Adding batch_size support for (almost) all pipelines (#13724)

* Tentative enabling of `batch_size` for pipelines.

* Add systematic test for pipeline batching.

* Enabling batch_size on almost all pipelines

- Not `zero-shot` (it's already passing stuff as batched so trickier)
- Not `QA` (preprocess uses squad features, we need to switch to real
tensors at this boundary.

* Adding `min_length_for_response` for conversational.

* Making CTC, speech mappings avaiable regardless of framework.

* Attempt at fixing automatic tests (ffmpeg not enabled for fast tests)

* Removing ffmpeg dependency in tests.

* Small fixes.

* Slight cleanup.

* Adding docs

and adressing comments.

* Quality.

* Update docs/source/main_classes/pipelines.rst

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/pipelines/question_answering.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/pipelines/zero_shot_classification.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Improving docs.

* Update docs/source/main_classes/pipelines.rst

Co-authored-by: Philipp Schmid <32632186+philschmid@users.noreply.github.com>

* N -> oberved_batch_size

softmax trick.

* Follow `padding_side`.

* Supporting image pipeline batching (and padding).

* Rename `unbatch` -> `loader_batch`.

* unbatch_size forgot.

* Custom padding for offset mappings.

* Attempt to remove librosa.

* Adding require_audio.

* torchaudio.

* Back to using datasets librosa.

* Adding help to set a pad_token on the tokenizer.

* Update src/transformers/pipelines/base.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/pipelines/base.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/pipelines/base.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Quality.

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Philipp Schmid <32632186+philschmid@users.noreply.github.com>

tests/test_pipelines_audio_classification.py

@@ -24,6 +24,7 @@ from transformers.testing_utils import (
     require_datasets,
     require_tf,
     require_torch,
+    require_torchaudio,
     slow,
 )
 
@@ -35,15 +36,16 @@ from .test_pipelines_common import ANY, PipelineTestCaseMeta
 class AudioClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
     model_mapping = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
 
-    @require_datasets
-    @slow
-    def run_pipeline_test(self, model, tokenizer, feature_extractor):
-        import datasets
-
+    def get_test_pipeline(self, model, tokenizer, feature_extractor):
         audio_classifier = AudioClassificationPipeline(model=model, feature_extractor=feature_extractor)
 
         # test with a raw waveform
         audio = np.zeros((34000,))
+        audio2 = np.zeros((14000,))
+        return audio_classifier, [audio2, audio]
+
+    def run_pipeline_test(self, audio_classifier, examples):
+        audio2, audio = examples
         output = audio_classifier(audio)
         # by default a model is initialized with num_labels=2
         self.assertEqual(
@@ -61,10 +63,17 @@ class AudioClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
             ],
         )
 
+        self.run_torchaudio(audio_classifier)
+
+    @require_datasets
+    @require_torchaudio
+    def run_torchaudio(self, audio_classifier):
+        import datasets
+
         # test with a local file
         dataset = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
-        filename = dataset[0]["file"]
-        output = audio_classifier(filename)
+        audio = dataset[0]["audio"]["array"]
+        output = audio_classifier(audio)
         self.assertEqual(
             output,
             [