Allow user-managed Pool in Wav2Vec2ProcessorWithLM.batch_decode (#18351)

* [Wav2Vec2] Allow user-managed Pool in Wav2Vec2ProcessorWithLM.batch_decode

* [Wav2Vec2] Add user-managed LM's pool tests and usage examples

* Improve styling

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

* [Wav2Vec2] Fix hyperlink references

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

docs/source/en/model_doc/wav2vec2.mdx

@@ -73,6 +73,61 @@ This model was contributed by [patrickvonplaten](https://huggingface.co/patrickv
     - batch_decode
     - decode
 
+### Decoding multiple audios
+
+If you are planning to decode multiple batches of audios, you should consider using [`~Wav2Vec2ProcessorWithLM.batch_decode`] and passing an instantiated `multiprocessing.Pool`.
+Otherwise, [`~Wav2Vec2ProcessorWithLM.batch_decode`] performance will be slower than calling [`~Wav2Vec2ProcessorWithLM.decode`] for each audio individually, as it internally instantiates a new `Pool` for every call. See the example below:
+
+```python
+>>> # Let's see how to use a user-managed pool for batch decoding multiple audios
+>>> from multiprocessing import get_context
+>>> from transformers import AutoTokenizer, AutoProcessor, AutoModelForCTC
+>>> from datasets import load_dataset
+>>> import datasets
+>>> import torch
+
+>>> # import model, feature extractor, tokenizer
+>>> model = AutoModelForCTC.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm").to("cuda")
+>>> processor = AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm")
+
+>>> # load example dataset
+>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+>>> dataset = dataset.cast_column("audio", datasets.Audio(sampling_rate=16_000))
+
+
+>>> def map_to_array(batch):
+...     batch["speech"] = batch["audio"]["array"]
+...     return batch
+
+
+>>> # prepare speech data for batch inference
+>>> dataset = dataset.map(map_to_array, remove_columns=["audio"])
+
+
+>>> def map_to_pred(batch, pool):
+...     inputs = processor(batch["speech"], sampling_rate=16_000, padding=True, return_tensors="pt")
+...     inputs = {k: v.to("cuda") for k, v in inputs.items()}
+
+...     with torch.no_grad():
+...         logits = model(**inputs).logits
+
+...     transcription = processor.batch_decode(logits.cpu().numpy(), pool).text
+...     batch["transcription"] = transcription
+...     return batch
+
+
+>>> # note: pool should be instantiated *after* `Wav2Vec2ProcessorWithLM`.
+>>> #       otherwise, the LM won't be available to the pool's sub-processes
+>>> # select number of processes and batch_size based on number of CPU cores available and on dataset size
+>>> with get_context("fork").Pool(processes=2) as pool:
+...     result = dataset.map(
+...         map_to_pred, batched=True, batch_size=2, fn_kwargs={"pool": pool}, remove_columns=["speech"]
+...     )
+
+>>> result["transcription"][:2]
+['MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL', "NOR IS MISTER COULTER'S MANNER LESS INTERESTING THAN HIS MATTER"]
+```
+
 ## Wav2Vec2 specific outputs
 
 [[autodoc]] models.wav2vec2_with_lm.processing_wav2vec2_with_lm.Wav2Vec2DecoderWithLMOutput