You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
PaddleSpeech/infer.py

153 lines
5.6 KiB

"""Inferer for DeepSpeech2 model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
import argparse
import functools
import paddle.fluid as fluid
from data_utils.data import DataGenerator
from model_utils.model import DeepSpeech2Model
from model_utils.model_check import check_cuda, check_version
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_samples', int, 10, "# of samples to infer.")
add_arg('beam_size', int, 500, "Beam search width.")
add_arg('num_proc_bsearch', int, 8, "# of CPUs for beam search.")
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('alpha', float, 2.5, "Coef of LM for beam search.")
add_arg('beta', float, 0.3, "Coef of WC for beam search.")
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('infer_manifest', str,
'data/librispeech/manifest.dev-clean',
"Filepath of manifest to infer.")
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/step_final',
"If None, the training starts from scratch, "
"otherwise, it resumes from the pre-trained model.")
add_arg('decoding_method', str,
'ctc_beam_search',
"Decoding method. Options: ctc_beam_search, ctc_greedy",
choices = ['ctc_beam_search', 'ctc_greedy'])
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()
def infer():
"""Inference for DeepSpeech2."""
# check if set use_gpu=True in paddlepaddle cpu version
check_cuda(args.use_gpu)
# check if paddlepaddle version is satisfied
check_version()
if args.use_gpu:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
data_generator = DataGenerator(
vocab_filepath=args.vocab_path,
mean_std_filepath=args.mean_std_path,
augmentation_config='{}',
specgram_type=args.specgram_type,
keep_transcription_text=True,
place = place,
is_training = False)
batch_reader = data_generator.batch_reader_creator(
manifest_path=args.infer_manifest,
batch_size=args.num_samples,
sortagrad=False,
shuffle_method=None)
infer_data = next(batch_reader())
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,
share_rnn_weights=args.share_rnn_weights,
place=place,
init_from_pretrained_model=args.model_path)
# decoders only accept string encoded in utf-8
vocab_list = [chars.encode("utf-8") for chars in data_generator.vocab_list]
if args.decoding_method == "ctc_greedy":
ds2_model.logger.info("start inference ...")
probs_split = ds2_model.infer_batch_probs(
infer_data=infer_data,
feeding_dict=data_generator.feeding)
result_transcripts = ds2_model.decode_batch_greedy(
probs_split=probs_split,
vocab_list=vocab_list)
else:
ds2_model.init_ext_scorer(args.alpha, args.beta, args.lang_model_path,
vocab_list)
ds2_model.logger.info("start inference ...")
probs_split= ds2_model.infer_batch_probs(
infer_data=infer_data,
feeding_dict=data_generator.feeding)
result_transcripts= ds2_model.decode_batch_beam_search(
probs_split=probs_split,
beam_alpha=args.alpha,
beam_beta=args.beta,
beam_size=args.beam_size,
cutoff_prob=args.cutoff_prob,
cutoff_top_n=args.cutoff_top_n,
vocab_list=vocab_list,
num_processes=args.num_proc_bsearch)
error_rate_func = cer if args.error_rate_type == 'cer' else wer
target_transcripts = infer_data[1]
for target, result in zip(target_transcripts, result_transcripts):
print("\nTarget Transcription: %s\nOutput Transcription: %s" %
(target, result))
print("Current error rate [%s] = %f" %
(args.error_rate_type, error_rate_func(target, result)))
ds2_model.logger.info("finish inference")
def main():
print_arguments(args)
infer()
if __name__ == '__main__':
main()