metadata

language: ja
tags:
  - audio
  - automatic-speech-recognition
license: mit
library_name: ctranslate2

Whisper kotoba-whisper-v2.0 model for CTranslate2

This repository contains the conversion of kotoba-tech/kotoba-whisper-v2.0 to the CTranslate2 model format.

This model can be used in CTranslate2 or projects based on CTranslate2 such as faster-whisper.

Example

Install library and download sample audio.

pip install faster-whisper
wget https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0-ggml/resolve/main/sample_ja_speech.wav

Inference with the kotoba-whisper-v2.0-faster.

from faster_whisper import WhisperModel

model = WhisperModel("kotoba-tech/kotoba-whisper-v2.0-faster")

segments, info = model.transcribe("sample_ja_speech.wav", language="ja", chunk_length=15, condition_on_previous_text=False)
for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))

Benchmark

Please refer to the kotoba-tech/kotoba-whisper-v1.0-faster for the detail of speed up here.

Conversion details

The original model was converted with the following command:

ct2-transformers-converter --model kotoba-tech/kotoba-whisper-v2.0 --output_dir kotoba-whisper-v2.0-faster \
    --copy_files tokenizer.json preprocessor_config.json --quantization float16

Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the compute_type option in CTranslate2.

More information

For more information about the kotoba-whisper-v2.0, refer to the original model card.