def causal_conv_bn_actv(layer_type,
name,
inputs,
filters,
kernel_size,
activation_fn,
strides,
padding,
regularizer,
training,
data_format,
bn_momentum,
bn_epsilon,
dilation=1):
"""
Defines a single dilated causal convolutional layer with batch norm
"""
block = conv_bn_actv(layer_type=layer_type,
name=name,
inputs=inputs,
filters=filters,
kernel_size=kernel_size,
activation_fn=activation_fn,
strides=strides,
padding=padding,
regularizer=regularizer,
training=training,
data_format=data_format,
bn_momentum=bn_momentum,
bn_epsilon=bn_epsilon,
dilation=dilation)
# pad the left side of the time-series with an amount of zeros based on the
# dilation rate
block = tf.pad(block, [[0, 0], [dilation * (kernel_size - 1), 0], [0, 0]])
return block
def _decode(self, input_dict):
"""
Decodes representation into data
Args:
input_dict (dict): Python dictionary with inputs to decoder. Must define:
* src_inputs - decoder input Tensor of shape [batch_size, time, dim]
or [time, batch_size, dim]
* src_lengths - decoder input lengths Tensor of shape [batch_size]
* tgt_inputs - Only during training. labels Tensor of the
shape [batch_size, time, num_features] or
[time, batch_size, num_features]
* stop_token_inputs - Only during training. labels Tensor of the
shape [batch_size, time, 1] or [time, batch_size, 1]
* tgt_lengths - Only during training. labels lengths
Tensor of the shape [batch_size]
Returns:
dict:
A python dictionary containing:
* outputs - array containing:
* decoder_output - tensor of shape [batch_size, time,
num_features] or [time, batch_size, num_features]. Spectrogram
representation learned by the decoder rnn
* spectrogram_prediction - tensor of shape [batch_size, time,
num_features] or [time, batch_size, num_features]. Spectrogram
containing the residual corrections from the postnet if enabled
* alignments - tensor of shape [batch_size, time, memory_size]
or [time, batch_size, memory_size]. The alignments learned by
the attention layer
* stop_token_prediction - tensor of shape [batch_size, time, 1]
or [time, batch_size, 1]. The stop token predictions
* final_sequence_lengths - tensor of shape [batch_size]
* stop_token_predictions - tensor of shape [batch_size, time, 1]
or [time, batch_size, 1]. The stop token predictions for use inside
the loss function.
"""
encoder_outputs = input_dict['encoder_output']['outputs']
enc_src_lengths = input_dict['encoder_output']['src_length']
if self._mode == "train":
spec = input_dict['target_tensors'][0] if 'target_tensors' in \
input_dict else None
spec_length = input_dict['target_tensors'][2] if 'target_tensors' in \
input_dict else None
_batch_size = encoder_outputs.get_shape().as_list()[0]
training = (self._mode == "train")
regularizer = self.params.get('regularizer', None)
if self.params.get('enable_postnet', True):
if "postnet_conv_layers" not in self.params:
raise ValueError(
"postnet_conv_layers must be passed from config file if postnet is"
"enabled"
)
if self._both:
num_audio_features = self._n_feats["mel"]
if self._mode == "train":
spec, _ = tf.split(
spec,
[self._n_feats['mel'], self._n_feats['magnitude']],
axis=2
)
else:
num_audio_features = self._n_feats
output_projection_layer = tf.layers.Dense(
name="output_proj",
units=num_audio_features,
use_bias=True,
)
stop_token_projection_layer = tf.layers.Dense(
name="stop_token_proj",
units=1,
use_bias=True,
)
prenet = None
if self.params.get('enable_prenet', True):
prenet = Prenet(
self.params.get('prenet_units', 256),
self.params.get('prenet_layers', 2),
self.params.get("prenet_activation", tf.nn.relu),
self.params["dtype"]
)
