Model reference · open weights
japanese-hubert-k2 is an open-weight embedding model from reazon-research. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Maker | reazon-research |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Parameters (lead) | 94M |
| Runs with | transformers |
| Released | 2025-09-04 |
| Popularity | 525 downloads / month |
| Licence | Open weights |
About
This is a Japanese Hubert Base model pre-trained on ReazonSpeech v2.0 corpus using the k2 framework.
This model is converted from the k2 model.
We also release the CTC models, reazon-research/japanese-hubert-base-k2-rs35kh and reazon-research/japanese-hubert-base-k2-rs35kh-bpe, derived from this model.
import librosa
import torch
from transformers import AutoFeatureExtractor, AutoModel
feature_extractor = AutoFeatureExtractor.from_pretrained("reazon-research/japanese-hubert-base-k2")
model = AutoModel.from_pretrained("reazon-research/japanese-hubert-base-k2")
audio, sr = librosa.load(audio_file, sr=16_000)
inputs = feature_extractor(
audio,
return_tensors="pt",
sampling_rate=sr,
)
with torch.inference_mode():
outputs = model(**inputs)
@misc{reazon-research-japanese-hubert-base-k2,
title={japanese-hubert-base-k2},
author={Sasaki, Yuta},
url = {https://huggingface.co/reazon-research/japanese-hubert-base-k2},
year = {2025}
}
@article{yang2024k2ssl,
title={k2SSL: A faster and better framework for self-supervised speech representation learning},
author={Yang, Yifan and Zhuo, Jianheng and Jin, Zengrui and Ma, Ziyang and Yang, Xiaoyu and Yao, Zengwei and Guo, Liyong and Kang, Wei and Kuang, Fangjun and Lin, Long and others},
journal={arXiv preprint arXiv:2411.17100},
year={2024}
}
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys japanese-hubert-k2 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (japanese-hubert-k2 below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"japanese-hubert-k2","input":"text to embed"}'
Create an account — your API key is available in the console. 5M tokens/month currently included with every new account at launch.