Fix decoder call in Device ASR/AVSR tutorials (#3572)

Summary:
Fixes decoder calls and related code in Device ASR/AVSR tutorials to account for changes to RNN-T decoder introduced in https://github.com/pytorch/audio/issues/3295.

Pull Request resolved: https://github.com/pytorch/audio/pull/3572

Reviewed By: mthrok

Differential Revision: D48629428

Pulled By: hwangjeff

fbshipit-source-id: 63ede307fb4412aa28f88972d56dca8405607b7a

examples/tutorials/device_asr.py

@@ -206,16 +206,15 @@ class Pipeline:
         self.beam_width = beam_width
 
         self.state = None
-        self.hypothesis = None
+        self.hypotheses = None
 
     def infer(self, segment: torch.Tensor) -> str:
         """Perform streaming inference"""
         features, length = self.feature_extractor(segment)
-        hypos, self.state = self.decoder.infer(
-            features, length, self.beam_width, state=self.state, hypothesis=self.hypothesis
+        self.hypotheses, self.state = self.decoder.infer(
+            features, length, self.beam_width, state=self.state, hypothesis=self.hypotheses
         )
-        self.hypothesis = hypos[0]
-        transcript = self.token_processor(self.hypothesis[0], lstrip=False)
+        transcript = self.token_processor(self.hypotheses[0][0], lstrip=False)
         return transcript
 
 
@@ -291,7 +290,7 @@ def main(device, src, bundle):
             chunk = q.get()
             segment = cacher(chunk[:, 0])
             transcript = pipeline.infer(segment)
-            print(transcript, end="", flush=True)
+            print(transcript, end="\r", flush=True)
 
     import torch.multiprocessing as mp
 

examples/tutorials/device_avsr.py

@@ -258,15 +258,14 @@ class InferencePipeline(torch.nn.Module):
         self.token_processor = token_processor
 
         self.state = None
-        self.hypothesis = None
+        self.hypotheses = None
 
     def forward(self, audio, video):
         audio, video = self.preprocessor(audio, video)
         feats = self.model(audio.unsqueeze(0), video.unsqueeze(0))
         length = torch.tensor([feats.size(1)], device=audio.device)
-        hypos, self.state = self.decoder.infer(feats, length, 10, state=self.state, hypothesis=self.hypothesis)
-        self.hypothesis = hypos[0]
-        transcript = self.token_processor(self.hypothesis[0], lstrip=False)
+        self.hypotheses, self.state = self.decoder.infer(feats, length, 10, state=self.state, hypothesis=self.hypotheses)
+        transcript = self.token_processor(self.hypotheses[0][0], lstrip=False)
         return transcript
 
 
@@ -370,7 +369,7 @@ def main(device, src, option=None):
             video, audio = cacher(video, audio)
             pipeline.state, pipeline.hypothesis = None, None
             transcript = pipeline(audio, video.float())
-            print(transcript, end="", flush=True)
+            print(transcript, end="\r", flush=True)
             num_video_frames = 0
             video_chunks = []
             audio_chunks = []