cell_params = {}
cell_params["num_units"] = self.params['decoder_cell_units']
decoder_cells = [
single_cell(
cell_class=self.params['decoder_cell_type'],
cell_params=cell_params,
zoneout_prob=self.params.get("zoneout_prob", 0.),
dp_output_keep_prob=1.-self.params.get("dropout_prob", 0.1),
training=training,
) for _ in range(self.params['decoder_layers'])
]
if self.params['attention_type'] is not None:
attention_mechanism = self._build_attention(
encoder_outputs, enc_src_lengths,
self.params.get("attention_bias", False)
)
attention_cell = tf.contrib.rnn.MultiRNNCell(decoder_cells)
attentive_cell = AttentionWrapper(
cell=attention_cell,
attention_mechanism=attention_mechanism,
alignment_history=True,
output_attention="both",
)
decoder_cell = attentive_cell
if self.params['attention_type'] is None:
decoder_cell = tf.contrib.rnn.MultiRNNCell(decoder_cells)
if self._mode == "train":
train_and_not_sampling = True
helper = TacotronTrainingHelper(
inputs=spec,
sequence_length=spec_length,
prenet=None,
model_dtype=self.params["dtype"],
mask_decoder_sequence=self.params.get("mask_decoder_sequence", True)
)
elif self._mode == "eval" or self._mode == "infer":
train_and_not_sampling = False
inputs = tf.zeros(
(_batch_size, 1, num_audio_features), dtype=self.params["dtype"]
)
helper = TacotronHelper(
inputs=inputs,
prenet=None,
mask_decoder_sequence=self.params.get("mask_decoder_sequence", True)
)
else:
raise ValueError("Unknown mode for decoder: {}".format(self._mode))
decoder = TacotronDecoder(
decoder_cell=decoder_cell,
helper=helper,
initial_decoder_state=decoder_cell.zero_state(
_batch_size, self.params["dtype"]
),
attention_type=self.params["attention_type"],
spec_layer=output_projection_layer,
stop_token_layer=stop_token_projection_layer,
prenet=prenet,
dtype=self.params["dtype"],
train=train_and_not_sampling
)
if self._mode == 'train':
maximum_iterations = tf.reduce_max(spec_length)
else:
maximum_iterations = tf.reduce_max(enc_src_lengths) * 10
outputs, final_state, sequence_lengths = tf.contrib.seq2seq.dynamic_decode(
# outputs, final_state, sequence_lengths, final_inputs = dynamic_decode(
decoder=decoder,
impute_finished=False,
maximum_iterations=maximum_iterations,
swap_memory=self.params.get("use_swap_memory", False),
output_time_major=self.params.get("time_major", False),
parallel_iterations=self.params.get("parallel_iterations", 32)
)
decoder_output = outputs.rnn_output
stop_token_logits = outputs.stop_token_output
with tf.variable_scope("decoder"):
# If we are in train and doing sampling, we need to do the projections
if train_and_not_sampling:
decoder_spec_output = output_projection_layer(decoder_output)
stop_token_logits = stop_token_projection_layer(decoder_spec_output)
decoder_output = decoder_spec_output
## Add the post net ##
if self.params.get('enable_postnet', True):
dropout_keep_prob = self.params.get('postnet_keep_dropout_prob', 0.5)
top_layer = decoder_output
for i, conv_params in enumerate(self.params['postnet_conv_layers']):
ch_out = conv_params['num_channels']
kernel_size = conv_params['kernel_size'] # [time, freq]
strides = conv_params['stride']
padding = conv_params['padding']
activation_fn = conv_params['activation_fn']
if ch_out == -1:
if self._both:
ch_out = self._n_feats["mel"]
else:
ch_out = self._n_feats
top_layer = conv_bn_actv(
layer_type="conv1d",
name="conv{}".format(i + 1),
inputs=top_layer,
filters=ch_out,
kernel_size=kernel_size,
activation_fn=activation_fn,
strides=strides,
padding=padding,
regularizer=regularizer,
training=training,
data_format=self.params.get('postnet_data_format', 'channels_last'),
