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提交 b56a548e 编写于 作者: X Xinghai Sun 提交者： GitHub
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Merge pull request #227 from xinghai-sun/refactor_config 

Reduce the config parsing codes (-360 Lines) for DS2 and make it looks cleaner.
上级 1337e3fb 2ad71281
隐藏空白更改
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Showing with 377 addition and 737 deletion
deep_speech_2/decoder.py
浏览文件 @ b56a548e
......@@ -9,8 +9,9 @@ from math import log
import multiprocessing
def ctc_best_path_decoder(probs_seq, vocabulary):
"""Best path decoder, also called argmax decoder or greedy decoder.
def ctc_greedy_decoder(probs_seq, vocabulary):
"""CTC greedy (best path) decoder.
Path consisting of the most probable tokens are further post-processed to
remove consecutive repetitions and all blanks.
......@@ -45,10 +46,12 @@ def ctc_beam_search_decoder(probs_seq,
cutoff_prob=1.0,
ext_scoring_func=None,
nproc=False):
"""Beam search decoder for CTC-trained network. It utilizes beam search
to approximately select top best decoding labels and returning results
in the descending order. The implementation is based on Prefix
Beam Search (https://arxiv.org/abs/1408.2873), and the unclear part is
"""CTC Beam search decoder.
It utilizes beam search to approximately select top best decoding
labels and returning results in the descending order.
The implementation is based on Prefix Beam Search
(https://arxiv.org/abs/1408.2873), and the unclear part is
redesigned. Two important modifications: 1) in the iterative computation
of probabilities, the assignment operation is changed to accumulation for
one prefix may comes from different paths; 2) the if condition "if l^+ not
......
deep_speech_2/demo_server.py
浏览文件 @ b56a548e
......@@ -3,123 +3,63 @@ import os
import time
import random
import argparse
import distutils.util
import functools
from time import gmtime, strftime
import SocketServer
import struct
import wave
import paddle.v2 as paddle
from utils import print_arguments
from data_utils.data import DataGenerator
from model import DeepSpeech2Model
from data_utils.utils import read_manifest
from utils import add_arguments, print_arguments
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--host_ip",
default="localhost",
type=str,
help="Server IP address. (default: %(default)s)")
parser.add_argument(
"--host_port",
default=8086,
type=int,
help="Server Port. (default: %(default)s)")
parser.add_argument(
"--speech_save_dir",
default="demo_cache",
type=str,
help="Directory for saving demo speech. (default: %(default)s)")
parser.add_argument(
"--vocab_filepath",
default='datasets/vocab/eng_vocab.txt',
type=str,
help="Vocabulary filepath. (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(
"--warmup_manifest_path",
default='datasets/manifest.test',
type=str,
help="Manifest path for warmup test. (default: %(default)s)")
parser.add_argument(
"--specgram_type",
default='linear',
type=str,
help="Feature type of audio data: 'linear' (power spectrum)"
" or 'mfcc'. (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=2048,
type=int,
help="RNN layer cell number. (default: %(default)s)")
parser.add_argument(
"--share_rnn_weights",
default=True,
type=distutils.util.strtobool,
help="Whether to share input-hidden weights between forword and backward "
"directional simple RNNs. Only available when use_gru=False. "
"(default: %(default)s)")
parser.add_argument(
"--use_gru",
default=False,
type=distutils.util.strtobool,
help="Use GRU or simple RNN. (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(
"--model_filepath",
default='checkpoints/params.latest.tar.gz',
type=str,
help="Model filepath. (default: %(default)s)")
parser.add_argument(
"--decode_method",
default='beam_search',
type=str,
help="Method for ctc decoding: best_path or beam_search. "
"(default: %(default)s)")
parser.add_argument(
"--beam_size",
default=100,
type=int,
help="Width for beam search decoding. (default: %(default)d)")
parser.add_argument(
"--language_model_path",
default="lm/data/common_crawl_00.prune01111.trie.klm",
type=str,
help="Path for language model. (default: %(default)s)")
parser.add_argument(
"--alpha",
default=0.36,
type=float,
help="Parameter associated with language model. (default: %(default)f)")
parser.add_argument(
"--beta",
default=0.25,
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)")
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
add_arg('host_port', int, 8086, "Server's IP port.")
add_arg('beam_size', int, 500, "Beam search width.")
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, 0.36, "Coef of LM for beam search.")
add_arg('beta', float, 0.25, "Coef of WC for beam search.")
add_arg('cutoff_prob', float, 0.99, "Cutoff probability 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('host_ip', str,
'localhost',
"Server's IP address.")
add_arg('speech_save_dir', str,
'demo_cache',
"Directory to save demo audios.")
add_arg('warmup_manifest', str,
'datasets/manifest.test',
"Filepath of manifest to warm up.")
add_arg('mean_std_path', str,
'mean_std.npz',
"Filepath of normalizer's mean & std.")
add_arg('vocab_path', str,
'datasets/vocab/eng_vocab.txt',
"Filepath of vocabulary.")
add_arg('model_path', str,
'./checkpoints/params.latest.tar.gz',
"If None, the training starts from scratch, "
"otherwise, it resumes from the pre-trained model.")
