@jolurf you can also use this decoder (GitHub - parlance/ctcdecode: PyTorch CTC Decoder bindings 66). Take the labels from your tokenizer and create a n-gram language model with KenLM. After that you can feed the logits from your Wav2Vec2 model into the decoder.
@patrickvonplaten Are there any updates on the transformer language model?
Voidful suggested to combine the wav2vec2 probabilities with those of the gpt2 model:
( huggingface_notebook/xlsr_gpt.ipynb at main · voidful/huggingface_notebook · GitHub 45 )
However, the CTC Vocab seems to match the GPT Vocab. Unfortunately, this is not the case in English. Is there already a solution?