def pooling_layer(self, x, pooling_type=None):
'''
Add a pooling layer across the whole utterance.
Input: [B, T, D]
--> Reduce along T
Statistics pooling output: [B, D * 2]
Average pooling output: [B, D]
'''
assert_rank3 = tf.debugging.assert_rank(x, 3)
with tf.control_dependencies([assert_rank3]):
x = tf.identity(x)
pooling_type = pooling_type if pooling_type else self.netconf[
'frame_pooling_type']
if pooling_type == 'stats':
with tf.name_scope('stats_pooling'):
mean, var = tf.nn.moments(x, 1)
x = tf.concat([mean, tf.sqrt(var + 1e-6)], 1)
elif pooling_type == 'average':
with tf.name_scope('average_pooling'):
mean, _ = tf.nn.moments(x, 1)
x = mean
else:
raise ValueError('Unsupported frame_pooling_type: %s' %
(pooling_type))
assert_rank2 = tf.debugging.assert_rank(x, 2)
with tf.control_dependencies([assert_rank2]):
x = tf.identity(x)
return x
def curvature_range(self):
# set up the curvature window
self._curv_win = tf.Variable(np.zeros([
self._curv_win_width,
]),
dtype=tf.float32,
name="curv_win",
trainable=False)
# we can use log smoothing for curvature range to follow trend faster
# self._curv_win = tf.scatter_update(
# self._curv_win, self._global_step % self._curv_win_width,
# tf.log(self._grad_norm_squared + EPS))
self._curv_win = tf.scatter_update(
self._curv_win, self._global_step % self._curv_win_width,
self._grad_norm_squared + EPS)
# note here the iterations start from iteration 0
valid_window = tf.slice(
self._curv_win, tf.constant([
0,
]),
tf.expand_dims(tf.minimum(tf.constant(self._curv_win_width),
self._global_step + 1),
dim=0))
if self._h_min_log_smooth:
self._h_min_t = tf.log(tf.reduce_min(valid_window) + EPS)
else:
self._h_min_t = tf.reduce_min(valid_window)
if self._h_max_log_smooth:
self._h_max_t = tf.log(tf.reduce_max(valid_window) + EPS)
else:
self._h_max_t = tf.reduce_max(valid_window)
curv_range_ops = []
with tf.control_dependencies([self._h_min_t, self._h_max_t]):
avg_op = self._moving_averager.apply(
[self._h_min_t, self._h_max_t])
with tf.control_dependencies([avg_op]):
if self._h_min_log_smooth:
self._h_min = tf.exp(
tf.identity(
self._moving_averager.average(self._h_min_t)))
else:
self._h_min = \
tf.identity(self._moving_averager.average(self._h_min_t))
if self._h_max_log_smooth:
self._h_max = tf.exp(
tf.identity(
self._moving_averager.average(self._h_max_t)))
else:
self._h_max = \
tf.identity(self._moving_averager.average(self._h_max_t))
if self._sparsity_debias:
self._h_min = self._h_min * self._sparsity_avg
self._h_max = self._h_max * self._sparsity_avg
curv_range_ops.append(avg_op)
return curv_range_ops
def apply_gradients(self, grads_tvars, global_step=None, name=None):
self._grads, self._tvars = zip(*[(g, t) for g, t in grads_tvars
if g is not None])
# for manual gradient clipping
if self._clip_thresh_var is not None:
self._grads, self._grads_norm = tf.clip_by_global_norm(
self._grads, self._clip_thresh_var)
# loosely adaptive clipping of gradient in case exploding gradient ruins statistics
if self._use_adapt_grad_clip:
thresh = tf.cond(
self._do_tune, lambda: tf.sqrt(self._stat_protect_fac * self.
_adapt_grad_clip_thresh**2),
lambda: tf.to_float(tf.constant(LARGE_FLOAT_VAL)))
self._grads, self._grads_norm = tf.clip_by_global_norm(
self._grads, thresh)
with tf.variable_scope("before_apply"):
before_apply_op = self.before_apply()
with tf.variable_scope("update_hyper"):
with tf.control_dependencies([before_apply_op]):
update_hyper_op = self.update_hyper_param()
with tf.variable_scope("apply_updates"):
with tf.control_dependencies([update_hyper_op]):
# clip exploding gradient according to h_max
if self._use_adapt_grad_clip:
thresh = tf.cond(
tf.greater(tf.global_norm(self._grads),
self._adapt_grad_clip_thresh),
lambda: self._adapt_grad_clip_target_val,
lambda: tf.to_float(tf.constant(LARGE_FLOAT_VAL)))
self._grads, self._grads_norm = tf.clip_by_global_norm(
self._grads, thresh)
apply_grad_op = self._optimizer.apply_gradients(
zip(self._grads, self._tvars), global_step, name)
with tf.control_dependencies([apply_grad_op]):
self._increment_global_step_op = tf.assign(self._global_step,
self._global_step + 1)
self._adapt_grad_clip_thresh_op = \
tf.assign(self._adapt_grad_clip_thresh, tf.sqrt(self._h_max) )
self._adapt_grad_clip_target_val_op = \
tf.assign(self._adapt_grad_clip_target_val, tf.sqrt(self._h_max) )
# self._adapt_grad_clip_target_val_op = \
# tf.assign(self._adapt_grad_clip_target_val, tf.sqrt(tf.sqrt(self._h_max * self._h_min)))
return tf.group(before_apply_op, update_hyper_op, apply_grad_op,
self._adapt_grad_clip_thresh_op,
self._adapt_grad_clip_target_val_op,
self._increment_global_step_op)
def call(self, audio_data, sample_rate=None):
"""
Caculate fbank && pitch(concat) features of wav.
:param audio_data: the audio signal from which to compute spectrum.
Should be an (1, N) tensor.
:param sample_rate: the samplerate of the signal we working with.
:return: A tensor with shape (num_frames, dim_features), containing
fbank && pitch feature of every frame in speech.
"""
p = self.config
with tf.name_scope('fbank_pitch'):
if sample_rate == None:
sample_rate = tf.constant(p.sample_rate, dtype=tf.int32)
assert_op = tf.assert_equal(tf.constant(p.sample_rate),
tf.cast(sample_rate, dtype=tf.int32))
with tf.control_dependencies([assert_op]):
fbank_feats = tf.squeeze(self.fbank(audio_data, sample_rate))
pitch_feats = tf.squeeze(self.pitch(audio_data, sample_rate))
fbank_pitch_feats = tf.concat([fbank_feats, pitch_feats], 1)
return fbank_pitch_feats
def call(self, audio_data, sample_rate=None):
"""
Caculate pitch features of audio data.
:param audio_data: the audio signal from which to compute spectrum. Should be an (1, N) tensor.
:param sample_rate: [option]the samplerate of the signal we working with, default is 16kHz.
:return: A float tensor of size (1, num_frames) containing pitch features of every frame in speech.
"""
p = self.config
with tf.name_scope('pitch'):
if sample_rate == None:
sample_rate = tf.constant(p.sample_rate, dtype=float)
assert_op = tf.assert_equal(tf.constant(p.sample_rate),
tf.cast(sample_rate, dtype=float))
with tf.control_dependencies([assert_op]):
pitch = py_x_ops.pitch(audio_data,
sample_rate,
window_length=p.window_length,
frame_length=p.frame_length,
thres_autoc=p.thres_autoc)
pitch = tf.squeeze(pitch)
pitch = tf.transpose(pitch[None, :])
return pitch
def grad_variance(self):
grad_var_ops = []
tensor_to_avg = []
for t, g in zip(self._tvars, self._grads):
if isinstance(g, ops.IndexedSlices):
tensor_to_avg.append(
tf.reshape(tf.unsorted_segment_sum(g.values, g.indices,
g.dense_shape[0]),
shape=t.get_shape()))
else:
tensor_to_avg.append(g)
avg_op = self._moving_averager.apply(tensor_to_avg)
grad_var_ops.append(avg_op)
with tf.control_dependencies([avg_op]):
self._grad_avg = [
self._moving_averager.average(val) for val in tensor_to_avg
]
self._grad_avg_squared = [tf.square(val) for val in self._grad_avg]
self._grad_var = tf.maximum(
tf.constant(EPS, dtype=self._grad_norm_squared_avg.dtype),
self._grad_norm_squared_avg -
tf.add_n([tf.reduce_sum(val) for val in self._grad_avg_squared]))
if self._sparsity_debias:
self._grad_var *= self._sparsity_avg
return grad_var_ops
def call(self, audio_data, sample_rate=None):
"""
Caculate power spectrum and phase spectrum of audio data.
:param audio_data: the audio signal from which to compute spectrum. Should be an (1, N) tensor.
:param sample_rate: [option]the samplerate of the signal we working with, default is 16kHz.
:return: Two returns:
power spectrum —— A float tensor of size (num_frames, num_frequencies) containing
power spectrum and of every frame in speech.
phase spectrum —— A float tensor of size (num_frames, num_frequencies) containing
phase spectrum and of every frame in speech.
"""
p = self.config
with tf.name_scope('analyfiltbank'):
if sample_rate == None:
sample_rate = tf.constant(p.sample_rate, dtype=tf.int32)
assert_op = tf.assert_equal(tf.constant(p.sample_rate),
tf.cast(sample_rate, dtype=tf.int32))
with tf.control_dependencies([assert_op]):
sample_rate = tf.cast(sample_rate, dtype=float)
power_spectrum, phase_spectrum = py_x_ops.analyfiltbank(
audio_data,
sample_rate,
window_length=p.window_length,
frame_length=p.frame_length)
return power_spectrum, phase_spectrum
def call(self, audio_data, sample_rate=None):
"""
Caculate mfcc features of audio data.
:param audio_data: the audio signal from which to compute spectrum. Should be an (1, N) tensor.
:param sample_rate: [option]the samplerate of the signal we working with, default is 16kHz.
:return: A float tensor of size (num_channels, num_frames, num_frequencies) containing
mfcc features of every frame in speech.
"""
p = self.config
with tf.name_scope('mfcc'):
if sample_rate == None:
sample_rate = tf.constant(p.sample_rate, dtype=tf.int32)
assert_op = tf.assert_equal(tf.constant(p.sample_rate),
tf.cast(sample_rate, dtype=tf.int32))
with tf.control_dependencies([assert_op]):
spectrum_feats = self.spect(audio_data, sample_rate)
spectrum_feats = tf.expand_dims(spectrum_feats, 0)
fbank_feats = self.fbank(audio_data, sample_rate)
mfcc = py_x_ops.mfcc(fbank_feats,
spectrum_feats,
sample_rate,
use_energy=p.use_energy,
cepstral_lifter=p.cepstral_lifter,
coefficient_count=p.coefficient_count)
return mfcc
def call(self, audio_data, sample_rate=None):
"""
Caculate cepstrum of audio data.
:param audio_data: the audio signal from which to compute spectrum. Should be an (1, N) tensor.
:param sample_rate: [option]the samplerate of the signal we working with, default is 16kHz.
:return:A float tensor of size (num_frames, ceps_subband_num) containing normalized cepstrum
(tag_ceps_mean_norm = True) or cepstrum (tag_ceps_mean_norm = False) of every frame in speech.
