change modality naming inconsistency (visual & video) (#3631)

* Update transforms.py

* Update train.py

examples/avsr/train.py

@@ -59,6 +59,7 @@ def parse_args():
         "--modality",
         type=str,
         help="Modality",
+        choices=["audio", "video", "audiovisual"],
         required=True,
     )
     parser.add_argument(

examples/avsr/transforms.py

@@ -55,7 +55,7 @@ def _extract_features(video_pipeline, audio_pipeline, samples, args):
     raw_videos = []
     raw_audios = []
     for sample in samples:
-        if args.modality == "visual":
+        if args.modality == "video":
             raw_videos.append(sample[0])
         if args.modality == "audio":
             raw_audios.append(sample[0])
@@ -64,7 +64,7 @@ def _extract_features(video_pipeline, audio_pipeline, samples, args):
             raw_audios.append(sample[0][: length * 640])
             raw_videos.append(sample[1][:length])
 
-    if args.modality == "visual" or args.modality == "audiovisual":
+    if args.modality == "video" or args.modality == "audiovisual":
         videos = torch.nn.utils.rnn.pad_sequence(raw_videos, batch_first=True)
         videos = video_pipeline(videos)
         video_lengths = torch.tensor([elem.shape[0] for elem in videos], dtype=torch.int32)
@@ -72,7 +72,7 @@ def _extract_features(video_pipeline, audio_pipeline, samples, args):
         audios = torch.nn.utils.rnn.pad_sequence(raw_audios, batch_first=True)
         audios = audio_pipeline(audios)
         audio_lengths = torch.tensor([elem.shape[0] // 640 for elem in audios], dtype=torch.int32)
-    if args.modality == "visual":
+    if args.modality == "video":
         return videos, video_lengths
     if args.modality == "audio":
         return audios, audio_lengths
@@ -105,7 +105,7 @@ class TrainTransform:
                 self.train_video_pipeline, self.train_audio_pipeline, samples, self.args
             )
             return Batch(audios, audio_lengths, targets, target_lengths)
-        if self.args.modality == "visual":
+        if self.args.modality == "video":
             videos, video_lengths = _extract_features(
                 self.train_video_pipeline, self.train_audio_pipeline, samples, self.args
             )
@@ -140,7 +140,7 @@ class ValTransform:
                 self.valid_video_pipeline, self.valid_audio_pipeline, samples, self.args
             )
             return Batch(audios, audio_lengths, targets, target_lengths)
-        if self.args.modality == "visual":
+        if self.args.modality == "video":
             videos, video_lengths = _extract_features(
                 self.valid_video_pipeline, self.valid_audio_pipeline, samples, self.args
             )