GitLab

* Input/Output tweaks for YAMNet and VGGish.

- Waveform input for YAMNet is now padded so that we get at least
  one patch of log mel spectrogram. The VGGish TF-Hub exporter
  uses YAMNet's feature computation so the VGGish export will
  also pad waveform input similarly.
- Added a 1024-D embedding output to YAMNet so we now produce
  predicted scores, log mel spectrogram features, and embeddings,
  to satisfy a variety of uses: class prediction, acoustic
  feature visualization, semantic feature extraction.
- Simplified usage of YAMNet in inference mode. Instead of trying
  to work around implicit batch size issues in the Model.predict()
  API, we simply __call__() the Model.
- Switched inference.py to TF 2 and Eager execution.
- Updated the visualization notebook: now uses TF2/Eager and
  can be loaded and run in Google Colab.

* Responded to DAn's comments in https://github.com/tensorflow/models/pull/9092

- Merged spectrogram computation and framing into a single function
  that returns both spectrogram and framed features.
- Extended waveform padding to pad up to an integral number of hops
  in addition to the final STFT analysis window.

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Add ops for extracting labels from sentences (get_*_labels() functions). This CL includes the ops for extracting labels for:
- BERT's next sentence prediction task
- ALBERT's sentence order prediction task

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* Added a TF-Hub-compatible SavedModel exporter for VGGish.

* Responded to DAn's comments, switched linspace to arange everywhere.

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Fixed a long-standing bug where the released VGGish model used
post-activation embedding output while the released embeddings
were pre-activation. There are still discrepancies due to
other reasons: differences in choice of YouTube transcode,
repeated resamplings with different resamplers, slight differences
in feature computation, etc.

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* Made VGGish and YAMNet work in TF2 without disabling TF2 behavior.

Allowed TF2 behavior and allowed passing in a features tensor into the
VGGish model definition. Both of these changes are needed for making
TF-Hub exports of these models. Lifted constraints on TF versions since
tf_slim has been updated to work with TF 2.

* Responded to DAn's comments in https://github.com/tensorflow/models/pull/9077

* Fixed typo in comment.

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The example config has b.ccc.a1 = 1.

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This is the likely reason for jobs that take forever.

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Move mock_task to utils/testing/

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