"""
p = self.config
with tf.name_scope('cepstrum'):
if sample_rate == None:
sample_rate = tf.constant(p.sample_rate, dtype=float)
assert_op = tf.assert_equal(
tf.constant(p.sample_rate), tf.cast(sample_rate, dtype=float))
with tf.control_dependencies([assert_op]):
cepstrum = py_x_ops.cepstrum(
audio_data,
sample_rate,
window_length=p.window_length,
frame_length=p.frame_length,
ceps_subband_num=p.ceps_subband_num,
tag_ceps_mean_norm=p.tag_ceps_mean_norm)
return cepstrum
def call(self, audio_data, sample_rate=None):
"""
Caculate plp features of audio data.
:param audio_data: the audio signal from which to compute spectrum. Should be an (1, N) tensor.
:param sample_rate: [option]the samplerate of the signal we working with, default is 16kHz.
:return:A float tensor of size (num_frames, (plp_order + 1)) containing plp features of every frame in speech.
"""
p = self.config
with tf.name_scope('plp'):
if sample_rate == None:
sample_rate = tf.constant(p.sample_rate, dtype=tf.int32)
assert_op = tf.assert_equal(tf.constant(p.sample_rate),
tf.cast(sample_rate, dtype=tf.int32))
with tf.control_dependencies([assert_op]):
sample_rate = tf.cast(sample_rate, dtype=float)
plp = py_x_ops.plp(audio_data,
sample_rate,
window_length=p.window_length,
frame_length=p.frame_length,
plp_order=p.plp_order)
return plp
def call(self, audio_data, sample_rate=None):
"""
Caculate power spectrum or log power spectrum of audio data.
:param audio_data: the audio signal from which to compute spectrum. Should be an (1, N) tensor.
:param sample_rate: [option]the samplerate of the signal we working with, default is 16kHz.
:return: A float tensor of size (num_frames, num_frequencies) containing power spectrum (output_type=1)
or log power spectrum (output_type=2) of every frame in speech.
"""
p = self.config
with tf.name_scope('spectrum'):
if sample_rate == None:
sample_rate = tf.constant(p.sample_rate, dtype=tf.int32)
assert_op = tf.assert_equal(tf.constant(p.sample_rate),
tf.cast(sample_rate, dtype=tf.int32))
with tf.control_dependencies([assert_op]):
sample_rate = tf.cast(sample_rate, dtype=float)
spectrum = py_x_ops.spectrum(
audio_data,
sample_rate,
window_length=p.window_length,
frame_length=p.frame_length,
output_type=p.output_type,
snip_edges=p.snip_edges,
raw_energy=p.raw_energy,
preEph_coeff=p.preeph_coeff,
window_type=p.window_type,
remove_dc_offset=p.remove_dc_offset,
is_fbank=p.is_fbank)
return spectrum
def call(self, audio_data, sample_rate=None):
"""
Caculate power spectrum or log power spectrum of audio data.
:param audio_data: the audio signal from which to compute spectrum. Should be an (1, N) tensor.
:param sample_rate: [option]the samplerate of the signal we working with, default is 16kHz.
:return: A float tensor of size N containing add-noise audio.
"""
p = self.config
with tf.name_scope('add_rir_noise_aecres'):
if sample_rate == None:
sample_rate = tf.constant(p.sample_rate, dtype=tf.int32)
assert_op = tf.assert_equal(
tf.constant(p.sample_rate), tf.cast(sample_rate, dtype=tf.int32))
with tf.control_dependencies([assert_op]):
sample_rate = tf.cast(sample_rate, dtype=float)
add_rir_noise_aecres_out = py_x_ops.add_rir_noise_aecres(
audio_data,
sample_rate,
if_add_rir=p.if_add_rir,
rir_filelist=p.rir_filelist,
if_add_noise=p.if_add_noise,
snr_min=p.snr_min,
snr_max=p.snr_max,
noise_filelist=p.noise_filelist,
if_add_aecres=p.if_add_aecres,
aecres_filelist=p.aecres_filelist)
return tf.squeeze(add_rir_noise_aecres_out)
def call(self, audio_data, sample_rate=None):
"""
Caculate fbank features of audio data.
