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import librosa
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import librosa.filters
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import numpy as np
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from scipy import signal
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from scipy.io import wavfile
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from src.utils.hparams import hparams as hp
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def load_wav(path, sr):
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return librosa.core.load(path, sr=sr)[0]
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def save_wav(wav, path, sr):
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wav *= 32767 / max(0.01, np.max(np.abs(wav)))
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wavfile.write(path, sr, wav.astype(np.int16))
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def save_wavenet_wav(wav, path, sr):
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librosa.output.write_wav(path, wav, sr=sr)
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def preemphasis(wav, k, preemphasize=True):
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if preemphasize:
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return signal.lfilter([1, -k], [1], wav)
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return wav
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def inv_preemphasis(wav, k, inv_preemphasize=True):
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if inv_preemphasize:
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return signal.lfilter([1], [1, -k], wav)
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return wav
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def get_hop_size():
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hop_size = hp.hop_size
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if hop_size is None:
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assert hp.frame_shift_ms is not None
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hop_size = int(hp.frame_shift_ms / 1000 * hp.sample_rate)
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return hop_size
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def linearspectrogram(wav):
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D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize))
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S = _amp_to_db(np.abs(D)) - hp.ref_level_db
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if hp.signal_normalization:
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return _normalize(S)
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return S
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def melspectrogram(wav):
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D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize))
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S = _amp_to_db(_linear_to_mel(np.abs(D))) - hp.ref_level_db
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if hp.signal_normalization:
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return _normalize(S)
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return S
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def _lws_processor():
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import lws
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return lws.lws(hp.n_fft, get_hop_size(), fftsize=hp.win_size, mode="speech")
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def _stft(y):
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if hp.use_lws:
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return _lws_processor(hp).stft(y).T
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else:
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return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=get_hop_size(), win_length=hp.win_size)
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def num_frames(length, fsize, fshift):
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"""Compute number of time frames of spectrogram
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"""
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pad = (fsize - fshift)
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if length % fshift == 0:
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M = (length + pad * 2 - fsize) // fshift + 1
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else:
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M = (length + pad * 2 - fsize) // fshift + 2
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return M
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def pad_lr(x, fsize, fshift):
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"""Compute left and right padding
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"""
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M = num_frames(len(x), fsize, fshift)
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pad = (fsize - fshift)
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T = len(x) + 2 * pad
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r = (M - 1) * fshift + fsize - T
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return pad, pad + r
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def librosa_pad_lr(x, fsize, fshift):
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return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0]
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_mel_basis = None
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def _linear_to_mel(spectogram):
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global _mel_basis
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if _mel_basis is None:
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_mel_basis = _build_mel_basis()
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return np.dot(_mel_basis, spectogram)
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def _build_mel_basis():
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assert hp.fmax <= hp.sample_rate // 2
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return librosa.filters.mel(sr=hp.sample_rate, n_fft=hp.n_fft, n_mels=hp.num_mels,
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fmin=hp.fmin, fmax=hp.fmax)
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def _amp_to_db(x):
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min_level = np.exp(hp.min_level_db / 20 * np.log(10))
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return 20 * np.log10(np.maximum(min_level, x))
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def _db_to_amp(x):
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return np.power(10.0, (x) * 0.05)
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def _normalize(S):
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if hp.allow_clipping_in_normalization:
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if hp.symmetric_mels:
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return np.clip((2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value,
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-hp.max_abs_value, hp.max_abs_value)
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else:
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return np.clip(hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)), 0, hp.max_abs_value)
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assert S.max() <= 0 and S.min() - hp.min_level_db >= 0
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if hp.symmetric_mels:
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return (2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value
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else:
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return hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db))
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def _denormalize(D):
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if hp.allow_clipping_in_normalization:
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if hp.symmetric_mels:
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return (((np.clip(D, -hp.max_abs_value,
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hp.max_abs_value) + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value))
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+ hp.min_level_db)
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else:
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return ((np.clip(D, 0, hp.max_abs_value) * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db)
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if hp.symmetric_mels:
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return (((D + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + hp.min_level_db)
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else:
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return ((D * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db)
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