mirror of https://github.com/coqui-ai/TTS.git
formatting audio.py
This commit is contained in:
parent
032bf312c6
commit
a2e900ef3b
|
@ -51,6 +51,7 @@ class AudioProcessor(object):
|
||||||
self.do_trim_silence = do_trim_silence
|
self.do_trim_silence = do_trim_silence
|
||||||
self.trim_db = trim_db
|
self.trim_db = trim_db
|
||||||
self.sound_norm = sound_norm
|
self.sound_norm = sound_norm
|
||||||
|
# setup stft parameters
|
||||||
if hop_length is None:
|
if hop_length is None:
|
||||||
self.n_fft, self.hop_length, self.win_length = self._stft_parameters()
|
self.n_fft, self.hop_length, self.win_length = self._stft_parameters()
|
||||||
else:
|
else:
|
||||||
|
@ -61,19 +62,11 @@ class AudioProcessor(object):
|
||||||
members = vars(self)
|
members = vars(self)
|
||||||
for key, value in members.items():
|
for key, value in members.items():
|
||||||
print(" | > {}:{}".format(key, value))
|
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):
|
### setting up the parameters ###
|
||||||
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))
|
|
||||||
|
|
||||||
def _build_mel_basis(self, ):
|
def _build_mel_basis(self, ):
|
||||||
if self.mel_fmax is not None:
|
if self.mel_fmax is not None:
|
||||||
assert self.mel_fmax <= self.sample_rate // 2
|
assert self.mel_fmax <= self.sample_rate // 2
|
||||||
|
@ -84,6 +77,16 @@ class AudioProcessor(object):
|
||||||
fmin=self.mel_fmin,
|
fmin=self.mel_fmin,
|
||||||
fmax=self.mel_fmax)
|
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):
|
def _normalize(self, S):
|
||||||
"""Put values in [0, self.max_norm] or [-self.max_norm, self.max_norm]"""
|
"""Put values in [0, self.max_norm] or [-self.max_norm, self.max_norm]"""
|
||||||
#pylint: disable=no-else-return
|
#pylint: disable=no-else-return
|
||||||
|
@ -121,23 +124,15 @@ class AudioProcessor(object):
|
||||||
else:
|
else:
|
||||||
return S
|
return S
|
||||||
|
|
||||||
def _stft_parameters(self, ):
|
### DB and AMP conversion ###
|
||||||
"""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
|
|
||||||
|
|
||||||
def _amp_to_db(self, x):
|
def _amp_to_db(self, x):
|
||||||
min_level = np.exp(self.min_level_db / 20 * np.log(10))
|
min_level = np.exp(self.min_level_db / 20 * np.log(10))
|
||||||
return 20 * np.log10(np.maximum(min_level, x))
|
return 20 * np.log10(np.maximum(min_level, x))
|
||||||
|
|
||||||
@staticmethod
|
def _db_to_amp(self, x):
|
||||||
def _db_to_amp(x):
|
|
||||||
return np.power(10.0, x * 0.05)
|
return np.power(10.0, x * 0.05)
|
||||||
|
|
||||||
|
### Preemphasis ###
|
||||||
def apply_preemphasis(self, x):
|
def apply_preemphasis(self, x):
|
||||||
if self.preemphasis == 0:
|
if self.preemphasis == 0:
|
||||||
raise RuntimeError(" [!] Preemphasis is set 0.0.")
|
raise RuntimeError(" [!] Preemphasis is set 0.0.")
|
||||||
|
@ -148,6 +143,13 @@ class AudioProcessor(object):
|
||||||
raise RuntimeError(" [!] Preemphasis is set 0.0.")
|
raise RuntimeError(" [!] Preemphasis is set 0.0.")
|
||||||
return scipy.signal.lfilter([1], [1, -self.preemphasis], x)
|
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):
|
def spectrogram(self, y):
|
||||||
if self.preemphasis != 0:
|
if self.preemphasis != 0:
|
||||||
D = self._stft(self.apply_preemphasis(y))
|
D = self._stft(self.apply_preemphasis(y))
|
||||||
|
@ -167,14 +169,14 @@ class AudioProcessor(object):
|
||||||
def inv_spectrogram(self, spectrogram):
|
def inv_spectrogram(self, spectrogram):
|
||||||
"""Converts spectrogram to waveform using librosa"""
|
"""Converts spectrogram to waveform using librosa"""
|
||||||
S = self._denormalize(spectrogram)
|
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
|
# Reconstruct phase
|
||||||
if self.preemphasis != 0:
|
if self.preemphasis != 0:
|
||||||
return self.apply_inv_preemphasis(self._griffin_lim(S**self.power))
|
return self.apply_inv_preemphasis(self._griffin_lim(S**self.power))
|
||||||
return self._griffin_lim(S**self.power)
|
return self._griffin_lim(S**self.power)
|
||||||
|
|
||||||
def inv_mel_spectrogram(self, mel_spectrogram):
|
def inv_melspectrogram(self, mel_spectrogram):
|
||||||
'''Converts mel spectrogram to waveform using librosa'''
|
'''Converts melspectrogram to waveform using librosa'''
|
||||||
D = self._denormalize(mel_spectrogram)
|
D = self._denormalize(mel_spectrogram)
|
||||||
S = self._db_to_amp(D + self.ref_level_db)
|
S = self._db_to_amp(D + self.ref_level_db)
|
||||||
S = self._mel_to_linear(S) # Convert back to linear
|
S = self._mel_to_linear(S) # Convert back to linear
|
||||||
|
@ -190,15 +192,7 @@ class AudioProcessor(object):
|
||||||
mel = self._normalize(S)
|
mel = self._normalize(S)
|
||||||
return mel
|
return mel
|
||||||
|
|
||||||
def _griffin_lim(self, S):
|
### STFT and ISTFT ###
|
||||||
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
|
|
||||||
|
|
||||||
def _stft(self, y):
|
def _stft(self, y):
|
||||||
return librosa.stft(
|
return librosa.stft(
|
||||||
y=y,
|
y=y,
|
||||||
|
@ -212,6 +206,16 @@ class AudioProcessor(object):
|
||||||
return librosa.istft(
|
return librosa.istft(
|
||||||
y, hop_length=self.hop_length, win_length=self.win_length)
|
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):
|
def find_endpoint(self, wav, threshold_db=-40, min_silence_sec=0.8):
|
||||||
window_length = int(self.sample_rate * min_silence_sec)
|
window_length = int(self.sample_rate * min_silence_sec)
|
||||||
hop_length = int(window_length / 4)
|
hop_length = int(window_length / 4)
|
||||||
|
@ -228,6 +232,21 @@ class AudioProcessor(object):
|
||||||
return librosa.effects.trim(
|
return librosa.effects.trim(
|
||||||
wav, top_db=self.trim_db, frame_length=self.win_length, hop_length=self.hop_length)[0]
|
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
|
@staticmethod
|
||||||
def mulaw_encode(wav, qc):
|
def mulaw_encode(wav, qc):
|
||||||
mu = 2 ** qc - 1
|
mu = 2 ** qc - 1
|
||||||
|
|
Loading…
Reference in New Issue