class AudioProcessor(object):
def __init__(self,
sample_rate=None,
num_mels=None,
min_level_db=None,
frame_shift_ms=None,
frame_length_ms=None,
hop_length=None,
win_length=None,
ref_level_db=None,
fft_size=1024,
power=None,
preemphasis=0.0,
signal_norm=None,
symmetric_norm=None,
max_norm=None,
mel_fmin=None,
mel_fmax=None,
spec_gain=20,
stft_pad_mode='reflect',
clip_norm=True,
griffin_lim_iters=None,
do_trim_silence=False,
trim_db=60,
do_sound_norm=False,
stats_path=None,
**_):
print(" > Setting up Audio Processor...")
# setup class attributed
self.sample_rate = sample_rate
self.num_mels = num_mels
self.min_level_db = min_level_db or 0
self.frame_shift_ms = frame_shift_ms
self.frame_length_ms = frame_length_ms
self.ref_level_db = ref_level_db
self.fft_size = fft_size
self.power = power
self.preemphasis = preemphasis
self.griffin_lim_iters = griffin_lim_iters
self.signal_norm = signal_norm
self.symmetric_norm = symmetric_norm
self.mel_fmin = mel_fmin or 0
self.mel_fmax = mel_fmax
self.spec_gain = float(spec_gain)
self.stft_pad_mode = stft_pad_mode
self.max_norm = 1.0 if max_norm is None else float(max_norm)
self.clip_norm = clip_norm
self.do_trim_silence = do_trim_silence
self.trim_db = trim_db
self.do_sound_norm = do_sound_norm
self.stats_path = stats_path
# setup stft parameters
if hop_length is None:
# compute stft parameters from given time values
self.hop_length, self.win_length = self._stft_parameters()
else:
# use stft parameters from config file
self.hop_length = hop_length
self.win_length = win_length
assert min_level_db != 0.0, " [!] min_level_db is 0"
assert self.win_length <= self.fft_size, " [!] win_length cannot be larger than fft_size"
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())
# setup scaler
if stats_path:
mel_mean, mel_std, linear_mean, linear_std, _ = self.load_stats(
stats_path)
self.setup_scaler(mel_mean, mel_std, linear_mean, linear_std)
self.signal_norm = True
self.max_norm = None
self.clip_norm = None
self.symmetric_norm = None
### setting up the parameters ###
def _build_mel_basis(self, ):
if self.mel_fmax is not None:
assert self.mel_fmax <= self.sample_rate // 2
return librosa.filters.mel(self.sample_rate,
self.fft_size,
n_mels=self.num_mels,
fmin=self.mel_fmin,
fmax=self.mel_fmax)
def _stft_parameters(self, ):
"""Compute necessary stft parameters with given time values"""
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 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
S = S.copy()
if self.signal_norm:
# mean-var scaling
if hasattr(self, 'mel_scaler'):
if S.shape[0] == self.num_mels:
return self.mel_scaler.transform(S.T).T
elif S.shape[0] == self.fft_size / 2:
return self.linear_scaler.transform(S.T).T
else:
raise RuntimeError(
' [!] Mean-Var stats does not match the given feature dimensions.'
)
# range normalization
S -= self.ref_level_db # discard certain range of DB assuming it is air noise
S_norm = ((S - self.min_level_db) / (-self.min_level_db))
if self.symmetric_norm:
S_norm = ((2 * self.max_norm) * S_norm) - self.max_norm
if self.clip_norm:
S_norm = np.clip(S_norm, -self.max_norm, self.max_norm) # pylint: disable=invalid-unary-operand-type
return S_norm
else:
S_norm = self.max_norm * S_norm
if self.clip_norm:
S_norm = np.clip(S_norm, 0, self.max_norm)
return S_norm
else:
return S
def _denormalize(self, S):
"""denormalize values"""
#pylint: disable=no-else-return
S_denorm = S.copy()
if self.signal_norm:
# mean-var scaling
if hasattr(self, 'mel_scaler'):
if S_denorm.shape[0] == self.num_mels:
return self.mel_scaler.inverse_transform(S_denorm.T).T
elif S_denorm.shape[0] == self.fft_size / 2:
return self.linear_scaler.inverse_transform(S_denorm.T).T
else:
raise RuntimeError(
' [!] Mean-Var stats does not match the given feature dimensions.'