add_arg('lang_model_path', str,
'lm/data/common_crawl_00.prune01111.trie.klm',
"Filepath for language 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('specgram_type', str,
'linear',
"Audio feature type. Options: linear, mfcc.",
choices=['linear', 'mfcc'])
# yapf: disable
args = parser.parse_args()
......@@ -200,8 +140,8 @@ def start_server():
"""Start the ASR server"""
# prepare data generator
data_generator = DataGenerator(
vocab_filepath=args.vocab_filepath,
mean_std_filepath=args.mean_std_filepath,
vocab_filepath=args.vocab_path,
mean_std_filepath=args.mean_std_path,
augmentation_config='{}',
specgram_type=args.specgram_type,
num_threads=1)
......@@ -212,7 +152,7 @@ def start_server():
num_rnn_layers=args.num_rnn_layers,
rnn_layer_size=args.rnn_layer_size,
use_gru=args.use_gru,
pretrained_model_path=args.model_filepath,
pretrained_model_path=args.model_path,
share_rnn_weights=args.share_rnn_weights)
# prepare ASR inference handler
......@@ -220,13 +160,13 @@ def start_server():
feature = data_generator.process_utterance(filename, "")
result_transcript = ds2_model.infer_batch(
infer_data=[feature],
decode_method=args.decode_method,
decoding_method=args.decoding_method,
beam_alpha=args.alpha,
beam_beta=args.beta,
beam_size=args.beam_size,
cutoff_prob=args.cutoff_prob,
vocab_list=data_generator.vocab_list,
language_model_path=args.language_model_path,
language_model_path=args.lang_model_path,
num_processes=1)
return result_transcript[0]
......@@ -235,7 +175,7 @@ def start_server():
print('Warming up ...')
warm_up_test(
audio_process_handler=file_to_transcript,
manifest_path=args.warmup_manifest_path,
manifest_path=args.warmup_manifest,
num_test_cases=3)
print('-----------------------------------------------------------')
......
deep_speech_2/evaluate.py
浏览文件 @ b56a548e
......@@ -3,147 +3,74 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import distutils.util
import argparse
import multiprocessing
import functools
import paddle.v2 as paddle
from data_utils.data import DataGenerator
from model import DeepSpeech2Model
from error_rate import wer, cer
import utils
from utils import add_arguments, print_arguments
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--batch_size",
default=128,
type=int,
help="Minibatch size for evaluation. (default: %(default)s)")
parser.add_argument(
"--trainer_count",
default=8,
type=int,
help="Trainer number. (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=2048,
type=int,
help="RNN layer cell number. (default: %(default)s)")
parser.add_argument(
"--share_rnn_weights",
default=True,
type=distutils.util.strtobool,
help="Whether to share input-hidden weights between forword and backward "
"directional simple RNNs. Only available when use_gru=False. "
"(default: %(default)s)")
parser.add_argument(
"--use_gru",
default=False,
type=distutils.util.strtobool,
help="Use GRU or simple RNN. (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(
"--num_threads_data",
default=multiprocessing.cpu_count() // 2,
type=int,
help="Number of cpu threads for preprocessing data. (default: %(default)s)")
parser.add_argument(
"--num_processes_beam_search",
default=multiprocessing.cpu_count() // 2,
type=int,
help="Number of cpu processes for beam search. (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',
type=str,
help="Method for ctc decoding, best_path or beam_search. "
"(default: %(default)s)")
parser.add_argument(
"--language_model_path",
default="lm/data/common_crawl_00.prune01111.trie.klm",
type=str,
help="Path for language model. (default: %(default)s)")
parser.add_argument(
"--alpha",
default=0.36,
type=float,
help="Parameter associated with language model. (default: %(default)f)")
parser.add_argument(
"--beta",
default=0.25,
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(
"--specgram_type",
default='linear',
type=str,
help="Feature type of audio data: 'linear' (power spectrum)"
" or 'mfcc'. (default: %(default)s)")
parser.add_argument(
"--decode_manifest_path",
default='datasets/manifest.test',
type=str,
help="Manifest path for decoding. (default: %(default)s)")
parser.add_argument(
"--model_filepath",
default='checkpoints/params.latest.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)")
parser.add_argument(
"--error_rate_type",
default='wer',
choices=['wer', 'cer'],
type=str,
help="Error rate type for evaluation. 'wer' for word error rate and 'cer' "
"for character error rate. "
"(default: %(default)s)")
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
add_arg('batch_size', int, 128, "Minibatch size.")
add_arg('trainer_count', int, 8, "# of Trainers (CPUs or GPUs).")
add_arg('beam_size', int, 500, "Beam search width.")
add_arg('num_proc_bsearch', int, 12, "# of CPUs for beam search.")
add_arg('num_proc_data', int, 12, "# of CPUs for data preprocessing.")
