Returning n-best hypotheses from Wav2Vec2ProcessorWithLM decoder
### Feature request

Currently, the [Wav2Vec2ProcessorWithLM](https://github.com/huggingface/transformers/blob/ae54e3c3b18bac0832ad62ea9b896dfd52a09850/src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py#L67) decode function returns [only the best hypothesis](https://github.com/huggingface/transformers/blob/ae54e3c3b18bac0832ad62ea9b896dfd52a09850/src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py#L572). Shall we extend its functionality and make it return n-best hypotheses, logit_scores, lm_scores, word_offsets so that people could rescore these hypotheses with a larger LM. 

For example, take a look at [NeMo article](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/asr_language_modeling.html#neural-rescoring) regarding the rescoring of n-best hypotheses.

### Motivation

 I suppose many people use n-gram models during the shallow fusion stage, the n-grams models are a good fit during the beam search because they are fast. People perform the rescoring of the n-best hypotheses with a larger LM (using them during the decoding is too slow so it makes sense to apply them during the rescoring of n-best hypotheses that come out of the ASR system). They fuse the score which comes out of the ASR with the perplexity-like score from the LM. If this external model is trained on the domain data it will drastically improve the WER of the resulting model.

### Your contribution

If it sounds like a good feature to you, that can be potentially adopted let me know and I'll prepare the PR 😃 
cc @sanchit-gandhi <|||||>> 

please, tell me your opinion on this feature :)