mirror of https://github.com/coqui-ai/TTS.git
251 lines
9.4 KiB
Python
251 lines
9.4 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 pprint import pprint
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from scipy import signal, io
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class AudioProcessor(object):
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def __init__(self,
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bits=None,
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sample_rate=None,
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num_mels=None,
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min_level_db=None,
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frame_shift_ms=None,
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frame_length_ms=None,
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ref_level_db=None,
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num_freq=None,
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power=None,
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preemphasis=None,
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signal_norm=None,
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symmetric_norm=None,
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max_norm=None,
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mel_fmin=None,
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mel_fmax=None,
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clip_norm=True,
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griffin_lim_iters=None,
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do_trim_silence=False,
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**kwargs):
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print(" > Setting up Audio Processor...")
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self.bits = bits
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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.preemphasis = preemphasis
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self.griffin_lim_iters = griffin_lim_iters
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self.signal_norm = signal_norm
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self.symmetric_norm = symmetric_norm
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self.mel_fmin = 0 if mel_fmin is None else mel_fmin
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self.mel_fmax = mel_fmax
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self.max_norm = 1.0 if max_norm is None else float(max_norm)
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self.clip_norm = clip_norm
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self.do_trim_silence = do_trim_silence
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self.n_fft, self.hop_length, self.win_length = self._stft_parameters()
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print(" | > Audio Processor attributes.")
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members = vars(self)
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for key, value in members.items():
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print(" | > {}:{}".format(key, value))
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def save_wav(self, wav, path):
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wav_norm = wav * (32767 / max(0.01, np.max(np.abs(wav))))
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io.wavfile.write(path, self.sample_rate, wav_norm.astype(np.int16))
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def _linear_to_mel(self, spectrogram):
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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 _mel_to_linear(self, mel_spec):
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inv_mel_basis = np.linalg.pinv(self._build_mel_basis())
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return np.maximum(1e-10, np.dot(inv_mel_basis, mel_spec))
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def _build_mel_basis(self, ):
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n_fft = (self.num_freq - 1) * 2
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if self.mel_fmax is not None:
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assert self.mel_fmax <= self.sample_rate // 2
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return librosa.filters.mel(
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self.sample_rate,
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n_fft,
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n_mels=self.num_mels,
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fmin=self.mel_fmin,
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fmax=self.mel_fmax)
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def _normalize(self, S):
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"""Put values in [0, self.max_norm] or [-self.max_norm, self.max_norm]"""
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if self.signal_norm:
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S_norm = ((S - self.min_level_db) / - self.min_level_db)
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if self.symmetric_norm:
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S_norm = ((2 * self.max_norm) * S_norm) - self.max_norm
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if self.clip_norm :
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S_norm = np.clip(S_norm, -self.max_norm, self.max_norm)
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return S_norm
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else:
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S_norm = self.max_norm * S_norm
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if self.clip_norm:
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S_norm = np.clip(S_norm, 0, self.max_norm)
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return S_norm
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else:
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return S
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def _denormalize(self, S):
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"""denormalize values"""
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S_denorm = S
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if self.signal_norm:
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if self.symmetric_norm:
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if self.clip_norm:
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S_denorm = np.clip(S_denorm, -self.max_norm, self.max_norm)
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S_denorm = ((S_denorm + self.max_norm) * -self.min_level_db / (2 * self.max_norm)) + self.min_level_db
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return S_denorm
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else:
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if self.clip_norm:
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S_denorm = np.clip(S_denorm, 0, self.max_norm)
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S_denorm = (S_denorm * -self.min_level_db /
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self.max_norm) + self.min_level_db
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return S_denorm
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else:
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return S
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def _stft_parameters(self, ):
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"""Compute necessary stft parameters with given time values"""
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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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print(" | > fft size: {}, hop length: {}, win length: {}".format(
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n_fft, hop_length, win_length))
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return n_fft, hop_length, win_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 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 != 0:
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D = self._stft(self.apply_preemphasis(y))
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else:
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D = self._stft(y)
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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 melspectrogram(self, y):
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if self.preemphasis != 0:
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D = self._stft(self.apply_preemphasis(y))
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else:
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D = self._stft(y)
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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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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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# Reconstruct phase
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if self.preemphasis != 0:
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return self.apply_inv_preemphasis(self._griffin_lim(S**self.power))
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else:
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return self._griffin_lim(S**self.power)
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def inv_mel_spectrogram(self, mel_spectrogram):
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'''Converts mel spectrogram to waveform using librosa'''
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D = self._denormalize(mel_spectrogram)
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S = self._db_to_amp(D + self.ref_level_db)
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S = self._mel_to_linear(S) # Convert back to linear
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if self.preemphasis != 0:
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return self.apply_inv_preemphasis(self._griffin_lim(S**self.power))
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else:
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return self._griffin_lim(S**self.power)
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def _griffin_lim(self, S):
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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 = self._istft(S_complex * angles)
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for i in range(self.griffin_lim_iters):
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angles = np.exp(1j * np.angle(self._stft(y)))
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y = self._istft(S_complex * angles)
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return y
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def _stft(self, y):
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return librosa.stft(
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y=y,
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n_fft=self.n_fft,
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hop_length=self.hop_length,
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win_length=self.win_length,
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)
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def _istft(self, y):
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return librosa.istft(
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y, hop_length=self.hop_length, win_length=self.win_length)
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def find_endpoint(self, wav, threshold_db=-40, min_silence_sec=0.8):
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window_length = int(self.sample_rate * min_silence_sec)
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hop_length = int(window_length / 4)
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threshold = self._db_to_amp(threshold_db)
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for x in range(hop_length, len(wav) - window_length, hop_length):
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if np.max(wav[x:x + window_length]) < threshold:
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return x + hop_length
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return len(wav)
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def trim_silence(self, wav):
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""" Trim silent parts with a threshold and 0.1 sec margin """
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margin = int(self.sample_rate * 0.1)
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wav = wav[margin:-margin]
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return librosa.effects.trim(
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wav, top_db=40, frame_length=1024, hop_length=256)[0]
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# WaveRNN repo specific functions
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# def mulaw_encode(self, wav, qc):
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# mu = qc - 1
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# wav_abs = np.minimum(np.abs(wav), 1.0)
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# magnitude = np.log(1 + mu * wav_abs) / np.log(1. + mu)
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# signal = np.sign(wav) * magnitude
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# # Quantize signal to the specified number of levels.
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# signal = (signal + 1) / 2 * mu + 0.5
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# return signal.astype(np.int32)
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# def mulaw_decode(self, wav, qc):
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# """Recovers waveform from quantized values."""
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# mu = qc - 1
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# # Map values back to [-1, 1].
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# casted = wav.astype(np.float32)
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# signal = 2 * (casted / mu) - 1
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# # Perform inverse of mu-law transformation.
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# magnitude = (1 / mu) * ((1 + mu) ** abs(signal) - 1)
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# return np.sign(signal) * magnitude
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def load_wav(self, filename, encode=False):
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x, sr = librosa.load(filename, sr=self.sample_rate)
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if self.do_trim_silence:
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x = self.trim_silence(x)
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# sr, x = io.wavfile.read(filename)
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assert self.sample_rate == sr
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return x
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def encode_16bits(self, x):
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return np.clip(x * 2**15, -2**15, 2**15 - 1).astype(np.int16)
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def quantize(self, x):
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return (x + 1.) * (2**self.bits - 1) / 2
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def dequantize(self, x):
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return 2 * x / (2**self.bits - 1) - 1
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