Add MAGICDATA importer

bin/import_magicdata.py

@@ -0,0 +1,88 @@
+#!/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 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(folder_with_archives, target_dir):
+    # First extract subset archives
+    for subset in ('train', 'dev', 'test'):
+        extract(os.path.join(folder_with_archives, 'magicdata_{}_set.tar.gz'.format(subset)), target_dir)
+
+    # Folder structure is now:
+    # - magicdata_{train,dev,test}.tar.gz
+    # - magicdata/
+    #   - train/*.wav
+    #   - train/TRANS.txt
+    #   - dev/*.wav
+    #   - dev/TRANS.txt
+    #   - test/*.wav
+    #   - test/TRANS.txt
+
+    # The TRANS files are CSVs with three columns, one containing the WAV file
+    # name, one containing the speaker ID, and one containing the transcription
+
+    def load_set(set_path):
+        transcripts = pandas.read_csv(os.path.join(set_path, 'TRANS.txt'), sep='\t', index_col=0)
+        glob_path = os.path.join(set_path, '*', '*.wav')
+        set_files = []
+        for wav in glob.glob(glob_path):
+            try:
+                wav_filename = wav
+                wav_filesize = os.path.getsize(wav)
+                transcript_key = os.path.basename(wav)
+                transcript = transcripts.loc[transcript_key, 'Transcription']
+                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(target_dir, subset))
+        df = pandas.DataFrame(data=subset_files, columns=COLUMN_NAMES)
+
+        # Trim train set to under 10s
+        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, 'magicdata_{}.csv'.format(subset))
+        print('Saving {} set into {}...'.format(subset, dest_csv))
+        df.to_csv(dest_csv, index=False)
+
+
+def main():
+    # https://openslr.org/68/
+    parser = argparse.ArgumentParser(description='Import MAGICDATA corpus')
+    parser.add_argument('folder_with_archives', help='Path to folder containing magicdata_{train,dev,test}.tar.gz')
+    parser.add_argument('--target_dir', default='', help='Target folder to extract files into and put the resulting CSVs. Defaults to a folder called magicdata next to the archives')
+    params = parser.parse_args()
+
+    if not params.target_dir:
+        params.target_dir = os.path.join(params.folder_with_archives, 'magicdata')
+
+    preprocess_data(params.folder_with_archives, params.target_dir)
+
+
+if __name__ == "__main__":
+    main()