Add AISHELL dataset importer

bin/import_aishell.py

@@ -0,0 +1,95 @@
+#!/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 tarfile
+import pandas
+
+
+COLUMNNAMES = ['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, 'data_aishell')
+
+    wav_archives_folder = os.path.join(main_folder, 'wav')
+    for targz in glob.glob(os.path.join(wav_archives_folder, '*.tar.gz')):
+        extract(targz, main_folder)
+
+    # Folder structure is now:
+    # - data_aishell/
+    #   - train/S****/*.wav
+    #   - dev/S****/*.wav
+    #   - test/S****/*.wav
+    #   - wav/S****.tar.gz
+    #   - transcript/aishell_transcript_v0.8.txt
+
+    # Transcripts file has one line per WAV file, where each line consists of
+    # the WAV file name without extension followed by a single space followed
+    # by the transcript.
+
+    # Since the transcripts themselves can contain spaces, we split on space but
+    # only once, then build a mapping from file name to transcript
+    transcripts_path = os.path.join(main_folder, 'transcript', 'aishell_transcript_v0.8.txt')
+    with open(transcripts_path) as fin:
+        transcripts = dict((line.split(' ', maxsplit=1) for line in fin))
+
+    def load_set(glob_path):
+        set_files = []
+        for wav in glob.glob(glob_path):
+            try:
+                wav_filename = wav
+                wav_filesize = os.path.getsize(wav)
+                transcript_key = os.path.splitext(os.path.basename(wav))[0]
+                transcript = transcripts[transcript_key].strip('\n')
+                set_files.append((wav_filename, wav_filesize, transcript))
+            except KeyError:
+                print('Warning: Missing transcript for WAV file {}.'.format(wav))
+        return set_files
+
+    for subset in ('train', 'dev', 'test'):
+        print('Loading {} set samples...'.format(subset))
+        subset_files = load_set(os.path.join(main_folder, subset, 'S*', '*.wav'))
+        df = pandas.DataFrame(data=subset_files, columns=COLUMNNAMES)
+
+        # Trim train set to under 10s by removing the last couple hundred samples
+        if subset == 'train':
+            durations = (df['wav_filesize'] - 44) / 16000 / 2
+            df = df[durations <= 10.0]
+            print('Trimming {} samples > 10 seconds'.format((durations > 10.0).sum()))
+
+        dest_csv = os.path.join(target_dir, 'aishell_{}.csv'.format(subset))
+        print('Saving {} set into {}...'.format(subset, dest_csv))
+        df.to_csv(dest_csv, index=False)
+
+
+def main():
+    # http://www.openslr.org/33/
+    parser = argparse.ArgumentParser(description='Import AISHELL corpus')
+    parser.add_argument('aishell_tgz_file', help='Path to data_aishell.tgz')
+    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.aishell_tgz_file)
+
+    preprocess_data(params.aishell_tgz_file, params.target_dir)
+
+
+if __name__ == "__main__":
+    main()