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95 lines
3.0 KiB
95 lines
3.0 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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from model import deep_speech2
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import gzip
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from itertools import groupby
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parser = argparse.ArgumentParser(
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description='Simpled version of DeepSpeech2 inference.')
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parser.add_argument(
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"--num_samples", default=10, type=int, help="Number of inference samples.")
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parser.add_argument(
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"--num_conv_layers", default=2, type=int, help="Convolution layer number.")
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parser.add_argument(
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"--num_rnn_layers", default=3, type=int, help="RNN layer number.")
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parser.add_argument(
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"--rnn_layer_size", default=512, type=int, help="RNN layer cell number.")
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parser.add_argument(
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"--use_gpu", default=True, type=bool, help="Use gpu or not.")
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args = parser.parse_args()
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def remove_duplicate_and_blank(id_list, blank_id):
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# remove consecutive duplicate tokens
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id_list = [x[0] for x in groupby(id_list)]
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# remove blank
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return [id for id in id_list if id != blank_id]
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def max_infer():
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# create network config
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_, vocab_list = audio_data_utils.get_vocabulary()
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dict_size = len(vocab_list)
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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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_, max_id = deep_speech2(
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audio_data=audio_data,
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text_data=text_data,
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dict_size=dict_size,
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num_conv_layers=args.num_conv_layers,
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num_rnn_layers=args.num_rnn_layers,
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rnn_size=args.rnn_layer_size)
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# load parameters
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parameters = paddle.parameters.Parameters.from_tar(
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gzip.open("params.tar.gz"))
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# prepare infer data
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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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test_batch_reader = audio_data_utils.padding_batch_reader(
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paddle.batch(
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audio_data_utils.reader_creator(
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manifest_path="./libri.manifest.test", sort_by_duration=False),
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batch_size=args.num_samples),
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padding=[-1, 1000])
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infer_data = test_batch_reader().next()
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# run inference
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max_id_results = paddle.infer(
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output_layer=max_id,
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parameters=parameters,
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input=infer_data,
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field=['id'])
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# postprocess
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instance_length = len(max_id_results) / args.num_samples
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instance_list = [
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max_id_results[i:i + instance_length]
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for i in xrange(0, args.num_samples)
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]
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for i, instance in enumerate(instance_list):
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id_list = remove_duplicate_and_blank(instance, dict_size)
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output_transcript = ''.join([vocab_list[id] for id in id_list])
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target_transcript = ''.join([vocab_list[id] for id in infer_data[i][1]])
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print("Target Transcript: %s \nOutput Transcript: %s \n" %
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(target_transcript, output_transcript))
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def main():
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paddle.init(use_gpu=args.use_gpu, trainer_count=1)
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max_infer()
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if __name__ == '__main__':
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main()
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