mirror of https://github.com/coqui-ai/TTS.git
109 lines
4.2 KiB
Python
109 lines
4.2 KiB
Python
import os
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import librosa
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import pickle
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import copy
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import numpy as np
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from scipy import signal
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import lws
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_mel_basis = None
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class AudioProcessor(object):
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def __init__(self, sample_rate, num_mels, min_level_db, frame_shift_ms,
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frame_length_ms, ref_level_db, num_freq, power, preemphasis,
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min_mel_freq, max_mel_freq, griffin_lim_iters=None, ):
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self.sample_rate = sample_rate
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self.num_mels = num_mels
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self.min_level_db = min_level_db
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self.frame_shift_ms = frame_shift_ms
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self.frame_length_ms = frame_length_ms
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self.ref_level_db = ref_level_db
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self.num_freq = num_freq
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self.power = power
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self.min_mel_freq = min_mel_freq
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self.max_mel_freq = max_mel_freq
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self.griffin_lim_iters = griffin_lim_iters
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self.preemphasis =preemphasis
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_, self.hop_length, self.win_length = self._stft_parameters()
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self.num_freq = (self.win_length // 2) + 1
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if preemphasis == 0:
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print(" | > Preemphasis is deactive.")
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def save_wav(self, wav, path):
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wav *= 32767 / max(0.01, np.max(np.abs(wav)))
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librosa.output.write_wav(path, wav.astype(np.float), self.sample_rate, norm=True)
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def _stft_parameters(self, ):
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n_fft = (self.num_freq - 1) * 2
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hop_length = int(self.frame_shift_ms / 1000.0 * self.sample_rate)
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win_length = int(self.frame_length_ms / 1000.0 * self.sample_rate)
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return n_fft, hop_length, win_length
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def _lws_processor(self):
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try:
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return lws.lws(self.win_length, self.hop_length ,mode="speech")
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except:
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raise RuntimeError(" !! WindowLength({}) is not multiple of HopLength({}).".format(self.win_length, self.hop_length))
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def _amp_to_db(self, x):
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min_level = np.exp(self.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(self, x):
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return np.power(10.0, x * 0.05)
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def _normalize(self, S):
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return np.clip((S - self.min_level_db) / -self.min_level_db, 0, 1)
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def _denormalize(self, S):
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return (np.clip(S, 0, 1) * -self.min_level_db) + self.min_level_db
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def apply_preemphasis(self, x):
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if self.preemphasis == 0:
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raise RuntimeError(" !! Preemphasis is applied with factor 0.0. ")
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return signal.lfilter([1, -self.preemphasis], [1], x)
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def apply_inv_preemphasis(self, x):
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if self.preemphasis == 0:
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raise RuntimeError(" !! Preemphasis is applied with factor 0.0. ")
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return signal.lfilter([1], [1, -self.preemphasis], x)
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def spectrogram(self, y):
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if self.preemphasis:
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D = self._lws_processor().stft(self.apply_preemphasis(y)).T
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else:
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D = self._lws_processor().stft(y).T
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S = self._amp_to_db(np.abs(D)) - self.ref_level_db
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return self._normalize(S)
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def inv_spectrogram(self, spectrogram):
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'''Converts spectrogram to waveform using librosa'''
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S = self._denormalize(spectrogram)
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S = self._db_to_amp(S + self.ref_level_db) # Convert back to linear
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processor = self._lws_processor()
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D = processor.run_lws(S.astype(np.float64).T ** self.power)
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y = processor.istft(D).astype(np.float32)
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# Reconstruct phase
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if self.preemphasis:
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return self.apply_inv_preemphasis(y)
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return y
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def _linear_to_mel(self, spectrogram):
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global _mel_basis
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if _mel_basis is None:
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_mel_basis = self._build_mel_basis()
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return np.dot(_mel_basis, spectrogram)
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def _build_mel_basis(self, ):
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return librosa.filters.mel(self.sample_rate, self.win_length, n_mels=self.num_mels)
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# fmin=self.min_mel_freq, fmax=self.max_mel_freq)
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def melspectrogram(self, y):
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if self.preemphasis:
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D = self._lws_processor().stft(self.apply_preemphasis(y)).T
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else:
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D = self._lws_processor().stft(y).T
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S = self._amp_to_db(self._linear_to_mel(np.abs(D))) - self.ref_level_db
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return self._normalize(S)
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