def embedding(self, x, is_training=False):
"""
:param x: shape=(n, t, n_mels)
:return: embedding. shape=(n, e)
"""
# frame-level embedding
x = tf.layers.dense(x, units=self.hidden_units, activation=tf.nn.relu) # (n, t, h)
out = conv1d_banks(x, K=self.num_banks, num_units=self.hidden_units, norm_type=self.norm_type,
is_training=is_training) # (n, t, k * h)
out = tf.layers.max_pooling1d(out, 2, 1, padding="same") # (n, t, k * h)
out = conv1d(out, self.hidden_units, 3, scope="conv1d_1") # (n, t, h)
out = normalize(out, type=self.norm_type, is_training=is_training, activation_fn=tf.nn.relu)
out = conv1d(out, self.hidden_units, 3, scope="conv1d_2") # (n, t, h)
out += x # (n, t, h) # residual connections
for i in range(self.num_highway):
out = highwaynet(out, num_units=self.hidden_units, scope='highwaynet_{}'.format(i)) # (n, t, h)
out = gru(out, self.hidden_units, False) # (n, t, h)
# take the last output
out = out[..., -1] # (n, h)
# embedding
out = tf.layers.dense(out, self.num_classes, name='projection') # (n, e)
out = tf.identity(out, name="embedding")
return out
def decode2(inputs, is_training=True, scope="decoder2", reuse=None):
'''
Args:
inputs: A 3d tensor with shape of [N, T', C'], where C'=hp.n_mels*hp.r,
dtype of float32. Log magnitude spectrogram of sound files.
is_training: Whether or not the layer is in training mode.
scope: Optional scope for `variable_scope`
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns
Predicted magnitude spectrogram tensor with shape of [N, T', C''],
where C'' = (1+hp.n_fft//2)*hp.r.
'''
with tf.variable_scope(scope, reuse=reuse):
# Decoder pre-net
prenet_out = mod.prenet(inputs,
is_training=is_training) # (N, T'', E/2)
# Decoder Post-processing net = CBHG
## Conv1D bank
dec = mod.conv1d_banks(prenet_out,
K=hp.decoder_num_banks,
is_training=is_training) # (N, T', E*K/2)
## Max pooling
dec = tf.layers.max_pooling1d(dec, 2, 1,
padding="same") # (N, T', E*K/2)
## Conv1D projections
dec = mod.conv1d(dec, hp.embed_size, 3, scope="conv1d_1") # (N, T', E)
dec = mod.normalize(dec,
type=hp.norm_type,
is_training=is_training,
activation_fn=tf.nn.relu,
scope="norm1")
dec = mod.conv1d(dec, hp.embed_size // 2, 3,
scope="conv1d_2") # (N, T', E/2)
dec = mod.normalize(dec,
type=hp.norm_type,
is_training=is_training,
activation_fn=None,
scope="norm2")
dec += prenet_out
## Highway Nets
for i in range(4):
dec = mod.highwaynet(
dec,
num_units=hp.embed_size // 2,
scope='highwaynet_{}'.format(i)) # (N, T, E/2)
## Bidirectional GRU
dec = mod.gru(dec, hp.embed_size // 2, True) # (N, T', E)
# Outputs => (N, T', (1+hp.n_fft//2)*hp.r)
out_dim = (1 + hp.n_fft // 2) * hp.r
outputs = tf.layers.dense(dec, out_dim)
return outputs
def encode(inputs, is_training = True, scope = 'encoder', reuse = None):
with tf.variable_scope(scope, reuse = reuse):
prenet_out = prenet(inputs, scope = 'prenet', is_training = is_training)
enc = conv1d_banks(
prenet_out, K = encoder_num_banks, is_training = is_training
)
enc = tf.layers.max_pooling1d(enc, 2, 1, padding = 'same')
enc = conv1d(enc, embed_size // 2, 3, scope = 'conv1d_1')
enc = normalize_in(enc, activation_fn = tf.nn.relu)
enc = conv1d(enc, embed_size // 2, 3, scope = 'conv1d_2')
enc = normalize_in(enc, activation_fn = tf.nn.relu)
enc += prenet_out
for i in range(num_highway_blocks):
enc = highwaynet(
enc, units = embed_size // 2, scope = 'highwaynet_%d' % (i)
)
memory = gru(enc, embed_size // 2, True)
return memory
def init_inference(self, config, is_training=False):
num_banks = config['num_banks']
hidden_units = config['hidden_units']
num_highway = config['num_highway']
norm_type = config['norm_type']
batch_size = config['batch_size']
num_rnn_layer = config['num_rnn_layer']
self._input_dim = input_dim = config['input_dim']
self._output_dim = output_dim = config['alphabet_size']
self._inputs = tf.placeholder(tf.float32,
[batch_size, None, input_dim])
self._seq_lens = tf.placeholder(tf.int32, shape=batch_size)
