metadata
language:
- ja
tags:
- audio
- automatic-speech-recognition
- whisper
- ctranslate2
- faster-whisper
base_model: efwkjn/whisper-ja-1.5B
pipeline_tag: automatic-speech-recognition
library_name: ctranslate2
whisper-ja-1.5B-ct2
CTranslate2 conversion of efwkjn/whisper-ja-1.5B with bfloat16 weights, for use with faster-whisper.
The original model is a Whisper large-v3 finetune for Japanese ASR, achieving competitive/SOTA CER across tested sets. See the original repo for details and benchmarks.
Usage
from faster_whisper import WhisperModel
model = WhisperModel("TransWithAI/whisper-ja-1.5B-ct2", device="cuda", compute_type="bfloat16")
segments, info = model.transcribe("audio.wav", language="ja")
for segment in segments:
print(f"[{segment.start:.2f} -> {segment.end:.2f}] {segment.text}")
Conversion
Converted with CTranslate2 4.7.2:
ct2-transformers-converter \
--model efwkjn/whisper-ja-1.5B \
--output_dir whisper-ja-1.5B-ct2 \
--quantization bfloat16 \
--copy_files tokenizer.json preprocessor_config.json
Acknowledgements
All credit for the model goes to efwkjn. Acknowledgements from the original model card:
- Train sets: OOPPEENN, Reazon, 小虫哥_, Common Voice 20, deepghs
- Test sets: KitsuneX07, TEDxJP, kotoba-tech, Saruwatari-lab, grider-withourai