Medix-AI/coqui-tts
Medix-AI
/
coqui-tts
mirror of https://github.com/coqui-ai/TTS.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

spec_gain fft_size and stft_pad_mode parameters for audio_processor

This commit is contained in:
erogol 2020-06-22 14:55:11 +02:00
parent 70c83671e6
commit 2f3f43844e
3 changed files with 18 additions and 18 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
train.py
View File

@ -473,8 +473,6 @@ def main(args): # pylint: disable=redefined-outer-name
model = setup_model(num_chars, num_speakers, c) model = setup_model(num_chars, num_speakers, c)
print(" | > Num output units : {}".format(ap.num_freq), flush=True)
params = set_weight_decay(model, c.wd) params = set_weight_decay(model, c.wd)
optimizer = RAdam(params, lr=c.lr, weight_decay=0) optimizer = RAdam(params, lr=c.lr, weight_decay=0)
if c.stopnet and c.separate_stopnet: if c.stopnet and c.separate_stopnet:
@ -542,7 +540,6 @@ def main(args): # pylint: disable=redefined-outer-name
if c.bidirectional_decoder: if c.bidirectional_decoder:
model.decoder_backward.set_r(r) model.decoder_backward.set_r(r)
print("\n > Number of output frames:", model.decoder.r) print("\n > Number of output frames:", model.decoder.r)
train_avg_loss_dict, global_step = train(model, criterion, optimizer, train_avg_loss_dict, global_step = train(model, criterion, optimizer,
optimizer_st, scheduler, ap, optimizer_st, scheduler, ap,
global_step, epoch) global_step, epoch)

28
utils/audio.py
View File

@ -17,7 +17,7 @@ class AudioProcessor(object):
hop_length=None, hop_length=None,
win_length=None, win_length=None,
ref_level_db=None, ref_level_db=None,
num_freq=None, fft_size=1024,
power=None, power=None,
preemphasis=0.0, preemphasis=0.0,
signal_norm=None, signal_norm=None,
@ -25,6 +25,8 @@ class AudioProcessor(object):
max_norm=None, max_norm=None,
mel_fmin=None, mel_fmin=None,
mel_fmax=None, mel_fmax=None,
spec_gain=20,
stft_pad_mode='reflect',
clip_norm=True, clip_norm=True,
griffin_lim_iters=None, griffin_lim_iters=None,
do_trim_silence=False, do_trim_silence=False,
@ -41,7 +43,7 @@ class AudioProcessor(object):
self.frame_shift_ms = frame_shift_ms self.frame_shift_ms = frame_shift_ms
self.frame_length_ms = frame_length_ms self.frame_length_ms = frame_length_ms
self.ref_level_db = ref_level_db self.ref_level_db = ref_level_db
self.num_freq = num_freq self.fft_size = fft_size
self.power = power self.power = power
self.preemphasis = preemphasis self.preemphasis = preemphasis
self.griffin_lim_iters = griffin_lim_iters self.griffin_lim_iters = griffin_lim_iters
@ -49,6 +51,8 @@ class AudioProcessor(object):
self.symmetric_norm = symmetric_norm self.symmetric_norm = symmetric_norm
self.mel_fmin = mel_fmin or 0 self.mel_fmin = mel_fmin or 0
self.mel_fmax = mel_fmax self.mel_fmax = mel_fmax
self.spec_gain = float(spec_gain)
self.stft_pad_mode = 'reflect'
self.max_norm = 1.0 if max_norm is None else float(max_norm) self.max_norm = 1.0 if max_norm is None else float(max_norm)
self.clip_norm = clip_norm self.clip_norm = clip_norm
self.do_trim_silence = do_trim_silence self.do_trim_silence = do_trim_silence
@ -58,12 +62,11 @@ class AudioProcessor(object):
# setup stft parameters # setup stft parameters
if hop_length is None: if hop_length is None:
# compute stft parameters from given time values # compute stft parameters from given time values
self.n_fft, self.hop_length, self.win_length = self._stft_parameters() self.hop_length, self.win_length = self._stft_parameters()
else: else:
# use stft parameters from config file # use stft parameters from config file
self.hop_length = hop_length self.hop_length = hop_length
self.win_length = win_length self.win_length = win_length
self.n_fft = (self.num_freq - 1) * 2
assert min_level_db != 0.0, " [!] min_level_db is 0" assert min_level_db != 0.0, " [!] min_level_db is 0"
members = vars(self) members = vars(self)
for key, value in members.items(): for key, value in members.items():
@ -86,19 +89,18 @@ class AudioProcessor(object):
assert self.mel_fmax <= self.sample_rate // 2 assert self.mel_fmax <= self.sample_rate // 2
return librosa.filters.mel( return librosa.filters.mel(
self.sample_rate, self.sample_rate,
self.n_fft, self.fft_size,
n_mels=self.num_mels, n_mels=self.num_mels,
fmin=self.mel_fmin, fmin=self.mel_fmin,
fmax=self.mel_fmax) fmax=self.mel_fmax)
def _stft_parameters(self, ): def _stft_parameters(self, ):
"""Compute necessary stft parameters with given time values""" """Compute necessary stft parameters with given time values"""
n_fft = (self.num_freq - 1) * 2
factor = self.frame_length_ms / self.frame_shift_ms factor = self.frame_length_ms / self.frame_shift_ms
assert (factor).is_integer(), " [!] frame_shift_ms should divide frame_length_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) hop_length = int(self.frame_shift_ms / 1000.0 * self.sample_rate)
win_length = int(hop_length * factor) win_length = int(hop_length * factor)
return n_fft, hop_length, win_length return hop_length, win_length
### normalization ### ### normalization ###
def _normalize(self, S): def _normalize(self, S):
@ -110,7 +112,7 @@ class AudioProcessor(object):
if hasattr(self, 'mel_scaler'): if hasattr(self, 'mel_scaler'):
if S.shape[0] == self.num_mels: if S.shape[0] == self.num_mels:
return self.mel_scaler.transform(S.T).T return self.mel_scaler.transform(S.T).T
elif S.shape[0] == self.n_fft / 2: elif S.shape[0] == self.fft_size / 2:
return self.linear_scaler.transform(S.T).T return self.linear_scaler.transform(S.T).T
else: else:
raise RuntimeError(' [!] Mean-Var stats does not match the given feature dimensions.') raise RuntimeError(' [!] Mean-Var stats does not match the given feature dimensions.')
@ -139,7 +141,7 @@ class AudioProcessor(object):
if hasattr(self, 'mel_scaler'): if hasattr(self, 'mel_scaler'):
if S_denorm.shape[0] == self.num_mels: if S_denorm.shape[0] == self.num_mels:
return self.mel_scaler.inverse_transform(S_denorm.T).T return self.mel_scaler.inverse_transform(S_denorm.T).T
elif S_denorm.shape[0] == self.n_fft / 2: elif S_denorm.shape[0] == self.fft_size / 2:
return self.linear_scaler.inverse_transform(S_denorm.T).T return self.linear_scaler.inverse_transform(S_denorm.T).T
else: else:
raise RuntimeError(' [!] Mean-Var stats does not match the given feature dimensions.') raise RuntimeError(' [!] Mean-Var stats does not match the given feature dimensions.')
@ -184,11 +186,11 @@ class AudioProcessor(object):
### DB and AMP conversion ### ### DB and AMP conversion ###
# pylint: disable=no-self-use # pylint: disable=no-self-use
def _amp_to_db(self, x): def _amp_to_db(self, x):
return 20 * np.log10(np.maximum(1e-5, x)) return self.spec_gain * np.log10(np.maximum(1e-5, x))
# pylint: disable=no-self-use # pylint: disable=no-self-use
def _db_to_amp(self, x): def _db_to_amp(self, x):
return np.power(10.0, x * 0.05) return np.power(10.0, x / self.spec_gain)
### Preemphasis ### ### Preemphasis ###
def apply_preemphasis(self, x): def apply_preemphasis(self, x):
@ -254,10 +256,10 @@ class AudioProcessor(object):
def _stft(self, y): def _stft(self, y):
return librosa.stft( return librosa.stft(
y=y, y=y,
n_fft=self.n_fft, n_fft=self.fft_size,
hop_length=self.hop_length, hop_length=self.hop_length,
win_length=self.win_length, win_length=self.win_length,
pad_mode='constant' pad_mode=self.stft_pad_mode,
) )
def _istft(self, y): def _istft(self, y):

