Fix the issue k2-fsa#13 by wrapping the codes with a main()

egs/librispeech/asr/simple_v1/prepare.py

@@ -11,48 +11,55 @@
 from lhotse import CutSet, Fbank, LilcomFilesWriter, WavAugmenter
 from lhotse.recipes.librispeech import dataset_parts_full, prepare_librispeech
 
-print("All dataset parts: ", dataset_parts_full)
-
-dataset_parts = ('dev-clean', 'test-clean', 'train-clean-100')
-
-print("Parts we will prepare: ", dataset_parts)
-
-corpus_dir = '/home/storage04/zhuangweiji/data/open-source-data/librispeech/LibriSpeech'
-output_dir = 'exp/data'
-librispeech_manifests = prepare_librispeech(corpus_dir, dataset_parts,
-                                            output_dir)
-
-use_data_augmentation = False
-augmenter = WavAugmenter.create_predefined(
-    'pitch_reverb_tdrop',
-    sampling_rate=16000) if use_data_augmentation else None
-# It seems when spawning multiple Python subprocesses with the same sox handle it raises "std::runtime_error: Couldn't close file"
-# The issue seems to happen only in a Jupyter notebook on Mac OS X, hence the work around below.
-if use_data_augmentation:
-    num_jobs = 1
-else:
-    num_jobs = os.cpu_count()
-    torch.set_num_threads(1)
-    torch.set_num_interop_threads(1)
-
-for partition, manifests in librispeech_manifests.items():
-    print(partition)
-    with LilcomFilesWriter(f'{output_dir}/feats_{partition}') as storage, \
-            ProcessPoolExecutor(num_jobs, mp_context=multiprocessing.get_context("spawn")) as ex:
-        cut_set = CutSet.from_manifests(
-            recordings=manifests['recordings'],
-            supervisions=manifests['supervisions'])
-        cut_set = cut_set.compute_and_store_features(
-            extractor=Fbank(),
-            storage=storage,
-            executor=ex)
-        if 'train' in partition:
-            # Duplicate the training set with an augmented version
-            augmented_cs = cut_set.compute_and_store_features(
-                extractor=Fbank(),
-                storage=storage,
-                augment_fn=augmenter,
-                executor=ex)
-            cut_set = cut_set + CutSet.from_cuts(c.with_id(c.id + '_aug') for c in augmented_cs)
-    librispeech_manifests[partition]['cuts'] = cut_set
-    cut_set.to_json(output_dir + f'/cuts_{partition}.json.gz')
+
+def main():
+  print("All dataset parts: ", dataset_parts_full)
+
+  dataset_parts = ('dev-clean', 'test-clean', 'train-clean-100')
+
+  print("Parts we will prepare: ", dataset_parts)
+
+  corpus_dir = '/home/storage04/zhuangweiji/data/open-source-data/librispeech/LibriSpeech'
+  output_dir = 'exp/data'
+  librispeech_manifests = prepare_librispeech(corpus_dir, dataset_parts,
+                                              output_dir)
+
+  use_data_augmentation = False
+  augmenter = WavAugmenter.create_predefined(
+      'pitch_reverb_tdrop',
+      sampling_rate=16000) if use_data_augmentation else None
+  # It seems when spawning multiple Python subprocesses with the same sox handle it raises "std::runtime_error: Couldn't close file"
+  # The issue seems to happen only in a Jupyter notebook on Mac OS X, hence the work around below.
+  if use_data_augmentation:
+      num_jobs = 1
+  else:
+      num_jobs = os.cpu_count()
+      torch.set_num_threads(1)
+      torch.set_num_interop_threads(1)
+
+  for partition, manifests in librispeech_manifests.items():
+      print(partition)
+      with LilcomFilesWriter(f'{output_dir}/feats_{partition}') as storage, \
+              ProcessPoolExecutor(num_jobs, mp_context=multiprocessing.get_context("spawn")) as ex:
+          cut_set = CutSet.from_manifests(
+              recordings=manifests['recordings'],
+              supervisions=manifests['supervisions'])
+          cut_set = cut_set.compute_and_store_features(
+              extractor=Fbank(),
+              storage=storage,
+              executor=ex)
+          if 'train' in partition:
+              # Duplicate the training set with an augmented version
+              augmented_cs = cut_set.compute_and_store_features(
+                  extractor=Fbank(),
+                  storage=storage,
+                  augment_fn=augmenter,
+                  executor=ex)
+              cut_set = cut_set + CutSet.from_cuts(c.with_id(c.id + '_aug') for c in augmented_cs)
+      librispeech_manifests[partition]['cuts'] = cut_set
+      cut_set.to_json(output_dir + f'/cuts_{partition}.json.gz')
+
+
+if __name__ == '__main__':
+    main()
+