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, 0.36, "Coef of LM for beam search.")
add_arg('beta', float, 0.25, "Coef of WC for beam search.")
add_arg('cutoff_prob', float, 0.99, "Cutoff probability 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('test_manifest', str,
'datasets/manifest.test',
"Filepath of manifest to evaluate.")
add_arg('mean_std_path', str,
'mean_std.npz',
"Filepath of normalizer's mean & std.")
add_arg('vocab_path', str,
'datasets/vocab/eng_vocab.txt',
"Filepath of vocabulary.")
add_arg('model_path', str,
'./checkpoints/params.latest.tar.gz',
"If None, the training starts from scratch, "
"otherwise, it resumes from the pre-trained model.")
add_arg('lang_model_path', str,
'lm/data/common_crawl_00.prune01111.trie.klm',
"Filepath for language 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 evaluate():
"""Evaluate on whole test data for DeepSpeech2."""
data_generator = DataGenerator(
vocab_filepath=args.vocab_filepath,
mean_std_filepath=args.mean_std_filepath,
vocab_filepath=args.vocab_path,
mean_std_filepath=args.mean_std_path,
augmentation_config='{}',
specgram_type=args.specgram_type,
num_threads=args.num_threads_data)
num_threads=args.num_proc_data)
batch_reader = data_generator.batch_reader_creator(
manifest_path=args.decode_manifest_path,
manifest_path=args.test_manifest,
batch_size=args.batch_size,
min_batch_size=1,
sortagrad=False,
......@@ -155,7 +82,7 @@ def evaluate():
num_rnn_layers=args.num_rnn_layers,
rnn_layer_size=args.rnn_layer_size,
use_gru=args.use_gru,
pretrained_model_path=args.model_filepath,
pretrained_model_path=args.model_path,
share_rnn_weights=args.share_rnn_weights)
error_rate_func = cer if args.error_rate_type == 'cer' else wer
......@@ -163,14 +90,14 @@ def evaluate():
for infer_data in batch_reader():
result_transcripts = ds2_model.infer_batch(
infer_data=infer_data,
decode_method=args.decode_method,
decoding_method=args.decoding_method,
beam_alpha=args.alpha,
beam_beta=args.beta,
beam_size=args.beam_size,
cutoff_prob=args.cutoff_prob,
vocab_list=data_generator.vocab_list,
language_model_path=args.language_model_path,
num_processes=args.num_processes_beam_search)
language_model_path=args.lang_model_path,
num_processes=args.num_proc_bsearch)
target_transcripts = [
''.join([data_generator.vocab_list[token] for token in transcript])
for _, transcript in infer_data
......@@ -185,7 +112,7 @@ def evaluate():
def main():
utils.print_arguments(args)
print_arguments(args)
paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
evaluate()
......
deep_speech_2/infer.py
浏览文件 @ b56a548e
......@@ -4,146 +4,72 @@ from __future__ import division
from __future__ import print_function
import argparse
import distutils.util
import multiprocessing
import functools
import paddle.v2 as paddle
from data_utils.data import DataGenerator
from model import DeepSpeech2Model
from error_rate import wer, cer
import utils
from utils import add_arguments, print_arguments
parser = argparse.ArgumentParser(description=__doc__)
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=2048,
type=int,
help="RNN layer cell number. (default: %(default)s)")
parser.add_argument(
"--share_rnn_weights",
default=True,
type=distutils.util.strtobool,
help="Whether to share input-hidden weights between forword and backward "
"directional simple RNNs. Only available when use_gru=False. "
"(default: %(default)s)")
parser.add_argument(
"--use_gru",
default=False,
type=distutils.util.strtobool,
help="Use GRU or simple RNN. (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(
"--num_threads_data",
default=1,
type=int,
help="Number of cpu threads for preprocessing data. (default: %(default)s)")
parser.add_argument(
"--num_processes_beam_search",
default=multiprocessing.cpu_count() // 2,
type=int,
help="Number of cpu processes for beam search. (default: %(default)s)")
parser.add_argument(
"--specgram_type",
default='linear',
type=str,
help="Feature type of audio data: 'linear' (power spectrum)"
" or 'mfcc'. (default: %(default)s)")
parser.add_argument(
"--trainer_count",
default=8,
type=int,
help="Trainer number. (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_manifest_path",
default='datasets/manifest.test',
type=str,
help="Manifest path for decoding. (default: %(default)s)")
parser.add_argument(
"--model_filepath",
default='checkpoints/params.latest.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)")
parser.add_argument(
"--decode_method",
default='beam_search',
type=str,
help="Method for ctc decoding: best_path or beam_search. "
"(default: %(default)s)")
parser.add_argument(
"--beam_size",
default=500,
type=int,
help="Width for beam search decoding. (default: %(default)d)")
parser.add_argument(
"--language_model_path",
default="lm/data/common_crawl_00.prune01111.trie.klm",
type=str,
help="Path for language model. (default: %(default)s)")
parser.add_argument(
"--alpha",
default=0.36,
type=float,
help="Parameter associated with language model. (default: %(default)f)")
parser.add_argument(
"--beta",
default=0.25,
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(
"--error_rate_type",
default='wer',
choices=['wer', 'cer'],
type=str,
help="Error rate type for evaluation. 'wer' for word error rate and 'cer' "
"for character error rate. "
"(default: %(default)s)")
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
add_arg('num_samples', int, 10, "# of samples to infer.")
add_arg('trainer_count', int, 8, "# of Trainers (CPUs or GPUs).")
add_arg('beam_size', int, 500, "Beam search width.")
add_arg('num_proc_bsearch', int, 12, "# 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, 0.36, "Coef of LM for beam search.")
add_arg('beta', float, 0.25, "Coef of WC for beam search.")
add_arg('cutoff_prob', float, 0.99, "Cutoff probability 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,
'datasets/manifest.dev',
"Filepath of manifest to infer.")
add_arg('mean_std_path', str,
'mean_std.npz',
"Filepath of normalizer's mean & std.")
add_arg('vocab_path', str,
'datasets/vocab/eng_vocab.txt',
"Filepath of vocabulary.")
add_arg('lang_model_path', str,
'lm/data/common_crawl_00.prune01111.trie.klm',
"Filepath for language model.")
add_arg('model_path', str,
'./checkpoints/params.latest.tar.gz',
"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."""
data_generator = DataGenerator(
vocab_filepath=args.vocab_filepath,
mean_std_filepath=args.mean_std_filepath,
vocab_filepath=args.vocab_path,
mean_std_filepath=args.mean_std_path,
augmentation_config='{}',
specgram_type=args.specgram_type,
num_threads=args.num_threads_data)
num_threads=1)
batch_reader = data_generator.batch_reader_creator(
manifest_path=args.decode_manifest_path,
manifest_path=args.infer_manifest,
batch_size=args.num_samples,
min_batch_size=1,
sortagrad=False,
......@@ -156,18 +82,18 @@ def infer():
num_rnn_layers=args.num_rnn_layers,
rnn_layer_size=args.rnn_layer_size,
use_gru=args.use_gru,
pretrained_model_path=args.model_filepath,
pretrained_model_path=args.model_path,
share_rnn_weights=args.share_rnn_weights)
result_transcripts = ds2_model.infer_batch(
infer_data=infer_data,
decode_method=args.decode_method,
decoding_method=args.decoding_method,
beam_alpha=args.alpha,
beam_beta=args.beta,
beam_size=args.beam_size,
cutoff_prob=args.cutoff_prob,
vocab_list=data_generator.vocab_list,
language_model_path=args.language_model_path,
num_processes=args.num_processes_beam_search)
language_model_path=args.lang_model_path,
num_processes=args.num_proc_bsearch)
error_rate_func = cer if args.error_rate_type == 'cer' else wer
target_transcripts = [
......@@ -182,7 +108,7 @@ def infer():
def main():
utils.print_arguments(args)
print_arguments(args)
paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
infer()
......
deep_speech_2/model.py
浏览文件 @ b56a548e
......@@ -146,7 +146,7 @@ class DeepSpeech2Model(object):
# run inference
return self._loss_inferer.infer(input=infer_data)
def infer_batch(self, infer_data, decode_method, beam_alpha, beam_beta,
def infer_batch(self, infer_data, decoding_method, beam_alpha, beam_beta,
beam_size, cutoff_prob, vocab_list, language_model_path,
num_processes):
"""Model inference. Infer the transcription for a batch of speech
......@@ -156,9 +156,9 @@ class DeepSpeech2Model(object):
consisting of a tuple of audio features and
transcription text (empty string).
:type infer_data: list
:param decode_method: Decoding method name, 'best_path' or
'beam search'.
:param decode_method: string
:param decoding_method: Decoding method name, 'ctc_greedy' or
'ctc_beam_search'.
:param decoding_method: string
:param beam_alpha: Parameter associated with language model.
:type beam_alpha: float
:param beam_beta: Parameter associated with word count.
......@@ -190,13 +190,13 @@ class DeepSpeech2Model(object):
]
# run decoder
results = []
if decode_method == "best_path":
if decoding_method == "ctc_greedy":
# best path decode
for i, probs in enumerate(probs_split):
output_transcription = ctc_best_path_decoder(
output_transcription = ctc_greedy_decoder(
probs_seq=probs, vocabulary=vocab_list)
results.append(output_transcription)
elif decode_method == "beam_search":
elif decoding_method == "ctc_beam_search":
# initialize external scorer
if self._ext_scorer == None:
self._ext_scorer = LmScorer(beam_alpha, beam_beta,
......@@ -205,7 +205,6 @@ class DeepSpeech2Model(object):
else:
self._ext_scorer.reset_params(beam_alpha, beam_beta)
assert self._loaded_lm_path == language_model_path
# beam search decode
beam_search_results = ctc_beam_search_decoder_batch(
probs_split=probs_split,
......@@ -219,7 +218,7 @@ class DeepSpeech2Model(object):
results = [result[0][1] for result in beam_search_results]
else:
raise ValueError("Decoding method [%s] is not supported." %
decode_method)
decoding_method)
return results
def _create_parameters(self, model_path=None):
......
