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--- |
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language: ja |
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tags: |
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- audio |
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- automatic-speech-recognition |
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license: apache-2.0 |
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--- |
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# Kotoba-Whisper: kotoba-whisper-v2.0 for Whisper cpp |
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This repository contains the model weights for [kotoba-tech/kotoba-whisper-v2.0](https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0) |
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converted to [GGML](https://github.com/ggerganov/ggml) format. GGML is the weight format expected by C/C++ packages |
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such as [Whisper.cpp](https://github.com/ggerganov/whisper.cpp), for which we provide an example below. |
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## Usage |
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Kotoba-Whisper can be run with the [Whisper.cpp](https://github.com/ggerganov/whisper.cpp) package with the original |
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sequential long-form transcription algorithm. |
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Steps for getting started: |
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1. Clone the Whisper.cpp repository: |
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``` |
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git clone https://github.com/ggerganov/whisper.cpp.git |
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cd whisper.cpp |
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``` |
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2. Download the GGML weights for `kotoba-tech/kotoba-whisper-v2.0`: |
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```bash |
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wget https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0-ggml/resolve/main/ggml-kotoba-whisper-v2.0.bin -P ./models |
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``` |
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3. Run inference using the provided sample audio: |
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```bash |
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wget https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0-ggml/resolve/main/sample_ja_speech.wav |
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make -j && ./main -m models/ggml-kotoba-whisper-v2.0.bin -f sample_ja_speech.wav --output-file transcription --output-json |
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``` |
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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: |
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``` |
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ffmpeg -i input.mp3 -ar 16000 -ac 1 -c:a pcm_s16le output.wav |
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``` |
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### Benchmark |
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Please refer to the [kotoba-tech/kotoba-whisper-v1.0-ggml](https://huggingface.co/kotoba-tech/kotoba-whisper-v1.0-ggml) for the detail of speed up. |
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### Quantized Model |
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To use the quantized model, download the quantized GGML weights: |
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```bash |
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wget https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0-ggml/resolve/main/ggml-kotoba-whisper-v2.0-q5_0.bin -P ./models |
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``` |
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Run inference on the sample audio: |
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```bash |
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make -j && ./main -m models/ggml-kotoba-whisper-v2.0-q5_0.bin -f sample_ja_speech.wav --output-file transcription.quantized --output-json |
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``` |
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Note that the benchmark results are almost identical to the raw non-quantized model weight. |
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### Conversion details |
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The original model was converted with the following command: |
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``` |
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# clone OpenAI whisper and whisper.cpp |
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git clone https://github.com/openai/whisper |
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git clone https://github.com/ggerganov/whisper.cpp |
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# get the models |
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cd whisper.cpp/models |
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git clone https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0 |
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# convert to ggml |
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python3 ./convert-h5-to-ggml.py ./kotoba-whisper-v2.0/ ../../whisper . |
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mv ggml-model.bin ggml-kotoba-whisper-v2.0 |
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# quantize ggml model |
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cd ../ |
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./quantize models/ggml-kotoba-whisper-v2.0.bin models/ggml-kotoba-whisper-v2.0-q5_0.bin q5_0 |
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``` |
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## Model Details |
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For more information about the kotoba-whisper-v2.0, refer to the original [model card](https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0). |
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