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PaddleSpeech/infer.py

138 lines
4.0 KiB

"""
Inference for a simplifed version of Baidu DeepSpeech2 model.
"""
import paddle.v2 as paddle
import distutils.util
import argparse
import gzip
from audio_data_utils import DataGenerator
from model import deep_speech2
from decoder import ctc_decode
parser = argparse.ArgumentParser(
description='Simplified version of DeepSpeech2 inference.')
parser.add_argument(
"--num_samples",
default=10,
type=int,
help="Number of samples for inference. (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(
"--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='data/eng_vocab.txt',
type=str,
help="Vocabulary filepath. (default: %(default)s)")
args = parser.parse_args()
def infer():
"""
Max-ctc-decoding for DeepSpeech2.
"""
# initialize data generator
data_generator = DataGenerator(
vocab_filepath=args.vocab_filepath,
normalizer_manifest_path=args.normalizer_manifest_path,
normalizer_num_samples=200,
max_duration=20.0,
min_duration=0.0,
stride_ms=10,
window_ms=20)
# create network config
dict_size = data_generator.vocabulary_size()
vocab_list = data_generator.vocabulary_list()
audio_data = paddle.layer.data(
name="audio_spectrogram",
height=161,
width=2000,
type=paddle.data_type.dense_vector(322000))
text_data = paddle.layer.data(
name="transcript_text",
type=paddle.data_type.integer_value_sequence(dict_size))
output_probs = deep_speech2(
audio_data=audio_data,
text_data=text_data,
dict_size=dict_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
feeding = data_generator.data_name_feeding()
test_batch_reader = data_generator.batch_reader_creator(
manifest_path=args.decode_manifest_path,
batch_size=args.num_samples,
padding_to=2000,
flatten=True,
sort_by_duration=False,
shuffle=False)
infer_data = test_batch_reader().next()
# run inference
infer_results = paddle.infer(
output_layer=output_probs, parameters=parameters, 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
for i, probs in enumerate(probs_split):
output_transcription = ctc_decode(
probs_seq=probs, vocabulary=vocab_list, method="best_path")
target_transcription = ''.join(
[vocab_list[index] for index in infer_data[i][1]])
print("Target Transcription: %s \nOutput Transcription: %s \n" %
(target_transcription, output_transcription))
def main():
paddle.init(use_gpu=args.use_gpu, trainer_count=1)
infer()
if __name__ == '__main__':
main()