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PaddleSpeech/tools/tune.py

229 lines
9.2 KiB

"""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()