datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
XModBenchXModBench
Benchmarking Cross-Modal Capabilities and Consistency in Omni-Language Models
🎉 Accepted at ICLR 2026
What is XModBench?
XModBench is the first tri-modal (audio / vision / text) multiple-choice
QA benchmark explicitly designed to measure cross-modal consistency — does
an omni-language model give the same correct answer when the same semantic
content is presented in different modalities?
Each item is a 4-choice question with a <context>… See the full description on the dataset page: https://huggingface.co/datasets/RyanWW/XModBench.MixEval-X
🚀 Project Page | 📜 arXiv | 👨💻 Github | 🏆 Leaderboard | 📝 blog | 🤗 HF Paper | 𝕏 Twitter
MixEval-X encompasses eight input-output modality combinations and can be further extended. Its data points reflect real-world task distributions. The last grid presents the scores of frontier organizations’ flagship models on MixEval-X, normalized to a 0-100 scale, with MMG tasks using win rates instead of Elo. Section C of the paper presents example data samples and model responses.… See the full description on the dataset page: https://huggingface.co/datasets/MixEval/MixEval-X.Xijinping-TTS-Voicebank
习近平音源
所有声音资料来自公开影像，属于公有领域目前有 1h30m 的截取后声音，足够进行 Fine-tuning
Usage
按句截取
python -m pip install -r requirement.txt
python split.py
新增声音资料后，使用 Whisper 产生带有时间标记的 JSON 档，并手动复制到 ./voice/[FILE].json
export OPENAI_API_KEY="API_KEY_HERE"
python whisper.py ./[FILE].[AUDIO_EXTENSION]
产生 Bert-VITS2 微调所需的 esd.list 档案
python index_to_list.py