:param audio_data: the audio signal from which to compute spectrum. Should be an (1, N) tensor.
:param sample_rate: [option]the samplerate of the signal we working with, default is 16kHz.
:return: A float tensor of size (num_channels, num_frames, num_frequencies) containing
fbank features of every frame in speech.
"""
p = self.config
with tf.name_scope('fbank'):
if sample_rate == None:
sample_rate = tf.constant(p.sample_rate, dtype=tf.int32)
if p.upper_frequency_limit <= 0:
p.upper_frequency_limit = p.sample_rate / 2.0 + p.upper_frequency_limit
elif (p.upper_frequency_limit <= p.lower_frequency_limit) or (
p.upper_frequency_limit > p.sample_rate / 2.0):
p.upper_frequency_limit = p.sample_rate / 2.0
assert_op = tf.assert_equal(tf.constant(p.sample_rate),
tf.cast(sample_rate, dtype=tf.int32))
with tf.control_dependencies([assert_op]):
spectrum = self.spect(audio_data, sample_rate)
spectrum = tf.expand_dims(spectrum, 0)
fbank = py_x_ops.fbank(
spectrum,
sample_rate,
upper_frequency_limit=p.upper_frequency_limit,
lower_frequency_limit=p.lower_frequency_limit,
filterbank_channel_count=p.filterbank_channel_count)
return fbank
def call(self, power_spectrum, phase_spectrum, sample_rate=None):
"""
Implement frequency domain to time domain conversion.
:param power_spectrum: a float tensor of size (num_frames, num_frequencies).
:param phase_spectrum: a float tensor of size (num_frames, num_frequencies).
:param sample_rate: a scalar tensor.
:return: audio data
"""
p = self.config
with tf.name_scope('synthfiltbank'):
if sample_rate == None:
sample_rate = tf.constant(p.sample_rate, dtype=tf.int32)
assert_op = tf.assert_equal(tf.constant(p.sample_rate),
tf.cast(sample_rate, dtype=tf.int32))
with tf.control_dependencies([assert_op]):
audio_data = py_x_ops.synthfiltbank(
power_spectrum,
phase_spectrum,
sample_rate,
window_length=p.window_length,
frame_length=p.frame_length)
return audio_data
def call(self, audio_data, sample_rate=None):
"""
Calculate the zero-crossing rate of speech.
:param audio_data: the audio signal from which to compute spectrum. Should be an (1, N) tensor.
:param sample_rate: [option]the samplerate of the signal we working with, default is 16kHz.
:return: A tensor with shape (1, num_frames), containing zero-crossing rate of every frame in speech.
"""
p = self.config
with tf.name_scope('zcr'):
if sample_rate == None:
sample_rate = tf.constant(p.sample_rate, dtype=tf.int32)
assert_op = tf.assert_equal(tf.constant(p.sample_rate),
tf.cast(sample_rate, dtype=tf.int32))
with tf.control_dependencies([assert_op]):
sample_rate = tf.cast(sample_rate, dtype=float)
zcr = py_x_ops.zcr(audio_data,
sample_rate,
window_length=p.window_length,
frame_length=p.frame_length)
return zcr
def call(self, audio_data, sample_rate=None):
"""
Caculate power of every frame in speech.
:param audio_data: the audio signal from which to compute spectrum. Should be an (1, N) tensor.
:param sample_rate: [option]the samplerate of the signal we working with, default is 16kHz.
:return:A float tensor of size (1, num_frames) containing power of every frame in speech.
