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PaddleSpeech/tune.py

155 lines
5.8 KiB

"""Beam search parameters tuning for DeepSpeech2 model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import distutils.util
import argparse
import multiprocessing
import paddle.v2 as paddle
from data_utils.data import DataGenerator
from model import DeepSpeech2Model
from error_rate import wer
NUM_CPU = multiprocessing.cpu_count() // 2
parser = argparse.ArgumentParser(description=__doc__)
def add_arg(argname, type, default, help, **kwargs):
type = distutils.util.strtobool if type == bool else type
parser.add_argument(
"--" + argname,
default=default,
type=type,
help=help + ' Default: %(default)s.',
**kwargs)
# yapf: disable
# configurations of overall
add_arg('num_samples', int, 100, "# of samples to infer.")
add_arg('trainer_count', int, 8, "# of Trainers (CPUs or GPUs).")
add_arg('use_gpu', bool, True, "Use GPU or not.")
add_arg('error_rate_type', str, 'wer', "Error rate type for evaluation.",
choices=['wer', 'cer'])
# configurations of tuning parameters
add_arg('alpha_from', float, 0.1, "Where alpha starts tuning from.")
add_arg('alpha_to', float, 0.36, "Where alpha ends tuning with.")
add_arg('num_alphas', int, 14, "# of alpha candidates for tuning.")
add_arg('beta_from', float, 0.05, "Where beta starts tuning from.")
add_arg('beta_to', float, 0.36, "Where beta ends tuning with.")
add_arg('num_betas', int, 20, "# of beta candidates for tuning.")
# configurations of decoder
add_arg('beam_size', int, 500, "Beam search width.")
add_arg('cutoff_prob', float, 0.99, "Cutoff probability for pruning.")
add_arg('parallels_bsearch',int, NUM_CPU,"# of CPUs for beam search.")
add_arg('lang_model_path', str,
'lm/data/common_crawl_00.prune01111.trie.klm',
"Filepath for language model.")
# configurations of data preprocess
add_arg('specgram_type', str,
'linear',
"Audio feature type. Options: linear, mfcc.",
choices=['linear', 'mfcc'])
# configurations of model structure
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('use_gru', bool, False, "Use GRUs instead of Simple RNNs.")
add_arg('share_rnn_weights',bool, True, "Share input-hidden weights across "
"bi-directional RNNs. Not for GRU.")
# configurations of data io
add_arg('tune_manifest', str,
'datasets/manifest.test',
"Filepath of manifest to tune.")
add_arg('mean_std_path', str,
'mean_std.npz',
"Filepath of normalizer's mean & std.")
add_arg('vocab_path', str,
'datasets/vocab/eng_vocab.txt',
"Filepath of vocabulary.")
# configurations of model io
add_arg('model_path', str,
'./checkpoints/params.latest.tar.gz',
"If None, the training starts from scratch, "
"otherwise, it resumes from the pre-trained model.")
args = parser.parse_args()
# yapf: disable
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!")
data_generator = DataGenerator(
vocab_filepath=args.vocab_path,
mean_std_filepath=args.mean_std_path,
augmentation_config='{}',
specgram_type=args.specgram_type,
num_threads=1)
batch_reader = data_generator.batch_reader_creator(
manifest_path=args.tune_manifest,
batch_size=args.num_samples,
sortagrad=False,
shuffle_method=None)
tune_data = batch_reader().next()
target_transcripts = [
''.join([data_generator.vocab_list[token] for token in transcript])
for _, transcript in tune_data
]
ds2_model = DeepSpeech2Model(
vocab_size=data_generator.vocab_size,
num_conv_layers=args.num_conv_layers,
num_rnn_layers=args.num_rnn_layers,
rnn_layer_size=args.rnn_layer_size,
use_gru=args.use_gru,
pretrained_model_path=args.model_path,
share_rnn_weights=args.share_rnn_weights)
# 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:
result_transcripts = ds2_model.infer_batch(
infer_data=tune_data,
decoder_method='ctc_beam_search',
beam_alpha=alpha,
beam_beta=beta,
beam_size=args.beam_size,
cutoff_prob=args.cutoff_prob,
vocab_list=data_generator.vocab_list,
language_model_path=args.lang_model_path,
num_processes=args.parallels_bsearch)
wer_sum, num_ins = 0.0, 0
for target, result in zip(target_transcripts, result_transcripts):
wer_sum += wer(target, result)
num_ins += 1
print("alpha = %f\tbeta = %f\tWER = %f" %
(alpha, beta, wer_sum / num_ins))
def print_arguments(args):
print("----------- Configuration Arguments -----------")
for arg, value in sorted(vars(args).iteritems()):
print("%s: %s" % (arg, value))
print("------------------------------------------------")
def main():
print_arguments(args)
paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
tune()
if __name__ == '__main__':
main()