Create 'tools' to hold tool scripts and add vocabulary dictionary building script.

deep_speech_2/README.md

@@ -40,13 +40,13 @@ python datasets/librispeech/librispeech.py --help
 ### Preparing for Training
 
 ```
-python compute_mean_std.py
+python tools/compute_mean_std.py
 ```
 
 It will compute mean and stdandard deviation for audio features, and save them to a file with a default name `./mean_std.npz`. This file will be used in both training and inferencing. The default feature of audio data is power spectrum, and the mfcc feature is also supported. To train and infer based on mfcc feature, please generate this file by
 
 ```
-python compute_mean_std.py --specgram_type mfcc
+python tools/compute_mean_std.py --specgram_type mfcc
 ```
 
 and specify ```--specgram_type mfcc``` when running train.py, infer.py, evaluator.py or tune.py.
@@ -54,7 +54,7 @@ and specify ```--specgram_type mfcc``` when running train.py, infer.py, evaluato
 More help for arguments:
 
 ```
-python compute_mean_std.py --help
+python tools/compute_mean_std.py --help
 ```
 
 ### Training

deep_speech_2/tools/_init_paths.py

+"""Set up paths for DS2"""
+
+import os.path
+import sys
+
+
+def add_path(path):
+    if path not in sys.path:
+        sys.path.insert(0, path)
+
+
+this_dir = os.path.dirname(__file__)
+
+# Add project path to PYTHONPATH
+proj_path = os.path.join(this_dir, '..')
+add_path(proj_path)

deep_speech_2/tools/build_vocab.py

+"""Build vocabulary dictionary from manifest files.
+
+Each item in vocabulary file is a character.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import codecs
+import json
+from collections import Counter
+import os.path
+
+parser = argparse.ArgumentParser(
+    description='Build vocabulary dictionary from transcription texts.')
+parser.add_argument(
+    "--manifest_paths",
+    type=str,
+    help="Manifest paths for building vocabulary dictionary."
+    "You can provide multiple manifest files.",
+    nargs='+',
+    required=True)
+parser.add_argument(
+    "--count_threshold",
+    default=0,
+    type=int,
+    help="Characters whose count below the threshold will be truncated. "
+    "(default: %(default)s)")
+parser.add_argument(
+    "--vocab_path",
+    default='datasets/vocab/zh_vocab.txt',
+    type=str,
+    help="Filepath to write vocabularies. (default: %(default)s)")
+args = parser.parse_args()
+
+
+def count_manifest(counter, manifest_path):
+    for json_line in codecs.open(manifest_path, 'r', 'utf-8'):
+        try:
+            json_data = json.loads(json_line)
+        except Exception as e:
+            raise Exception('Error parsing manifest: %s, %s' % \
+                    (manifest_path, e))
+        text = json_data['text']
+        for char in text:
+            counter.update(char)
+
+
+def main():
+    counter = Counter()
+    for manifest_path in args.manifest_paths:
+        count_manifest(counter, manifest_path)
+
+    count_sorted = sorted(counter.items(), key=lambda x: x[1], reverse=True)
+    with codecs.open(args.vocab_path, 'w', 'utf-8') as fout:
+        for item_pair in count_sorted:
+            if item_pair[1] < args.count_threshold: break
+            fout.write(item_pair[0] + '\n')
+
+
+if __name__ == '__main__':
+    main()

deep_speech_2/compute_mean_std.py → deep_speech_2/tools/compute_mean_std.py

@@ -4,6 +4,7 @@ from __future__ import division
 from __future__ import print_function
 
 import argparse
+import _init_paths
 from data_utils.normalizer import FeatureNormalizer
 from data_utils.augmentor.augmentation import AugmentationPipeline
 from data_utils.featurizer.audio_featurizer import AudioFeaturizer