BS.1770 meter
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loudness = meter.integrated_loudness(wav)
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wav = pyln.normalize.loudness(wav, loudness, -22.0)
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if np.abs(wav).max() > 1:
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wav = wav / np.abs(wav).max()
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# get amplitude spectrogram
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x_stft = librosa.stft(wav, n_fft=fft_size, hop_length=hop_size,
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win_length=win_length, window=window, pad_mode="constant")
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linear_spc = np.abs(x_stft) # (n_bins, T)
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# get mel basis
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fmin = 0 if fmin == -1 else fmin
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fmax = sample_rate / 2 if fmax == -1 else fmax
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mel_basis = librosa.filters.mel(sr=sample_rate, n_fft=fft_size, n_mels=num_mels, fmin=fmin, fmax=fmax)
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# calculate mel spec
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mel = mel_basis @ linear_spc
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mel = np.log10(np.maximum(eps, mel)) # (n_mel_bins, T)
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l_pad, r_pad = librosa_pad_lr(wav, fft_size, hop_size, 1)
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wav = np.pad(wav, (l_pad, r_pad), mode='constant', constant_values=0.0)
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wav = wav[:mel.shape[1] * hop_size]
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# log linear spec
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linear_spc = np.log10(np.maximum(eps, linear_spc))
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return {'wav': wav, 'mel': mel.T, 'linear': linear_spc.T, 'mel_basis': mel_basis, 'wav_orig': wav_orig}
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def get_wav_num_frames(path, sr=None):
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try:
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with wave.open(path, 'rb') as f:
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sr_ = f.getframerate()
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if sr is None:
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sr = sr_
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return int(f.getnframes() / (sr_ / sr))
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except wave.Error:
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wav_file, sr_ = sf.read(path, dtype='float32')
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if sr is None:
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sr = sr_
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return int(len(wav_file) / (sr_ / sr))
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except:
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wav_file, sr_ = librosa.core.load(path, sr=sr)
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return len(wav_file)
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