Add Audio Spectogram Transformer (#19981)

* First draft

* Make conversion script work

* Add id2label mapping, run code quality

* Fix copies

* Add first draft of feature extractor

* Update conversion script to use feature extractor

* Make more tests pass

* Add docs

* update input_features to input_values + pad by default to max length

* Fix doc tests

* Add feature extractor tests

* Add proper padding/truncation to feature extractor

* Add support for conversion of all audioset checkpoints

* Improve docs and extend conversion script

* Fix README

* Rename spectogram to spectrogram

* Fix copies

* Add integration test

* Remove dummy conv

* Update to ast

* Update organization

* Fix init

* Rename model to AST

* Add require_torchaudio annotator

* Move import of ASTFeatureExtractor under a is_speech_available

* Fix rebase

* Add pipeline config

* Update name of classifier head

* Rename time_dimension and frequency_dimension for clarity

* Remove print statement

* Fix pipeline test

* Fix pipeline test

* Fix index table

* Fix init

* Fix conversion script

* Rename to ForAudioClassification

* Fix index table

Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

tests/pipelines/test_pipelines_common.py

@@ -155,6 +155,12 @@ def get_tiny_feature_extractor_from_checkpoint(checkpoint, tiny_config, feature_
     if hasattr(tiny_config, "image_size") and feature_extractor:
         feature_extractor = feature_extractor.__class__(size=tiny_config.image_size, crop_size=tiny_config.image_size)
 
+    # Audio Spectogram Transformer specific.
+    if feature_extractor.__class__.__name__ == "ASTFeatureExtractor":
+        feature_extractor = feature_extractor.__class__(
+            max_length=tiny_config.max_length, num_mel_bins=tiny_config.num_mel_bins
+        )
+
     # Speech2TextModel specific.
     if hasattr(tiny_config, "input_feat_per_channel") and feature_extractor:
         feature_extractor = feature_extractor.__class__(