metadata
language:
- ja
license: mit
tags:
- audio
- speech
- preference
- anime
library_name: transformers
pipeline_tag: audio-classification
AnimeScore
Try the interactive demo: AnimeScore Demo Space.
A learned scorer for anime-like speech style. Given an audio clip, it returns a scalar score; higher is more anime-like.
This is the official Huggingface model repository for the paper "AnimeScore: A Preference-Based Dataset and Framework for Evaluating Anime-Like Speech Style".
For more details, please visit our GitHub Repository.
Checkpoint
We release the HuBERT-based model, which achieved the best performance among the backbones we evaluated (pairwise accuracy 82.4%, AUC 0.908).
| File | Size | Notes |
|---|---|---|
model.safetensors |
~9 MB | Released head weights |
config.json |
— | Model config |
modeling_animescore.py |
— | Custom modeling code (loaded via trust_remote_code=True) |
How to use
pip install -r requirements.txt
import torch, torchaudio
from transformers import AutoModel
device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModel.from_pretrained(
"spellbrush/animescore",
trust_remote_code=True,
).eval().to(device)
wav, sr = torchaudio.load("sample.wav")
if wav.size(0) > 1:
wav = wav.mean(0, keepdim=True) # mono
if sr != 16000:
wav = torchaudio.functional.resample(wav, sr, 16000)
with torch.no_grad():
s = model.score(wav.to(device)).item()
print(f"AnimeScore: {s:.3f}")
Pairwise probability:
sa = model.score(wav_a.to(device))
sb = model.score(wav_b.to(device))
p_a_gt_b = torch.sigmoid(sa - sb).item()
CLI: python example_inference.py --ckpt . --wav sample.wav
or deploy this directory as a HuggingFace Space (SDK = gradio).
Citation
@inproceedings{park2026animescore,
title = {AnimeScore: A Preference-Based Dataset and Framework for
Evaluating Anime-Like Speech Style},
author = {Park, Joonyong and Li, Jerry},
booktitle = {Interspeech},
year = {2026}
}
License
MIT License.