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| Model File | Size | Quantization | Description |
|---|---|---|---|
kotoba-whisper-v2.2-ggml.bin | ~1.4 GB | FP16 | Original precision, best quality |
kotoba-whisper-v2.2-ggml-q8_0.bin | ~780 MB | Q8_0 | 8-bit quantization, good balance |
kotoba-whisper-v2.2-ggml-q5_0.bin | ~513 MB | Q5_0 | 5-bit quantization, smaller size |
kotoba-whisper-v2.2-ggml.bin (FP16)kotoba-whisper-v2.2-ggml-q8_0.bin (Q8_0)kotoba-whisper-v2.2-ggml-q5_0.bin (Q5_0)1# Clone and build whisper.cpp
2git clone https://github.com/ggerganov/whisper.cpp
3cd whisper.cpp
4cmake -B build -DCMAKE_BUILD_TYPE=Release
5cmake --build build --config Release
6
7# Download model (example with Q8_0)
8# Place the downloaded .bin file in the models/ directory
9
10# Run inference
11./build/bin/whisper-cli -m models/kotoba-whisper-v2.2-ggml-q8_0.bin -f audio.wav -l ja1from whispercpp import Whisper
2
3w = Whisper.from_pretrained("kotoba-whisper-v2.2-ggml-q8_0.bin")
4result = w.transcribe("audio.wav")
5print(result)convert-h5-to-ggml.py script from whisper.cpp and quantized using the built-in quantize tool.1# Convert to GGML
2python models/convert-h5-to-ggml.py <model_dir> <whisper_repo> <output_dir>
3
4# Quantize to Q8_0
5./build/bin/quantize ggml-model.bin ggml-model-q8_0.bin q8_0
6
7# Quantize to Q5_0
8./build/bin/quantize ggml-model.bin ggml-model-q5_0.bin q5_01@misc{kotoba-whisper,
2 author = {Kotoba Technologies},
3 title = {kotoba-whisper-v2.2},
4 year = {2024},
5 url = {https://huggingface.co/kotoba-tech/kotoba-whisper-v2.2}
6}