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| import librosa | |
| import librosa.filters | |
| import numpy as np | |
| from scipy import signal | |
| from scipy.io import wavfile | |
| import soundfile as sf | |
| def load_wav(path, sr): | |
| return librosa.core.load(path, sr=sr)[0] | |
| def save_wav(wav, path, sr): | |
| wav *= 32767 / max(0.01, np.max(np.abs(wav))) | |
| #proposed by @dsmiller | |
| wavfile.write(path, sr, wav.astype(np.int16)) | |
| def save_wavenet_wav(wav, path, sr): | |
| sf.write(path, wav.astype(np.float32), sr) | |
| def preemphasis(wav, k, preemphasize=True): | |
| if preemphasize: | |
| return signal.lfilter([1, -k], [1], wav) | |
| return wav | |
| def inv_preemphasis(wav, k, inv_preemphasize=True): | |
| if inv_preemphasize: | |
| return signal.lfilter([1], [1, -k], wav) | |
| return wav | |
| #From https://github.com/r9y9/wavenet_vocoder/blob/master/audio.py | |
| def start_and_end_indices(quantized, silence_threshold=2): | |
| for start in range(quantized.size): | |
| if abs(quantized[start] - 127) > silence_threshold: | |
| break | |
| for end in range(quantized.size - 1, 1, -1): | |
| if abs(quantized[end] - 127) > silence_threshold: | |
| break | |
| assert abs(quantized[start] - 127) > silence_threshold | |
| assert abs(quantized[end] - 127) > silence_threshold | |
| return start, end | |
| def get_hop_size(hparams): | |
| hop_size = hparams.hop_size | |
| if hop_size is None: | |
| assert hparams.frame_shift_ms is not None | |
| hop_size = int(hparams.frame_shift_ms / 1000 * hparams.sample_rate) | |
| return hop_size | |
| def linearspectrogram(wav, hparams): | |
| D = _stft(preemphasis(wav, hparams.preemphasis, hparams.preemphasize), hparams) | |
| S = _amp_to_db(np.abs(D), hparams) - hparams.ref_level_db | |
| if hparams.signal_normalization: | |
| return _normalize(S, hparams) | |
| return S | |
| def melspectrogram(wav, hparams): | |
| D = _stft(preemphasis(wav, hparams.preemphasis, hparams.preemphasize), hparams) | |
| S = _amp_to_db(_linear_to_mel(np.abs(D), hparams), hparams) - hparams.ref_level_db | |
| if hparams.signal_normalization: | |
| return _normalize(S, hparams) | |
| return S | |
| def inv_linear_spectrogram(linear_spectrogram, hparams): | |
| """Converts linear spectrogram to waveform using librosa""" | |
| if hparams.signal_normalization: | |
| D = _denormalize(linear_spectrogram, hparams) | |
| else: | |
| D = linear_spectrogram | |
| S = _db_to_amp(D + hparams.ref_level_db) #Convert back to linear | |
| if hparams.use_lws: | |
| processor = _lws_processor(hparams) | |
| D = processor.run_lws(S.astype(np.float64).T ** hparams.power) | |
| y = processor.istft(D).astype(np.float32) | |
| return inv_preemphasis(y, hparams.preemphasis, hparams.preemphasize) | |
| else: | |
| return inv_preemphasis(_griffin_lim(S ** hparams.power, hparams), hparams.preemphasis, hparams.preemphasize) | |
| def inv_mel_spectrogram(mel_spectrogram, hparams): | |
| """Converts mel spectrogram to waveform using librosa""" | |
| if hparams.signal_normalization: | |
| D = _denormalize(mel_spectrogram, hparams) | |
| else: | |
| D = mel_spectrogram | |
| S = _mel_to_linear(_db_to_amp(D + hparams.ref_level_db), hparams) # Convert back to linear | |
| if hparams.use_lws: | |
| processor = _lws_processor(hparams) | |
| D = processor.run_lws(S.astype(np.float64).T ** hparams.power) | |
| y = processor.istft(D).astype(np.float32) | |
| return inv_preemphasis(y, hparams.preemphasis, hparams.preemphasize) | |
| else: | |
| return inv_preemphasis(_griffin_lim(S ** hparams.power, hparams), hparams.preemphasis, hparams.preemphasize) | |
| def _lws_processor(hparams): | |
| import lws | |
| return lws.lws(hparams.n_fft, get_hop_size(hparams), fftsize=hparams.win_size, mode="speech") | |
| def _griffin_lim(S, hparams): | |
| """librosa implementation of Griffin-Lim | |
| Based on https://github.com/librosa/librosa/issues/434 | |
| """ | |
