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189 lines
5.8 KiB
189 lines
5.8 KiB
7 years ago
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import paddle.v2 as paddle
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import audio_data_utils
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import argparse
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parser = argparse.ArgumentParser(
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description='Simpled version of DeepSpeech2 trainer.')
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parser.add_argument(
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"--batch_size", default=512, type=int, help="Minibatch size.")
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parser.add_argument("--trainer", default=1, type=int, help="Trainer number.")
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parser.add_argument(
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"--num_passes", default=20, type=int, help="Training pass number.")
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args = parser.parse_args()
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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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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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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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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):
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conv1 = 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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conv2 = conv_bn_layer(
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input=conv1,
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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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conv3 = conv_bn_layer(
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input=conv2,
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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 conv3
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def rnn_group(input, size, num_stacks):
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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, text_data, dict_size):
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conv_group_output = conv_group(input=audio_data)
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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_output = rnn_group(input=conv2seq, size=256, num_stacks=5)
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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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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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return cost
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def train():
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# create network config
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dict_size = audio_data_utils.get_vocabulary_size()
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audio_data = paddle.layer.data(
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name="audio_spectrogram",
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height=161,
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width=1000,
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type=paddle.data_type.dense_vector(161000))
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text_data = paddle.layer.data(
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name="transcript_text",
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type=paddle.data_type.integer_value_sequence(dict_size))
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cost = deep_speech2(audio_data, text_data, dict_size)
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# create parameters and optimizer
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parameters = paddle.parameters.create(cost)
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optimizer = paddle.optimizer.Adam(
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learning_rate=5e-5,
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gradient_clipping_threshold=5,
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regularization=paddle.optimizer.L2Regularization(rate=8e-4))
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trainer = paddle.trainer.SGD(
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cost=cost, parameters=parameters, update_equation=optimizer)
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return
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# create data readers
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feeding = {
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"audio_spectrogram": 0,
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"transcript_text": 1,
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}
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train_batch_reader = audio_data_utils.padding_batch_reader(
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paddle.batch(
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audio_data_utils.reader_creator("./libri.manifest.dev"),
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batch_size=args.batch_size // args.trainer),
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padding=[-1, 1000])
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test_batch_reader = audio_data_utils.padding_batch_reader(
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paddle.batch(
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audio_data_utils.reader_creator("./libri.manifest.test"),
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batch_size=args.batch_size // args.trainer),
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padding=[-1, 1000])
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# create event handler
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def event_handler(event):
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if isinstance(event, paddle.event.EndIteration):
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if event.batch_id % 10 == 0:
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print "Pass: %d, Batch: %d, TrainCost: %f, %s" % (
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event.pass_id, event.batch_id, event.cost, event.metrics)
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else:
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sys.stdout.write('.')
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sys.stdout.flush()
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if isinstance(event, paddle.event.EndPass):
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result = trainer.test(reader=test_batch_reader, feeding=feeding)
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print "Pass: %d, TestCost: %f, %s" % (event.pass_id, event.cost,
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result.metrics)
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with gzip.open("params.tar.gz", 'w') as f:
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parameters.to_tar(f)
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# run train
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trainer.train(
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reader=train_batch_reader,
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event_handler=event_handler,
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num_passes=10,
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feeding=feeding)
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def main():
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train()
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if __name__ == '__main__':
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main()
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