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Contribution: Add new audio/speech metrics for generative audio #2464
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Hi! thanks for your contribution!, great first issue! |
Hi @d-caviedes, |
Hi @SkafteNicki Cool. Should I just work on my branch and go for pull request afterwards? |
yes, as soon you feel you want to share your work or need some guidance, please open a draft PR :) |
hello @d-caviedes still interested in contributing? do you need some more guidance? |
Hi yes, I would like to. Actually I have what I think it is a functional WARPQ in my local torchmetrics, but I would need a bit more guidance yes :) |
sure, just ping me on slack or Discord 🦩 |
🚀 Feature
Add new audio metrics for generative audio processing
Motivation
The evaluation of speech processing (denoising, dereverberation and in general enhancement) highly depends on audio metrics. Nowadays, generative AI is heavily used for speech/audio enhancement, becoming the new SOTA. However, the performance evaluation of speech enhancement with generative AI needs of reference/target less metrics that highly correlate with MOS (Mean Opinion Score). Currently implemented metrics do not allow for the correct assessment of generative speech enhancement algorithms (e.g. those based on diffusion or GANs) because they heavily rely on reference/target audio.
Newer metrics, such as DNSMOS, NISQA, CDPAM, WARPQ allow for a fundamented assessment of the performance of such algorithms (they are either reference-less or designed for generative methods). In addition, they have shown outperformance over traditional metrics (PESQ, STOI...) regarding MOS correlation.
Pitch
It would be great to have these metrics included, as they are currently available in scattered repositories
WARPQ
DNSMOS
CDPAM
NISQA
Alternatives
I cannot think of any
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