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145 lines
4.2 KiB
145 lines
4.2 KiB
"""
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Inference for a simplifed version of Baidu DeepSpeech2 model.
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"""
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import paddle.v2 as paddle
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from itertools import groupby
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import distutils.util
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import argparse
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import gzip
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from audio_data_utils import DataGenerator
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from model import deep_speech2
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parser = argparse.ArgumentParser(
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description='Simplified version of DeepSpeech2 inference.')
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parser.add_argument(
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"--num_samples",
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default=10,
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type=int,
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help="Number of samples for inference. (default: %(default)s)")
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parser.add_argument(
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"--num_conv_layers",
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default=2,
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type=int,
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help="Convolution layer number. (default: %(default)s)")
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parser.add_argument(
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"--num_rnn_layers",
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default=3,
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type=int,
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help="RNN layer number. (default: %(default)s)")
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parser.add_argument(
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"--rnn_layer_size",
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default=512,
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type=int,
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help="RNN layer cell number. (default: %(default)s)")
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parser.add_argument(
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"--use_gpu",
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default=True,
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type=distutils.util.strtobool,
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help="Use gpu or not. (default: %(default)s)")
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parser.add_argument(
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"--normalizer_manifest_path",
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default='./manifest.libri.train-clean-100',
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type=str,
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help="Manifest path for normalizer. (default: %(default)s)")
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parser.add_argument(
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"--decode_manifest_path",
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default='./manifest.libri.test-clean',
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type=str,
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help="Manifest path for decoding. (default: %(default)s)")
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parser.add_argument(
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"--model_filepath",
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default='./params.tar.gz',
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type=str,
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help="Model filepath. (default: %(default)s)")
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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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"""
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Postprocessing for max-ctc-decoder.
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- remove consecutive duplicate tokens.
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- remove blanks.
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"""
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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 blanks
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return [id for id in id_list if id != blank_id]
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def best_path_decode():
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"""
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Max-ctc-decoding for DeepSpeech2.
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"""
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# initialize data generator
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data_generator = DataGenerator(
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vocab_filepath='eng_vocab.txt',
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normalizer_manifest_path=args.normalizer_manifest_path,
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normalizer_num_samples=200,
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max_duration=20.0,
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min_duration=0.0,
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stride_ms=10,
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window_ms=20)
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# create network config
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dict_size = data_generator.vocabulary_size()
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vocab_list = data_generator.vocabulary_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=2000,
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type=paddle.data_type.dense_vector(322000))
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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(args.model_filepath))
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# prepare infer data
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feeding = data_generator.data_name_feeding()
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test_batch_reader = data_generator.batch_reader_creator(
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manifest_path=args.decode_manifest_path,
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batch_size=args.num_samples,
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padding_to=2000,
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flatten=True,
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sort_by_duration=False,
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shuffle=False)
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infer_data = test_batch_reader().next()
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# run max-ctc-decoding
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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 * instance_length:(i + 1) * 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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best_path_decode()
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if __name__ == '__main__':
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main()
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