86 lines
3.7 KiB
Python
86 lines
3.7 KiB
Python
import math
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import paddle.fluid.dygraph as dg
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import paddle.fluid as fluid
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import paddle.fluid.layers as layers
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from parakeet.modules.customized import Conv1D
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class PostConvNet(dg.Layer):
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def __init__(self,
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n_mels=80,
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num_hidden=512,
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filter_size=5,
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padding=0,
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num_conv=5,
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outputs_per_step=1,
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use_cudnn=True,
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dropout=0.1,
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batchnorm_last=False):
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super(PostConvNet, self).__init__()
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self.dropout = dropout
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self.num_conv = num_conv
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self.batchnorm_last = batchnorm_last
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self.conv_list = []
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k = math.sqrt(1 / (n_mels * outputs_per_step))
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self.conv_list.append(Conv1D(num_channels = n_mels * outputs_per_step,
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num_filters = num_hidden,
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filter_size = filter_size,
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padding = padding,
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param_attr = fluid.ParamAttr(initializer=fluid.initializer.XavierInitializer()),
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bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)),
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use_cudnn = use_cudnn))
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k = math.sqrt(1 / num_hidden)
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for _ in range(1, num_conv-1):
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self.conv_list.append(Conv1D(num_channels = num_hidden,
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num_filters = num_hidden,
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filter_size = filter_size,
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padding = padding,
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param_attr = fluid.ParamAttr(initializer=fluid.initializer.XavierInitializer()),
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bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)),
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use_cudnn = use_cudnn))
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self.conv_list.append(Conv1D(num_channels = num_hidden,
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num_filters = n_mels * outputs_per_step,
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filter_size = filter_size,
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padding = padding,
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param_attr = fluid.ParamAttr(initializer=fluid.initializer.XavierInitializer()),
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bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)),
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use_cudnn = use_cudnn))
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for i, layer in enumerate(self.conv_list):
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self.add_sublayer("conv_list_{}".format(i), layer)
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self.batch_norm_list = [dg.BatchNorm(num_hidden,
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data_layout='NCHW') for _ in range(num_conv-1)]
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if self.batchnorm_last:
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self.batch_norm_list.append(dg.BatchNorm(n_mels * outputs_per_step,
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data_layout='NCHW'))
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for i, layer in enumerate(self.batch_norm_list):
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self.add_sublayer("batch_norm_list_{}".format(i), layer)
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def forward(self, input):
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"""
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Post Conv Net.
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Args:
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input (Variable): Shape(B, T, C), dtype: float32. The input value.
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Returns:
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output (Variable), Shape(B, T, C), the result after postconvnet.
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"""
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input = layers.transpose(input, [0,2,1])
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len = input.shape[-1]
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for i in range(self.num_conv-1):
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batch_norm = self.batch_norm_list[i]
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conv = self.conv_list[i]
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input = layers.dropout(layers.tanh(batch_norm(conv(input)[:,:,:len])), self.dropout)
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conv = self.conv_list[self.num_conv-1]
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input = conv(input)[:,:,:len]
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if self.batchnorm_last:
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batch_norm = self.batch_norm_list[self.num_conv-1]
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input = layers.dropout(batch_norm(input), self.dropout)
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output = layers.transpose(input, [0,2,1])
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return output |