wav2vec2_with_lm
I refer to the example and run kenlm on the Chinese test set, it is ok ,but the language model weight, no matter how I adjust it, the result remains the same, this is my debugging code

 # dict
    vocabfile = os.path.join(model_path,'vocab.json')
    with open(vocabfile,'r',encoding="utf-8") as f:
        vocab_dict = json.load(f)

    labels = list(vocab_dict.keys())
    # kenlm deocder 

    decoder = build_ctcdecoder(
            labels,
            kenlm_model_path=kenlm_path,
            alpha = alpha,
            beta = beta
            )
    # processor_with_lm 

    processor_with_lm = Wav2Vec2ProcessorWithLM(
             feature_extractor=processor.feature_extractor,
             tokenizer=processor.tokenizer,
             decoder=decoder
             )

    # decode with lm 
    text_tmp = processor_with_lm.batch_decode(logits.numpy()).text[0].split('<s>')
    text_lm = ''.join(text_tmp)
    print ("lm text:{}".format(text_lm))

i  adjust   alpha  beta , how can i do  ？

You can run a hyperparameter search by inferencing a test set with different alpha and beta values. But if you have a small language model there might not be a difference. Maybe use a larger language model or larger test set.<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.<|||||>> I refer to the example and run kenlm on the Chinese test set, it is ok ,but the language model weight, no matter how I adjust it, the result remains the same, this is my debugging code
> 
> # dict
> ```
> vocabfile = os.path.join(model_path,'vocab.json')
> with open(vocabfile,'r',encoding="utf-8") as f:
>     vocab_dict = json.load(f)
> 
> labels = list(vocab_dict.keys())
> # kenlm deocder 
> 
> decoder = build_ctcdecoder(
>         labels,
>         kenlm_model_path=kenlm_path,
>         alpha = alpha,
>         beta = beta
>         )
> # processor_with_lm 
> 
> processor_with_lm = Wav2Vec2ProcessorWithLM(
>          feature_extractor=processor.feature_extractor,
>          tokenizer=processor.tokenizer,
>          decoder=decoder
>          )
> 
> # decode with lm 
> text_tmp = processor_with_lm.batch_decode(logits.numpy()).text[0].split('<s>')
> text_lm = ''.join(text_tmp)
> print ("lm text:{}".format(text_lm))
> ```
> 
> i adjust alpha beta , how can i do ？

Check your kenlm model is char-level or word-level. To wav2vec2_with_lm, it bases `pyctcdecode`, which only support word-level kenlm. If you need to use char-level language model, you need to tune codes of `pyctcdecode`.