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156 lines
4.7 KiB
156 lines
4.7 KiB
"""
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A simplifed version of Baidu DeepSpeech2 model.
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"""
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import paddle.v2 as paddle
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#TODO: add bidirectional rnn.
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def conv_bn_layer(input, filter_size, num_channels_in, num_channels_out, stride,
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padding, act):
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"""
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Convolution layer with batch normalization.
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"""
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conv_layer = paddle.layer.img_conv(
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input=input,
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filter_size=filter_size,
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num_channels=num_channels_in,
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num_filters=num_channels_out,
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stride=stride,
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padding=padding,
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act=paddle.activation.Linear(),
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bias_attr=False)
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return paddle.layer.batch_norm(input=conv_layer, act=act)
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def bidirectonal_simple_rnn_bn_layer(name, input, size, act):
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"""
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Bidirectonal simple rnn layer with batch normalization.
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The batch normalization is only performed on input-state projection
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(sequence-wise normalization).
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Question: does mean and variance statistics computed over the whole sequence
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or just on each individual time steps?
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"""
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def __simple_rnn_step__(input):
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last_state = paddle.layer.memory(name=name + "_state", size=size)
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input_fc = paddle.layer.fc(
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input=input,
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size=size,
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act=paddle.activation.Linear(),
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bias_attr=False)
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# batch norm is only performed on input-state projection
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input_fc_bn = paddle.layer.batch_norm(
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input=input_fc, act=paddle.activation.Linear())
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state_fc = paddle.layer.fc(
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input=last_state,
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size=size,
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act=paddle.activation.Linear(),
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bias_attr=False)
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return paddle.layer.addto(
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name=name + "_state", input=[input_fc_bn, state_fc], act=act)
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forward = paddle.layer.recurrent_group(
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step=__simple_rnn_step__, input=input)
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return forward
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# argument reverse is not exposed in V2 recurrent_group
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#backward = paddle.layer.recurrent_group(
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#step=__simple_rnn_step__,
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#input=input,
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#reverse=True)
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#return paddle.layer.concat(input=[forward, backward])
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def conv_group(input, num_stacks):
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"""
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Convolution group with several stacking convolution layers.
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"""
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conv = conv_bn_layer(
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input=input,
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filter_size=(11, 41),
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num_channels_in=1,
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num_channels_out=32,
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stride=(3, 2),
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padding=(5, 20),
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act=paddle.activation.BRelu())
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for i in xrange(num_stacks - 1):
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conv = conv_bn_layer(
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input=conv,
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filter_size=(11, 21),
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num_channels_in=32,
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num_channels_out=32,
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stride=(1, 2),
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padding=(5, 10),
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act=paddle.activation.BRelu())
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return conv
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def rnn_group(input, size, num_stacks):
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"""
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RNN group with several stacking RNN layers.
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"""
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output = input
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for i in xrange(num_stacks):
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output = bidirectonal_simple_rnn_bn_layer(
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name=str(i), input=output, size=size, act=paddle.activation.BRelu())
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return output
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def deep_speech2(audio_data,
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text_data,
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dict_size,
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num_conv_layers=2,
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num_rnn_layers=3,
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rnn_size=256):
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"""
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The whole DeepSpeech2 model structure (a simplified version).
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:param audio_data: Audio spectrogram data layer.
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:type audio_data: LayerOutput
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:param text_data: Transcription text data layer.
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:type text_data: LayerOutput
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:param dict_size: Dictionary size for tokenized transcription.
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:type dict_size: int
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:param num_conv_layers: Number of stacking convolution layers.
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:type num_conv_layers: int
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:param num_rnn_layers: Number of stacking RNN layers.
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:type num_rnn_layers: int
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:param rnn_size: RNN layer size (number of RNN cells).
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:type rnn_size: int
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:return: Tuple of the cost layer and the max_id decoder layer.
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:rtype: tuple of LayerOutput
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"""
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# convolution group
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conv_group_output = conv_group(input=audio_data, num_stacks=num_conv_layers)
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# convert data form convolution feature map to sequence of vectors
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conv2seq = paddle.layer.block_expand(
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input=conv_group_output,
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num_channels=32,
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stride_x=1,
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stride_y=1,
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block_x=1,
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block_y=21)
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# rnn group
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rnn_group_output = rnn_group(
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input=conv2seq, size=rnn_size, num_stacks=num_rnn_layers)
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# output token distribution
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fc = paddle.layer.fc(
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input=rnn_group_output,
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size=dict_size + 1,
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act=paddle.activation.Linear(),
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bias_attr=True)
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# ctc cost
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cost = paddle.layer.warp_ctc(
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input=fc,
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label=text_data,
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size=dict_size + 1,
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blank=dict_size,
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norm_by_times=True)
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# max decoder
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max_id = paddle.layer.max_id(input=fc)
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return cost, max_id
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