bn_momentum=self.params.get('postnet_bn_momentum', 0.1),
bn_epsilon=self.params.get('postnet_bn_epsilon', 1e-5),
)
top_layer = tf.layers.dropout(
top_layer, rate=1. - dropout_keep_prob, training=training
)
else:
top_layer = tf.zeros(
[
_batch_size, maximum_iterations,
outputs.rnn_output.get_shape()[-1]
],
dtype=self.params["dtype"]
)
if regularizer and training:
vars_to_regularize = []
vars_to_regularize += attentive_cell.trainable_variables
vars_to_regularize += attention_mechanism.memory_layer.trainable_variables
vars_to_regularize += output_projection_layer.trainable_variables
vars_to_regularize += stop_token_projection_layer.trainable_variables
for weights in vars_to_regularize:
if "bias" not in weights.name:
# print("Added regularizer to {}".format(weights.name))
if weights.dtype.base_dtype == tf.float16:
tf.add_to_collection(
'REGULARIZATION_FUNCTIONS', (weights, regularizer)
)
else:
tf.add_to_collection(
ops.GraphKeys.REGULARIZATION_LOSSES, regularizer(weights)
)
if self.params.get('enable_prenet', True):
prenet.add_regularization(regularizer)
if self.params['attention_type'] is not None:
alignments = tf.transpose(
final_state.alignment_history.stack(), [1, 0, 2]
)
else:
alignments = tf.zeros([_batch_size, _batch_size, _batch_size])
spectrogram_prediction = decoder_output + top_layer
if self._both:
mag_spec_prediction = spectrogram_prediction
mag_spec_prediction = conv_bn_actv(
layer_type="conv1d",
name="conv_0",
inputs=mag_spec_prediction,
filters=256,
kernel_size=4,
activation_fn=tf.nn.relu,
strides=1,
padding="SAME",
regularizer=regularizer,
training=training,
data_format=self.params.get('postnet_data_format', 'channels_last'),
bn_momentum=self.params.get('postnet_bn_momentum', 0.1),
bn_epsilon=self.params.get('postnet_bn_epsilon', 1e-5),
)
mag_spec_prediction = conv_bn_actv(
layer_type="conv1d",
name="conv_1",
inputs=mag_spec_prediction,
filters=512,
kernel_size=4,
activation_fn=tf.nn.relu,
strides=1,
padding="SAME",
regularizer=regularizer,
training=training,
data_format=self.params.get('postnet_data_format', 'channels_last'),
bn_momentum=self.params.get('postnet_bn_momentum', 0.1),
bn_epsilon=self.params.get('postnet_bn_epsilon', 1e-5),
)
if self._model.get_data_layer()._exp_mag:
mag_spec_prediction = tf.exp(mag_spec_prediction)
mag_spec_prediction = tf.layers.conv1d(
mag_spec_prediction,
self._n_feats["magnitude"],
1,
name="post_net_proj",
use_bias=False,
)
else:
mag_spec_prediction = tf.zeros([_batch_size, _batch_size, _batch_size])
stop_token_prediction = tf.sigmoid(stop_token_logits)
outputs = [
decoder_output, spectrogram_prediction, alignments,
stop_token_prediction, sequence_lengths, mag_spec_prediction
]
return {
'outputs': outputs,
'stop_token_prediction': stop_token_logits,
}
def _encode(self, input_dict):
"""Creates TensorFlow graph for Tacotron-2 like encoder.
Args:
input_dict (dict): dictionary with inputs.
Must define:
source_tensors - array containing [
* source_sequence: tensor of shape [batch_size, sequence length]
* src_length: tensor of shape [batch_size]
]
Returns:
dict: A python dictionary containing:
* outputs - tensor containing the encoded text to be passed to the
attention layer
* src_length - the length of the encoded text
"""
text = input_dict['source_tensors'][0]
text_len = input_dict['source_tensors'][1]
training = (self._mode == "train")
regularizer = self.params.get('regularizer', None)
data_format = self.params.get('data_format', 'channels_last')
src_vocab_size = self._model.get_data_layer().params['src_vocab_size']
zoneout_prob = self.params.get('zoneout_prob', 0.)