)
if self.symmetric_norm:
if self.clip_norm:
S_denorm = np.clip(S_denorm, -self.max_norm, self.max_norm) #pylint: disable=invalid-unary-operand-type
S_denorm = ((S_denorm + self.max_norm) * -self.min_level_db /
(2 * self.max_norm)) + self.min_level_db
return S_denorm + self.ref_level_db
else:
if self.clip_norm:
S_denorm = np.clip(S_denorm, 0, self.max_norm)
S_denorm = (S_denorm * -self.min_level_db /
self.max_norm) + self.min_level_db
return S_denorm + self.ref_level_db
else:
return S_denorm
### Mean-STD scaling ###
def load_stats(self, stats_path):
stats = np.load(stats_path, allow_pickle=True).item() #pylint: disable=unexpected-keyword-arg
mel_mean = stats['mel_mean']
mel_std = stats['mel_std']
linear_mean = stats['linear_mean']
linear_std = stats['linear_std']
stats_config = stats['audio_config']
# check all audio parameters used for computing stats
skip_parameters = [
'griffin_lim_iters', 'stats_path', 'do_trim_silence',
'ref_level_db', 'power'
]
for key in stats_config.keys():
if key in skip_parameters:
continue
if key != 'sample_rate':
assert stats_config[key] == self.__dict__[key],\
f" [!] Audio param {key} does not match the value used for computing mean-var stats. {stats_config[key]} vs {self.__dict__[key]}"
return mel_mean, mel_std, linear_mean, linear_std, stats_config
# pylint: disable=attribute-defined-outside-init
def setup_scaler(self, mel_mean, mel_std, linear_mean, linear_std):
self.mel_scaler = StandardScaler()
self.mel_scaler.set_stats(mel_mean, mel_std)
self.linear_scaler = StandardScaler()
self.linear_scaler.set_stats(linear_mean, linear_std)
### DB and AMP conversion ###
# pylint: disable=no-self-use
def _amp_to_db(self, x):
return self.spec_gain * np.log10(np.maximum(1e-5, x))
# pylint: disable=no-self-use
def _db_to_amp(self, x):
return np.power(10.0, x / self.spec_gain)
### Preemphasis ###
def apply_preemphasis(self, x):
if self.preemphasis == 0:
raise RuntimeError(" [!] Preemphasis is set 0.0.")
return scipy.signal.lfilter([1, -self.preemphasis], [1], x)
def apply_inv_preemphasis(self, x):
if self.preemphasis == 0:
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))
else:
D = self._stft(y)
S = self._amp_to_db(np.abs(D))
return self._normalize(S)
def melspectrogram(self, y):
if self.preemphasis != 0:
D = self._stft(self.apply_preemphasis(y))
else:
D = self._stft(y)
S = self._amp_to_db(self._linear_to_mel(np.abs(D)))
return self._normalize(S)
def inv_spectrogram(self, spectrogram):
"""Converts spectrogram to waveform using librosa"""
S = self._denormalize(spectrogram)
S = self._db_to_amp(S)
# 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_melspectrogram(self, mel_spectrogram):
'''Converts melspectrogram to waveform using librosa'''
D = self._denormalize(mel_spectrogram)
S = self._db_to_amp(D)
S = self._mel_to_linear(S) # Convert back to linear
if self.preemphasis != 0:
return self.apply_inv_preemphasis(self._griffin_lim(S**self.power))
return self._griffin_lim(S**self.power)
def out_linear_to_mel(self, linear_spec):
S = self._denormalize(linear_spec)
S = self._db_to_amp(S)
S = self._linear_to_mel(np.abs(S))
S = self._amp_to_db(S)
mel = self._normalize(S)
return mel
### STFT and ISTFT ###
def _stft(self, y):
return librosa.stft(
y=y,
n_fft=self.fft_size,
hop_length=self.hop_length,
win_length=self.win_length,
pad_mode=self.stft_pad_mode,
)
def _istft(self, y):
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
def compute_stft_paddings(self, x, pad_sides=1):
'''compute right padding (final frame) or both sides padding (first and final frames)
'''
assert pad_sides in (1, 2)
pad = (x.shape[0] // self.hop_length +
1) * self.hop_length - x.shape[0]
if pad_sides == 1:
return 0, pad
return pad // 2, pad // 2 + pad % 2
### Compute F0 ###
def compute_f0(self, x):
f0, t = pw.dio(
x.astype(np.double),
fs=self.sample_rate,
f0_ceil=self.mel_fmax,
