""" Inference for a simplifed version of Baidu DeepSpeech2 model. """ import paddle.v2 as paddle import distutils.util import argparse import gzip from audio_data_utils import DataGenerator from model import deep_speech2 from decoder import * from error_rate import wer parser = argparse.ArgumentParser( description='Simplified version of DeepSpeech2 inference.') parser.add_argument( "--num_samples", default=10, type=int, help="Number of samples for inference. (default: %(default)s)") parser.add_argument( "--num_conv_layers", default=2, type=int, help="Convolution layer number. (default: %(default)s)") parser.add_argument( "--num_rnn_layers", default=3, type=int, help="RNN layer number. (default: %(default)s)") parser.add_argument( "--rnn_layer_size", default=512, type=int, help="RNN layer cell number. (default: %(default)s)") parser.add_argument( "--use_gpu", default=True, type=distutils.util.strtobool, help="Use gpu or not. (default: %(default)s)") parser.add_argument( "--normalizer_manifest_path", default='data/manifest.libri.train-clean-100', type=str, help="Manifest path for normalizer. (default: %(default)s)") parser.add_argument( "--decode_manifest_path", default='data/manifest.libri.test-clean', type=str, help="Manifest path for decoding. (default: %(default)s)") parser.add_argument( "--model_filepath", default='./params.tar.gz', type=str, help="Model filepath. (default: %(default)s)") parser.add_argument( "--vocab_filepath", default='data/eng_vocab.txt', type=str, help="Vocabulary filepath. (default: %(default)s)") parser.add_argument( "--decode_method", default='beam_search_nproc', type=str, help="Method for ctc decoding, best_path, beam_search or beam_search_nproc. (default: %(default)s)" ) parser.add_argument( "--beam_size", default=50, type=int, help="Width for beam search decoding. (default: %(default)d)") parser.add_argument( "--num_results_per_sample", default=1, type=int, help="Number of output per sample in beam search. (default: %(default)d)") parser.add_argument( "--language_model_path", default="./data/1Billion.klm", type=str, help="Path for language model. (default: %(default)s)") parser.add_argument( "--alpha", default=0.0, type=float, help="Parameter associated with language model. (default: %(default)f)") parser.add_argument( "--beta", default=0.0, type=float, help="Parameter associated with word count. (default: %(default)f)") args = parser.parse_args() def infer(): """ Inference for DeepSpeech2. """ # initialize data generator data_generator = DataGenerator( vocab_filepath=args.vocab_filepath, normalizer_manifest_path=args.normalizer_manifest_path, normalizer_num_samples=200, max_duration=20.0, min_duration=0.0, stride_ms=10, window_ms=20) # create network config dict_size = data_generator.vocabulary_size() vocab_list = data_generator.vocabulary_list() audio_data = paddle.layer.data( name="audio_spectrogram", height=161, width=2000, type=paddle.data_type.dense_vector(322000)) text_data = paddle.layer.data( name="transcript_text", type=paddle.data_type.integer_value_sequence(dict_size)) output_probs = deep_speech2( audio_data=audio_data, text_data=text_data, dict_size=dict_size, num_conv_layers=args.num_conv_layers, num_rnn_layers=args.num_rnn_layers, rnn_size=args.rnn_layer_size, is_inference=True) # load parameters parameters = paddle.parameters.Parameters.from_tar( gzip.open(args.model_filepath)) # prepare infer data feeding = data_generator.data_name_feeding() test_batch_reader = data_generator.batch_reader_creator( manifest_path=args.decode_manifest_path, batch_size=args.num_samples, padding_to=2000, flatten=True, sort_by_duration=False, shuffle=False) infer_data = test_batch_reader().next() # run inference infer_results = paddle.infer( output_layer=output_probs, parameters=parameters, input=infer_data) num_steps = len(infer_results) / len(infer_data) probs_split = [ infer_results[i * num_steps:(i + 1) * num_steps] for i in xrange(0, len(infer_data)) ] ## decode and print # best path decode wer_sum, wer_counter = 0, 0 if args.decode_method == "best_path": for i, probs in enumerate(probs_split): target_transcription = ''.join( [vocab_list[index] for index in infer_data[i][1]]) best_path_transcription = ctc_best_path_decode( probs_seq=probs, vocabulary=vocab_list) print("\nTarget Transcription: %s\nOutput Transcription: %s" % (target_transcription, best_path_transcription)) wer_cur = wer(target_transcription, best_path_transcription) wer_sum += wer_cur wer_counter += 1 print("cur wer = %f, average wer = %f" % (wer_cur, wer_sum / wer_counter)) # beam search decode elif args.decode_method == "beam_search": ext_scorer = Scorer(args.alpha, args.beta, args.language_model_path) for i, probs in enumerate(probs_split): target_transcription = ''.join( [vocab_list[index] for index in infer_data[i][1]]) beam_search_result = ctc_beam_search_decoder( probs_seq=probs, vocabulary=vocab_list, beam_size=args.beam_size, ext_scoring_func=ext_scorer.evaluate, blank_id=len(vocab_list)) print("\nTarget Transcription:\t%s" % target_transcription) for index in range(args.num_results_per_sample): result = beam_search_result[index] #output: index, log prob, beam result print("Beam %d: %f \t%s" % (index, result[0], result[1])) wer_cur = wer(target_transcription, beam_search_result[0][1]) wer_sum += wer_cur wer_counter += 1 print("cur wer = %f , average wer = %f" % (wer_cur, wer_sum / wer_counter)) # beam search in multiple processes elif args.decode_method == "beam_search_nproc": ext_scorer = Scorer(args.alpha, args.beta, args.language_model_path) beam_search_nproc_results = ctc_beam_search_decoder_nproc( probs_split=probs_split, vocabulary=vocab_list, beam_size=args.beam_size, #ext_scoring_func=ext_scorer.evaluate, ext_scoring_func=None, blank_id=len(vocab_list)) for i, beam_search_result in enumerate(beam_search_nproc_results): target_transcription = ''.join( [vocab_list[index] for index in infer_data[i][1]]) print("\nTarget Transcription:\t%s" % target_transcription) for index in range(args.num_results_per_sample): result = beam_search_result[index] #output: index, log prob, beam result print("Beam %d: %f \t%s" % (index, result[0], result[1])) wer_cur = wer(target_transcription, beam_search_result[0][1]) wer_sum += wer_cur wer_counter += 1 print("cur wer = %f , average wer = %f" % (wer_cur, wer_sum / wer_counter)) else: raise ValueError("Decoding method [%s] is not supported." % method) def main(): paddle.init(use_gpu=args.use_gpu, trainer_count=1) infer() if __name__ == '__main__': main()