self._out_lens = self._seq_lens
# TODO, awni, for now on the client to remember to initialize these.
self._mean = tf.get_variable("mean", shape=input_dim, trainable=False)
self._std = tf.get_variable("std", shape=input_dim, trainable=False)
std_inputs = (self._inputs - self._mean) / self._std
x = conv1d(self._inputs, hidden_units, 1, scope="conv1d")
out = conv1d_banks(x,
K=num_banks,
num_units=hidden_units,
norm_type=norm_type,
is_training=is_training) # (n, t, k * h)
out = tf.layers.max_pooling1d(out, 2, 1,
padding="same") # (n, t, k * h)
out = conv1d(out, hidden_units, 3, scope="conv1d_1") # (n, t, h)
out = normalize(out,
type=norm_type,
is_training=is_training,
activation_fn=tf.nn.relu)
out = conv1d(out, hidden_units, 3, scope="conv1d_2") # (n, t, h)
out += x # (n, t, h) # residual connections
for i in range(num_highway):
out = highwaynet(out,
num_units=hidden_units,
scope='highwaynet_{}'.format(i)) # (n, t, h)
rnn_out, state, initial_state = gru(
out,
hidden_units,
False,
seqlens=self._seq_lens,
num_layers=num_rnn_layer,
is_training=is_training) # (n, t, h)
self._initial_state = initial_state
self._rnn_state = state
rnn_out = tf.transpose(rnn_out, [1, 0, 2])
# Collapse time and batch dims pre softmax.
rnn_out = tf.reshape(rnn_out, (-1, hidden_units))
logits, probas = _add_softmax_linear(
rnn_out,
hidden_units,
output_dim,
initializer=tf.contrib.layers.xavier_initializer())
# Reshape to time-major.
self._logits = tf.reshape(logits, (-1, batch_size, output_dim))
self._probas = tf.reshape(probas, (-1, batch_size, output_dim))
self._init_inference = True
def encode(inputs, is_training=True, scope="encoder", reuse=None):
'''
Args:
inputs: A 2d tensor with shape of [N, T], dtype of int32. N: batch_size T: real length
seqlens: A 1d tensor with shape of [N,], dtype of int32.
masks: A 3d tensor with shape of [N, T, 1], dtype of float32.
is_training: Whether or not the layer is in training mode.
scope: Optional scope for `variable_scope`
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns: E is the spectrogram filter N
A collection of Hidden vectors, whose shape is (N, T, E). N seqs, each with T characters, and each of them encoded to E dimension latent representation
'''
with tf.variable_scope(scope, reuse=reuse):
# Load vocabulary
#char2idx, idx2char = load_vocab()
# Character Embedding N seqs
#inputs = mod.embed(inputs, len(char2idx), hp.embed_size) # (N, T, E) shape=(32, ?, 256)
# Encoder pre-net: dense(E)--dropout--dense(E/2)--dropout
#ipdb.set_trace()
inputs = mod.pre_spectro(inputs, is_training=is_training) # (N, T, E)
prenet_out = mod.prenet(inputs, is_training=is_training) # (N, T, E/2)
# Encoder CBHG
## Conv1D bank
enc = mod.conv1d_banks(prenet_out,
K=hp.encoder_num_banks,
is_training=is_training) # (N, T, K * E / 2)
### Max pooling
enc = tf.layers.max_pooling1d(enc, 2, 1,
padding="same") # (N, T, K * E / 2)
### Conv1D projections
enc = mod.conv1d(enc, hp.embed_size // 2, 3,
scope="conv1d_1") # (N, T, E/2)
enc = mod.normalize(enc,
type=hp.norm_type,
is_training=is_training,
activation_fn=tf.nn.relu,
scope="norm1")
enc = mod.conv1d(enc, hp.embed_size // 2, 3,
scope="conv1d_2") # (N, T, E/2)
enc = mod.normalize(enc,
type=hp.norm_type,
is_training=is_training,
activation_fn=None,
scope="norm2")
enc += prenet_out # (N, T, E/2) # residual connections
### Highway Nets
for i in range(hp.num_highwaynet_blocks):
enc = mod.highwaynet(
enc,
num_units=hp.embed_size // 2,
scope='highwaynet_{}'.format(i)) # (N, T, E/2)
### Bidirectional GRU---apply nonlineararity
memory = mod.gru(
enc, hp.embed_size // 2, False
) # (N, T, E) what the network represent the input text input
return memory