Update forced alignment tutorial (#2544)

Summary:
1. Fix initialization.
Previously, the SOS token score was initialized to 0 across the time axis.
This was biasing the alignment to delay the start.
The proper way to delay the SOS is via blank token.
The new initilization takes the cumulated sum of blank scores.
2. Fill the end of trellis with Inf
Similar to the start, at the end where there remaining time frame is less
than the number of tokens, it is no longer possible to align the text, thus
we fill with Inf for better visualization.
3. Clean up asset management code.

Pull Request resolved: https://github.com/pytorch/audio/pull/2544

Reviewed By: nateanl

Differential Revision: D38276478

Pulled By: mthrok

fbshipit-source-id: 6d934cc850a0790b8c463a4f69f8f1143633d299

examples/tutorials/forced_alignment_tutorial.py

@@ -11,6 +11,16 @@ Recognition <https://arxiv.org/abs/2007.09127>`__.
 
 """
 
+import torch
+import torchaudio
+
+print(torch.__version__)
+print(torchaudio.__version__)
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(device)
+
 
 ######################################################################
 # Overview
@@ -37,32 +47,18 @@ Recognition <https://arxiv.org/abs/2007.09127>`__.
 
 # %matplotlib inline
 
-import os
 from dataclasses import dataclass
 
 import IPython
 import matplotlib
 import matplotlib.pyplot as plt
-import requests
-import torch
-import torchaudio
 
 matplotlib.rcParams["figure.figsize"] = [16.0, 4.8]
 
 torch.random.manual_seed(0)
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-print(torch.__version__)
-print(torchaudio.__version__)
-print(device)
 
-SPEECH_URL = "https://download.pytorch.org/torchaudio/tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav"
-SPEECH_FILE = "_assets/speech.wav"
+SPEECH_FILE = torchaudio.utils.download_asset("tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav")
 
-if not os.path.exists(SPEECH_FILE):
-    os.makedirs("_assets", exist_ok=True)
-    with open(SPEECH_FILE, "wb") as file:
-        file.write(requests.get(SPEECH_URL).content)
 
 ######################################################################
 # Generate frame-wise label probability
@@ -156,8 +152,12 @@ def get_trellis(emission, tokens, blank_id=0):
     # Trellis has extra diemsions for both time axis and tokens.
     # The extra dim for tokens represents <SoS> (start-of-sentence)
     # The extra dim for time axis is for simplification of the code.
-    trellis = torch.full((num_frame + 1, num_tokens + 1), -float("inf"))
-    trellis[:, 0] = 0
+    trellis = torch.empty((num_frame + 1, num_tokens + 1))
+    trellis[0, 0] = 0
+    trellis[1:, 0] = torch.cumsum(emission[:, 0], 0)
+    trellis[0, -num_tokens:] = -float("inf")
+    trellis[-num_tokens:, 0] = float("inf")
+
     for t in range(num_frame):
         trellis[t + 1, 1:] = torch.maximum(
             # Score for staying at the same token
@@ -250,7 +250,8 @@ def backtrack(trellis, emission, tokens, blank_id=0):
 
 
 path = backtrack(trellis, emission, tokens)
-print(path)
+for p in path:
+    print(p)
 
 
 ################################################################################
@@ -449,6 +450,8 @@ plot_alignments(
 )
 plt.show()
 
+################################################################################
+#
 
 # A trick to embed the resulting audio to the generated file.
 # `IPython.display.Audio` has to be the last call in a cell,
@@ -458,10 +461,9 @@ def display_segment(i):
     word = word_segments[i]
     x0 = int(ratio * word.start)
     x1 = int(ratio * word.end)
-    filename = f"_assets/{i}_{word.label}.wav"
-    torchaudio.save(filename, waveform[:, x0:x1], bundle.sample_rate)
     print(f"{word.label} ({word.score:.2f}): {x0 / bundle.sample_rate:.3f} - {x1 / bundle.sample_rate:.3f} sec")
-    return IPython.display.Audio(filename)
+    segment = waveform[:, x0:x1]
+    return IPython.display.Audio(segment.numpy(), rate=bundle.sample_rate)
 
 
 ######################################################################