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Yes, we actually did research and spent a significant amount of GPU time. Thanks to my luck of stumbling into the right people at the right time, I could afford

Yes, we actually did research and spent a significant amount of GPU time. Thanks to my luck of stumbling into the right people at the right time, I could afford cloud-scale training because OVH granted me very generous rebates... The main innovation is that we prevent loss mis-allocation during training of the acoustic AI model by pre-weighting things with the loss of the language model. Or in short: We don't train what you don't need to hear If you want to play around with the TEVR tokenizer design, here's the source for that: https://huggingface.co/fxtentacle/tevr-token-entropy-predictor-de https://huggingface.co/fxtentacle/tevr-token-entropy-predict...

cgeier 4y ago
> The main innovation is that we prevent loss mis-allocation during training of the acoustic AI model by pre-weighting things with the loss of the language model. Or in short: > We don't train what you don't need to hear This does sound a lot more interesting than the ~280 lines of code.
- fxtentacle 4y ago
  For a researcher, yes. But for understanding the trick there, you need to have read and understood the CTC loss paper. For people like my industry clients, on the other hand, "code that is easy to audit and easy to install" is a core feature. They don't care about the research, they just want to make audio files search-able.
tialaramex 4y ago
So does this means the recogniser would be worse at recognising unexpected utterances, which is roughly what you'd see with human recognition ? What's the German equivalent of "How to Wreck a Nice Beach"?
- yorwba 4y ago
  Eishockey, Kanufahren, Wirsing. (Alles okay. Kann noch fahren. Wiedersehen!)
- fxtentacle 4y ago
  Yes and no. With perfect audio quality, it'll write down almost verbatim what you said. But as the audio gets more noisy, it'll shift more and more towards the most likely interpretation.

cgeier 4y ago

> The main innovation is that we prevent loss mis-allocation during training of the acoustic AI model by pre-weighting things with the loss of the language model. Or in short: > We don't train what you don't need to hear This does sound a lot more interesting than the ~280 lines of code.

fxtentacle 4y ago
For a researcher, yes. But for understanding the trick there, you need to have read and understood the CTC loss paper. For people like my industry clients, on the other hand, "code that is easy to audit and easy to install" is a core feature. They don't care about the research, they just want to make audio files search-able.

tialaramex 4y ago

So does this means the recogniser would be worse at recognising unexpected utterances, which is roughly what you'd see with human recognition ? What's the German equivalent of "How to Wreck a Nice Beach"?

yorwba 4y ago
Eishockey, Kanufahren, Wirsing. (Alles okay. Kann noch fahren. Wiedersehen!)
fxtentacle 4y ago
Yes and no. With perfect audio quality, it'll write down almost verbatim what you said. But as the audio gets more noisy, it'll shift more and more towards the most likely interpretation.