ValueFX9507/Tifa-DeepsexV2-7b-MGRPO-GGUF-Q4
Reinforcement Learning
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Ayaan Sharif
Ayaan-Sharif
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NLP, LLM, TEXT, Languages
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sanchit-gandhi/whisper-jax:The whisper jax demo is not working. Error messages
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The whisper jax demo is not working. Error messages
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#18 opened almost 2 years ago
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ray608
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what if we segment the audio first and then transcribe tho its some extra compute to throw in but imo it would resul tin better result !
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Panda-70M is a new large-scale video dataset comprising 70 million high-quality video clips, each paired with textual captions, designed to be used as pre-training for video understanding tasks.
Key Points:
* Automatic Caption Generation: Utilizes an automatic pipeline with multiple cross-modality teacher models to generate captions for video clips.
* Fine-tuned Caption Selection: Employs a fine-tuned retrieval model to select the most appropriate caption from multiple candidates for each video clip.
* Improved Performance: Pre-training on Panda-70M shows significant performance gains in video captioning, text-video retrieval, and text-driven video generation.
Paper: Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers (2402.19479)
Project page: https://snap-research.github.io/Panda-70M/
Code: https://github.com/snap-research/Panda-70M
Congrats to the authors @tschen , @aliaksandr-siarohin et al. for their work!
Key Points:
* Automatic Caption Generation: Utilizes an automatic pipeline with multiple cross-modality teacher models to generate captions for video clips.
* Fine-tuned Caption Selection: Employs a fine-tuned retrieval model to select the most appropriate caption from multiple candidates for each video clip.
* Improved Performance: Pre-training on Panda-70M shows significant performance gains in video captioning, text-video retrieval, and text-driven video generation.
Paper: Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers (2402.19479)
Project page: https://snap-research.github.io/Panda-70M/
Code: https://github.com/snap-research/Panda-70M
Congrats to the authors @tschen , @aliaksandr-siarohin et al. for their work!
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