Regenerate 0.12.1

This incorporates #2629, which is a fix for the latest Sphinx-Gallery.
pytorch · Oct 21, 2022 · dce4f16 · dce4f16
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diff --git a/0.12.1/_sources/tutorials/asr_inference_with_ctc_decoder_tutorial.rst.txt b/0.12.1/_sources/tutorials/asr_inference_with_ctc_decoder_tutorial.rst.txt
diff --git a/0.12.1/_sources/tutorials/audio_data_augmentation_tutorial.rst.txt b/0.12.1/_sources/tutorials/audio_data_augmentation_tutorial.rst.txt
@@ -88,9 +88,9 @@ First, we import the modules and download the audio assets we use in this tutori
 
  .. code-block:: none
 
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@@ -1305,7 +1305,7 @@ Codec aplied:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  14.010 seconds)
+   **Total running time of the script:** ( 0 minutes  14.318 seconds)
 
 
 .. _sphx_glr_download_tutorials_audio_data_augmentation_tutorial.py:

diff --git a/0.12.1/_sources/tutorials/audio_datasets_tutorial.rst.txt b/0.12.1/_sources/tutorials/audio_datasets_tutorial.rst.txt
@@ -172,7 +172,7 @@ Here, we show how to use the
 
  .. code-block:: none
 
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     <IPython.lib.display.Audio object>
     <IPython.lib.display.Audio object>
     <IPython.lib.display.Audio object>
@@ -183,7 +183,7 @@ Here, we show how to use the
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  2.034 seconds)
+   **Total running time of the script:** ( 0 minutes  1.943 seconds)
 
 
 .. _sphx_glr_download_tutorials_audio_datasets_tutorial.py:

diff --git a/0.12.1/_sources/tutorials/audio_feature_augmentation_tutorial.rst.txt b/0.12.1/_sources/tutorials/audio_feature_augmentation_tutorial.rst.txt
@@ -316,7 +316,7 @@ FrequencyMasking
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  1.877 seconds)
+   **Total running time of the script:** ( 0 minutes  1.788 seconds)
 
 
 .. _sphx_glr_download_tutorials_audio_feature_augmentation_tutorial.py:

diff --git a/0.12.1/_sources/tutorials/audio_feature_extractions_tutorial.rst.txt b/0.12.1/_sources/tutorials/audio_feature_extractions_tutorial.rst.txt
@@ -876,7 +876,7 @@ and it is available as :py:func:`torchaudio.functional.compute_kaldi_pitch`.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  7.207 seconds)
+   **Total running time of the script:** ( 0 minutes  6.272 seconds)
 
 
 .. _sphx_glr_download_tutorials_audio_feature_extractions_tutorial.py:

diff --git a/0.12.1/_sources/tutorials/audio_io_tutorial.rst.txt b/0.12.1/_sources/tutorials/audio_io_tutorial.rst.txt
@@ -96,9 +96,9 @@ First, we import the modules and download the audio assets we use in this tutori
 
  .. code-block:: none
 
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@@ -452,7 +452,7 @@ The following examples illustrate this.
 
  .. code-block:: none
 
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+      0%|          | 0.00/110k [00:00<?, ?B/s]    100%|##########| 110k/110k [00:00<00:00, 22.0MB/s]
 
 
 
@@ -635,7 +635,7 @@ the provided data fit
  .. code-block:: none
 
     ----------
-    Source: /tmp/tmpsaca1umy/save_example_default.wav
+    Source: /tmp/tmpqytfyo0t/save_example_default.wav
     ----------
      - File size: 217658 bytes
      - AudioMetaData(sample_rate=16000, num_frames=54400, num_channels=1, bits_per_sample=32, encoding=PCM_F)
@@ -668,7 +668,7 @@ The resulting file occupies half the storage but loses precision
  .. code-block:: none
 
     ----------
-    Source: /tmp/tmps_6bvvwb/save_example_PCM_S16.wav
+    Source: /tmp/tmplij5hkl4/save_example_PCM_S16.wav
     ----------
      - File size: 108844 bytes
      - AudioMetaData(sample_rate=16000, num_frames=54400, num_channels=1, bits_per_sample=16, encoding=PCM_S)
@@ -724,37 +724,37 @@ To name a few:
  .. code-block:: none
 
     ----------
-    Source: /tmp/tmpfdz7yaip/save_example.flac
+    Source: /tmp/tmp2057vo77/save_example.flac
     ----------
      - File size: 37141 bytes
      - AudioMetaData(sample_rate=8000, num_frames=27200, num_channels=1, bits_per_sample=24, encoding=FLAC)
 
     ----------
-    Source: /tmp/tmpfdz7yaip/save_example.vorbis
+    Source: /tmp/tmp2057vo77/save_example.vorbis
     ----------
      - File size: 12588 bytes
      - AudioMetaData(sample_rate=8000, num_frames=27200, num_channels=1, bits_per_sample=0, encoding=VORBIS)
 
     ----------
-    Source: /tmp/tmpfdz7yaip/save_example.sph
+    Source: /tmp/tmp2057vo77/save_example.sph
     ----------
      - File size: 109824 bytes
      - AudioMetaData(sample_rate=8000, num_frames=27200, num_channels=1, bits_per_sample=32, encoding=PCM_S)
 
     ----------
-    Source: /tmp/tmpfdz7yaip/save_example.amb
+    Source: /tmp/tmp2057vo77/save_example.amb
     ----------
      - File size: 108858 bytes
      - AudioMetaData(sample_rate=8000, num_frames=27200, num_channels=1, bits_per_sample=32, encoding=PCM_F)
 
