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update Conv1D and Linear

This commit is contained in:
lifuchen 2020-02-12 08:51:32 +00:00 committed by chenfeiyu
parent 53f569a519
commit f5ac04b1a3
9 changed files with 74 additions and 79 deletions
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26
examples/FastSpeech/config/fastspeech.yaml
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@ -1,14 +1,14 @@
audio:
num_mels: 80
n_fft: 2048
sr: 22050
preemphasis: 0.97
hop_length: 256
win_length: 1024
power: 1.2
min_level_db: -100
ref_level_db: 20
outputs_per_step: 1
num_mels: 80 #the number of mel bands when calculating mel spectrograms.
n_fft: 2048 #the number of fft components.
sr: 22050 #the sampling rate of audio data file.
preemphasis: 0.97 #the preemphasis coefficient.
hop_length: 256 #the number of samples to advance between frames.
win_length: 1024 #the length (width) of the window function.
power: 1.2 #the power to raise before griffin-lim.
min_level_db: -100 #the minimum level db.
ref_level_db: 20 #the reference level db.
outputs_per_step: 1 #the outputs per step.
encoder_n_layer: 6
encoder_head: 2
@ -35,12 +35,12 @@ epochs: 10000
lr: 0.001
save_step: 500
use_gpu: True
use_data_parallel: False
use_data_parallel: True
data_path: ../../dataset/LJSpeech-1.1
transtts_path: ../TransformerTTS/checkpoint/
transformer_step: 200000
transformer_step: 160000
save_path: ./checkpoint
log_dir: ./log
#checkpoint_path: ./checkpoint
#ransformer_step: 97000
#transformer_step: 97000

1
examples/FastSpeech/train.py
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@ -51,7 +51,6 @@ def main(cfg):
with fluid.unique_name.guard():
transformerTTS = TransformerTTS(cfg)
model_dict, _ = load_checkpoint(str(cfg.transformer_step), os.path.join(cfg.transtts_path, "transformer"))
transformerTTS.set_dict(model_dict)
transformerTTS.eval()

2
examples/TransformerTTS/config/train_transformer.yaml
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@ -23,7 +23,7 @@ lr: 0.001
save_step: 1000
image_step: 2000
use_gpu: True
use_data_parallel: True
use_data_parallel: False
stop_token: False
data_path: ../../dataset/LJSpeech-1.1

27
parakeet/models/fastspeech/LengthRegulator.py
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@ -83,21 +83,21 @@ class DurationPredictor(dg.Layer):
self.dropout = dropout
k = math.sqrt(1 / self.input_size)
self.conv1 = Conv1D(in_channels = self.input_size,
out_channels = self.out_channels,
self.conv1 = Conv1D(num_channels = self.input_size,
num_filters = self.out_channels,
filter_size = self.filter_size,
padding=1,
param_attr = fluid.ParamAttr(initializer=fluid.initializer.XavierInitializer()),
bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)),
data_format='NTC')
bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)))
#data_format='NTC')
k = math.sqrt(1 / self.out_channels)
self.conv2 = Conv1D(in_channels = self.out_channels,
out_channels = self.out_channels,
self.conv2 = Conv1D(num_channels = self.out_channels,
num_filters = self.out_channels,
filter_size = self.filter_size,
padding=1,
param_attr = fluid.ParamAttr(initializer=fluid.initializer.XavierInitializer()),
bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)),
data_format='NTC')
bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)))
#data_format='NTC')
self.layer_norm1 = dg.LayerNorm(self.out_channels)
self.layer_norm2 = dg.LayerNorm(self.out_channels)
@ -118,11 +118,18 @@ class DurationPredictor(dg.Layer):
out (Variable), Shape(B, T, C), the output of duration predictor.
"""
# encoder_output.shape(N, T, C)
out = layers.dropout(layers.relu(self.layer_norm1(self.conv1(encoder_output))), self.dropout)
out = layers.dropout(layers.relu(self.layer_norm2(self.conv2(out))), self.dropout)
out = layers.transpose(encoder_output, [0,2,1])
out = self.conv1(out)
out = layers.transpose(out, [0,2,1])
out = layers.dropout(layers.relu(self.layer_norm1(out)), self.dropout)
out = layers.transpose(out, [0,2,1])
out = self.conv2(out)
out = layers.transpose(out, [0,2,1])
out = layers.dropout(layers.relu(self.layer_norm2(out)), self.dropout)
out = layers.relu(self.linear(out))
out = layers.squeeze(out, axes=[-1])
return out

