Add importer for Free ST Chinese Mandarin Corpus

bin/import_freestmandarin.py

@@ -0,0 +1,96 @@
+#!/usr/bin/env python
+from __future__ import absolute_import, division, print_function
+
+# Make sure we can import stuff from util/
+# This script needs to be run from the root of the DeepSpeech repository
+import os
+import sys
+sys.path.insert(1, os.path.join(sys.path[0], '..'))
+
+import argparse
+import glob
+import numpy as np
+import pandas
+import tarfile
+
+
+COLUMN_NAMES = ['wav_filename', 'wav_filesize', 'transcript']
+
+
+def extract(archive_path, target_dir):
+    print('Extracting {} into {}...'.format(archive_path, target_dir))
+    with tarfile.open(archive_path) as tar:
+        tar.extractall(target_dir)
+
+
+def preprocess_data(tgz_file, target_dir):
+    # First extract main archive and sub-archives
+    extract(tgz_file, target_dir)
+    main_folder = os.path.join(target_dir, 'ST-CMDS-20170001_1-OS')
+
+    # Folder structure is now:
+    # - ST-CMDS-20170001_1-OS/
+    #   - *.wav
+    #   - *.txt
+    #   - *.metadata
+
+    def load_set(glob_path):
+        set_files = []
+        for wav in glob.glob(glob_path):
+            wav_filename = wav
+            wav_filesize = os.path.getsize(wav)
+            txt_filename = os.path.splitext(wav_filename)[0] + '.txt'
+            with open(txt_filename, 'r') as fin:
+                transcript = fin.read()
+            set_files.append((wav_filename, wav_filesize, transcript))
+        return set_files
+
+    # Load all files, then deterministically split into train/dev/test sets
+    all_files = load_set(os.path.join(main_folder, '*.wav'))
+    df = pandas.DataFrame(data=all_files, columns=COLUMN_NAMES)
+    df.sort_values(by='wav_filename', inplace=True)
+
+    indices = np.arange(0, len(df))
+    np.random.seed(12345)
+    np.random.shuffle(indices)
+
+    # Total corpus size: 102600 samples. 5000 samples gives us 99% confidence
+    # level with a margin of error of under 2%.
+    test_indices = indices[-5000:]
+    dev_indices = indices[-10000:-5000]
+    train_indices = indices[:-10000]
+
+    train_files = df.iloc[train_indices]
+    durations = (train_files['wav_filesize'] - 44) / 16000 / 2
+    train_files = train_files[durations <= 10.0]
+    print('Trimming {} samples > 10 seconds'.format((durations > 10.0).sum()))
+    dest_csv = os.path.join(target_dir, 'freestmandarin_train.csv')
+    print('Saving train set into {}...'.format(dest_csv))
+    train_files.to_csv(dest_csv, index=False)
+
+    dev_files = df.iloc[dev_indices]
+    dest_csv = os.path.join(target_dir, 'freestmandarin_dev.csv')
+    print('Saving dev set into {}...'.format(dest_csv))
+    dev_files.to_csv(dest_csv, index=False)
+
+    test_files = df.iloc[test_indices]
+    dest_csv = os.path.join(target_dir, 'freestmandarin_test.csv')
+    print('Saving test set into {}...'.format(dest_csv))
+    test_files.to_csv(dest_csv, index=False)
+
+
+def main():
+    # https://www.openslr.org/38/
+    parser = argparse.ArgumentParser(description='Import Free ST Chinese Mandarin corpus')
+    parser.add_argument('tgz_file', help='Path to ST-CMDS-20170001_1-OS.tar.gz')
+    parser.add_argument('--target_dir', default='', help='Target folder to extract files into and put the resulting CSVs. Defaults to same folder as the main archive.')
+    params = parser.parse_args()
+
+    if not params.target_dir:
+        params.target_dir = os.path.dirname(params.tgz_file)
+
+    preprocess_data(params.tgz_file, params.target_dir)
+
+
+if __name__ == "__main__":
+    main()