frame_period=1000 * self.hop_length / self.sample_rate,
)
f0 = pw.stonemask(x.astype(np.double), f0, t, self.sample_rate)
return f0
### 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)
threshold = self._db_to_amp(threshold_db)
for x in range(hop_length, len(wav) - window_length, hop_length):
if np.max(wav[x:x + window_length]) < threshold:
return x + hop_length
return len(wav)
def trim_silence(self, wav):
""" Trim silent parts with a threshold and 0.01 sec margin """
margin = int(self.sample_rate * 0.01)
wav = wav[margin:-margin]
return librosa.effects.trim(wav,
top_db=self.trim_db,
frame_length=self.win_length,
hop_length=self.hop_length)[0]
@staticmethod
def sound_norm(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)
assert self.sample_rate == sr, "%s vs %s" % (self.sample_rate, sr)
else:
x, sr = librosa.load(filename, sr=sr)
if self.do_trim_silence:
try:
x = self.trim_silence(x)
except ValueError:
print(f' [!] File cannot be trimmed for silence - {filename}')
if self.do_sound_norm:
x = self.sound_norm(x)
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
# wav_abs = np.minimum(np.abs(wav), 1.0)
signal = np.sign(wav) * np.log(1 + mu * np.abs(wav)) / np.log(1. + mu)
# Quantize signal to the specified number of levels.
signal = (signal + 1) / 2 * mu + 0.5
return np.floor(signal, )
@staticmethod
def mulaw_decode(wav, qc):
"""Recovers waveform from quantized values."""
mu = 2**qc - 1
x = np.sign(wav) / mu * ((1 + mu)**np.abs(wav) - 1)
return x
@staticmethod
def encode_16bits(x):
return np.clip(x * 2**15, -2**15, 2**15 - 1).astype(np.int16)
@staticmethod
def quantize(x, bits):
return (x + 1.) * (2**bits - 1) / 2
@staticmethod
def dequantize(x, bits):
return 2 * x / (2**bits - 1) - 1
def setup_scaler(self, mel_mean, mel_std, linear_mean, linear_std):
self.mel_scaler = StandardScaler()
self.mel_scaler.set_stats(mel_mean, mel_std)
self.linear_scaler = StandardScaler()
self.linear_scaler.set_stats(linear_mean, linear_std)
class AudioProcessor(object):
"""Audio Processor for TTS used by all the data pipelines.
Note:
All the class arguments are set to default values to enable a flexible initialization
of the class with the model config. They are not meaningful for all the arguments.
Args:
sample_rate (int, optional): target audio sampling rate. Defaults to None.
resample (bool, optional): enable/disable resampling of the audio clips when the target sampling rate does not match the original sampling rate. Defaults to False.
num_mels (int, optional): number of melspectrogram dimensions. Defaults to None.
min_level_db (int, optional): minimum db threshold for the computed melspectrograms. Defaults to None.
frame_shift_ms (int, optional): milliseconds of frames between STFT columns. Defaults to None.
frame_length_ms (int, optional): milliseconds of STFT window length. Defaults to None.
hop_length (int, optional): number of frames between STFT columns. Used if ```frame_shift_ms``` is None. Defaults to None.
win_length (int, optional): STFT window length. Used if ```frame_length_ms``` is None. Defaults to None.
ref_level_db (int, optional): reference DB level to avoid background noise. In general <20DB corresponds to the air noise. Defaults to None.
fft_size (int, optional): FFT window size for STFT. Defaults to 1024.
power (int, optional): Exponent value applied to the spectrogram before GriffinLim. Defaults to None.
preemphasis (float, optional): Preemphasis coefficient. Preemphasis is disabled if == 0.0. Defaults to 0.0.
signal_norm (bool, optional): enable/disable signal normalization. Defaults to None.
symmetric_norm (bool, optional): enable/disable symmetric normalization. If set True normalization is performed in the range [-k, k] else [0, k], Defaults to None.
max_norm (float, optional): ```k``` defining the normalization range. Defaults to None.
