[BUG] the vq result is not well

[didadida-r]

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Hi,

I debug the gpt result and finally find that the have some problem in the vq stage,

`mispronounce`:  the pronouncation is not right in the vq stage with Self reduction
新的轮回，便会开始 --》 新的轮回，定会开始

`timber change`: the reproduced audio in vq, the timber will be different with the origin audio, 

Can you share some ideas about these cases and how to optimize it. Is making the ar4 or ar8 will help? or changing to vq with vq loss

Thanks 

To Reproduce
Steps to reproduce the behavior:

git clone lastest fish code and latest offical model

download the demo page audio as input_audio_path

python tools/vqgan/inference.py \
        -i "$input_audio_path" \
        -o "$vq_restore_wav_path" \
        -ckpt "$vqgan_path"

Expected behavior
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Additional context
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[leng-yue]

That's why we introduced the VITS decoder, which greatly reduced mispronounce and increased timbre similarity.

[didadida-r]

Thanks, I'm curious as to why the vq and vits modules are separated. Would there be a specific reason not to combine them into a single module, similar to the approach taken with gpt-sovits? and adding hubert module?

[didadida-r]

That's why we introduced the VITS decoder, which greatly reduced mispronounce and increased timbre similarity.

Is the issues with mispronunciation and timber change attributing to a low token rate? As these problems do not seem to be as apparent in codec and dac