"""Beam search parameters tuning for DeepSpeech2 model.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import sys import os import numpy as np import argparse import functools import gzip import logging import paddle.v2 as paddle import _init_paths from data_utils.data import DataGenerator from decoders.swig_wrapper import Scorer from decoders.swig_wrapper import ctc_beam_search_decoder_batch from model_utils.model import deep_speech_v2_network from utils.error_rate import wer, cer from utils.utility import add_arguments, print_arguments parser = argparse.ArgumentParser(description=__doc__) add_arg = functools.partial(add_arguments, argparser=parser) # yapf: disable add_arg('num_batches', int, -1, "# of batches tuning on. " "Default -1, on whole dev set.") add_arg('batch_size', int, 256, "# of samples per batch.") add_arg('trainer_count', int, 8, "# of Trainers (CPUs or GPUs).") add_arg('beam_size', int, 500, "Beam search width.") add_arg('num_proc_bsearch', int, 8, "# of CPUs for beam search.") add_arg('num_proc_data', int, 8, "# of CPUs for data preprocessing.") add_arg('num_conv_layers', int, 2, "# of convolution layers.") add_arg('num_rnn_layers', int, 3, "# of recurrent layers.") add_arg('rnn_layer_size', int, 2048, "# of recurrent cells per layer.") add_arg('num_alphas', int, 45, "# of alpha candidates for tuning.") add_arg('num_betas', int, 8, "# of beta candidates for tuning.") add_arg('alpha_from', float, 1.0, "Where alpha starts tuning from.") add_arg('alpha_to', float, 3.2, "Where alpha ends tuning with.") add_arg('beta_from', float, 0.1, "Where beta starts tuning from.") add_arg('beta_to', float, 0.45, "Where beta ends tuning with.") add_arg('cutoff_prob', float, 1.0, "Cutoff probability for pruning.") add_arg('cutoff_top_n', int, 40, "Cutoff number for pruning.") add_arg('use_gru', bool, False, "Use GRUs instead of simple RNNs.") add_arg('use_gpu', bool, True, "Use GPU or not.") add_arg('share_rnn_weights',bool, True, "Share input-hidden weights across " "bi-directional RNNs. Not for GRU.") add_arg('tune_manifest', str, 'data/librispeech/manifest.dev-clean', "Filepath of manifest to tune.") add_arg('mean_std_path', str, 'data/librispeech/mean_std.npz', "Filepath of normalizer's mean & std.") add_arg('vocab_path', str, 'data/librispeech/vocab.txt', "Filepath of vocabulary.") add_arg('lang_model_path', str, 'models/lm/common_crawl_00.prune01111.trie.klm', "Filepath for language model.") add_arg('model_path', str, './checkpoints/libri/params.latest.tar.gz', "If None, the training starts from scratch, " "otherwise, it resumes from the pre-trained model.") add_arg('error_rate_type', str, 'wer', "Error rate type for evaluation.", choices=['wer', 'cer']) add_arg('specgram_type', str, 'linear', "Audio feature type. Options: linear, mfcc.", choices=['linear', 'mfcc']) # yapf: disable args = parser.parse_args() logging.basicConfig( format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)d] %(message)s') def tune(): """Tune parameters alpha and beta incrementally.""" 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!") data_generator = DataGenerator( vocab_filepath=args.vocab_path, mean_std_filepath=args.mean_std_path, augmentation_config='{}', specgram_type=args.specgram_type, num_threads=args.num_proc_data) 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_speech_v2_network( 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, use_gru=args.use_gru, share_rnn_weights=args.share_rnn_weights) batch_reader = data_generator.batch_reader_creator( manifest_path=args.tune_manifest, batch_size=args.batch_size, sortagrad=False, shuffle_method=None) # load parameters if not os.path.isfile(args.model_path): raise IOError("Invaid model path: %s" % args.model_path) parameters = paddle.parameters.Parameters.from_tar( gzip.open(args.model_path)) inferer = paddle.inference.Inference( output_layer=output_probs, parameters=parameters) # decoders only accept string encoded in utf-8 vocab_list = [chars.encode("utf-8") for chars in data_generator.vocab_list] # init logger logger = logging.getLogger("") logger.setLevel(level=logging.INFO) # init external scorer logger.info("begin to initialize the external scorer for tuning") if not os.path.isfile(args.lang_model_path): raise IOError("Invaid language model path: %s" % args.lang_model_path) ext_scorer = Scorer( alpha=args.alpha_from, beta=args.beta_from, model_path=args.lang_model_path, vocabulary=vocab_list) logger.info("language model: " "is_character_based = %d," % ext_scorer.is_character_based() + " max_order = %d," % ext_scorer.get_max_order() + " dict_size = %d" % ext_scorer.get_dict_size()) logger.info("end initializing scorer. Start tuning ...") error_rate_func = cer if args.error_rate_type == 'cer' else wer # 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] err_sum = [0.0 for i in xrange(len(params_grid))] err_ave = [0.0 for i in xrange(len(params_grid))] num_ins, cur_batch = 0, 0 ## incremental tuning parameters over multiple batches for infer_data in batch_reader(): if (args.num_batches >= 0) and (cur_batch >= args.num_batches): break infer_results = inferer.infer(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)) ] target_transcripts = [ ''.join([data_generator.vocab_list[token] for token in transcript]) for _, transcript in infer_data ] num_ins += len(target_transcripts) # grid search for index, (alpha, beta) in enumerate(params_grid): # reset alpha & beta ext_scorer.reset_params(alpha, beta) beam_search_results = ctc_beam_search_decoder_batch( probs_split=probs_split, vocabulary=vocab_list, beam_size=args.beam_size, num_processes=args.num_proc_bsearch, cutoff_prob=args.cutoff_prob, cutoff_top_n=args.cutoff_top_n, ext_scoring_func=ext_scorer, ) result_transcripts = [res[0][1] for res in beam_search_results] for target, result in zip(target_transcripts, result_transcripts): err_sum[index] += error_rate_func(target, result) err_ave[index] = err_sum[index] / num_ins if index % 2 == 0: sys.stdout.write('.') sys.stdout.flush() # output on-line tuning result at the end of current batch err_ave_min = min(err_ave) min_index = err_ave.index(err_ave_min) print("\nBatch %d [%d/?], current opt (alpha, beta) = (%s, %s), " " min [%s] = %f" %(cur_batch, num_ins, "%.3f" % params_grid[min_index][0], "%.3f" % params_grid[min_index][1], args.error_rate_type, err_ave_min)) cur_batch += 1 # output WER/CER at every (alpha, beta) print("\nFinal %s:\n" % args.error_rate_type) for index in xrange(len(params_grid)): print("(alpha, beta) = (%s, %s), [%s] = %f" % ("%.3f" % params_grid[index][0], "%.3f" % params_grid[index][1], args.error_rate_type, err_ave[index])) err_ave_min = min(err_ave) min_index = err_ave.index(err_ave_min) print("\nFinish tuning on %d batches, final opt (alpha, beta) = (%s, %s)" % (args.num_batches, "%.3f" % params_grid[min_index][0], "%.3f" % params_grid[min_index][1])) logger.info("finish tuning") def main(): print_arguments(args) paddle.init(use_gpu=args.use_gpu, rnn_use_batch=True, trainer_count=args.trainer_count) tune() if __name__ == '__main__': main()