Merge branch 'develop' of upstream into develop

pull/2/head
Yibing Liu 7 years ago
commit 19eb6343b8

@ -4,13 +4,18 @@ 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 model_utils.model import DeepSpeech2Model
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
@ -66,6 +71,9 @@ add_arg('specgram_type', str,
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:
@ -79,29 +87,59 @@ def tune():
augmentation_config='{}',
specgram_type=args.specgram_type,
num_threads=1)
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)
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)
# 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)
@ -116,6 +154,13 @@ def tune():
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])
@ -125,18 +170,18 @@ def tune():
num_ins += len(target_transcripts)
# grid search
for index, (alpha, beta) in enumerate(params_grid):
result_transcripts = ds2_model.infer_batch(
infer_data=infer_data,
decoding_method='ctc_beam_search',
beam_alpha=alpha,
beam_beta=beta,
# 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,
vocab_list=vocab_list,
language_model_path=args.lang_model_path,
num_processes=args.num_proc_bsearch)
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
@ -167,7 +212,7 @@ def tune():
% (args.num_batches, "%.3f" % params_grid[min_index][0],
"%.3f" % params_grid[min_index][1]))
ds2_model.logger.info("finish inference")
logger.info("finish tuning")
def main():

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