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Btw, did anyone notice they removed access to "who starred the project" recently. You can only check your own repos, not external ones. It was very nice to find related people before.



From the sources it is based on BL606P

https://en.bouffalolab.com/product/?type=detail&id=16

voice processing is in hardware unfortunately, but it exposes some things like DOA


You'd better use offline stt/tts for this.


Kroko models are more accurate and their size is just a hundred megabytes compared to parakeet (2.5 gigabytes in default fp32)


Do you have a link to results confirming this? Kroko does not seem to be on the Open ASR Leaderboard. Parakeet has an average WER of 6.32 across several common datasets.


Kroko's website says benchmarks aren't formalized yet. FWIW, this url says 5% WER for English [0]. though it doesn't specify the dataset, so not directly comparable to Parakeet's 6.32 on the Open ASR Leaderboard

Best way to judge is to try it on your own audio

[0] https://huggingface.co/hudaiapa88/sherpa-stt-onnx


> Running a family was a brutal two-person job -- and the kids had to dive in to help out the second they could lift something heavier than a couple pounds.

Orphanes did struggle but most families were not just two person, families were big and supported by community.


You can check whale sound recognition project https://arxiv.org/abs/2104.08614


And moreover, you can not tune those models for practical applications. The model is originally trained on very clean data, so lower layers are also not very stable for diverse inputs. To finetune you have to update the whole model, not just upper layers.


In section 5.7.5, they fine-tune for "11 low-resource languages, with between 5-10 hours of training data and at least 1 hour of validation splits." "CTC fine-tuning takes ≈1 hour of walltime on 32 GPUs for the 300M scale." If that's too expensive, you also have the option of supplying additional context for the LLM-based model (section 5.5).

As for "very clean data," see section 5.7.4: "Omnilingual + OMSF ASR was intentionally curated to represent naturalistic (i.e., often noisy) audio conditions, diverse speaker identities, and spontaneous, expressive speech."


This model is actually expected to be bad for popular languages, just like previous MMS it is not accurate at all, it wins by supporting something rare well but never had good ASR accuracy even for Swedish etc. It is more a research thing than a real tool. Unlike Whisper.


It is useless actually. Very slow and quality is suboptimal and it is just speech generation component. See discussion here:

https://github.com/SesameAILabs/csm/issues/80


No, there are mathematical reasons LLMs are better. They are trained with multiobjective loss (coding skills, translation skills, etc) so they understand the world much better than MLM. Original post discuss that but with more words and points than necessary.


GPTs also get gradients from all tokens, BERT only on 15% masked tokens. GPTs are more effective.


Call it a CLM vs MLM, not LLM vs MLM. Soon LMLM's will exist, which will be LLMs too...


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