jeremy110

@BakingBrains You could consider increasing the dataset to around 1,000 hours. In my own tests, the WER drops to around 18–20% at that point.

@Amarnath1906 Hi~ If you're like me and using a 4090 with only 24GB of memory, you can try my approach. If you have an 80GB GPU, I recommend training the...

@BakingBrains Hi~ Basically, you can also use the scripts provided by NeMo, but at the time I wanted to keep things simple, so I wrote my own version. If you're...

@Chonlasitsk Hi~ Which model are you using? This method only works if you use the same model and only change the vocabulary. So, if it’s 0.6B, you need to initialize...

@Chonlasitsk hi~ If you only want to train Thai, then you just need to change to the Thai vocabulary, and you can train directly. If you want to keep English...

@Chonlasitsk Hi~ I remember that after I changed the vocabulary, it was still able to generate English results normally. I might give you a script later today or tomorrow, and...

@Chonlasitsk hi~ I just tried the v3 model, and it really doesn’t work—I’m not too sure why. Also, I noticed that every time I change the dictionary, the transcription output...

@Chonlasitsk Yes, I guess it’s probably related to the special tokens. But you can still initialize it this way, and after a bit of training, the English part should recover....

@mleharsh2ai Hi~~ 1. If you’re using 80GB of memory, you can unfreeze all parameters. With only 24GB, you’ll need to freeze half of them. 2. I’d recommend using Lhotse—compared to...

如果用cpu推理，那麼這個時間是合理的