deep_speech_2/tests/test_decoders.py
浏览文件 @ b56a548e
......@@ -49,16 +49,16 @@ class TestDecoders(unittest.TestCase):
0.15882358, 0.1235788, 0.23376776, 0.20510435, 0.00279306,
0.05294827, 0.22298418
]]
self.best_path_result = ["ac'bdc", "b'da"]
self.greedy_result = ["ac'bdc", "b'da"]
self.beam_search_result = ['acdc', "b'a"]
def test_best_path_decoder_1(self):
bst_result = ctc_best_path_decoder(self.probs_seq1, self.vocab_list)
self.assertEqual(bst_result, self.best_path_result[0])
def test_greedy_decoder_1(self):
bst_result = ctc_greedy_decoder(self.probs_seq1, self.vocab_list)
self.assertEqual(bst_result, self.greedy_result[0])
def test_best_path_decoder_2(self):
bst_result = ctc_best_path_decoder(self.probs_seq2, self.vocab_list)
self.assertEqual(bst_result, self.best_path_result[1])
def test_greedy_decoder_2(self):
bst_result = ctc_greedy_decoder(self.probs_seq2, self.vocab_list)
self.assertEqual(bst_result, self.greedy_result[1])
def test_beam_search_decoder_1(self):
beam_result = ctc_beam_search_decoder(
......
deep_speech_2/tools/build_vocab.py
浏览文件 @ b56a548e
......@@ -7,32 +7,29 @@ from __future__ import division
from __future__ import print_function
import argparse
import functools
import codecs
import json
from collections import Counter
import os.path
import _init_paths
from data_utils import utils
from utils import add_arguments, print_arguments
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--manifest_paths",
type=str,
help="Manifest paths for building vocabulary."
"You can provide multiple manifest files.",
nargs='+',
required=True)
parser.add_argument(
"--count_threshold",
default=0,
type=int,
help="Characters whose counts are below the threshold will be truncated. "
"(default: %(default)i)")
parser.add_argument(
"--vocab_path",
default='datasets/vocab/zh_vocab.txt',
type=str,
help="File path to write the vocabulary. (default: %(default)s)")
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
add_arg('count_threshold', int, 0, "Truncation threshold for char counts.")
add_arg('vocab_path', str,
'datasets/vocab/zh_vocab.txt',
"Filepath to write the vocabulary.")
add_arg('manifest_paths', str,
None,
"Filepaths of manifests for building vocabulary. "
"You can provide multiple manifest files.",
nargs='+',
required=True)
# yapf: disable
args = parser.parse_args()
......@@ -44,6 +41,8 @@ def count_manifest(counter, manifest_path):
def main():
print_arguments(args)
counter = Counter()
for manifest_path in args.manifest_paths:
count_manifest(counter, manifest_path)
......
deep_speech_2/tools/compute_mean_std.py
浏览文件 @ b56a548e
......@@ -4,48 +4,35 @@ from __future__ import division
from __future__ import print_function
import argparse
import functools
import _init_paths
from data_utils.normalizer import FeatureNormalizer
from data_utils.augmentor.augmentation import AugmentationPipeline
from data_utils.featurizer.audio_featurizer import AudioFeaturizer
parser = argparse.ArgumentParser(
description='Computing mean and stddev for feature normalizer.')
parser.add_argument(
"--specgram_type",
default='linear',
type=str,
help="Feature type of audio data: 'linear' (power spectrum)"
" or 'mfcc'. (default: %(default)s)")
parser.add_argument(
"--manifest_path",
default='datasets/manifest.train',
type=str,
help="Manifest path for computing normalizer's mean and stddev."
"(default: %(default)s)")
parser.add_argument(
"--num_samples",
default=2000,
type=int,
help="Number of samples for computing mean and stddev. "
"(default: %(default)s)")
parser.add_argument(
"--augmentation_config",
default='{}',
type=str,
help="Augmentation configuration in json-format. "
"(default: %(default)s)")
parser.add_argument(
"--output_file",
default='mean_std.npz',
type=str,
help="Filepath to write mean and std to (.npz)."
"(default: %(default)s)")
from utils 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, 2000, "# of samples to for statistics.")
add_arg('specgram_type', str,
'linear',
"Audio feature type. Options: linear, mfcc.",
choices=['linear', 'mfcc'])
add_arg('manifest_path', str,
'datasets/manifest.train',
"Filepath of manifest to compute normalizer's mean and stddev.")
add_arg('output_path', str,
'mean_std.npz',
"Filepath of write mean and stddev to (.npz).")