28
parakeet/models/transformerTTS/CBHG.py
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@ -24,22 +24,20 @@ class CBHG(dg.Layer):
self.projection_size = projection_size
self.conv_list = []
k = math.sqrt(1 / projection_size)
self.conv_list.append(Conv1D(in_channels = projection_size,
out_channels = hidden_size,
self.conv_list.append(Conv1D(num_channels = projection_size,
num_filters = hidden_size,
filter_size = 1,
padding = int(np.floor(1/2)),
param_attr = fluid.ParamAttr(initializer=fluid.initializer.XavierInitializer()),
bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)),
data_format = "NCT"))
bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k))))
k = math.sqrt(1 / hidden_size)
for i in range(2,K+1):
self.conv_list.append(Conv1D(in_channels = hidden_size,
out_channels = hidden_size,
self.conv_list.append(Conv1D(num_channels = hidden_size,
num_filters = hidden_size,
filter_size = i,
padding = int(np.floor(i/2)),
param_attr = fluid.ParamAttr(initializer=fluid.initializer.XavierInitializer()),
bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)),
data_format = "NCT"))
bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k))))
for i, layer in enumerate(self.conv_list):
self.add_sublayer("conv_list_{}".format(i), layer)
@ -55,22 +53,20 @@ class CBHG(dg.Layer):
conv_outdim = hidden_size * K
k = math.sqrt(1 / conv_outdim)
self.conv_projection_1 = Conv1D(in_channels = conv_outdim,
out_channels = hidden_size,
self.conv_projection_1 = Conv1D(num_channels = conv_outdim,
num_filters = hidden_size,
filter_size = 3,
padding = int(np.floor(3/2)),
param_attr = fluid.ParamAttr(initializer=fluid.initializer.XavierInitializer()),
bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)),
data_format = "NCT")
bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)))
k = math.sqrt(1 / hidden_size)
self.conv_projection_2 = Conv1D(in_channels = hidden_size,
out_channels = projection_size,
self.conv_projection_2 = Conv1D(num_channels = hidden_size,
num_filters = projection_size,
filter_size = 3,
padding = int(np.floor(3/2)),
param_attr = fluid.ParamAttr(initializer=fluid.initializer.XavierInitializer()),
bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)),
data_format = "NCT")
bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)))
self.batchnorm_proj_1 = dg.BatchNorm(hidden_size,
data_layout='NCHW')

14
parakeet/models/transformerTTS/encoderprenet.py
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@ -17,24 +17,22 @@ class EncoderPrenet(dg.Layer):
padding_idx = None)
self.conv_list = []
k = math.sqrt(1 / embedding_size)
self.conv_list.append(Conv1D(in_channels = embedding_size,
out_channels = num_hidden,
self.conv_list.append(Conv1D(num_channels = embedding_size,
num_filters = num_hidden,
filter_size = 5,
padding = int(np.floor(5/2)),
param_attr = fluid.ParamAttr(initializer=fluid.initializer.XavierInitializer()),
bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)),
use_cudnn = use_cudnn,
data_format = "NCT"))
use_cudnn = use_cudnn))
k = math.sqrt(1 / num_hidden)
for _ in range(2):
self.conv_list.append(Conv1D(in_channels = num_hidden,
out_channels = num_hidden,
self.conv_list.append(Conv1D(num_channels = num_hidden,
num_filters = num_hidden,
filter_size = 5,
padding = int(np.floor(5/2)),
param_attr = fluid.ParamAttr(initializer=fluid.initializer.XavierInitializer()),
bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)),
use_cudnn = use_cudnn,
data_format = "NCT"))
use_cudnn = use_cudnn))
for i, layer in enumerate(self.conv_list):
self.add_sublayer("conv_list_{}".format(i), layer)

