infer.py

"""
   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 using 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()