# if src_vocab_size % 8 != 0:
# src_vocab_size += 8 - (src_vocab_size % 8)
# ----- Embedding layer -----------------------------------------------
enc_emb_w = tf.get_variable(
name="EncoderEmbeddingMatrix",
shape=[src_vocab_size, self.params['src_emb_size']],
dtype=self.params['dtype'],
# initializer=tf.random_normal_initializer()
)
embedded_inputs = tf.cast(tf.nn.embedding_lookup(
enc_emb_w,
text,
), self.params['dtype'])
# ----- Convolutional layers -----------------------------------------------
input_layer = embedded_inputs
if data_format == 'channels_last':
top_layer = input_layer
else:
top_layer = tf.transpose(input_layer, [0, 2, 1])
for i, conv_params in enumerate(self.params['conv_layers']):
ch_out = conv_params['num_channels']
kernel_size = conv_params['kernel_size'] # [time, freq]
strides = conv_params['stride']
padding = conv_params['padding']
if padding == "VALID":
text_len = (text_len - kernel_size[0] +
strides[0]) // strides[0]
else:
text_len = (text_len + strides[0] - 1) // strides[0]
top_layer = conv_bn_actv(
layer_type="conv1d",
name="conv{}".format(i + 1),
inputs=top_layer,
filters=ch_out,
kernel_size=kernel_size,
activation_fn=self.params['activation_fn'],
strides=strides,
padding=padding,
regularizer=regularizer,
training=training,
data_format=data_format,
bn_momentum=self.params.get('bn_momentum', 0.1),
bn_epsilon=self.params.get('bn_epsilon', 1e-5),
)
top_layer = tf.layers.dropout(top_layer,
rate=self.params["cnn_dropout_prob"],
training=training)
if data_format == 'channels_first':
top_layer = tf.transpose(top_layer, [0, 2, 1])
# ----- RNN ---------------------------------------------------------------
num_rnn_layers = self.params['num_rnn_layers']
if num_rnn_layers > 0:
cell_params = {}
cell_params["num_units"] = self.params['rnn_cell_dim']
rnn_type = self.params['rnn_type']
rnn_input = top_layer
rnn_vars = []
if self.params["use_cudnn_rnn"]:
if self._mode == "infer":
cell = lambda: tf.contrib.cudnn_rnn.CudnnCompatibleLSTMCell(
cell_params["num_units"])
cells_fw = [cell() for _ in range(1)]
cells_bw = [cell() for _ in range(1)]
(top_layer, _,
_) = tf.contrib.rnn.stack_bidirectional_dynamic_rnn(
cells_fw,
cells_bw,
rnn_input,
sequence_length=text_len,
dtype=rnn_input.dtype,
time_major=False)
else:
all_cudnn_classes = [
i[1] for i in inspect.getmembers(
tf.contrib.cudnn_rnn, inspect.isclass)
]
if not rnn_type in all_cudnn_classes:
raise TypeError("rnn_type must be a Cudnn RNN class")
if zoneout_prob != 0.:
raise ValueError(
"Zoneout is currently not supported for cudnn rnn classes"
)
rnn_input = tf.transpose(top_layer, [1, 0, 2])
if self.params['rnn_unidirectional']:
direction = cudnn_rnn_ops.CUDNN_RNN_UNIDIRECTION
else:
direction = cudnn_rnn_ops.CUDNN_RNN_BIDIRECTION
rnn_block = rnn_type(num_layers=num_rnn_layers,
num_units=cell_params["num_units"],
direction=direction,
dtype=rnn_input.dtype,
name="cudnn_rnn")
rnn_block.build(rnn_input.get_shape())
top_layer, _ = rnn_block(rnn_input)
top_layer = tf.transpose(top_layer, [1, 0, 2])
rnn_vars += rnn_block.trainable_variables
else:
multirnn_cell_fw = tf.nn.rnn_cell.MultiRNNCell([
single_cell(cell_class=rnn_type,
cell_params=cell_params,
zoneout_prob=zoneout_prob,
