README.md

---
language: ja
tags:
  - audio
  - automatic-speech-recognition
license: apache-2.0
---

# Kotoba-Whisper: kotoba-whisper-v1.0 for Whisper cpp
This repository contains the model weights for [kotoba-tech/kotoba-whisper-v1.0](https://huggingface.co/kotoba-tech/kotoba-whisper-v1.0)
converted to [GGML](https://github.com/ggerganov/ggml) format. GGML is the weight format expected by C/C++ packages 
such as [Whisper.cpp](https://github.com/ggerganov/whisper.cpp), for which we provide an example below.

## Usage
Kotoba-Whisper can be run with the [Whisper.cpp](https://github.com/ggerganov/whisper.cpp) package with the original 
sequential long-form transcription algorithm.

Steps for getting started:

1. Clone the Whisper.cpp repository:
```
git clone https://github.com/ggerganov/whisper.cpp.git
cd whisper.cpp
```
2. Download the GGML weights for `kotoba-tech/kotoba-whisper-v1.0`:

```bash
wget https://huggingface.co/kotoba-tech/kotoba-whisper-v1.0-ggml/resolve/main/ggml-kotoba-whisper-v1.0.bin -P ./models
```

3. Run inference using the provided sample audio:

```bash
wget https://huggingface.co/kotoba-tech/kotoba-whisper-v1.0-ggml/resolve/main/sample_ja_speech.wav
make -j && ./main -m models/ggml-kotoba-whisper-v1.0.bin -f sample_ja_speech.wav --output-file transcription --output-json
```

Note that it runs only with 16-bit WAV files, so make sure to convert your input before running the tool. For example, you can use ffmpeg like this:
```
ffmpeg -i input.mp3 -ar 16000 -ac 1 -c:a pcm_s16le output.wav
```

### Benchmark
We measure the inference speed with four different Japanese speech audio on MacBook Pro with the following spec:
- Apple M2 Pro
- 32GB
- 14-inch, 2023
- OS Sonoma Version 14.4.1 (23E224)


| audio file | audio duration (min)| inference time (sec) |
|--|---------------------|-------------|
|audio 1 | 50.3 | 581       |
|audio 2 | 5.6  | 41       |
|audio 3 | 4.9  | 30       |
|audio 4 | 5.6  | 35       |


### Quantized Model
To use the quantized model, download the quantized GGML weights:

```bash
wget https://huggingface.co/kotoba-tech/kotoba-whisper-v1.0-ggml/resolve/main/ggml-kotoba-whisper-v1.0-q5_0.bin -P ./models
```

Run inference on the sample audio:
```bash
make -j && ./main -m models/ggml-kotoba-whisper-v1.0-q5_0.bin -f sample_ja_speech.wav --output-file transcription.quantized --output-json
```

Note that the benchmark results are almost identical to the raw non-quantized model weight.


## Model Details

For more information about the kotoba-whisper-v1.0, refer to the original [model card](https://huggingface.co/kotoba-tech/kotoba-whisper-v1.0).