mel_fmin (int, optional): minimum filter frequency for computing melspectrograms. Defaults to None.
mel_fmax (int, optional): maximum filter frequency for computing melspectrograms.. Defaults to None.
spec_gain (int, optional): gain applied when converting amplitude to DB. Defaults to 20.
stft_pad_mode (str, optional): Padding mode for STFT. Defaults to 'reflect'.
clip_norm (bool, optional): enable/disable clipping the our of range values in the normalized audio signal. Defaults to True.
griffin_lim_iters (int, optional): Number of GriffinLim iterations. Defaults to None.
do_trim_silence (bool, optional): enable/disable silence trimming when loading the audio signal. Defaults to False.
trim_db (int, optional): DB threshold used for silence trimming. Defaults to 60.
do_sound_norm (bool, optional): enable/disable signal normalization. Defaults to False.
stats_path (str, optional): Path to the computed stats file. Defaults to None.
verbose (bool, optional): enable/disable logging. Defaults to True.
"""
def __init__(
self,
sample_rate=None,
resample=False,
num_mels=None,
log_func="np.log10",
min_level_db=None,
frame_shift_ms=None,
frame_length_ms=None,
hop_length=None,
win_length=None,
ref_level_db=None,
fft_size=1024,
power=None,
preemphasis=0.0,
signal_norm=None,
symmetric_norm=None,
max_norm=None,
mel_fmin=None,
mel_fmax=None,
spec_gain=20,
stft_pad_mode="reflect",
clip_norm=True,
griffin_lim_iters=None,
do_trim_silence=False,
trim_db=60,
do_sound_norm=False,
stats_path=None,
verbose=True,
**_,
):
# setup class attributed
self.sample_rate = sample_rate
self.resample = resample
self.num_mels = num_mels
self.min_level_db = min_level_db or 0
self.frame_shift_ms = frame_shift_ms
self.frame_length_ms = frame_length_ms
self.ref_level_db = ref_level_db
self.fft_size = fft_size
self.power = power
self.preemphasis = preemphasis
self.griffin_lim_iters = griffin_lim_iters
self.signal_norm = signal_norm
self.symmetric_norm = symmetric_norm
self.mel_fmin = mel_fmin or 0
self.mel_fmax = mel_fmax
self.spec_gain = float(spec_gain)
self.stft_pad_mode = stft_pad_mode
self.max_norm = 1.0 if max_norm is None else float(max_norm)
self.clip_norm = clip_norm
self.do_trim_silence = do_trim_silence
self.trim_db = trim_db
self.do_sound_norm = do_sound_norm
self.stats_path = stats_path
# setup exp_func for db to amp conversion
if log_func == "np.log":
self.base = np.e
elif log_func == "np.log10":
self.base = 10
else:
raise ValueError(" [!] unknown `log_func` value.")
# setup stft parameters
if hop_length is None:
# compute stft parameters from given time values
self.hop_length, self.win_length = self._stft_parameters()
else:
# use stft parameters from config file
self.hop_length = hop_length
self.win_length = win_length
assert min_level_db != 0.0, " [!] min_level_db is 0"
assert self.win_length <= self.fft_size, " [!] win_length cannot be larger than fft_size"
members = vars(self)
if verbose:
print(" > Setting up Audio Processor...")
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())
# setup scaler
if stats_path and signal_norm:
mel_mean, mel_std, linear_mean, linear_std, _ = self.load_stats(
stats_path)
self.setup_scaler(mel_mean, mel_std, linear_mean, linear_std)
self.signal_norm = True
self.max_norm = None
self.clip_norm = None
self.symmetric_norm = None
### setting up the parameters ###
def _build_mel_basis(self, ):
if self.mel_fmax is not None:
assert self.mel_fmax <= self.sample_rate // 2
return librosa.filters.mel(self.sample_rate,
self.fft_size,
n_mels=self.num_mels,
fmin=self.mel_fmin,
fmax=self.mel_fmax)
def _stft_parameters(self, ):
"""Compute necessary stft parameters with given time values"""
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 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
S = S.copy()
if self.signal_norm:
# mean-var scaling
if hasattr(self, "mel_scaler"):
if S.shape[0] == self.num_mels:
return self.mel_scaler.transform(S.T).T
elif S.shape[0] == self.fft_size / 2:
return self.linear_scaler.transform(S.T).T
else:
raise RuntimeError(
" [!] Mean-Var stats does not match the given feature dimensions."
