Medix-AI/coqui-tts
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coqui-tts
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formatting audio.py

This commit is contained in:
erogol 2020-03-09 21:05:10 +01:00
parent 032bf312c6
commit a2e900ef3b
1 changed files with 54 additions and 35 deletions
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89
utils/audio.py
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@ -51,6 +51,7 @@ class AudioProcessor(object):
self.do_trim_silence = do_trim_silence
self.trim_db = trim_db
self.sound_norm = sound_norm
# setup stft parameters
if hop_length is None:
self.n_fft, self.hop_length, self.win_length = self._stft_parameters()
else:
@ -61,19 +62,11 @@ class AudioProcessor(object):
members = vars(self)
for key, value in members.items():
print(" | > {}:{}".format(key, value))
# create spectrogram utils
self.mel_basis = self._build_mel_basis()
self.inv_mel_basis = np.linalg.pinv(self._build_mel_basis())
def save_wav(self, wav, path):
wav_norm = wav * (32767 / max(0.01, np.max(np.abs(wav))))
scipy.io.wavfile.write(path, self.sample_rate, wav_norm.astype(np.int16))
def _linear_to_mel(self, spectrogram):
_mel_basis = self._build_mel_basis()
return np.dot(_mel_basis, spectrogram)
def _mel_to_linear(self, mel_spec):
inv_mel_basis = np.linalg.pinv(self._build_mel_basis())
return np.maximum(1e-10, np.dot(inv_mel_basis, mel_spec))
### setting up the parameters ###
def _build_mel_basis(self, ):
if self.mel_fmax is not None:
assert self.mel_fmax <= self.sample_rate // 2
@ -84,6 +77,16 @@ class AudioProcessor(object):
fmin=self.mel_fmin,
fmax=self.mel_fmax)
def _stft_parameters(self, ):
"""Compute necessary stft parameters with given time values"""
n_fft = (self.num_freq - 1) * 2
factor = self.frame_length_ms / self.frame_shift_ms
assert (factor).is_integer(), " [!] frame_shift_ms should divide frame_length_ms"
hop_length = int(self.frame_shift_ms / 1000.0 * self.sample_rate)
win_length = int(hop_length * factor)
return n_fft, hop_length, win_length
### normalization ###
def _normalize(self, S):
"""Put values in [0, self.max_norm] or [-self.max_norm, self.max_norm]"""
#pylint: disable=no-else-return
@ -121,23 +124,15 @@ class AudioProcessor(object):
else:
return S
def _stft_parameters(self, ):
"""Compute necessary stft parameters with given time values"""
n_fft = (self.num_freq - 1) * 2
factor = self.frame_length_ms / self.frame_shift_ms
assert (factor).is_integer(), " [!] frame_shift_ms should divide frame_length_ms"
hop_length = int(self.frame_shift_ms / 1000.0 * self.sample_rate)
win_length = int(hop_length * factor)
return n_fft, hop_length, win_length
### DB and AMP conversion ###
def _amp_to_db(self, x):
min_level = np.exp(self.min_level_db / 20 * np.log(10))
return 20 * np.log10(np.maximum(min_level, x))
@staticmethod
def _db_to_amp(x):
def _db_to_amp(self, x):
return np.power(10.0, x * 0.05)
### Preemphasis ###
def apply_preemphasis(self, x):
if self.preemphasis == 0:
raise RuntimeError(" [!] Preemphasis is set 0.0.")
@ -148,6 +143,13 @@ class AudioProcessor(object):
raise RuntimeError(" [!] Preemphasis is set 0.0.")
return scipy.signal.lfilter([1], [1, -self.preemphasis], x)
### SPECTROGRAMs ###
def _linear_to_mel(self, spectrogram):
return np.dot(self.mel_basis, spectrogram)
def _mel_to_linear(self, mel_spec):
return np.maximum(1e-10, np.dot(self.inv_mel_basis, mel_spec))
def spectrogram(self, y):
if self.preemphasis != 0:
D = self._stft(self.apply_preemphasis(y))
@ -167,14 +169,14 @@ class AudioProcessor(object):
def inv_spectrogram(self, spectrogram):
"""Converts spectrogram to waveform using librosa"""
S = self._denormalize(spectrogram)
S = self._db_to_amp(S + self.ref_level_db) # Convert back to linear
S = self._db_to_amp(S + self.ref_level_db)
# Reconstruct phase
if self.preemphasis != 0:
return self.apply_inv_preemphasis(self._griffin_lim(S**self.power))
return self._griffin_lim(S**self.power)
def inv_mel_spectrogram(self, mel_spectrogram):
'''Converts mel spectrogram to waveform using librosa'''
def inv_melspectrogram(self, mel_spectrogram):
'''Converts melspectrogram to waveform using librosa'''
D = self._denormalize(mel_spectrogram)
S = self._db_to_amp(D + self.ref_level_db)
S = self._mel_to_linear(S) # Convert back to linear
@ -190,15 +192,7 @@ class AudioProcessor(object):
mel = self._normalize(S)
return mel
def _griffin_lim(self, S):
angles = np.exp(2j * np.pi * np.random.rand(*S.shape))
S_complex = np.abs(S).astype(np.complex)
y = self._istft(S_complex * angles)
for _ in range(self.griffin_lim_iters):
angles = np.exp(1j * np.angle(self._stft(y)))
y = self._istft(S_complex * angles)
return y
### STFT and ISTFT ###
def _stft(self, y):
return librosa.stft(
y=y,
@ -212,6 +206,16 @@ class AudioProcessor(object):
return librosa.istft(
y, hop_length=self.hop_length, win_length=self.win_length)
def _griffin_lim(self, S):
angles = np.exp(2j * np.pi * np.random.rand(*S.shape))
S_complex = np.abs(S).astype(np.complex)
y = self._istft(S_complex * angles)
for _ in range(self.griffin_lim_iters):
angles = np.exp(1j * np.angle(self._stft(y)))
y = self._istft(S_complex * angles)
return y
### Audio Processing ###
def find_endpoint(self, wav, threshold_db=-40, min_silence_sec=0.8):
window_length = int(self.sample_rate * min_silence_sec)
hop_length = int(window_length / 4)
@ -228,6 +232,21 @@ class AudioProcessor(object):
return librosa.effects.trim(
wav, top_db=self.trim_db, frame_length=self.win_length, hop_length=self.hop_length)[0]
def sound_norm(self, x):
return x / abs(x).max() * 0.9
### save and load ###
def load_wav(self, filename, sr=None):
if sr is None:
x, sr = sf.read(filename)
else:
x, sr = librosa.load(filename, sr=sr)
return x
def save_wav(self, wav, path):
wav_norm = wav * (32767 / max(0.01, np.max(np.abs(wav))))
scipy.io.wavfile.write(path, self.sample_rate, wav_norm.astype(np.int16))
@staticmethod
def mulaw_encode(wav, qc):
mu = 2 ** qc - 1