Merge pull request #191 from pkuyym/build_vocab

Add vocabulary dictionary building script

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

tools/_init_paths.py

+"""Set up paths for DS2"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+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)

tools/build_vocab.py

+"""Build vocabulary 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
+import _init_paths
+from data_utils import utils
+
+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)")
+args = parser.parse_args()
+
+
+def count_manifest(counter, manifest_path):
+    manifest_jsons = utils.read_manifest(manifest_path)
+    for line_json in manifest_jsons:
+        for char in line_json['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 char, count in count_sorted:
+            if count < args.count_threshold: break
+            fout.write(char + '\n')
+
+
+if __name__ == '__main__':
+    main()

compute_mean_std.py → 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