Re-style the config codes for tools in DS2.

demo_server.py

@@ -13,8 +13,6 @@ from data_utils.data import DataGenerator
 from model import DeepSpeech2Model
 from data_utils.utils import read_manifest
 
-parser = argparse.ArgumentParser(description=__doc__)
-
 
 def add_arg(argname, type, default, help, **kwargs):
     type = distutils.util.strtobool if type == bool else type
@@ -27,6 +25,7 @@ def add_arg(argname, type, default, help, **kwargs):
 
 
 # yapf: disable
+parser = argparse.ArgumentParser(description=__doc__)
 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.")

evaluate.py

@@ -5,15 +5,11 @@ from __future__ import print_function
 
 import distutils.util
 import argparse
-import multiprocessing
 import paddle.v2 as paddle
 from data_utils.data import DataGenerator
 from model import DeepSpeech2Model
 from error_rate import wer, cer
 
-NUM_CPU = multiprocessing.cpu_count() // 2
-parser = argparse.ArgumentParser(description=__doc__)
-
 
 def add_arg(argname, type, default, help, **kwargs):
     type = distutils.util.strtobool if type == bool else type
@@ -26,11 +22,12 @@ def add_arg(argname, type, default, help, **kwargs):
 
 
 # yapf: disable
+parser = argparse.ArgumentParser(description=__doc__)
 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('parallels_bsearch',int,    NUM_CPU,"# of CPUs for beam search.")
-add_arg('parallels_data',   int,    NUM_CPU,"# of CPUs for data preprocessing.")
+add_arg('parallels_bsearch',int,    12,     "# of CPUs for beam search.")
+add_arg('parallels_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.")

infer.py

@@ -5,15 +5,11 @@ from __future__ import print_function
 
 import argparse
 import distutils.util
-import multiprocessing
 import paddle.v2 as paddle
 from data_utils.data import DataGenerator
 from model import DeepSpeech2Model
 from error_rate import wer, cer
 
-NUM_CPU = multiprocessing.cpu_count() // 2
-parser = argparse.ArgumentParser(description=__doc__)
-
 
 def add_arg(argname, type, default, help, **kwargs):
     type = distutils.util.strtobool if type == bool else type
@@ -26,11 +22,11 @@ def add_arg(argname, type, default, help, **kwargs):
 
 
 # yapf: disable
-# configurations of overall
+parser = argparse.ArgumentParser(description=__doc__)
 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('parallels_bsearch',int,    NUM_CPU,"# of CPUs for beam search.")
+add_arg('parallels_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.")

tools/build_vocab.py

@@ -14,26 +14,31 @@ import os.path
 import _init_paths
 from data_utils import utils
 
+
+def add_arg(argname, type, default, help, **kwargs):
+    type = distutils.util.strtobool if type == bool else type
+    parser.add_argument(
+        "--" + argname,
+        default=default,
+        type=type,
+        help=help + ' Default: %(default)s.',
+        **kwargs)
+
+
+# yapf: disable
 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('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)
 args = parser.parse_args()
+# yapf: disable
 
 
 def count_manifest(counter, manifest_path):
@@ -43,7 +48,16 @@ def count_manifest(counter, manifest_path):
             counter.update(char)
 
 
+def print_arguments(args):
+    print("-----------  Configuration Arguments -----------")
+    for arg, value in sorted(vars(args).iteritems()):
+        print("%s: %s" % (arg, value))
+    print("------------------------------------------------")
+
+
 def main():
+    print_arguments(args)
+
     counter = Counter()
     for manifest_path in args.manifest_paths:
         count_manifest(counter, manifest_path)

tools/compute_mean_std.py

@@ -9,43 +9,45 @@ 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)")
+
+def add_arg(argname, type, default, help, **kwargs):
+    type = distutils.util.strtobool if type == bool else type
+    parser.add_argument(
+        "--" + argname,
+        default=default,
+        type=type,
+        help=help + ' Default: %(default)s.',
+        **kwargs)
+
+
+# yapf: disable
+parser = argparse.ArgumentParser(description=__doc__)
+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).")
 args = parser.parse_args()
+# yapf: disable
+
+
+def print_arguments(args):
+    print("-----------  Configuration Arguments -----------")
+    for arg, value in sorted(vars(args).iteritems()):
+        print("%s: %s" % (arg, value))
+    print("------------------------------------------------")
 
 
 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 +59,7 @@ 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__':

train.py

@@ -5,14 +5,10 @@ from __future__ import print_function
 
 import argparse
 import distutils.util
-import multiprocessing
 import paddle.v2 as paddle
 from model import DeepSpeech2Model
 from data_utils.data import DataGenerator
 
-NUM_CPU = multiprocessing.cpu_count() // 2
-parser = argparse.ArgumentParser(description=__doc__)
-
 
 def add_arg(argname, type, default, help, **kwargs):
     type = distutils.util.strtobool if type == bool else type
@@ -25,10 +21,11 @@ def add_arg(argname, type, default, help, **kwargs):
 
 
 # yapf: disable
+parser = argparse.ArgumentParser(description=__doc__)
 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('parallels_data',   int,    NUM_CPU,"# of CPUs for data preprocessing.")
+add_arg('parallels_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.")

tune.py

@@ -6,15 +6,11 @@ from __future__ import print_function
 import numpy as np
 import distutils.util
 import argparse
-import multiprocessing
 import paddle.v2 as paddle
 from data_utils.data import DataGenerator
 from model import DeepSpeech2Model
 from error_rate import wer
 
-NUM_CPU = multiprocessing.cpu_count() // 2
-parser = argparse.ArgumentParser(description=__doc__)
-
 
 def add_arg(argname, type, default, help, **kwargs):
     type = distutils.util.strtobool if type == bool else type
@@ -27,10 +23,11 @@ def add_arg(argname, type, default, help, **kwargs):
 
 
 # yapf: disable
+parser = argparse.ArgumentParser(description=__doc__)
 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('parallels_bsearch',int,    NUM_CPU,"# of CPUs for beam search.")
+add_arg('parallels_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.")
@@ -73,6 +70,7 @@ args = parser.parse_args()
 # yapf: disable
 
 
+
 def tune():
     """Tune parameters alpha and beta on one minibatch."""
     if not args.num_alphas >= 0: