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202 lines
8.2 KiB
202 lines
8.2 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Beam search parameters tuning for DeepSpeech2 model."""
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import sys
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import os
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import numpy as np
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import argparse
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import functools
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import gzip
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import logging
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import paddle.fluid as fluid
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import _init_paths
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from data_utils.data import DataGenerator
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from model_utils.model import DeepSpeech2Model
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from utils.error_rate import char_errors, word_errors
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from utils.utility import add_arguments, print_arguments
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parser = argparse.ArgumentParser(description=__doc__)
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add_arg = functools.partial(add_arguments, argparser=parser)
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# yapf: disable
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add_arg('num_batches', int, -1, "# of batches tuning on. "
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"Default -1, on whole dev set.")
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add_arg('batch_size', int, 256, "# of samples per batch.")
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add_arg('trainer_count', int, 8, "# of Trainers (CPUs or GPUs).")
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add_arg('beam_size', int, 500, "Beam search width.")
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add_arg('num_proc_bsearch', int, 8, "# of CPUs for beam search.")
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add_arg('num_conv_layers', int, 2, "# of convolution layers.")
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add_arg('num_rnn_layers', int, 3, "# of recurrent layers.")
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add_arg('rnn_layer_size', int, 2048, "# of recurrent cells per layer.")
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add_arg('num_alphas', int, 45, "# of alpha candidates for tuning.")
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add_arg('num_betas', int, 8, "# of beta candidates for tuning.")
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add_arg('alpha_from', float, 1.0, "Where alpha starts tuning from.")
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add_arg('alpha_to', float, 3.2, "Where alpha ends tuning with.")
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add_arg('beta_from', float, 0.1, "Where beta starts tuning from.")
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add_arg('beta_to', float, 0.45, "Where beta ends tuning with.")
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add_arg('cutoff_prob', float, 1.0, "Cutoff probability for pruning.")
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add_arg('cutoff_top_n', int, 40, "Cutoff number for pruning.")
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add_arg('use_gru', bool, False, "Use GRUs instead of simple RNNs.")
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add_arg('use_gpu', bool, True, "Use GPU or not.")
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add_arg('share_rnn_weights',bool, True, "Share input-hidden weights across "
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"bi-directional RNNs. Not for GRU.")
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add_arg('tune_manifest', str,
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'data/librispeech/manifest.dev-clean',
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"Filepath of manifest to tune.")
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add_arg('mean_std_path', str,
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'data/librispeech/mean_std.npz',
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"Filepath of normalizer's mean & std.")
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add_arg('vocab_path', str,
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'data/librispeech/vocab.txt',
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"Filepath of vocabulary.")
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add_arg('lang_model_path', str,
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'models/lm/common_crawl_00.prune01111.trie.klm',
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"Filepath for language model.")
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add_arg('model_path', str,
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'./checkpoints/libri/params.latest.tar.gz',
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"If None, the training starts from scratch, "
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"otherwise, it resumes from the pre-trained model.")
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add_arg('error_rate_type', str,
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'wer',
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"Error rate type for evaluation.",
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choices=['wer', 'cer'])
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add_arg('specgram_type', str,
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'linear',
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"Audio feature type. Options: linear, mfcc.",
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choices=['linear', 'mfcc'])
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# yapf: disable
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args = parser.parse_args()
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def tune():
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"""Tune parameters alpha and beta incrementally."""
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if not args.num_alphas >= 0:
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raise ValueError("num_alphas must be non-negative!")
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if not args.num_betas >= 0:
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raise ValueError("num_betas must be non-negative!")