# yapf: disable
args = parser.parse_args()
def main():
augmentation_pipeline = AugmentationPipeline(args.augmentation_config)
print_arguments(args)
augmentation_pipeline = AugmentationPipeline('{}')
audio_featurizer = AudioFeaturizer(specgram_type=args.specgram_type)
def augment_and_featurize(audio_segment):
......@@ -57,7 +44,7 @@ def main():
manifest_path=args.manifest_path,
featurize_func=augment_and_featurize,
num_samples=args.num_samples)
normalizer.write_to_file(args.output_file)
normalizer.write_to_file(args.output_path)
if __name__ == '__main__':
......
deep_speech_2/train.py
浏览文件 @ b56a548e
......@@ -4,174 +4,91 @@ from __future__ import division
from __future__ import print_function
import argparse
import distutils.util
import multiprocessing
import functools
import paddle.v2 as paddle
from model import DeepSpeech2Model
from data_utils.data import DataGenerator
import utils
from utils import add_arguments, print_arguments
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--batch_size", default=256, type=int, help="Minibatch size.")
parser.add_argument(
"--num_passes",
default=200,
type=int,
help="Training pass number. (default: %(default)s)")
parser.add_argument(
"--num_iterations_print",
default=100,
type=int,
help="Number of iterations for every train cost printing. "
"(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=2048,
type=int,
help="RNN layer cell number. (default: %(default)s)")
parser.add_argument(
"--share_rnn_weights",
default=True,
type=distutils.util.strtobool,
help="Whether to share input-hidden weights between forword and backward "
"directional simple RNNs. Only available when use_gru=False. "
"(default: %(default)s)")
parser.add_argument(
"--use_gru",
default=False,
type=distutils.util.strtobool,
help="Use GRU or simple RNN. (default: %(default)s)")
parser.add_argument(
"--adam_learning_rate",
default=5e-4,
type=float,
help="Learning rate for ADAM Optimizer. (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(
"--use_sortagrad",
default=True,
type=distutils.util.strtobool,
help="Use sortagrad or not. (default: %(default)s)")
parser.add_argument(
"--specgram_type",
default='linear',
type=str,
help="Feature type of audio data: 'linear' (power spectrum)"
" or 'mfcc'. (default: %(default)s)")
parser.add_argument(
"--max_duration",
default=27.0,
type=float,
help="Audios with duration larger than this will be discarded. "
"(default: %(default)s)")
parser.add_argument(
"--min_duration",
default=0.0,
type=float,
help="Audios with duration smaller than this will be discarded. "
"(default: %(default)s)")
parser.add_argument(
"--shuffle_method",
default='batch_shuffle_clipped',
type=str,
help="Shuffle method: 'instance_shuffle', 'batch_shuffle', "
"'batch_shuffle_batch'. (default: %(default)s)")
parser.add_argument(
"--trainer_count",
default=8,
type=int,
help="Trainer number. (default: %(default)s)")
parser.add_argument(
"--num_threads_data",
default=multiprocessing.cpu_count() // 2,
type=int,
help="Number of cpu threads for preprocessing data. (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(
"--train_manifest_path",
default='datasets/manifest.train',
type=str,
help="Manifest path for training. (default: %(default)s)")
parser.add_argument(
"--dev_manifest_path",
default='datasets/manifest.dev',
type=str,
help="Manifest path for validation. (default: %(default)s)")
parser.add_argument(
"--vocab_filepath",
default='datasets/vocab/eng_vocab.txt',
type=str,
help="Vocabulary filepath. (default: %(default)s)")
parser.add_argument(
"--init_model_path",
default=None,
type=str,
help="If set None, the training will start from scratch. "
"Otherwise, the training will resume from "
"the existing model of this path. (default: %(default)s)")
parser.add_argument(
"--output_model_dir",
default="./checkpoints",
type=str,
help="Directory for saving models. (default: %(default)s)")
parser.add_argument(
"--augmentation_config",
default=open('conf/augmentation.config', 'r').read(),
type=str,
help="Augmentation configuration in json-format. "
"(default: %(default)s)")
parser.add_argument(
"--is_local",
default=True,
type=distutils.util.strtobool,
help="Set to false if running with pserver in paddlecloud. "
"(default: %(default)s)")
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
add_arg('batch_size', int, 256, "Minibatch size.")
add_arg('trainer_count', int, 8, "# of Trainers (CPUs or GPUs).")
add_arg('num_passes', int, 200, "# of training epochs.")
add_arg('num_proc_data', int, 12, "# of CPUs for data preprocessing.")
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('num_iter_print', int, 100, "Every # iterations for printing "
"train cost.")
add_arg('learning_rate', float, 5e-4, "Learning rate.")
add_arg('max_duration', float, 27.0, "Longest audio duration allowed.")
add_arg('min_duration', float, 0.0, "Shortest audio duration allowed.")
add_arg('use_sortagrad', bool, True, "Use SortaGrad or not.")
add_arg('use_gpu', bool, True, "Use GPU or not.")
add_arg('is_local', bool, True, "Use pserver or not.")
add_arg('use_gru', bool, False, "Use GRUs instead of simple RNNs.")
add_arg('share_rnn_weights',bool, True, "Share input-hidden weights across "
"bi-directional RNNs. Not for GRU.")
add_arg('train_manifest', str,
'datasets/manifest.train',
"Filepath of train manifest.")
add_arg('dev_manifest', str,
'datasets/manifest.dev',
"Filepath of validation manifest.")
add_arg('mean_std_path', str,
'mean_std.npz',
"Filepath of normalizer's mean & std.")
add_arg('vocab_path', str,
'datasets/vocab/eng_vocab.txt',
"Filepath of vocabulary.")
add_arg('init_model_path', str,
None,
"If None, the training starts from scratch, "
"otherwise, it resumes from the pre-trained model.")
