Downloads · 30 days
51.9K
39% of all-time downloads
sbintuitions/kana-whisper
kana-whisper is a machine learning model from sbintuitions. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Downloads · 30 days
51.9K
39% of all-time downloads
All-time downloads
132K
Public
Parameters
809M
3.2 GB on disk
Likes
20
Trending 1
Click a slice to open those files.
.safetensors3.2 GB · 100%
From the Hugging Face model README
Kana Whisper is a fine-tuned Whisper large-v3-turbo model that transcribes Japanese speech directly into katakana sequences.
This model is the ASR component of the Joyo Kanji Yomi Benchmark, a systematic evaluation framework for kanji-level reading accuracy of Japanese TTS systems. It was developed as part of the Sarashina2.2-TTS project, where it serves as the backbone of the Kana CER (Kana Character Error Rate) metric proposed in our paper.
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
import torch
model_id = "sbintuitions/kana-whisper"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
use_safetensors=True,
).to("cuda")
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
torch_dtype=torch.float16,
device="cuda",
generate_kwargs={"language": "ja", "task": "transcribe"},
)
result = pipe("path/to/audio.wav")
print(result["text"])
# Example output: "キョーワイイテンキデスネ"
@misc{liu2026sarashina22ttstacklingkanjipolyphony,
title={Sarashina2.2-TTS: Tackling Kanji Polyphony in Japanese Speech Generation via Data Scaling and Targeted Data Synthesis},
author={Lianbo Liu and Shiao Zhu and Kai Washizaki and Reo Yoneyama and Haesung Jeon and Mengjie Zhao and Yusuke Fujita and Hao Shi and Nao Yoshida and Yuan Gao and Roman Koshkin and Yukiya Hono and Yui Sudo},
year={2026},
eprint={2606.25369},
archivePrefix={arXiv},
primaryClass={cs.SD},
url={https://arxiv.org/abs/2606.25369},
}