Add mel spectrogram visualization to Streaming ASR tutorial (#2974)

Summary:
Per the suggestion by nateanl, adding the visualization of feature fed to ASR.

<img width="688" alt="Screen Shot 2023-01-12 at 8 19 59 PM" src="https://user-images.githubusercontent.com/855818/212215190-23be7553-4c04-40d9-944e-3ee2ff69c49b.png">

Pull Request resolved: https://github.com/pytorch/audio/pull/2974

Reviewed By: nateanl

Differential Revision: D42484088

Pulled By: mthrok

fbshipit-source-id: 2c839492869416554eac04aa06cd12078db21bd7

examples/tutorials/online_asr_tutorial.py

@@ -48,6 +48,7 @@ print(torchaudio.__version__)
 ######################################################################
 #
 import IPython
+import matplotlib.pyplot as plt
 
 try:
     from torchaudio.io import StreamReader
@@ -195,10 +196,28 @@ state, hypothesis = None, None
 stream_iterator = streamer.stream()
 
 
+def _plot(feats, num_iter, unit=25):
+    unit_dur = segment_length / sample_rate * unit
+    num_plots = num_iter // unit + (1 if num_iter % unit else 0)
+    fig, axes = plt.subplots(num_plots, 1)
+    t0 = 0
+    for i, ax in enumerate(axes):
+        feats_ = feats[i*unit:(i+1)*unit]
+        t1 = t0 + segment_length / sample_rate * len(feats_)
+        feats_ = torch.cat([f[2:-2] for f in feats_])  # remove boundary effect and overlap
+        ax.imshow(feats_.T, extent=[t0, t1, 0, 1], aspect="auto", origin="lower")
+        ax.tick_params(which='both', left=False, labelleft=False)
+        ax.set_xlim(t0, t0 + unit_dur)
+        t0 = t1
+    fig.suptitle("MelSpectrogram Feature")
+    plt.tight_layout()
+
+
 @torch.inference_mode()
-def run_inference(num_iter=200):
+def run_inference(num_iter=100):
     global state, hypothesis
     chunks = []
+    feats = []
     for i, (chunk,) in enumerate(stream_iterator, start=1):
         segment = cacher(chunk[:, 0])
         features, length = feature_extractor(segment)
@@ -208,9 +227,12 @@ def run_inference(num_iter=200):
         print(transcript, end="", flush=True)
 
         chunks.append(chunk)
+        feats.append(features)
         if i == num_iter:
             break
 
+    # Plot the features
+    _plot(feats, num_iter)
     return IPython.display.Audio(torch.cat(chunks).T.numpy(), rate=bundle.sample_rate)
 
 
@@ -249,6 +271,36 @@ run_inference()
 
 run_inference()
 
+######################################################################
+#
+
+run_inference()
+
+######################################################################
+#
+
+run_inference()
+
+######################################################################
+#
+
+run_inference()
+
+######################################################################
+#
+
+run_inference()
+
+######################################################################
+#
+
+run_inference()
+
+######################################################################
+#
+
+run_inference()
+
 ######################################################################
 #
 # Tag: :obj:`torchaudio.io`