)
# range normalization
S -= self.ref_level_db # discard certain range of DB assuming it is air noise
S_norm = (S - self.min_level_db) / (-self.min_level_db)
if self.symmetric_norm:
S_norm = ((2 * self.max_norm) * S_norm) - self.max_norm
if self.clip_norm:
S_norm = np.clip(
S_norm,
-self.max_norm,
self.max_norm # pylint: disable=invalid-unary-operand-type
)
return S_norm
else:
S_norm = self.max_norm * S_norm
if self.clip_norm:
S_norm = np.clip(S_norm, 0, self.max_norm)
return S_norm
else:
return S
def denormalize(self, S):
"""denormalize values"""
# pylint: disable=no-else-return
S_denorm = S.copy()
if self.signal_norm:
# mean-var scaling
if hasattr(self, "mel_scaler"):
if S_denorm.shape[0] == self.num_mels:
return self.mel_scaler.inverse_transform(S_denorm.T).T
elif S_denorm.shape[0] == self.fft_size / 2:
return self.linear_scaler.inverse_transform(S_denorm.T).T
else:
raise RuntimeError(
" [!] Mean-Var stats does not match the given feature dimensions."
)
if self.symmetric_norm:
if self.clip_norm:
S_denorm = np.clip(
S_denorm,
-self.max_norm,
self.max_norm # pylint: disable=invalid-unary-operand-type
)
S_denorm = ((S_denorm + self.max_norm) * -self.min_level_db /
(2 * self.max_norm)) + self.min_level_db
return S_denorm + self.ref_level_db
else:
if self.clip_norm:
S_denorm = np.clip(S_denorm, 0, self.max_norm)
S_denorm = (S_denorm * -self.min_level_db /
self.max_norm) + self.min_level_db
return S_denorm + self.ref_level_db
else:
return S_denorm
### Mean-STD scaling ###
def load_stats(self, stats_path):
stats = np.load(stats_path, allow_pickle=True).item() # pylint: disable=unexpected-keyword-arg
mel_mean = stats["mel_mean"]
mel_std = stats["mel_std"]
linear_mean = stats["linear_mean"]
linear_std = stats["linear_std"]
stats_config = stats["audio_config"]
# check all audio parameters used for computing stats
skip_parameters = [
"griffin_lim_iters", "stats_path", "do_trim_silence",
"ref_level_db", "power"
]
for key in stats_config.keys():
if key in skip_parameters:
continue
if key not in ["sample_rate", "trim_db"]:
assert (
stats_config[key] == self.__dict__[key]
), f" [!] Audio param {key} does not match the value used for computing mean-var stats. {stats_config[key]} vs {self.__dict__[key]}"
return mel_mean, mel_std, linear_mean, linear_std, stats_config
# pylint: disable=attribute-defined-outside-init
def setup_scaler(self, mel_mean, mel_std, linear_mean, linear_std):
self.mel_scaler = StandardScaler()
self.mel_scaler.set_stats(mel_mean, mel_std)
self.linear_scaler = StandardScaler()
self.linear_scaler.set_stats(linear_mean, linear_std)
### DB and AMP conversion ###
# pylint: disable=no-self-use
def _amp_to_db(self, x):
return self.spec_gain * _log(np.maximum(1e-5, x), self.base)
# pylint: disable=no-self-use
def _db_to_amp(self, x):
return _exp(x / self.spec_gain, self.base)
### Preemphasis ###
def apply_preemphasis(self, x):
if self.preemphasis == 0:
raise RuntimeError(" [!] Preemphasis is set 0.0.")
