"""Inferer for DeepSpeech2 model.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import gzip import distutils.util import multiprocessing import paddle.v2 as paddle from data_utils.data import DataGenerator from model import deep_speech2 from decoder import * from scorer import Scorer from error_rate import wer import utils parser = argparse.ArgumentParser(description=__doc__) parser.add_argument( "--num_samples", default=100, 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( "--num_threads_data", default=multiprocessing.cpu_count(), type=int, help="Number of cpu threads for preprocessing data. (default: %(default)s)") parser.add_argument( "--mean_std_filepath", default='mean_std.npz', type=str, help="Manifest path for normalizer. (default: %(default)s)") parser.add_argument( "--decode_manifest_path", default='data/manifest.libri.test-100sample', 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='datasets/vocab/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=500, 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/en.00.UNKNOWN.klm", type=str, help="Path for language model. (default: %(default)s)") parser.add_argument( "--alpha", default=0.26, type=float, help="Parameter associated with language model. (default: %(default)f)") parser.add_argument( "--beta", default=0.1, type=float, help="Parameter associated with word count. (default: %(default)f)") parser.add_argument( "--cutoff_prob", default=0.99, type=float, help="The cutoff probability of pruning" "in beam search. (default: %(default)f)") args = parser.parse_args() def infer(): """Inference for DeepSpeech2.""" # initialize data generator data_generator = DataGenerator( vocab_filepath=args.vocab_filepath, mean_std_filepath=args.mean_std_filepath, augmentation_config='{}', num_threads=args.num_threads_data) # create network config # paddle.data_type.dense_array is used for variable batch input. # The size 161 * 161 is only an placeholder value and the real shape # of input batch data will be induced during training. audio_data = paddle.layer.data( name="audio_spectrogram", type=paddle.data_type.dense_array(161 * 161)) text_data = paddle.layer.data( name="transcript_text", type=paddle.data_type.integer_value_sequence(data_generator.vocab_size)) output_probs = deep_speech2( audio_data=audio_data, text_data=text_data, dict_size=data_generator.vocab_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 batch_reader = data_generator.batch_reader_creator( manifest_path=args.decode_manifest_path, batch_size=args.num_samples, sortagrad=False, shuffle_method=None) infer_data = 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(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([ data_generator.vocab_list[index] for index in infer_data[i][1] ]) best_path_transcription = ctc_best_path_decode( probs_seq=probs, vocabulary=data_generator.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([ data_generator.vocab_list[index] for index in infer_data[i][1] ]) beam_search_result = ctc_beam_search_decoder( probs_seq=probs, vocabulary=data_generator.vocab_list, beam_size=args.beam_size, blank_id=len(data_generator.vocab_list), cutoff_prob=args.cutoff_prob, ext_scoring_func=ext_scorer, ) print("\nTarget Transcription:\t%s" % target_transcription) for index in xrange(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)) 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=data_generator.vocab_list, beam_size=args.beam_size, blank_id=len(data_generator.vocab_list), cutoff_prob=args.cutoff_prob, ext_scoring_func=ext_scorer, ) for i, beam_search_result in enumerate(beam_search_nproc_results): target_transcription = ''.join([ data_generator.vocab_list[index] for index in infer_data[i][1] ]) print("\nTarget Transcription:\t%s" % target_transcription) for index in xrange(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." % decode_method) def main(): utils.print_arguments(args) paddle.init(use_gpu=args.use_gpu, trainer_count=1) infer() if __name__ == '__main__': main()