Qwen/Qwen2.5-Omni-7B
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fireblade2534
fireblade2534
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To Meta AI Research: I would like to fold
ylacombe/expresso into the training mix of an Apache TTS model series. Can you relax the Expresso dataset license to CC-BY or more permissive?
Barring that, can I have an individual exception to train on the materials and distribute trained Apache models, without direct redistribution of the original files? Thanks!
CC (Expresso paper authors whose handles I could find on HF) @wnhsu @adavirro @bowenshi @itaigat @TalRemez @JadeCopet @hassid @felixkreuk @adiyoss @edupoux
Barring that, can I have an individual exception to train on the materials and distribute trained Apache models, without direct redistribution of the original files? Thanks!
CC (Expresso paper authors whose handles I could find on HF) @wnhsu @adavirro @bowenshi @itaigat @TalRemez @JadeCopet @hassid @felixkreuk @adiyoss @edupoux
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📢 Our EMOVA paper has been accepted by CVPR 2025, and we are glad to release all resources, including code (training & inference), datasets (training & evaluation), and checkpoints (EMOVA-3B/7B/72B)!
🤗 EMOVA is a novel end-to-end omni-modal LLM that can see, hear and speak. Given omni-modal (i.e., textual, visual and speech) inputs, EMOVA can generate both textual and speech responses with vivid emotional controls by utilizing the speech decoder and a style controller.
✨ EMOVA Highlights
✅ State-of-the-art omni-modality: EMOVA achieves SoTA comparable results on both vision-language and speech benchmarks simultaneously.
✅ Device adaptation: our codebase supports training/inference on both NVIDIA GPUs (e.g., A800 & H20) and Ascend NPUs (e.g., 910B3)!
✅ Modular design: we integrate multiple implementations of vision encoder, vision projector, and language model, even including the most recent DeepSeekMoE-tiny!
🔥 You are all welcome to try and star!
- Project page: https://emova-ollm.github.io/
- Github: https://github.com/emova-ollm/EMOVA
- Demo: Emova-ollm/EMOVA-demo
🤗 EMOVA is a novel end-to-end omni-modal LLM that can see, hear and speak. Given omni-modal (i.e., textual, visual and speech) inputs, EMOVA can generate both textual and speech responses with vivid emotional controls by utilizing the speech decoder and a style controller.
✨ EMOVA Highlights
✅ State-of-the-art omni-modality: EMOVA achieves SoTA comparable results on both vision-language and speech benchmarks simultaneously.
✅ Device adaptation: our codebase supports training/inference on both NVIDIA GPUs (e.g., A800 & H20) and Ascend NPUs (e.g., 910B3)!
✅ Modular design: we integrate multiple implementations of vision encoder, vision projector, and language model, even including the most recent DeepSeekMoE-tiny!
🔥 You are all welcome to try and star!
- Project page: https://emova-ollm.github.io/
- Github: https://github.com/emova-ollm/EMOVA
- Demo: Emova-ollm/EMOVA-demo