@ -13,186 +13,32 @@
# limitations under the License.
""" Inferer for DeepSpeech2 model. """
import sys
import argparse
import functools
from model_utils . model_check import check_cuda , check_version
from utils . utility import add_arguments , print_arguments
from utils . error_rate import wer , cer
from data_utils . data import DataGenerator
from data_utils . dataset import create_dataloader
from model_utils . model import DeepSpeech2Model
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 main_sp ( config , args ) :
exp = Tester ( config , args )
exp . setup ( )
exp . run_test ( )
def infer( ) :
""" Inference for DeepSpeech2. """
def main ( config , args ) :
main_sp ( config , args )
# check if set use_gpu=True in paddlepaddle cpu version
check_cuda ( args . use_gpu )
# check if paddlepaddle version is satisfied
check_version ( )
# 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)
batch_reader = create_dataloader (
manifest_path = args . infer_manifest ,
vocab_filepath = args . vocab_path ,
mean_std_filepath = args . mean_std_path ,
augmentation_config = ' {} ' ,
max_duration = float ( ' inf ' ) ,
min_duration = 0.0 ,
stride_ms = 10.0 ,
window_ms = 20.0 ,
max_freq = None ,
specgram_type = args . specgram_type ,
use_dB_normalization = True ,
random_seed = 0 ,
keep_transcription_text = False ,
is_training = False ,
batch_size = args . num_samples ,
sortagrad = False ,
shuffle_method = None )
#for audio, text, audio_len, text_len in batch_reader:
# print(audio.shape)
# print(text.shape)
# print(audio_len)
# print(text_len)
# break
reader = batch_reader ( )
infer_data = reader . next ( )
print ( infer_data )
from model_utils . network2 import DeepSpeech2
feat_dim = 161
model = DeepSpeech2 (
feat_size = feat_dim ,
dict_size = batch_reader . dataset . 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 ,
)
output = model ( * infer_data )
print ( output )
# 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 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 ( ) :
if __name__ == " __main__ " :
parser = default_argument_parser ( )
args = parser . parse_args ( )
print_arguments ( args )
infer ( )
if __name__ == ' __main__ ' :
main ( )
# https://yaml.org/type/float.html
config = get_cfg_defaults ( )
if args . config :
config . merge_from_file ( args . config )
if args . opts :
config . merge_from_list ( args . opts )
config . freeze ( )
print ( config )
if args . dump_config :
with open ( args . dump_config , ' w ' ) as f :
print ( config , file = f )
main ( config , args )
Hui Zhang
5 years ago