training=training,
residual_connections=False)
for _ in range(num_rnn_layers)
])
rnn_vars += multirnn_cell_fw.trainable_variables
if self.params['rnn_unidirectional']:
top_layer, _ = tf.nn.dynamic_rnn(
cell=multirnn_cell_fw,
inputs=rnn_input,
sequence_length=text_len,
dtype=rnn_input.dtype,
time_major=False,
)
else:
multirnn_cell_bw = tf.nn.rnn_cell.MultiRNNCell([
single_cell(cell_class=rnn_type,
cell_params=cell_params,
zoneout_prob=zoneout_prob,
training=training,
residual_connections=False)
for _ in range(num_rnn_layers)
])
top_layer, _ = tf.nn.bidirectional_dynamic_rnn(
cell_fw=multirnn_cell_fw,
cell_bw=multirnn_cell_bw,
inputs=rnn_input,
sequence_length=text_len,
dtype=rnn_input.dtype,
time_major=False)
# concat 2 tensors [B, T, n_cell_dim] --> [B, T, 2*n_cell_dim]
top_layer = tf.concat(top_layer, 2)
rnn_vars += multirnn_cell_bw.trainable_variables
if regularizer and training:
cell_weights = []
cell_weights += rnn_vars
cell_weights += [enc_emb_w]
for weights in cell_weights:
if "bias" not in weights.name:
# print("Added regularizer to {}".format(weights.name))
if weights.dtype.base_dtype == tf.float16:
tf.add_to_collection('REGULARIZATION_FUNCTIONS',
(weights, regularizer))
else:
tf.add_to_collection(
ops.GraphKeys.REGULARIZATION_LOSSES,
regularizer(weights))
# -- end of rnn------------------------------------------------------------
top_layer = tf.layers.dropout(top_layer,
rate=self.params["rnn_dropout_prob"],
training=training)
outputs = top_layer
return {'outputs': outputs, 'src_length': text_len}
def _encode(self, input_dict):
"""Creates TensorFlow graph for DeepSpeech-2 like encoder.
Args:
input_dict (dict): input dictionary that has to contain
the following fields::
input_dict = {
"source_tensors": [
src_sequence (shape=[batch_size, sequence length, num features]),
src_length (shape=[batch_size])
]
}
Returns:
dict: dictionary with the following tensors::
{
'outputs': hidden state, shape=[batch_size, sequence length, n_hidden]
'src_length': tensor, shape=[batch_size]
}
"""
source_sequence, src_length = input_dict['source_tensors']
training = (self._mode == "train")
dropout_keep_prob = self.params[
'dropout_keep_prob'] if training else 1.0
regularizer = self.params.get('regularizer', None)
data_format = self.params.get('data_format', 'channels_last')
bn_momentum = self.params.get('bn_momentum', 0.99)
bn_epsilon = self.params.get('bn_epsilon', 1e-3)
input_layer = tf.expand_dims(source_sequence, axis=-1) # BTFC
# print("<<< input :", input_layer.get_shape().as_list())
batch_size = input_layer.get_shape().as_list()[0]
freq = input_layer.get_shape().as_list()[2]
# supported data_formats:
# BTFC = channel_last (legacy)
# BCTF = channel_first(legacy)
# BFTC
# BCFT
if data_format == 'channels_last' or data_format == 'BTFC':
layout = 'BTFC'
dformat = 'channels_last'
elif data_format == 'channels_first' or data_format == 'BCTF':
layout = 'BCTF'
dformat = 'channels_first'
elif data_format == 'BFTC':
layout = 'BFTC'
dformat = 'channels_last'
elif data_format == 'BCFT':
layout = 'BCFT'
dformat = 'channels_first'
else:
print(
"WARNING: unsupported data format: will use channels_last (BTFC) instead"
)
layout = 'BTFC'
dformat = 'channels_last'