add_arg('output_model_dir', str,
"./checkpoints",
"Directory for saving checkpoints.")
add_arg('augment_conf_path',str,
'conf/augmentation.config',
"Filepath of augmentation configuration file (json-format).")
add_arg('specgram_type', str,
'linear',
"Audio feature type. Options: linear, mfcc.",
choices=['linear', 'mfcc'])
add_arg('shuffle_method', str,
'batch_shuffle_clipped',
"Shuffle method.",
choices=['instance_shuffle', 'batch_shuffle', 'batch_shuffle_clipped'])
# yapf: disable
args = parser.parse_args()
def train():
"""DeepSpeech2 training."""
train_generator = DataGenerator(
vocab_filepath=args.vocab_filepath,
mean_std_filepath=args.mean_std_filepath,
augmentation_config=args.augmentation_config,
vocab_filepath=args.vocab_path,
mean_std_filepath=args.mean_std_path,
augmentation_config=open(args.augment_conf_path, 'r').read(),
max_duration=args.max_duration,
min_duration=args.min_duration,
specgram_type=args.specgram_type,
num_threads=args.num_threads_data)
num_threads=args.num_proc_data)
dev_generator = DataGenerator(
vocab_filepath=args.vocab_filepath,
mean_std_filepath=args.mean_std_filepath,
vocab_filepath=args.vocab_path,
mean_std_filepath=args.mean_std_path,
augmentation_config="{}",
specgram_type=args.specgram_type,
num_threads=args.num_threads_data)
num_threads=args.num_proc_data)
train_batch_reader = train_generator.batch_reader_creator(
manifest_path=args.train_manifest_path,
manifest_path=args.train_manifest,
batch_size=args.batch_size,
min_batch_size=args.trainer_count,
sortagrad=args.use_sortagrad if args.init_model_path is None else False,
shuffle_method=args.shuffle_method)
dev_batch_reader = dev_generator.batch_reader_creator(
manifest_path=args.dev_manifest_path,
manifest_path=args.dev_manifest,
batch_size=args.batch_size,
min_batch_size=1, # must be 1, but will have errors.
sortagrad=False,
......@@ -184,21 +101,21 @@ def train():
rnn_layer_size=args.rnn_layer_size,
use_gru=args.use_gru,
pretrained_model_path=args.init_model_path,
share_rnn_weights=args.share_rnn_weights)
share_rnn_weights=args.share_weights)
ds2_model.train(
train_batch_reader=train_batch_reader,
dev_batch_reader=dev_batch_reader,
feeding_dict=train_generator.feeding,
learning_rate=args.adam_learning_rate,
learning_rate=args.learning_rate,
gradient_clipping=400,
num_passes=args.num_passes,
num_iterations_print=args.num_iterations_print,
num_iterations_print=args.num_iter_print,
output_model_dir=args.output_model_dir,
is_local=args.is_local)
def main():
utils.print_arguments(args)
print_arguments(args)
paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
train()
......
deep_speech_2/tune.py
浏览文件 @ b56a548e
"""Parameters tuning for DeepSpeech2 model."""
"""Beam search parameters tuning for DeepSpeech2 model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import distutils.util
import argparse
import multiprocessing
import functools
import paddle.v2 as paddle
from data_utils.data import DataGenerator
from model import DeepSpeech2Model
from error_rate import wer
import utils
from utils import add_arguments, print_arguments
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--num_samples",
default=100,
type=int,
help="Number of samples for parameters tuning. (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=2048,
type=int,
help="RNN layer cell number. (default: %(default)s)")
parser.add_argument(
"--share_rnn_weights",
default=True,
type=distutils.util.strtobool,
help="Whether to share input-hidden weights between forword and backward "
"directional simple RNNs. Only available when use_gru=False. "
"(default: %(default)s)")
parser.add_argument(
"--use_gru",
default=False,
type=distutils.util.strtobool,
help="Use GRU or simple RNN. (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(
"--trainer_count",
default=8,
type=int,
help="Trainer number. (default: %(default)s)")
parser.add_argument(
"--num_threads_data",
default=1,
type=int,
help="Number of cpu threads for preprocessing data. (default: %(default)s)")
parser.add_argument(
"--num_processes_beam_search",
default=multiprocessing.cpu_count() // 2,
type=int,
help="Number of cpu processes for beam search. (default: %(default)s)")
parser.add_argument(
"--specgram_type",
default='linear',
type=str,
help="Feature type of audio data: 'linear' (power spectrum)"
" or 'mfcc'. (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(
"--tune_manifest_path",
default='datasets/manifest.dev',
type=str,
help="Manifest path for tuning. (default: %(default)s)")
parser.add_argument(
"--model_filepath",
default='checkpoints/params.latest.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)")
parser.add_argument(
"--beam_size",
default=500,
type=int,
help="Width for beam search decoding. (default: %(default)d)")
parser.add_argument(
"--language_model_path",
default="lm/data/common_crawl_00.prune01111.trie.klm",
type=str,
help="Path for language model. (default: %(default)s)")
parser.add_argument(
"--alpha_from",
default=0.1,
type=float,
help="Where alpha starts from. (default: %(default)f)")
parser.add_argument(
"--num_alphas",
default=14,
type=int,
help="Number of candidate alphas. (default: %(default)d)")
parser.add_argument(
"--alpha_to",
default=0.36,
type=float,
help="Where alpha ends with. (default: %(default)f)")
parser.add_argument(
"--beta_from",
default=0.05,
type=float,
help="Where beta starts from. (default: %(default)f)")
parser.add_argument(
"--num_betas",
default=20,
type=float,
help="Number of candidate betas. (default: %(default)d)")
parser.add_argument(
"--beta_to",
default=1.0,
type=float,
help="Where beta ends with. (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)")
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
add_arg('num_samples', int, 100, "# of samples to infer.")
