which resulted in different greedy transcripts for 4.5% of English and 6.5% of Japanese
test utterances. In Japanese, some differences changed entire words.
This change:
* uses `log(x + 2^-24)` instead of clamping to the log floor
* uses a symmetric Hann window, equivalent to `torch.hann_window(periodic=False)`
* adds the normalization epsilon to the standard deviation instead of inside the square root
Only the `lfm2a` preprocessor opts into these behaviors. Other audio preprocessors are unchanged.
Tested on top of 84e76d8 using `llama-server` with CUDA and `temperature=0`, compared against
http://github.com/Liquid4All/liquid-audio fp32.
Test set:
* 200 LibriSpeech `test-clean` utterances (EN)
* 200 Common Voice `ja` test utterances (JP)
* identical 16 kHz audio passed to both implementations
| Greedy transcript identical to `liquid-audio` | Without fix | With fix |
| --------------------------------------------- | ----------: | ----------: |
| EN F16 | 191/200 | 200/200 |
| JP F32 | 187/200 | 200/200 |
| JP F16 | 187/200 | 199/200 |
The remaining JP F16 difference is a comma and matches the reference implementation's own bf16
output.
Mel relative L2 error versus `liquid-audio`:
* EN: 3.2% -> ~2e-6 median
* JP: 3.9% -> ~2e-6 median
Yuri Khrustalev
fcc891545b