#input_layer is BTFC
if layout == 'BCTF':
top_layer = tf.transpose(input_layer, [0, 3, 1, 2])
elif layout == 'BFTC':
top_layer = tf.transpose(input_layer, [0, 2, 1, 3])
elif layout == 'BCFT':
top_layer = tf.transpose(input_layer, [0, 3, 2, 1])
else:
top_layer = input_layer
# print("<<< pre-conv:", top_layer.get_shape().as_list())
# ----- Convolutional layers ---------------------------------------------
conv_layers = self.params['conv_layers']
for idx_conv in range(len(conv_layers)):
ch_out = conv_layers[idx_conv]['num_channels']
kernel_size = conv_layers[idx_conv]['kernel_size'] # [T,F] format
strides = conv_layers[idx_conv]['stride'] # [T,F] format
padding = conv_layers[idx_conv]['padding']
if padding == "VALID":
src_length = (src_length - kernel_size[0] +
strides[0]) // strides[0]
freq = (freq - kernel_size[1] + strides[1]) // strides[1]
else:
src_length = (src_length + strides[0] - 1) // strides[0]
freq = (freq + strides[1] - 1) // strides[1]
if layout == 'BFTC' or layout == 'BCFT':
kernel_size = kernel_size[::-1]
strides = strides[::-1]
# print(kernel_size, strides)
top_layer = conv_bn_actv(
layer_type="conv2d",
name="conv{}".format(idx_conv + 1),
inputs=top_layer,
filters=ch_out,
kernel_size=kernel_size,
activation_fn=self.params['activation_fn'],
strides=strides,
padding=padding,
regularizer=regularizer,
training=training,
data_format=dformat,
bn_momentum=bn_momentum,
bn_epsilon=bn_epsilon,
)
# print(idx_conv, "++++", top_layer.get_shape().as_list())
# convert layout --> BTFC
# if data_format == 'channels_first':
# top_layer = tf.transpose(top_layer, [0, 2, 3, 1])
if layout == 'BCTF': # BCTF --> BTFC
top_layer = tf.transpose(top_layer, [0, 2, 3, 1])
elif layout == 'BFTC': # BFTC --> BTFC
top_layer = tf.transpose(top_layer, [0, 2, 1, 3])
elif layout == 'BCFT': # BCFT --> BTFC
top_layer = tf.transpose(top_layer, [0, 3, 2, 1])
# print(">>> post-conv:", top_layer.get_shape().as_list())
# reshape to [B, T, FxC]
f = top_layer.get_shape().as_list()[2]
c = top_layer.get_shape().as_list()[3]
fc = f * c
top_layer = tf.reshape(top_layer, [batch_size, -1, fc])
# ----- RNN ---------------------------------------------------------------
num_rnn_layers = self.params['num_rnn_layers']
if num_rnn_layers > 0:
rnn_cell_dim = self.params['rnn_cell_dim']
rnn_type = self.params['rnn_type']
if self.params['use_cudnn_rnn']:
# reshape to [B, T, C] --> [T, B, C]
rnn_input = tf.transpose(top_layer, [1, 0, 2])
if self.params['rnn_unidirectional']:
direction = cudnn_rnn_ops.CUDNN_RNN_UNIDIRECTION
else:
direction = cudnn_rnn_ops.CUDNN_RNN_BIDIRECTION
if rnn_type == "cudnn_gru" or rnn_type == "gru":
# pylint: disable=no-member
rnn_block = tf.contrib.cudnn_rnn.CudnnGRU(
num_layers=num_rnn_layers,
num_units=rnn_cell_dim,
direction=direction,
dropout=1.0 - dropout_keep_prob,
dtype=rnn_input.dtype,
name="cudnn_gru",
)
elif rnn_type == "cudnn_lstm" or rnn_type == "lstm":
# pylint: disable=no-member
rnn_block = tf.contrib.cudnn_rnn.CudnnLSTM(
num_layers=num_rnn_layers,
num_units=rnn_cell_dim,
direction=direction,
dropout=1.0 - dropout_keep_prob,
dtype=rnn_input.dtype,
name="cudnn_lstm",
)
else:
raise ValueError(
"{} is not a valid rnn_type for cudnn_rnn layers".