"""
p = self.config
with tf.name_scope('framepow'):
if sample_rate == None:
sample_rate = tf.constant(p.sample_rate, dtype=float)
assert_op = tf.assert_equal(
tf.constant(p.sample_rate), tf.cast(sample_rate, dtype=float))
with tf.control_dependencies([assert_op]):
framepow = py_x_ops.frame_pow(
audio_data,
sample_rate,
window_length=p.window_length,
frame_length=p.frame_length)
return framepow
def splice(feat, left_context, right_context):
'''
splice frame with context
param: feat, tf.float32, [batch, time, feat]
return: feat, tf.float32, [batch, time, feat*(left_context + 1 + right_context)]
reference:
https://github.com/kaldi-asr/kaldi/src/feat/feature-functions.cc#L205:6
'''
def _loop_continue(time, end_time, context, unused_left_context,
right_context, unused_output_tas):
del unused_output_tas
del unused_left_context
return time < end_time
def _loop_body(time, end_time, context, left_context, right_context,
output_tas):
shape = tf.shape(context)
B, _, D = shape[0], shape[1], shape[2]
N = (1 + left_context + right_context) * D
new_feat = context[:, time:time + left_context + 1 + right_context, :]
new_feat = tf.reshape(new_feat, [B, N])
new_output_tas = output_tas.write(time, new_feat)
return (time + 1, end_time, context, left_context, right_context,
new_output_tas)
with tf.control_dependencies([
tf.assert_greater_equal(left_context, 0),
tf.assert_greater_equal(right_context, 0)
]):
T = tf.shape(feat)[1]
output_tas = _new_tensor_array('splice_feat_ta', T, dtype=tf.float32)
time = tf.constant(0, tf.int32)
first = tf.tile(feat[:, 0:1, :], [1, left_context, 1])
last = tf.tile(feat[:, -1:, :], [1, right_context, 1])
context = tf.concat([first, feat], axis=1)
context = tf.concat([context, last], axis=1)
loop_vars = (time, T, context, left_context, right_context, output_tas)
parallel_iterations = 10
shape_invariants = tf.nest.map_structure(
lambda t: tf.TensorShape(None), loop_vars)
(time, end_time, context, left_context, right_context,
output_tas) = tf.while_loop(_loop_continue,
_loop_body,
loop_vars=loop_vars,
shape_invariants=shape_invariants,
parallel_iterations=parallel_iterations,
swap_memory=False)
del context
del left_context
del right_context
batch_spliced_feats = output_tas.stack()
batch_spliced_feats = tf.transpose(batch_spliced_feats, [1, 0, 2])
return batch_spliced_feats
def before_apply(self):
self._moving_averager = tf.train.ExponentialMovingAverage(
decay=self._beta, zero_debias=self._zero_debias)
assert self._grads is not None and len(self._grads) > 0
before_apply_ops = []
# get per var g**2 and norm**2
self._grad_squared = []
self._grad_norm_squared = []
for v, g in zip(self._tvars, self._grads):
if g is None:
continue
with ops.colocate_with(v):
self._grad_squared.append(tf.square(g))
self._grad_norm_squared = [
tf.reduce_sum(grad_squared) for grad_squared in self._grad_squared
]
if self._sparsity_debias:
avg_op_sparsity = self.grad_sparsity()
before_apply_ops.append(avg_op_sparsity)
# the following running average on squared norm of gradient is shared
# by `grad_variance` and `dist_to_opt`
avg_op = self._moving_averager.apply(self._grad_norm_squared)
with tf.control_dependencies([avg_op]):
self._grad_norm_squared_avg = [
self._moving_averager.average(val)
for val in self._grad_norm_squared
]
self._grad_norm_squared = tf.add_n(self._grad_norm_squared)
self._grad_norm_squared_avg = tf.add_n(self._grad_norm_squared_avg)
before_apply_ops.append(avg_op)
with tf.control_dependencies([avg_op]):
curv_range_ops = self.curvature_range()
before_apply_ops += curv_range_ops
grad_var_ops = self.grad_variance()
before_apply_ops += grad_var_ops
dist_to_opt_ops = self.dist_to_opt()
before_apply_ops += dist_to_opt_ops
return tf.group(*before_apply_ops)
def get_train_op(self, loss, global_step=None):
"""Get the training operator."""