     ----------
-    Source: /tmp/tmpfdz7yaip/save_example.amr-nb
+    Source: /tmp/tmp2057vo77/save_example.amr-nb
     ----------
      - File size: 2008 bytes
      - AudioMetaData(sample_rate=8000, num_frames=27200, num_channels=1, bits_per_sample=0, encoding=AMR_NB)
 
     ----------
-    Source: /tmp/tmpfdz7yaip/save_example.gsm
+    Source: /tmp/tmp2057vo77/save_example.gsm
     ----------
      - File size: 5610 bytes
      - AudioMetaData(sample_rate=8000, num_frames=0, num_channels=1, bits_per_sample=0, encoding=GSM)
@@ -803,7 +803,7 @@ required.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  1.810 seconds)
+   **Total running time of the script:** ( 0 minutes  2.095 seconds)
 
 
 .. _sphx_glr_download_tutorials_audio_io_tutorial.py:

diff --git a/0.12.1/_sources/tutorials/audio_resampling_tutorial.rst.txt b/0.12.1/_sources/tutorials/audio_resampling_tutorial.rst.txt
@@ -810,42 +810,42 @@ To elaborate on the results:
     librosa: 0.9.2
                     downsample (48 -> 44.1 kHz) time (ms)                      
                                                   librosa functional transforms
-    sinc (width 64)                                   NaN      25.22       0.47
-    sinc (width 16)                                   NaN      35.48       0.38
-    kaiser_best                                     33.13      26.47       0.45
-    kaiser_fast                                     17.98      20.25       0.38
+    sinc (width 64)                                   NaN      35.79       0.43
+    sinc (width 16)                                   NaN      26.77       0.45
+    kaiser_best                                     57.25      27.38       0.53
+    kaiser_fast                                     19.67      36.56       0.46
     torchaudio: 0.12.1
     librosa: 0.9.2
                     downsample (16 -> 8 kHz) time (ms)                      
                                                librosa functional transforms
-    sinc (width 64)                                NaN       1.11       0.76
-    sinc (width 16)                                NaN       0.56       0.34
-    kaiser_best                                  12.42       1.27       2.71
-    kaiser_fast                                   4.34       0.72       0.38
+    sinc (width 64)                                NaN       1.21       0.85
+    sinc (width 16)                                NaN       0.70       0.38
+    kaiser_best                                  29.19       7.09      13.77
+    kaiser_fast                                   6.63       0.86       1.35
     torchaudio: 0.12.1
     librosa: 0.9.2
                     upsample (44.1 -> 48 kHz) time (ms)                      
                                                 librosa functional transforms
-    sinc (width 64)                                 NaN      33.37       0.46
-    sinc (width 16)                                 NaN      36.13       0.39
-    kaiser_best                                   33.39      26.42       0.46
-    kaiser_fast                                   18.12      21.50       0.39
+    sinc (width 64)                                 NaN      38.06       0.48
+    sinc (width 16)                                 NaN      28.19       0.39
+    kaiser_best                                   33.69      28.36       0.44
+    kaiser_fast                                   10.11      25.23       0.39
     torchaudio: 0.12.1
     librosa: 0.9.2
                     upsample (8 -> 16 kHz) time (ms)                      
                                              librosa functional transforms
-    sinc (width 64)                              NaN       6.29       0.39
-    sinc (width 16)                              NaN       0.53       0.21
-    kaiser_best                                12.75       0.94       0.39
-    kaiser_fast                                 6.26       0.73       0.23
+    sinc (width 64)                              NaN       0.79       0.36
+    sinc (width 16)                              NaN       0.51       0.20
+    kaiser_best                                21.12       0.96       0.37
+    kaiser_fast                                 4.58       0.68       0.29
 
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  5.029 seconds)
+   **Total running time of the script:** ( 0 minutes  6.002 seconds)
 
 
 .. _sphx_glr_download_tutorials_audio_resampling_tutorial.py:

diff --git a/0.12.1/_sources/tutorials/forced_alignment_tutorial.rst.txt b/0.12.1/_sources/tutorials/forced_alignment_tutorial.rst.txt
@@ -1210,7 +1210,7 @@ perform CTC segmentation for forced alignment.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  3.023 seconds)
+   **Total running time of the script:** ( 0 minutes  3.601 seconds)
 
 
 .. _sphx_glr_download_tutorials_forced_alignment_tutorial.py:

diff --git a/0.12.1/_sources/tutorials/mvdr_tutorial.rst.txt b/0.12.1/_sources/tutorials/mvdr_tutorial.rst.txt
@@ -156,8 +156,8 @@ which was generated with:
 
  .. code-block:: none
 
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+      0%|          | 0.00/1.95M [00:00<?, ?B/s]    100%|##########| 1.95M/1.95M [00:00<00:00, 85.8MB/s]
 
 
 
@@ -856,7 +856,7 @@ We can then obtain the enhanced waveform by passing this output to the
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  2.279 seconds)
+   **Total running time of the script:** ( 0 minutes  2.999 seconds)
 
 
 .. _sphx_glr_download_tutorials_mvdr_tutorial.py:

diff --git a/0.12.1/_sources/tutorials/online_asr_tutorial.rst.txt b/0.12.1/_sources/tutorials/online_asr_tutorial.rst.txt
@@ -144,9 +144,9 @@ which is a Emformer RNN-T model trained on LibriSpeech dataset.
 
  .. code-block:: none
 
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@@ -572,7 +572,7 @@ repeatedly.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  50.720 seconds)
+   **Total running time of the script:** ( 2 minutes  23.720 seconds)
 
 
 .. _sphx_glr_download_tutorials_online_asr_tutorial.py: