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Martin Viewegger

Viewegger

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Phr00t/LTX2-Rapid-Merges

reacted to YerbaPage's post with 🔥 33 minutes ago

🔥 SWE-Pruner can save up to 40% of your Claude Code cost without sacrificing performance. Try it out! 📚 https://huggingface.co/papers/2601.16746 💻 https://github.com/Ayanami1314/swe-pruner Drawing inspiration from how human programmers “selectively skim” source code during development and debugging, SWE-Pruner performs task-aware adaptive pruning for long contexts.

reacted to consome2's post with ❤️ 1 day ago

We’ve released two conversational speech datasets from oto on Hugging Face 🤗 Both are based on real, casual, full-duplex conversations, but with slightly different focuses. Dataset 1: Processed / curated subset https://huggingface.co/datasets/otoearth/otoSpeech-full-duplex-processed-141h * Full-duplex, spontaneous multi-speaker conversations * Participants filtered for high audio quality * PII removal and audio enhancement applied * Designed for training and benchmarking S2S or dialogue models Dataset 2: Larger raw(er) release https://huggingface.co/datasets/otoearth/otoSpeech-full-duplex-280h * Same collection pipeline, with broader coverage * More diversity in speakers, accents, and conversation styles * Useful for analysis, filtering, or custom preprocessing experiments We intentionally split the release to support different research workflows: clean and ready-to-use vs. more exploratory and research-oriented use. The datasets are currently private, but we’re happy to approve access requests — feel free to request access if you’re interested. If you’re working on speech-to-speech (S2S) models or are curious about full-duplex conversational data, we’d love to discuss and exchange ideas together. Feedback and ideas are very welcome!

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liked a model 28 minutes ago

Phr00t/LTX2-Rapid-Merges

Image-Text-to-Video • Updated about 12 hours ago • 135

reacted to YerbaPage's post with 🔥 33 minutes ago

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🔥 SWE-Pruner can save up to 40% of your Claude Code cost without sacrificing performance. Try it out!

📚 SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents (2601.16746)
💻 https://github.com/Ayanami1314/swe-pruner

Drawing inspiration from how human programmers “selectively skim” source code during development and debugging, SWE-Pruner performs task-aware adaptive pruning for long contexts.

reacted to consome2's post with ❤️ 1 day ago

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4930

We’ve released two conversational speech datasets from oto on Hugging Face 🤗
Both are based on real, casual, full-duplex conversations, but with slightly different focuses.

Dataset 1: Processed / curated subset
otoearth/otoSpeech-full-duplex-processed-141h
* Full-duplex, spontaneous multi-speaker conversations
* Participants filtered for high audio quality
* PII removal and audio enhancement applied
* Designed for training and benchmarking S2S or dialogue models

Dataset 2: Larger raw(er) release
otoearth/otoSpeech-full-duplex-280h
* Same collection pipeline, with broader coverage
* More diversity in speakers, accents, and conversation styles
* Useful for analysis, filtering, or custom preprocessing experiments

We intentionally split the release to support different research workflows:
clean and ready-to-use vs. more exploratory and research-oriented use.

The datasets are currently private, but we’re happy to approve access requests — feel free to request access if you’re interested.

If you’re working on speech-to-speech (S2S) models or are curious about full-duplex conversational data, we’d love to discuss and exchange ideas together.

Feedback and ideas are very welcome!