README.md

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

# Whisper kotoba-whisper-v1.0 model for CTranslate2

This repository contains the conversion of [kotoba-tech/kotoba-whisper-v1.0](https://huggingface.co/kotoba-tech/kotoba-whisper-v1.0) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format.

This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/systran/faster-whisper).

## Example
Install library and download sample audio.
```shell
pip install faster-whisper
wget https://huggingface.co/kotoba-tech/kotoba-whisper-v1.0-ggml/resolve/main/sample_ja_speech.wav
```
Inference with the kotoba-whisper-v1.0-faster.

```python
from faster_whisper import WhisperModel

model = WhisperModel("kotoba-tech/kotoba-whisper-v1.0-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))
```

## Conversion details

The original model was converted with the following command:

```
ct2-transformers-converter --model kotoba-tech/kotoba-whisper-v1.0 --output_dir kotoba-whisper-v1.0-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](https://opennmt.net/CTranslate2/quantization.html).

## More information

**For more information about the original model, see its [model card](https://huggingface.co/distil-whisper/distil-large-v2).**