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AnyAudio Judge 7B

AnyAudio-Judge-7B is a dynamic rubric-based audio judge built on top of 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...

ArtText modelBasismodellQwen/Qwen2.5-Omni-7BVersionspk_dictVerleger@cucl2CBewertung

Modellquelle

Art
Text model
Dateigröße
253 KB
Basismodell
Qwen/Qwen2.5-Omni-7B
Version
vspk_dict
Aktualisiert
3. Okt. 2026
Verleger
@cucl2
Downloads
185
Parameter
7B
Quelle
Hugging Face

Quellenbeschreibung

AnyAudio-Judge-7B

AnyAudio-Judge-7B is a dynamic rubric-based audio judge built on top of 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 Companion corpus: cucl2/AnyAudio-Judge-Corpus Companion 30B model: 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

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 for the full pipeline including external rubric decomposition.)

License

Apache-2.0, inheriting the license of the base Qwen2.5-Omni-7B model.

Citation

@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}
}

Quellen

1 Quelle
Hugging FacePrimärVersion f939a6512a00cdd112162a045daf6eac697e338f
huggingface.co/cucl2/AnyAudio-Judge-7B/tree/f939a6512a00cdd112162a045daf6eac697e338f

Verifiziert 3. Okt.

Modellartefakte

1 Artefakt

spk_dict.pt

pt · 253 KB · SHA-256 6a05609b28f5…2a18 · Hugging Face

Download file

Quellenauszüge

3 Auszüge

--- 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} } ```

Source context: 185 downloads · 0 likes · Pipeline audio-text-to-text · Library transformers · Repo cucl2/AnyAudio-Judge-7B

Source context: 87 downloads · 0 likes · Pipeline audio-text-to-text · Library transformers · Repo cucl2/AnyAudio-Judge-7B