Wave2vec custom training tokenizer bug
## Environment info
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- `transformers` version: master
- Platform: win 10
- Python version: 3.8
- PyTorch version (GPU?): GPU
- Tensorflow version (GPU?):
- Using GPU in script?:
- Using distributed or parallel set-up in script?:

### Who can help
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HF projects:

- nlp datasets: [different repo](https://github.com/huggingface/nlp)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
 -->
@patrickvonplaten 

## Information

Model I am using (Bert, XLNet ...):

The problem arises when using:
*  [X] the official example scripts: (give details below)
wave2vec Training example
* [ ] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [ ] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:
I was just running the wave2vec Training on pretrained base modell with the german common voice dataset. I modified the dataset that it fits into the format of librispeech so there are no changes in the example script.

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## Expected behavior

<!-- A clear and concise description of what you would expect to happen. -->
While Training the wer on eval dataset is exactly 1.
After Training, while evaluation I recognized that the transcribed predictions are unk tokens only and the tokenizer is missing. I am using the pretrained tokenizer that's why.
The words and sentences seems to be correct (counting the same on both sides), only instead of words only the unk tokens are returned after detokenizing.
I will add a notebook on how to fine-tune Wav2Vec2 on languages other than English next week (think I'll also go for the German Common Voice dataset). We only today added the multi-lingual checkpoint, so you probably used the English checkpoint which cannot handle German. If you didn't see the notebook within ~1,2 weeks, please ping me here again<|||||>Thanks for your answer
For understanding, why does the english checkpoint does not support german ?
I didn't see any reason for that, where is the point I was blind at ?<|||||>@flozi00 did you use a default English tokenizer/processor? You need to load a custom tokenizer, e.g. by `tokenizer = Wav2Vec2CTCTokenizer(vocab_file='path/to/custom/vocab.json')`, where vocab_file is the path to vocabulary of German characters. After that you can create a custom Wav2Vec2Processor:
`processor = Wav2Vec2Processor(feature_extractor=Wav2Vec2FeatureExtractor.from_pretrained("facebook/wav2vec2-base"), tokenizer=tokenizer)`

Regarding the model, I'm not completely sure whether you can load the pretrained base model directly or instead convert the fairseq pytorch checkpoint manually. If I understand correctly, the script for converting Wav2Vec2 checkpoint requires letter dictionary (e.g. https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt). So if the Huggingface Transformers Wav2Vec2 model stores this letter dictionary, you probably need to convert the model manually with your own dict.ltr.txt with German letters included, as well as set the vocabulary size during conversion.