Fix bugs of s2t fairseq model converting
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This fix bugs in s2t fairseq model converting into HF one.

Fixes
- Fix typo in add_argument
- Fix `_Missing key(s) in state_dict_` error while loading fairseq model weights into HF model. This specifcally fixes issue cased by fairseq model's fixed positional embedding in both encoder and decoder.

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Models:

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The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_15593). All of your documentation changes will be reflected on that endpoint.<|||||>>  @patil-suraj it's also possible that the position embedding weights are present when converting from Fairseq no? Or are they always missing?

Sinusoidal embeddings in fairseq are not registered as parameters, so they are always missing from the `state_dict`<|||||>> > @patil-suraj it's also possible that the position embedding weights are present when converting from Fairseq no? Or are they always missing?
> 
> Sinusoidal embeddings in fairseq are not registered as parameters, so they are always missing from the `state_dict`

@patrickvonplaten good point, technically positional embedding weights can be present if fairseq model is trained using learned embedding. Say if you give `learned=True`  as a 4th argument in [PositionalEmbedding function](https://github.com/pytorch/fairseq/blob/5f2515e676985efd2944f6c87fe1b086cb94bdee/fairseq/modules/positional_embedding.py#L12~L16)  which is being called in [encoder](https://github.com/pytorch/fairseq/blob/5f2515e676985efd2944f6c87fe1b086cb94bdee/fairseq/models/speech_to_text/s2t_transformer.py#L331~#L333) and [decoder](https://github.com/pytorch/fairseq/blob/5f2515e676985efd2944f6c87fe1b086cb94bdee/fairseq/models/transformer/transformer_decoder.py#L97~#L103).
@patil-suraj , kindly correct me if I am wrong.

So in short, depending on training h-parameter settings, `missing` lists can be 4 cases.
`[], ["encoder.embed_positions.weights"], ["decoder.embed_positions.weights"], ["encoder.embed_positions.weights", "decoder.embed_positions.weights"]`, although if the model is trained straight-forward way(using fixed positional embedding), both of encoder, decoder positional embedding weights must be missing.

Based on @patrickvonplaten suggestion, I have modifed to code to cover all thoses cases.
Thank you for suggestion.

Please review reflected changes @patil-suraj @patrickvonplaten 
<|||||>hmm I kind of did something wrong with git by force-push ing it. Please let me know if it can be a problem :( <|||||>@patil-suraj could you take a final look here and merge if ok?<|||||>Hey guys, I have trained s2t model with fairseq and for some reason conversion still fails. https://github.com/huggingface/transformers/blob/main/src/transformers/models/speech_to_text/convert_s2t_fairseq_to_tfms.py#L56 here it seems args is actually none. It seems that ['cfg']['model'] is a thing code actually requires. with this change conversion is successful but I am missing preprocessor_config.json can anyone help me with this one?<|||||>@patil-suraj could you take a quick look here?<|||||>Also while we are at it, conversion does not take into account about model.vocab file, the easiest way I find possible to successfully transcribe audio with hf was to copy spm_unigram10000.vocab and rename it to vocab.json, also grapped this one as a pre processor https://huggingface.co/facebook/s2t-small-librispeech-asr/blob/main/preprocessor_config.json. I think vocab should be saved with fairseq pt or script should require its path at least...  also Speech2TextForConditionalGeneration  generate function does not take input_ids it takes inputs. I will be happy to help in any way ;)