5
utils/generic_utils.py
View File

@ -146,7 +146,7 @@ def setup_model(num_chars, num_speakers, c):
model = MyModel(num_chars=num_chars, model = MyModel(num_chars=num_chars,
num_speakers=num_speakers, num_speakers=num_speakers,
r=c.r, r=c.r,
postnet_output_dim=c.audio['num_freq'], postnet_output_dim=int(c.audio['fft_size'] / 2 + 1),
decoder_output_dim=c.audio['num_mels'], decoder_output_dim=c.audio['num_mels'],
gst=c.use_gst, gst=c.use_gst,
memory_size=c.memory_size, memory_size=c.memory_size,
@ -252,7 +252,7 @@ def check_config(c):
# audio processing parameters # audio processing parameters
_check_argument('num_mels', c['audio'], restricted=True, val_type=int, min_val=10, max_val=2056) _check_argument('num_mels', c['audio'], restricted=True, val_type=int, min_val=10, max_val=2056)
_check_argument('num_freq', c['audio'], restricted=True, val_type=int, min_val=128, max_val=4058) _check_argument('fft_size', c['audio'], restricted=True, val_type=int, min_val=128, max_val=4058)
_check_argument('sample_rate', c['audio'], restricted=True, val_type=int, min_val=512, max_val=100000) _check_argument('sample_rate', c['audio'], restricted=True, val_type=int, min_val=512, max_val=100000)
_check_argument('frame_length_ms', c['audio'], restricted=True, val_type=float, min_val=10, max_val=1000, alternative='win_length') _check_argument('frame_length_ms', c['audio'], restricted=True, val_type=float, min_val=10, max_val=1000, alternative='win_length')
_check_argument('frame_shift_ms', c['audio'], restricted=True, val_type=float, min_val=1, max_val=1000, alternative='hop_length') _check_argument('frame_shift_ms', c['audio'], restricted=True, val_type=float, min_val=1, max_val=1000, alternative='hop_length')
@ -278,6 +278,7 @@ def check_config(c):
_check_argument('clip_norm', c['audio'], restricted=True, val_type=bool) _check_argument('clip_norm', c['audio'], restricted=True, val_type=bool)
_check_argument('mel_fmin', c['audio'], restricted=True, val_type=float, min_val=0.0, max_val=1000) _check_argument('mel_fmin', c['audio'], restricted=True, val_type=float, min_val=0.0, max_val=1000)
_check_argument('mel_fmax', c['audio'], restricted=True, val_type=float, min_val=500.0) _check_argument('mel_fmax', c['audio'], restricted=True, val_type=float, min_val=500.0)
_check_argument('spec_gain', c['audio'], restricted=True, val_type=float, min_val=1, max_val=100)
_check_argument('do_trim_silence', c['audio'], restricted=True, val_type=bool) _check_argument('do_trim_silence', c['audio'], restricted=True, val_type=bool)
_check_argument('trim_db', c['audio'], restricted=True, val_type=int) _check_argument('trim_db', c['audio'], restricted=True, val_type=int)