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138 lines
4.0 KiB
138 lines
4.0 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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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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from decoder import ctc_decode
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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='data/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='data/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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parser.add_argument(
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"--vocab_filepath",
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default='data/eng_vocab.txt',
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type=str,
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help="Vocabulary filepath. (default: %(default)s)")
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args = parser.parse_args()
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def infer():
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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=args.vocab_filepath,
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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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output_probs = 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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is_inference=True)
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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 inference
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infer_results = paddle.infer(
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output_layer=output_probs, parameters=parameters, input=infer_data)
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num_steps = len(infer_results) / len(infer_data)
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probs_split = [
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infer_results[i * num_steps:(i + 1) * num_steps]
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for i in xrange(0, len(infer_data))
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]
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# decode and print
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for i, probs in enumerate(probs_split):
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output_transcription = ctc_decode(
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probs_seq=probs, vocabulary=vocab_list, method="best_path")
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target_transcription = ''.join(
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[vocab_list[index] for index in infer_data[i][1]])
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print("Target Transcription: %s \nOutput Transcription: %s \n" %
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(target_transcription, output_transcription))
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def main():
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paddle.init(use_gpu=args.use_gpu, trainer_count=1)
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infer()
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if __name__ == '__main__':
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main()
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