return scipy.signal.lfilter([1, -self.preemphasis], [1], x)
def apply_inv_preemphasis(self, x):
if self.preemphasis == 0:
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))
else:
D = self._stft(y)
S = self._amp_to_db(np.abs(D))
return self.normalize(S)
def melspectrogram(self, y):
if self.preemphasis != 0:
D = self._stft(self.apply_preemphasis(y))
else:
D = self._stft(y)
S = self._amp_to_db(self._linear_to_mel(np.abs(D)))
return self.normalize(S)
def inv_spectrogram(self, spectrogram):
"""Converts spectrogram to waveform using librosa"""
S = self.denormalize(spectrogram)
S = self._db_to_amp(S)
# 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_melspectrogram(self, mel_spectrogram):
"""Converts melspectrogram to waveform using librosa"""
D = self.denormalize(mel_spectrogram)
S = self._db_to_amp(D)
S = self._mel_to_linear(S) # Convert back to linear
if self.preemphasis != 0:
return self.apply_inv_preemphasis(self._griffin_lim(S**self.power))
return self._griffin_lim(S**self.power)
def out_linear_to_mel(self, linear_spec):
S = self.denormalize(linear_spec)
S = self._db_to_amp(S)
S = self._linear_to_mel(np.abs(S))
S = self._amp_to_db(S)
mel = self.normalize(S)
return mel
### STFT and ISTFT ###
def _stft(self, y):
return librosa.stft(
y=y,
n_fft=self.fft_size,
hop_length=self.hop_length,
win_length=self.win_length,
pad_mode=self.stft_pad_mode,
window="hann",
center=True,
)
def _istft(self, y):
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
def compute_stft_paddings(self, x, pad_sides=1):
"""compute right padding (final frame) or both sides padding (first and final frames)"""
assert pad_sides in (1, 2)
pad = (x.shape[0] // self.hop_length +
1) * self.hop_length - x.shape[0]
if pad_sides == 1:
return 0, pad
return pad // 2, pad // 2 + pad % 2
### Compute F0 ###
# TODO: pw causes some dep issues
# def compute_f0(self, x):
# f0, t = pw.dio(
# x.astype(np.double),
# fs=self.sample_rate,
# f0_ceil=self.mel_fmax,
# frame_period=1000 * self.hop_length / self.sample_rate,
# )
# f0 = pw.stonemask(x.astype(np.double), f0, t, self.sample_rate)
# return f0
### 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)
threshold = self._db_to_amp(threshold_db)
for x in range(hop_length, len(wav) - window_length, hop_length):
if np.max(wav[x:x + window_length]) < threshold:
return x + hop_length
return len(wav)
def trim_silence(self, wav):
""" Trim silent parts with a threshold and 0.01 sec margin """
margin = int(self.sample_rate * 0.01)
wav = wav[margin:-margin]
return librosa.effects.trim(wav,
top_db=self.trim_db,
frame_length=self.win_length,
hop_length=self.hop_length)[0]
@staticmethod
def sound_norm(x):
return x / abs(x).max() * 0.95
### save and load ###
def load_wav(self, filename, sr=None):
if self.resample:
x, sr = librosa.load(filename, sr=self.sample_rate)
elif sr is None:
x, sr = sf.read(filename)
assert self.sample_rate == sr, "%s vs %s" % (self.sample_rate, sr)
else:
x, sr = librosa.load(filename, sr=sr)
if self.do_trim_silence:
try:
x = self.trim_silence(x)
except ValueError:
print(f" [!] File cannot be trimmed for silence - {filename}")
if self.do_sound_norm:
x = self.sound_norm(x)
return x
def save_wav(self, wav, path, sr=None):
wav_norm = wav * (32767 / max(0.01, np.max(np.abs(wav))))
scipy.io.wavfile.write(path, sr if sr else self.sample_rate,
wav_norm.astype(np.int16))
@staticmethod
def mulaw_encode(wav, qc):
mu = 2**qc - 1
# wav_abs = np.minimum(np.abs(wav), 1.0)
signal = np.sign(wav) * np.log(1 + mu * np.abs(wav)) / np.log(1.0 + mu)
# Quantize signal to the specified number of levels.
signal = (signal + 1) / 2 * mu + 0.5
return np.floor(signal, )
@staticmethod
def mulaw_decode(wav, qc):
"""Recovers waveform from quantized values."""
mu = 2**qc - 1
x = np.sign(wav) / mu * ((1 + mu)**np.abs(wav) - 1)
return x
@staticmethod
def encode_16bits(x):
return np.clip(x * 2**15, -(2**15), 2**15 - 1).astype(np.int16)
@staticmethod
def quantize(x, bits):
return (x + 1.0) * (2**bits - 1) / 2
@staticmethod
def dequantize(x, bits):
return 2 * x / (2**bits - 1) - 1