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PaddleSpeech/tune.py

197 lines
6.0 KiB

"""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
import utils
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(
"--trainer_count",
default=8,
type=int,
help="Trainer number. (default: %(default)s)")
parser.add_argument(
"--num_threads_data",
default=1,
type=int,
help="Number of cpu threads for preprocessing data. (default: %(default)s)")
parser.add_argument(
"--num_processes_beam_search",
default=multiprocessing.cpu_count() // 2,
type=int,
help="Number of cpu processes for beam search. (default: %(default)s)")
parser.add_argument(
"--specgram_type",
default='linear',
type=str,
help="Feature type of audio data: 'linear' (power spectrum)"
" or 'mfcc'. (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(
"--tune_manifest_path",
default='datasets/manifest.dev',
type=str,
help="Manifest path for tuning. (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/common_crawl_00.prune01111.trie.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!")
data_generator = DataGenerator(
vocab_filepath=args.vocab_filepath,
mean_std_filepath=args.mean_std_filepath,
augmentation_config='{}',
specgram_type=args.specgram_type,
num_threads=args.num_threads_data)
batch_reader = data_generator.batch_reader_creator(
manifest_path=args.tune_manifest_path,
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,
pretrained_model_path=args.model_filepath)
# 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,
decode_method='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.language_model_path,
num_processes=args.num_processes_beam_search)
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 main():
utils.print_arguments(args)
paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
tune()
if __name__ == '__main__':
main()