Hey,

I added some longer generation examples, by enforcing `min_new_tokens`. Definitely can lose itself a bit more but still pretty decent I think :)

Check it out:
pages.cs.huji.ac.il/adiyoss-lab/...

And feel free to generate anything with a single line of code:
github.com/slp-rl/slamkit

We generated samples with a max length, but the model can predict an "end" token before. One could play with sampling params to make the model keep talking:)

I will try get time to generate longer samples, but also encourage everyone to play around themselves. We tried to make it relatively easy🙏

And about this - yes!
We are accepting PRs to add more tokenisers, better optimisers, efficient attention implementations and anything that seems relevant :)

Feel free to reach out 💪

Hey!
Really pleased you liked our work:) I think with the help of the open source community we can push results even further.

About generation length - the model context is 1024~=40 seconds of audio, but we used a setup like TWIST for evaluation. Definitely worth testing longer generations!

🔜🗣️It was shown to be really useful for training SpeechLMs. We are working on some stuff now to hopefully make it even easier. More to come soon!💪

For instance, in my opinion, in this example it feels unlikely that people would use stress to convey these meanings. Happy for all and any suggestions and insights :)

🥇Project page (+leaderboard) - pages.cs.huji.ac.il/adiyoss-lab/...
📜Paper - arxiv.org/abs/2409.07437
💻Code - github.com/slp-rl/salmon
🤗 Data - huggingface.co/datasets/slp...

🪙 I assume sentiment improved because of style tokens (also shown in STSP metric from SpiritLM). I wonder what is limiting performance - data? modelling? tokens? We welcome suggestions and new SLMs!

Great list! I’d be happy to join as well :)