| angles = np.exp(2j * np.pi * np.random.rand(*S.shape)) | |
| S_complex = np.abs(S).astype(np.complex) | |
| y = _istft(S_complex * angles, hparams) | |
| for i in range(hparams.griffin_lim_iters): | |
| angles = np.exp(1j * np.angle(_stft(y, hparams))) | |
| y = _istft(S_complex * angles, hparams) | |
| return y | |
| def _stft(y, hparams): | |
| if hparams.use_lws: | |
| return _lws_processor(hparams).stft(y).T | |
| else: | |
| return librosa.stft(y=y, n_fft=hparams.n_fft, hop_length=get_hop_size(hparams), win_length=hparams.win_size) | |
| def _istft(y, hparams): | |
| return librosa.istft(y, hop_length=get_hop_size(hparams), win_length=hparams.win_size) | |
| ########################################################## | |
| #Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!) | |
| def num_frames(length, fsize, fshift): | |
| """Compute number of time frames of spectrogram | |
| """ | |
| pad = (fsize - fshift) | |
| if length % fshift == 0: | |
| M = (length + pad * 2 - fsize) // fshift + 1 | |
| else: | |
| M = (length + pad * 2 - fsize) // fshift + 2 | |
| return M | |
| def pad_lr(x, fsize, fshift): | |
| """Compute left and right padding | |
| """ | |
| M = num_frames(len(x), fsize, fshift) | |
| pad = (fsize - fshift) | |
| T = len(x) + 2 * pad | |
| r = (M - 1) * fshift + fsize - T | |
| return pad, pad + r | |
| ########################################################## | |
| #Librosa correct padding | |
| def librosa_pad_lr(x, fsize, fshift): | |
| return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0] | |
| # Conversions | |
| _mel_basis = None | |
| _inv_mel_basis = None | |
| def _linear_to_mel(spectogram, hparams): | |
| global _mel_basis | |
| if _mel_basis is None: | |
| _mel_basis = _build_mel_basis(hparams) | |
| return np.dot(_mel_basis, spectogram) | |
| def _mel_to_linear(mel_spectrogram, hparams): | |
| global _inv_mel_basis | |
| if _inv_mel_basis is None: | |
| _inv_mel_basis = np.linalg.pinv(_build_mel_basis(hparams)) | |
| return np.maximum(1e-10, np.dot(_inv_mel_basis, mel_spectrogram)) | |
| def _build_mel_basis(hparams): | |
| assert hparams.fmax <= hparams.sample_rate // 2 | |
| return librosa.filters.mel(hparams.sample_rate, hparams.n_fft, n_mels=hparams.num_mels, | |
| fmin=hparams.fmin, fmax=hparams.fmax) | |
| def _amp_to_db(x, hparams): | |
| min_level = np.exp(hparams.min_level_db / 20 * np.log(10)) | |
| return 20 * np.log10(np.maximum(min_level, x)) | |
| def _db_to_amp(x): | |
| return np.power(10.0, (x) * 0.05) | |
| def _normalize(S, hparams): | |
| if hparams.allow_clipping_in_normalization: | |
| if hparams.symmetric_mels: | |
| return np.clip((2 * hparams.max_abs_value) * ((S - hparams.min_level_db) / (-hparams.min_level_db)) - hparams.max_abs_value, | |
| -hparams.max_abs_value, hparams.max_abs_value) | |
| else: | |
| return np.clip(hparams.max_abs_value * ((S - hparams.min_level_db) / (-hparams.min_level_db)), 0, hparams.max_abs_value) | |
| assert S.max() <= 0 and S.min() - hparams.min_level_db >= 0 | |
| if hparams.symmetric_mels: | |
| return (2 * hparams.max_abs_value) * ((S - hparams.min_level_db) / (-hparams.min_level_db)) - hparams.max_abs_value | |
| else: | |
| return hparams.max_abs_value * ((S - hparams.min_level_db) / (-hparams.min_level_db)) | |
| def _denormalize(D, hparams): | |
| if hparams.allow_clipping_in_normalization: | |
| if hparams.symmetric_mels: | |
| return (((np.clip(D, -hparams.max_abs_value, | |
| hparams.max_abs_value) + hparams.max_abs_value) * -hparams.min_level_db / (2 * hparams.max_abs_value)) | |
| + hparams.min_level_db) | |
| else: | |
| return ((np.clip(D, 0, hparams.max_abs_value) * -hparams.min_level_db / hparams.max_abs_value) + hparams.min_level_db) | |
| if hparams.symmetric_mels: | |
| return (((D + hparams.max_abs_value) * -hparams.min_level_db / (2 * hparams.max_abs_value)) + hparams.min_level_db) | |
| else: | |
| return ((D * -hparams.min_level_db / hparams.max_abs_value) + hparams.min_level_db) | |