apply_gradient_op = self.get_apply_gradients_op(loss, global_step)
# model average
self.var_avg(global_step)
# model average after apply gradients
with tf.control_dependencies([apply_gradient_op]):
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
train_op = tf.group(*update_ops)
utils.log_vars('moving vars', tf.moving_average_variables())
return train_op
def dist_to_opt(self):
dist_to_opt_ops = []
# running average of the norm of gradeint
self._grad_norm = tf.sqrt(self._grad_norm_squared)
avg_op = self._moving_averager.apply([
self._grad_norm,
])
dist_to_opt_ops.append(avg_op)
with tf.control_dependencies([avg_op]):
self._grad_norm_avg = self._moving_averager.average(
self._grad_norm)
# single iteration distance estimation
# note that self._grad_norm_avg is per variable
self._dist_to_opt = (self._grad_norm_avg /
(self._grad_norm_squared_avg + EPS))
# running average of distance
avg_op = self._moving_averager.apply([self._dist_to_opt])
dist_to_opt_ops.append(avg_op)
with tf.control_dependencies([avg_op]):
self._dist_to_opt_avg = tf.identity(
self._moving_averager.average(self._dist_to_opt))
if self._sparsity_debias:
self._dist_to_opt_avg /= (tf.sqrt(self._sparsity_avg) + EPS)
return dist_to_opt_ops
def update_hyper_param(self):
assign_hyper_ops = []
self._mu = tf.identity(
tf.cond(self._do_tune, lambda: self.get_mu_tensor(),
lambda: self._mu_var))
with tf.control_dependencies([self._mu]):
self._lr = tf.identity(
tf.cond(self._do_tune, lambda: self.get_lr_tensor(),
lambda: self._lr_var))
with tf.control_dependencies([self._mu, self._lr]):
if self._use_unsmoothed_lr_mu:
assign_hyper_ops.append(tf.assign(self._mu_var, self._mu))
assign_hyper_ops.append(tf.assign(self._lr_var, self._lr))
else:
self._mu = self._beta * self._mu_var + (1 -
self._beta) * self._mu
self._lr = self._beta * self._lr_var + (1 -
self._beta) * self._lr
with tf.control_dependencies([self._mu, self._lr]):
assign_hyper_ops.append(tf.assign(self._mu_var, self._mu))
assign_hyper_ops.append(tf.assign(self._lr_var, self._lr))
assign_hyper_op = tf.group(*assign_hyper_ops)
return assign_hyper_op
def accuracy(logits, labels):
''' accuracy candies
params:
logits: [B, ..., D]
labels: [B, ...]
return:
accuracy tensor
'''
with tf.name_scope('accuracy'):
assert_rank = tf.assert_equal(tf.rank(logits), tf.rank(labels) + 1)
assert_shape = tf.assert_equal(tf.shape(logits)[:-1], tf.shape(labels))
with tf.control_dependencies([assert_rank, assert_shape]):
predictions = tf.argmax(logits, axis=-1, output_type=tf.int64)
labels = tf.cast(labels, tf.int64)
return tf.reduce_mean(
tf.cast(tf.equal(predictions, labels), dtype=tf.float32))
def call(self, wavfile):
"""
Get audio data and sample rate from a wavfile.
:param wavfile: filepath of wav
:return: 2 values. The first is a Tensor of audio data. The second return value is the sample rate of the input wav
file, which is a tensor with float dtype.