21
parakeet/models/transformerTTS/post_convnet.py
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@ -22,34 +22,31 @@ class PostConvNet(dg.Layer):
self.batchnorm_last = batchnorm_last
self.conv_list = []
k = math.sqrt(1 / (n_mels * outputs_per_step))
self.conv_list.append(Conv1D(in_channels = n_mels * outputs_per_step,
out_channels = num_hidden,
self.conv_list.append(Conv1D(num_channels = n_mels * outputs_per_step,
num_filters = num_hidden,
filter_size = filter_size,
padding = padding,
param_attr = fluid.ParamAttr(initializer=fluid.initializer.XavierInitializer()),
bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)),
use_cudnn = use_cudnn,
data_format = "NCT"))
use_cudnn = use_cudnn))
k = math.sqrt(1 / num_hidden)
for _ in range(1, num_conv-1):
self.conv_list.append(Conv1D(in_channels = num_hidden,
out_channels = num_hidden,
self.conv_list.append(Conv1D(num_channels = num_hidden,
num_filters = num_hidden,
filter_size = filter_size,
padding = padding,
param_attr = fluid.ParamAttr(initializer=fluid.initializer.XavierInitializer()),
bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)),
use_cudnn = use_cudnn,
data_format = "NCT") )
use_cudnn = use_cudnn))
self.conv_list.append(Conv1D(in_channels = num_hidden,
out_channels = n_mels * outputs_per_step,
self.conv_list.append(Conv1D(num_channels = num_hidden,
num_filters = n_mels * outputs_per_step,
filter_size = filter_size,
padding = padding,
param_attr = fluid.ParamAttr(initializer=fluid.initializer.XavierInitializer()),
bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)),
use_cudnn = use_cudnn,
data_format = "NCT"))
use_cudnn = use_cudnn))
for i, layer in enumerate(self.conv_list):
self.add_sublayer("conv_list_{}".format(i), layer)

14
parakeet/models/transformerTTS/vocoder.py
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@ -10,15 +10,13 @@ class Vocoder(dg.Layer):
"""
def __init__(self, config):
super(Vocoder, self).__init__()
self.pre_proj = Conv1D(in_channels = config.audio.num_mels,
out_channels = config.hidden_size,
filter_size=1,
data_format = "NCT")
self.pre_proj = Conv1D(num_channels = config.audio.num_mels,
num_filters = config.hidden_size,
filter_size=1)
self.cbhg = CBHG(config.hidden_size, config.batch_size)
self.post_proj = Conv1D(in_channels = config.hidden_size,
out_channels = (config.audio.n_fft // 2) + 1,
filter_size=1,
data_format = "NCT")
self.post_proj = Conv1D(num_channels = config.hidden_size,
num_filters = (config.audio.n_fft // 2) + 1,
filter_size=1)
def forward(self, mel):
mel = layers.transpose(mel, [0,2,1])

18
parakeet/modules/ffn.py
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@ -14,23 +14,21 @@ class PositionwiseFeedForward(dg.Layer):
self.dropout = dropout
k = math.sqrt(1 / d_in)
self.w_1 = Conv1D(in_channels = d_in,
out_channels = num_hidden,
self.w_1 = Conv1D(num_channels = d_in,
num_filters = num_hidden,
filter_size = filter_size,
padding=padding,
param_attr = fluid.ParamAttr(initializer=fluid.initializer.XavierInitializer()),
bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)),
use_cudnn = use_cudnn,
data_format = "NTC")
use_cudnn = use_cudnn)
k = math.sqrt(1 / num_hidden)
self.w_2 = Conv1D(in_channels = num_hidden,
out_channels = d_in,
self.w_2 = Conv1D(num_channels = num_hidden,
num_filters = d_in,
filter_size = filter_size,
padding=padding,
param_attr = fluid.ParamAttr(initializer=fluid.initializer.XavierInitializer()),
bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)),
use_cudnn = use_cudnn,
data_format = "NTC")
use_cudnn = use_cudnn)
self.layer_norm = dg.LayerNorm(d_in)
def forward(self, input):
@ -42,12 +40,14 @@ class PositionwiseFeedForward(dg.Layer):
Returns:
output (Variable), Shape(B, T, C), the result after FFN.
"""
x = layers.transpose(input, [0,2,1])
#FFN Networt
x = self.w_2(layers.relu(self.w_1(input)))
x = self.w_2(layers.relu(self.w_1(x)))
# dropout
x = layers.dropout(x, self.dropout)
x = layers.transpose(x, [0,2,1])
# residual connection
x = x + input