"""Parameters tuning for DeepSpeech2 model.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import distutils.util import argparse import gzip import paddle.v2 as paddle from data_utils.data import DataGenerator from model import deep_speech2 from decoder import * from lm.lm_scorer import LmScorer from error_rate import wer parser = argparse.ArgumentParser(description=__doc__) parser.add_argument( "--num_samples", default=100, type=int, help="Number of samples for parameters tuning. (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( "--num_processes_beam_search", default=multiprocessing.cpu_count(), type=int, help="Number of cpu processes for beam search. (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='datasets/manifest.test', type=str, help="Manifest path for decoding. (default: %(default)s)") parser.add_argument( "--model_filepath", default='checkpoints/params.latest.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( "--beam_size", default=500, type=int, help="Width for beam search decoding. (default: %(default)d)") parser.add_argument( "--language_model_path", default="lm/data/en.00.UNKNOWN.klm", type=str, help="Path for language model. (default: %(default)s)") parser.add_argument( "--alpha_from", default=0.1, type=float, help="Where alpha starts from. (default: %(default)f)") parser.add_argument( "--num_alphas", default=14, type=int, help="Number of candidate alphas. (default: %(default)d)") parser.add_argument( "--alpha_to", default=0.36, type=float, help="Where alpha ends with. (default: %(default)f)") parser.add_argument( "--beta_from", default=0.05, type=float, help="Where beta starts from. (default: %(default)f)") parser.add_argument( "--num_betas", default=20, type=float, help="Number of candidate betas. (default: %(default)d)") parser.add_argument( "--beta_to", default=1.0, type=float, help="Where beta ends with. (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 tune(): """Tune parameters alpha and beta on one minibatch.""" if not args.num_alphas >= 0: raise ValueError("num_alphas must be non-negative!") if not args.num_betas >= 0: raise ValueError("num_betas must be non-negative!") # 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) # get one batch data for tuning 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(0, len(infer_data)) ] # create grid for search cand_alphas = np.linspace(args.alpha_from, args.alpha_to, args.num_alphas) cand_betas = np.linspace(args.beta_from, args.beta_to, args.num_betas) params_grid = [(alpha, beta) for alpha in cand_alphas for beta in cand_betas] ## tune parameters in loop for (alpha, beta) in params_grid: wer_sum, wer_counter = 0, 0 ext_scorer = LmScorer(alpha, beta, args.language_model_path) # beam search using multiple processes beam_search_results = ctc_beam_search_decoder_batch( probs_split=probs_split, vocabulary=data_generator.vocab_list, beam_size=args.beam_size, cutoff_prob=args.cutoff_prob, blank_id=len(data_generator.vocab_list), num_processes=args.num_processes_beam_search, ext_scoring_func=ext_scorer, ) for i, beam_search_result in enumerate(beam_search_results): target_transcription = ''.join([ data_generator.vocab_list[index] for index in infer_data[i][1] ]) wer_sum += wer(target_transcription, beam_search_result[0][1]) wer_counter += 1 print("alpha = %f\tbeta = %f\tWER = %f" % (alpha, beta, wer_sum / wer_counter)) def main(): paddle.init(use_gpu=args.use_gpu, trainer_count=1) tune() if __name__ == '__main__': main()