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if args.use_gpu:
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place = fluid.CUDAPlace(0)
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else:
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place = fluid.CPUPlace()
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data_generator = DataGenerator(
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vocab_filepath=args.vocab_path,
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mean_std_filepath=args.mean_std_path,
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augmentation_config='{}',
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specgram_type=args.specgram_type,
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keep_transcription_text=True,
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place = place,
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is_training = False)
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batch_reader = data_generator.batch_reader_creator(
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manifest_path=args.tune_manifest,
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batch_size=args.batch_size,
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sortagrad=False,
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shuffle_method=None)
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ds2_model = DeepSpeech2Model(
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vocab_size=data_generator.vocab_size,
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num_conv_layers=args.num_conv_layers,
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num_rnn_layers=args.num_rnn_layers,
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rnn_layer_size=args.rnn_layer_size,
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use_gru=args.use_gru,
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place=place,
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init_from_pretrained_model=args.model_path,
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share_rnn_weights=args.share_rnn_weights)
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# decoders only accept string encoded in utf-8
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vocab_list = [chars for chars in data_generator.vocab_list]
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errors_func = char_errors if args.error_rate_type == 'cer' else word_errors
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# create grid for search
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cand_alphas = np.linspace(args.alpha_from, args.alpha_to, args.num_alphas)
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cand_betas = np.linspace(args.beta_from, args.beta_to, args.num_betas)
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params_grid = [(alpha, beta) for alpha in cand_alphas
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for beta in cand_betas]
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err_sum = [0.0 for i in range(len(params_grid))]
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err_ave = [0.0 for i in range(len(params_grid))]
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num_ins, len_refs, cur_batch = 0, 0, 0
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# initialize external scorer
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ds2_model.init_ext_scorer(args.alpha_from, args.beta_from,
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args.lang_model_path, vocab_list)
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## incremental tuning parameters over multiple batches
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ds2_model.logger.info("start tuning ...")
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for infer_data in batch_reader():
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if (args.num_batches >= 0) and (cur_batch >= args.num_batches):
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break
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probs_split = ds2_model.infer_batch_probs(
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infer_data=infer_data,
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feeding_dict=data_generator.feeding)
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target_transcripts = infer_data[1]
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num_ins += len(target_transcripts)
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# grid search
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for index, (alpha, beta) in enumerate(params_grid):
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result_transcripts = ds2_model.decode_batch_beam_search(
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probs_split=probs_split,
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beam_alpha=alpha,
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beam_beta=beta,
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beam_size=args.beam_size,
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cutoff_prob=args.cutoff_prob,
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cutoff_top_n=args.cutoff_top_n,
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vocab_list=vocab_list,
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num_processes=args.num_proc_bsearch)
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for target, result in zip(target_transcripts, result_transcripts):
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errors, len_ref = errors_func(target, result)
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err_sum[index] += errors
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# accumulate the length of references of every batch
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# in the first iteration
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if args.alpha_from == alpha and args.beta_from == beta:
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len_refs += len_ref
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err_ave[index] = err_sum[index] / len_refs
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if index % 2 == 0:
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sys.stdout.write('.')
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sys.stdout.flush()
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# output on-line tuning result at the end of current batch
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err_ave_min = min(err_ave)
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min_index = err_ave.index(err_ave_min)
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print("\nBatch %d [%d/?], current opt (alpha, beta) = (%s, %s), "
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" min [%s] = %f" %(cur_batch, num_ins,
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"%.3f" % params_grid[min_index][0],
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"%.3f" % params_grid[min_index][1],
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args.error_rate_type, err_ave_min))
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cur_batch += 1
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# output WER/CER at every (alpha, beta)
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print("\nFinal %s:\n" % args.error_rate_type)
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for index in range(len(params_grid)):
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print("(alpha, beta) = (%s, %s), [%s] = %f"
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% ("%.3f" % params_grid[index][0], "%.3f" % params_grid[index][1],
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args.error_rate_type, err_ave[index]))
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err_ave_min = min(err_ave)
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min_index = err_ave.index(err_ave_min)
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print("\nFinish tuning on %d batches, final opt (alpha, beta) = (%s, %s)"
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% (cur_batch, "%.3f" % params_grid[min_index][0],
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"%.3f" % params_grid[min_index][1]))
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ds2_model.logger.info("finish tuning")
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def main():
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print_arguments(args)
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tune()
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if __name__ == '__main__':
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main()
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