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PaddleSpeech/evaluate.py

217 lines
7.4 KiB

"""Evaluation for DeepSpeech2 model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle.v2 as paddle
import distutils.util
import argparse
import gzip
from data_utils.data import DataGenerator
from model import deep_speech2
from decoder import *
from error_rate import wer
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--num_samples",
default=100,
type=int,
help="Number of samples for evaluation. (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(
"--mean_std_filepath",
default='mean_std.npz',
type=str,
help="Manifest path for normalizer. (default: %(default)s)")
parser.add_argument(
"--decode_method",
default='beam_search_nproc',
type=str,
help="Method for ctc decoding, best_path, "
"beam_search or beam_search_nproc. (default: %(default)s)")
parser.add_argument(
"--language_model_path",
default="data/1Billion.klm",
type=str,
help="Path for language model. (default: %(default)s)")
parser.add_argument(
"--alpha",
default=0.26,
type=float,
help="Parameter associated with language model. (default: %(default)f)")
parser.add_argument(
"--beta",
default=0.1,
type=float,
help="Parameter associated with word count. (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)")
parser.add_argument(
"--beam_size",
default=500,
type=int,
help="Width for beam search decoding. (default: %(default)d)")
parser.add_argument(
"--normalizer_manifest_path",
default='data/manifest.libri.train-clean-100',
type=str,
help="Manifest path for normalizer. (default: %(default)s)")
parser.add_argument(
"--decode_manifest_path",
default='data/manifest.libri.test-clean',
type=str,
help="Manifest path for decoding. (default: %(default)s)")
parser.add_argument(
"--model_filepath",
default='./params.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)")
args = parser.parse_args()
def evaluate():
"""Evaluate on whole test data for DeepSpeech2."""
# initialize data generator
data_generator = DataGenerator(
vocab_filepath=args.vocab_filepath,
mean_std_filepath=args.mean_std_filepath,
augmentation_config='{}')
# create network config
# paddle.data_type.dense_array is used for variable batch input.
# The size 161 * 161 is only an placeholder value and the real shape
# of input batch data will be induced during training.
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_speech2(
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,
is_inference=True)
# load parameters
parameters = paddle.parameters.Parameters.from_tar(
gzip.open(args.model_filepath))
# prepare infer data
batch_reader = data_generator.batch_reader_creator(
manifest_path=args.decode_manifest_path,
batch_size=args.num_samples,
sortagrad=False,
shuffle_method=None)
# define inferer
inferer = paddle.inference.Inference(
output_layer=output_probs, parameters=parameters)
# initialize external scorer for beam search decoding
if args.decode_method == 'beam_search' or \
args.decode_method == 'beam_search_nproc':
ext_scorer = Scorer(args.alpha, args.beta, args.language_model_path)
wer_counter, wer_sum = 0, 0.0
for infer_data in batch_reader():
# run inference
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(0, len(infer_data))
]
# decode and print
# best path decode
if args.decode_method == "best_path":
for i, probs in enumerate(probs_split):
output_transcription = ctc_best_path_decode(
probs_seq=probs, vocabulary=data_generator.vocab_list)
target_transcription = ''.join([
data_generator.vocab_list[index]
for index in infer_data[i][1]
])
wer_sum += wer(target_transcription, output_transcription)
wer_counter += 1
# beam search decode in single process
elif args.decode_method == "beam_search":
for i, probs in enumerate(probs_split):
target_transcription = ''.join([
data_generator.vocab_list[index]
for index in infer_data[i][1]
])
beam_search_result = ctc_beam_search_decoder(
probs_seq=probs,
vocabulary=data_generator.vocab_list,
beam_size=args.beam_size,
blank_id=len(data_generator.vocab_list),
ext_scoring_func=ext_scorer,
cutoff_prob=args.cutoff_prob, )
wer_sum += wer(target_transcription, beam_search_result[0][1])
wer_counter += 1
# beam search using multiple processes
elif args.decode_method == "beam_search_nproc":
beam_search_nproc_results = ctc_beam_search_decoder_nproc(
probs_split=probs_split,
vocabulary=data_generator.vocab_list,
beam_size=args.beam_size,
blank_id=len(data_generator.vocab_list),
ext_scoring_func=ext_scorer,
cutoff_prob=args.cutoff_prob, )
for i, beam_search_result in enumerate(beam_search_nproc_results):
target_transcription = ''.join([
data_generator.vocab_list[index]
for index in infer_data[i][1]
])
wer_sum += wer(target_transcription, beam_search_result[0][1])
wer_counter += 1
else:
raise ValueError("Decoding method [%s] is not supported." %
decode_method)
print("Cur WER = %f" % (wer_sum / wer_counter))
print("Final WER = %f" % (wer_sum / wer_counter))
def main():
paddle.init(use_gpu=args.use_gpu, trainer_count=1)
evaluate()
if __name__ == '__main__':
main()