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_text_classification.py

@@ -72,9 +72,12 @@ class TextClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTestC
         outputs = text_classifier("Birds are a type of animal")
         self.assertEqual(nested_simplify(outputs), [{"label": "POSITIVE", "score": 0.988}])
 
-    def run_pipeline_test(self, model, tokenizer, feature_extractor):
+    def get_test_pipeline(self, model, tokenizer, feature_extractor):
         text_classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
+        return text_classifier, ["HuggingFace is in", "This is another test"]
 
+    def run_pipeline_test(self, text_classifier, _):
+        model = text_classifier.model
         # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
         valid_inputs = "HuggingFace is in"
         outputs = text_classifier(valid_inputs)