format(rnn_type))
top_layer, state = rnn_block(rnn_input)
top_layer = tf.transpose(top_layer, [1, 0, 2])
else:
rnn_input = top_layer
multirnn_cell_fw = tf.nn.rnn_cell.MultiRNNCell([
rnn_cell(rnn_cell_dim=rnn_cell_dim,
layer_type=rnn_type,
dropout_keep_prob=dropout_keep_prob)
for _ in range(num_rnn_layers)
])
if self.params['rnn_unidirectional']:
top_layer, state = tf.nn.dynamic_rnn(
cell=multirnn_cell_fw,
inputs=rnn_input,
sequence_length=src_length,
dtype=rnn_input.dtype,
time_major=False,
)
else:
multirnn_cell_bw = tf.nn.rnn_cell.MultiRNNCell([
rnn_cell(rnn_cell_dim=rnn_cell_dim,
layer_type=rnn_type,
dropout_keep_prob=dropout_keep_prob)
for _ in range(num_rnn_layers)
])
top_layer, state = tf.nn.bidirectional_dynamic_rnn(
cell_fw=multirnn_cell_fw,
cell_bw=multirnn_cell_bw,
inputs=rnn_input,
sequence_length=src_length,
dtype=rnn_input.dtype,
time_major=False)
# concat 2 tensors [B, T, n_cell_dim] --> [B, T, 2*n_cell_dim]
top_layer = tf.concat(top_layer, 2)
# -- end of rnn------------------------------------------------------------
if self.params['row_conv']:
channels = top_layer.get_shape().as_list()[-1]
top_layer = row_conv(
name="row_conv",
input_layer=top_layer,
batch=batch_size,
channels=channels,
activation_fn=self.params['activation_fn'],
width=self.params['row_conv_width'],
regularizer=regularizer,
training=training,
data_format=data_format,
bn_momentum=bn_momentum,
bn_epsilon=bn_epsilon,
)
# Reshape [B, T, C] --> [B*T, C]
c = top_layer.get_shape().as_list()[-1]
top_layer = tf.reshape(top_layer, [-1, c])
# --- hidden layer with clipped ReLU activation and dropout---------------
top_layer = tf.layers.dense(
inputs=top_layer,
units=self.params['n_hidden'],
kernel_regularizer=regularizer,
activation=self.params['activation_fn'],
name='fully_connected',
)
outputs = tf.nn.dropout(x=top_layer, keep_prob=dropout_keep_prob)
# reshape from [B*T,A] --> [B, T, A].
# Output shape: [batch_size, n_steps, n_hidden]
outputs = tf.reshape(
outputs,
[batch_size, -1, self.params['n_hidden']],
)
return {
'outputs': outputs,
'src_length': src_length,
}
def _embed_style(self, style_spec, style_len):
"""
Code that implements the reference encoder as described in "Towards
end-to-end prosody transfer for expressive speech synthesis with Tacotron",
and "Style Tokens: Unsupervised Style Modeling, Control and Transfer in
End-to-End Speech Synthesis"
Config parameters:
* **conv_layers** (list) --- See the conv_layers parameter for the
Tacotron-2 model.
* **num_rnn_layers** (int) --- Number of rnn layers in the reference encoder
* **rnn_cell_dim** (int) --- Size of rnn layer
* **rnn_unidirectional** (bool) --- Uni- or bi-directional rnn.
* **rnn_type** --- Must be a valid tf rnn cell class
* **emb_size** (int) --- Size of gst
* **attention_layer_size** (int) --- Size of linear layers in attention
* **num_tokens** (int) --- Number of tokens for gst
* **num_heads** (int) --- Number of attention heads
"""
training = (self._mode == "train")
regularizer = self.params.get('regularizer', None)
data_format = self.params.get('data_format', 'channels_last')
batch_size = style_spec.get_shape().as_list()[0]
top_layer = tf.expand_dims(style_spec, -1)
params = self.params['style_embedding_params']
if "conv_layers" in params:
for i, conv_params in enumerate(params['conv_layers']):
ch_out = conv_params['num_channels']
kernel_size = conv_params['kernel_size'] # [time, freq]
strides = conv_params['stride']
padding = conv_params['padding']
if padding == "VALID":
style_len = (style_len - kernel_size[0] +
strides[0]) // strides[0]
else:
style_len = (style_len + strides[0] - 1) // strides[0]
top_layer = conv_bn_actv(
layer_type="conv2d",
name="conv{}".format(i + 1),
inputs=top_layer,
filters=ch_out,
kernel_size=kernel_size,
activation_fn=self.params['activation_fn'],
strides=strides,
padding=padding,
regularizer=regularizer,
training=training,
data_format=data_format,
bn_momentum=self.params.get('bn_momentum', 0.1),
bn_epsilon=self.params.get('bn_epsilon', 1e-5),
)
if data_format == 'channels_first':
top_layer = tf.transpose(top_layer, [0, 2, 1])
top_layer = tf.concat(tf.unstack(top_layer, axis=2), axis=-1)
num_rnn_layers = params['num_rnn_layers']
if num_rnn_layers > 0:
cell_params = {}
cell_params["num_units"] = params['rnn_cell_dim']
rnn_type = params['rnn_type']
rnn_input = top_layer
rnn_vars = []
multirnn_cell_fw = tf.nn.rnn_cell.MultiRNNCell([
single_cell(cell_class=rnn_type,
cell_params=cell_params,
training=training,
residual_connections=False)
for _ in range(num_rnn_layers)
])
rnn_vars += multirnn_cell_fw.trainable_variables
if params['rnn_unidirectional']:
top_layer, final_state = tf.nn.dynamic_rnn(
cell=multirnn_cell_fw,
inputs=rnn_input,
sequence_length=style_len,
dtype=rnn_input.dtype,
time_major=False,
)
final_state = final_state[0]
else:
multirnn_cell_bw = tf.nn.rnn_cell.MultiRNNCell([
single_cell(cell_class=rnn_type,
cell_params=cell_params,
training=training,
residual_connections=False)
for _ in range(num_rnn_layers)
])
top_layer, final_state = tf.nn.bidirectional_dynamic_rnn(
cell_fw=multirnn_cell_fw,
cell_bw=multirnn_cell_bw,
inputs=rnn_input,
sequence_length=style_len,
dtype=rnn_input.dtype,
time_major=False)
# concat 2 tensors [B, T, n_cell_dim] --> [B, T, 2*n_cell_dim]
final_state = tf.concat(
(final_state[0][0].h, final_state[1][0].h), 1)
rnn_vars += multirnn_cell_bw.trainable_variables
top_layer = final_state
# Apply linear layer
top_layer = tf.layers.dense(top_layer,
128,
activation=tf.nn.tanh,
kernel_regularizer=regularizer,
name="reference_activation")
if regularizer and training:
cell_weights = rnn_vars
for weights in cell_weights:
if "bias" not in weights.name:
# print("Added regularizer to {}".format(weights.name))
if weights.dtype.base_dtype == tf.float16:
tf.add_to_collection('REGULARIZATION_FUNCTIONS',
(weights, regularizer))
else:
tf.add_to_collection(
ops.GraphKeys.REGULARIZATION_LOSSES,
regularizer(weights))
num_units = params["num_tokens"]
att_size = params["attention_layer_size"]
# Randomly initilized tokens
gst_embedding = tf.get_variable(
"token_embeddings",
shape=[num_units, params["emb_size"]],
dtype=self.params["dtype"],
initializer=tf.random_uniform_initializer(
minval=-1., maxval=1., dtype=self.params["dtype"]),
trainable=False)
attention = attention_layer.Attention(params["attention_layer_size"],
params["num_heads"],
0.,
training,
mode="bahdanau")
top_layer = tf.expand_dims(top_layer, 1)
gst_embedding = tf.nn.tanh(gst_embedding)
gst_embedding = tf.expand_dims(gst_embedding, 0)
gst_embedding = tf.tile(gst_embedding, [batch_size, 1, 1])
token_embeddings = attention(top_layer, gst_embedding, None)
token_embeddings = tf.squeeze(token_embeddings, 1)
return token_embeddings