Update online ASR tutorial (#2226)

Summary:
https://554729-90321822-gh.circle-artifacts.com/0/docs/tutorials/online_asr_tutorial.html

1. Add figure to explain the caching
2. Fix the initialization of stream iterator

Pull Request resolved: https://github.com/pytorch/audio/pull/2226

Reviewed By: carolineechen

Differential Revision: D34265971

Pulled By: mthrok

fbshipit-source-id: 243301e74c4040f4b8cd111b363e70da60e5dae4

examples/tutorials/online_asr_tutorial.py

@@ -72,7 +72,11 @@ token_processor = bundle.get_token_processor()
 
 ######################################################################
 # Streaming inference works on input data with overlap.
-# Emformer RNN-T expects right context like the following.
+# Emformer RNN-T model treats the newest portion of the input data
+# as the "right context" — a preview of future context.
+# In each inference call, the model expects the main segment
+# to start from this right context from the previous inference call.
+# The following figure illustrates this.
 #
 # .. image:: https://download.pytorch.org/torchaudio/tutorial-assets/emformer_rnnt_context.png
 #
@@ -115,14 +119,20 @@ print(streamer.get_src_stream_info(0))
 print(streamer.get_out_stream_info(0))
 
 ######################################################################
-# `Streamer` iterate the source media without overlap, so we make a
-# helper structure that caches a chunk and return it with right context
-# appended when the next chunk is given.
+# As previously explained, Emformer RNN-T model expects input data with
+# overlaps; however, `Streamer` iterates the source media without overlap,
+# so we make a helper structure that caches a part of input data from
+# `Streamer` as right context and then appends it to the next input data from
+# `Streamer`.
+#
+# The following figure illustrates this.
+#
+# .. image:: https://download.pytorch.org/torchaudio/tutorial-assets/emformer_rnnt_streamer_context.png
 #
 
 
 class ContextCacher:
-    """Cache the previous chunk and combine it with the new chunk
+    """Cache the end of input data and prepend the next input data with it.
 
     Args:
         segment_length (int): The size of main segment.
@@ -166,12 +176,14 @@ state, hypothesis = None, None
 # repeatedly.
 #
 
+stream_iterator = streamer.stream()
+
 
 @torch.inference_mode()
 def run_inference(num_iter=200):
     global state, hypothesis
     chunks = []
-    for i, (chunk,) in enumerate(streamer.stream(), start=1):
+    for i, (chunk,) in enumerate(stream_iterator, start=1):
         segment = cacher(chunk[:, 0])
         features, length = feature_extractor(segment)
         hypos, state = decoder.infer(features, length, 10, state=state, hypothesis=hypothesis)