"""
p = self.config
contents = tf.io.read_file(wavfile)
audio_data, sample_rate = tf.audio.decode_wav(
contents, desired_channels=p.audio_channels)
assert_op = tf.assert_equal(tf.constant(p.sample_rate),
tf.cast(sample_rate, dtype=float))
with tf.control_dependencies([assert_op]):
return tf.squeeze(audio_data, axis=-1), tf.cast(sample_rate,
dtype=float)
def grad_sparsity(self):
# If the sparse minibatch gradient has 10 percent of its entries
# non-zero, its sparsity is 0.1.
# The norm of dense gradient averaged from full dataset
# are roughly estimated norm of minibatch
# sparse gradient norm * sqrt(sparsity)
# An extension maybe only correct the sparse blob.
non_zero_cnt = tf.add_n([tf.count_nonzero(g) for g in self._grads])
all_entry_cnt = tf.add_n([tf.size(g) for g in self._grads])
self._sparsity = tf.cast(non_zero_cnt, self._grads[0].dtype) \
/ tf.cast(all_entry_cnt, self._grads[0].dtype)
avg_op = self._moving_averager.apply([
self._sparsity,
])
with tf.control_dependencies([avg_op]):
self._sparsity_avg = self._moving_averager.average(self._sparsity)
return avg_op
def ctc_lambda_loss(logits, labels, input_length, label_length, blank_index=0):
'''
ctc loss function
psram: logits, (B, T, D)
psram: input_length, (B, 1), input length of encoder
psram: labels, (B, T)
psram: label_length, (B, 1), label length for convert dense label to sparse
returns: loss, scalar
'''
ilen = tf.cond(
pred=tf.equal(tf.rank(input_length), 1),
true_fn=lambda: input_length,
false_fn=lambda: tf.squeeze(input_length),
)
ilen = tf.cast(ilen, tf.int32)
olen = tf.cond(
pred=tf.equal(tf.rank(label_length), 1),
true_fn=lambda: label_length,
false_fn=lambda: tf.squeeze(label_length))
olen = tf.cast(olen, tf.int32)
deps = [
tf.assert_rank(labels, 2, name='label_rank_check'),
tf.assert_rank(logits, 3, name='logits_rank_check'),
tf.assert_rank(ilen, 1, name='src_len_rank_check'), # input_length
tf.assert_rank(olen, 1, name='tgt_len_rank_check'), # output_length
]
labels, logits = ctc_data_transform(labels, logits, blank_index)
with tf.control_dependencies(deps):
# (B, 1)
# blank index is consistent with Espnet, zero
batch_loss = tf.nn.ctc_loss(
labels=labels,
inputs=logits,
sequence_length=ilen,
time_major=False,
preprocess_collapse_repeated=False,
ctc_merge_repeated=True,
ignore_longer_outputs_than_inputs=False)
return batch_loss
def get_train_op(self, loss, multitask, global_step=None):
"""Get the training operator."""
# quantize training
quantconf = self.config['solver']['quantization']
quantization = quantconf['enable']
if quantization:
quant_delay = quantconf['quant_delay']
logging.info('Quantization training with {} delay'.format(quant_delay))
tf.contrib.quantize.create_training_graph(quant_delay=quant_delay)
apply_gradient_op = self.get_apply_gradients_op(loss, multitask,
global_step)
# model average
self.var_avg(global_step)
# model average after apply gradients
with tf.control_dependencies([apply_gradient_op]):
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
train_op = tf.group(*update_ops)
utils.log_vars('moving vars', tf.moving_average_variables())
return train_op
def call(self, filename, audio_data, sample_rate=None):
"""
Write wav using audio_data[tensor].
:param filename: filepath of wav.
:param audio_data: a tensor containing data of a wav.
:param sample_rate: [option]the samplerate of the signal we working with, default is 16kHz.
:return: write wav opration.
"""
p = self.config
filename = tf.constant(filename)
if sample_rate == None:
sample_rate = tf.constant(p.sample_rate, dtype=tf.int32)
assert_op = tf.assert_equal(
tf.constant(p.sample_rate), tf.cast(sample_rate, dtype=tf.int32))
with tf.control_dependencies([assert_op]):
audio_data = tf.cast(audio_data, dtype=tf.float32)
contents = tf.audio.encode_wav(
tf.expand_dims(audio_data, 1), tf.cast(sample_rate, dtype=tf.int32))
w = tf.io.write_file(filename, contents)
return w