add_arg('trainer_count', int, 8, "# of Trainers (CPUs or GPUs).")
add_arg('beam_size', int, 500, "Beam search width.")
add_arg('num_proc_bsearch', int, 12, "# 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('num_alphas', int, 14, "# of alpha candidates for tuning.")
add_arg('num_betas', int, 20, "# of beta candidates for tuning.")
add_arg('alpha_from', float, 0.1, "Where alpha starts tuning from.")
add_arg('alpha_to', float, 0.36, "Where alpha ends tuning with.")
add_arg('beta_from', float, 0.05, "Where beta starts tuning from.")
add_arg('beta_to', float, 0.36, "Where beta ends tuning with.")
add_arg('cutoff_prob', float, 0.99, "Cutoff probability 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('tune_manifest', str,
'datasets/manifest.test',
"Filepath of manifest to tune.")
add_arg('mean_std_path', str,
'mean_std.npz',
"Filepath of normalizer's mean & std.")
add_arg('vocab_path', str,
'datasets/vocab/eng_vocab.txt',
"Filepath of vocabulary.")
add_arg('lang_model_path', str,
'lm/data/common_crawl_00.prune01111.trie.klm',
"Filepath for language model.")
add_arg('model_path', str,
'./checkpoints/params.latest.tar.gz',
"If None, the training starts from scratch, "
"otherwise, it resumes from the pre-trained model.")
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()
......@@ -149,13 +69,13 @@ def tune():
raise ValueError("num_betas must be non-negative!")
data_generator = DataGenerator(
vocab_filepath=args.vocab_filepath,
mean_std_filepath=args.mean_std_filepath,
vocab_filepath=args.vocab_path,
mean_std_filepath=args.mean_std_path,
augmentation_config='{}',
specgram_type=args.specgram_type,
num_threads=args.num_threads_data)
num_threads=1)
batch_reader = data_generator.batch_reader_creator(
manifest_path=args.tune_manifest_path,
manifest_path=args.tune_manifest,
batch_size=args.num_samples,
sortagrad=False,
shuffle_method=None)
......@@ -171,7 +91,7 @@ def tune():
num_rnn_layers=args.num_rnn_layers,
rnn_layer_size=args.rnn_layer_size,
use_gru=args.use_gru,
pretrained_model_path=args.model_filepath,
pretrained_model_path=args.model_path,
share_rnn_weights=args.share_rnn_weights)
# create grid for search
......@@ -184,14 +104,14 @@ def tune():
for alpha, beta in params_grid:
result_transcripts = ds2_model.infer_batch(
infer_data=tune_data,
decode_method='beam_search',
decoding_method='ctc_beam_search',
beam_alpha=alpha,
beam_beta=beta,
beam_size=args.beam_size,
cutoff_prob=args.cutoff_prob,
vocab_list=data_generator.vocab_list,
language_model_path=args.language_model_path,
num_processes=args.num_processes_beam_search)
language_model_path=args.lang_model_path,
num_processes=args.num_proc_bsearch)
wer_sum, num_ins = 0.0, 0
for target, result in zip(target_transcripts, result_transcripts):
wer_sum += wer(target, result)
......@@ -201,7 +121,7 @@ def tune():
def main():
utils.print_arguments(args)
print_arguments(args)
paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
tune()
......
deep_speech_2/utils.py
浏览文件 @ b56a548e
......@@ -3,6 +3,8 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import distutils.util
def print_arguments(args):
"""Print argparse's arguments.
......@@ -19,7 +21,27 @@ def print_arguments(args):
:param args: Input argparse.Namespace for printing.
:type args: argparse.Namespace
"""
print("----- Configuration Arguments -----")
for arg, value in vars(args).iteritems():
print("----------- Configuration Arguments -----------")
for arg, value in sorted(vars(args).iteritems()):
print("%s: %s" % (arg, value))
print("------------------------------------")
print("------------------------------------------------")
def add_arguments(argname, type, default, help, argparser, **kwargs):
"""Add argparse's argument.
Usage:
.. code-block:: python
parser = argparse.ArgumentParser()
add_argument("name", str, "Jonh", "User name.", parser)
args = parser.parse_args()
"""
type = distutils.util.strtobool if type == bool else type
argparser.add_argument(
"--" + argname,
default=default,
type=type,
help=help + ' Default: %(default)s.',
**kwargs)
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