mirror of
https://github.com/babysor/MockingBird.git
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207 lines
7.6 KiB
Python
207 lines
7.6 KiB
Python
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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import soundfile as sf
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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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#proposed by @dsmiller
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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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sf.write(path, wav.astype(np.float32), 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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#From https://github.com/r9y9/wavenet_vocoder/blob/master/audio.py
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def start_and_end_indices(quantized, silence_threshold=2):
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for start in range(quantized.size):
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if abs(quantized[start] - 127) > silence_threshold:
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break
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for end in range(quantized.size - 1, 1, -1):
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if abs(quantized[end] - 127) > silence_threshold:
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break
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assert abs(quantized[start] - 127) > silence_threshold
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assert abs(quantized[end] - 127) > silence_threshold
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return start, end
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def get_hop_size(hparams):
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hop_size = hparams.hop_size
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if hop_size is None:
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assert hparams.frame_shift_ms is not None
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hop_size = int(hparams.frame_shift_ms / 1000 * hparams.sample_rate)
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return hop_size
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def linearspectrogram(wav, hparams):
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D = _stft(preemphasis(wav, hparams.preemphasis, hparams.preemphasize), hparams)
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S = _amp_to_db(np.abs(D), hparams) - hparams.ref_level_db
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if hparams.signal_normalization:
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return _normalize(S, hparams)
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return S
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def melspectrogram(wav, hparams):
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D = _stft(preemphasis(wav, hparams.preemphasis, hparams.preemphasize), hparams)
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S = _amp_to_db(_linear_to_mel(np.abs(D), hparams), hparams) - hparams.ref_level_db
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if hparams.signal_normalization:
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return _normalize(S, hparams)
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return S
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def inv_linear_spectrogram(linear_spectrogram, hparams):
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"""Converts linear spectrogram to waveform using librosa"""
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if hparams.signal_normalization:
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D = _denormalize(linear_spectrogram, hparams)
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else:
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D = linear_spectrogram
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S = _db_to_amp(D + hparams.ref_level_db) #Convert back to linear
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if hparams.use_lws:
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processor = _lws_processor(hparams)
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D = processor.run_lws(S.astype(np.float64).T ** hparams.power)
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y = processor.istft(D).astype(np.float32)
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return inv_preemphasis(y, hparams.preemphasis, hparams.preemphasize)
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else:
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return inv_preemphasis(_griffin_lim(S ** hparams.power, hparams), hparams.preemphasis, hparams.preemphasize)
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def inv_mel_spectrogram(mel_spectrogram, hparams):
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"""Converts mel spectrogram to waveform using librosa"""
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if hparams.signal_normalization:
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D = _denormalize(mel_spectrogram, hparams)
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else:
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D = mel_spectrogram
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S = _mel_to_linear(_db_to_amp(D + hparams.ref_level_db), hparams) # Convert back to linear
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if hparams.use_lws:
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processor = _lws_processor(hparams)
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D = processor.run_lws(S.astype(np.float64).T ** hparams.power)
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y = processor.istft(D).astype(np.float32)
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return inv_preemphasis(y, hparams.preemphasis, hparams.preemphasize)
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else:
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return inv_preemphasis(_griffin_lim(S ** hparams.power, hparams), hparams.preemphasis, hparams.preemphasize)
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def _lws_processor(hparams):
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import lws
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return lws.lws(hparams.n_fft, get_hop_size(hparams), fftsize=hparams.win_size, mode="speech")
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def _griffin_lim(S, hparams):
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"""librosa implementation of Griffin-Lim
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Based on https://github.com/librosa/librosa/issues/434
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"""
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angles = np.exp(2j * np.pi * np.random.rand(*S.shape))
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S_complex = np.abs(S).astype(np.complex)
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y = _istft(S_complex * angles, hparams)
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for i in range(hparams.griffin_lim_iters):
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angles = np.exp(1j * np.angle(_stft(y, hparams)))
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y = _istft(S_complex * angles, hparams)
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return y
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def _stft(y, hparams):
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if hparams.use_lws:
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return _lws_processor(hparams).stft(y).T
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else:
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return librosa.stft(y=y, n_fft=hparams.n_fft, hop_length=get_hop_size(hparams), win_length=hparams.win_size)
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def _istft(y, hparams):
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return librosa.istft(y, hop_length=get_hop_size(hparams), win_length=hparams.win_size)
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##########################################################
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#Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!)
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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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##########################################################
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#Librosa correct padding
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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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# Conversions
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_mel_basis = None
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_inv_mel_basis = None
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def _linear_to_mel(spectogram, hparams):
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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(hparams)
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return np.dot(_mel_basis, spectogram)
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def _mel_to_linear(mel_spectrogram, hparams):
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global _inv_mel_basis
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if _inv_mel_basis is None:
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_inv_mel_basis = np.linalg.pinv(_build_mel_basis(hparams))
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return np.maximum(1e-10, np.dot(_inv_mel_basis, mel_spectrogram))
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def _build_mel_basis(hparams):
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assert hparams.fmax <= hparams.sample_rate // 2
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return librosa.filters.mel(sr=hparams.sample_rate, n_fft=hparams.n_fft, n_mels=hparams.num_mels,
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fmin=hparams.fmin, fmax=hparams.fmax)
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def _amp_to_db(x, hparams):
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min_level = np.exp(hparams.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, hparams):
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if hparams.allow_clipping_in_normalization:
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if hparams.symmetric_mels:
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return np.clip((2 * hparams.max_abs_value) * ((S - hparams.min_level_db) / (-hparams.min_level_db)) - hparams.max_abs_value,
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-hparams.max_abs_value, hparams.max_abs_value)
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else:
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return np.clip(hparams.max_abs_value * ((S - hparams.min_level_db) / (-hparams.min_level_db)), 0, hparams.max_abs_value)
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assert S.max() <= 0 and S.min() - hparams.min_level_db >= 0
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if hparams.symmetric_mels:
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return (2 * hparams.max_abs_value) * ((S - hparams.min_level_db) / (-hparams.min_level_db)) - hparams.max_abs_value
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else:
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return hparams.max_abs_value * ((S - hparams.min_level_db) / (-hparams.min_level_db))
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def _denormalize(D, hparams):
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if hparams.allow_clipping_in_normalization:
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if hparams.symmetric_mels:
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return (((np.clip(D, -hparams.max_abs_value,
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hparams.max_abs_value) + hparams.max_abs_value) * -hparams.min_level_db / (2 * hparams.max_abs_value))
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+ hparams.min_level_db)
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else:
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return ((np.clip(D, 0, hparams.max_abs_value) * -hparams.min_level_db / hparams.max_abs_value) + hparams.min_level_db)
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if hparams.symmetric_mels:
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return (((D + hparams.max_abs_value) * -hparams.min_level_db / (2 * hparams.max_abs_value)) + hparams.min_level_db)
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else:
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return ((D * -hparams.min_level_db / hparams.max_abs_value) + hparams.min_level_db)
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