--- license: apache-2.0 language: - en - zh library_name: transformers base_model: Qwen/Qwen2.5-Omni-7B tags: - audio - audio-language-model - instruction-following - rubric-based-evaluation - judge-model pipeline_tag: audio-text-to-text --- # AnyAudio-Judge-7B `AnyAudio-Judge-7B` is a **dynamic rubric-based audio judge** built on top of [Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B). It predicts, for each yes/no rubric item describing one verifiable aspect of an audio caption, whether the audio satisfies that aspect — together with a short evidence string. This is the smaller variant of the AnyAudio-Judge family. The larger `AnyAudio-Judge-30B` (initialized from Qwen3-Omni-30B-A3B-Captioner) is the variant reported in the paper. The 7B model is trained on the same SFT corpus and is intended for users who need a more efficient evaluator. > Companion benchmark: [`cucl2/AnyAudio-Judge-Bench`](https://huggingface.co/datasets/cucl2/AnyAudio-Judge-Bench) > Companion corpus: [`cucl2/AnyAudio-Judge-Corpus`](https://huggingface.co/datasets/cucl2/AnyAudio-Judge-Corpus) > Companion 30B model: [`cucl2/AnyAudio-Judge-30B`](https://huggingface.co/cucl2/AnyAudio-Judge-30B) ## Training - **Base**: Qwen2.5-Omni-7B - **Corpus**: 105K (audio, instruction, rubric, CoT) tuples (see `cucl2/AnyAudio-Judge-Corpus`) - **Stage**: full-parameter SFT for 1 epoch - 16 × H20 96GB - per-device batch size 4, grad accumulation 1 - learning rate 1e-5 ## Usage ```python from anyaudio_judge import AnyAudioJudge, decompose_instruction caption = "A gentle, delicate female voice, with soft and smooth pitch, calm and restrained throughout." rubric = decompose_instruction(caption) # external LLM call judge = AnyAudioJudge.from_pretrained("cucl2/AnyAudio-Judge-7B") result = judge.judge("./demo.wav", rubric) print("alignment_score:", result.score) for item in result.items: print(item.question, "->", item.answer) ``` (See the [GitHub repo](https://github.com/) for the full pipeline including external rubric decomposition.) ## License Apache-2.0, inheriting the license of the base Qwen2.5-Omni-7B model. ## Citation ```bibtex @misc{anyaudiojudge2026, title = {AnyAudio-Judge: A Dynamic Rubric-Based Benchmark and Evaluator for Audio Instruction Following}, author = {Anonymous Authors}, year = {2026}, note = {Preprint, under submission} } ```
