Create README.md

README.md

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+---
+language: ja
+tags:
+  - audio
+  - automatic-speech-recognition
+license: apache-2.0
+---
+
+# Kotoba-Whisper: kotoba-whisper-v2.0 for Whisper cpp
+This repository contains the model weights for [kotoba-tech/kotoba-whisper-v2.0](https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0)
+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-v2.0`:
+
+```bash
+wget https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0-ggml/resolve/main/ggml-kotoba-whisper-v2.0.bin -P ./models
+```
+
+3. Run inference using the provided sample audio:
+
+```bash
+wget https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0-ggml/resolve/main/sample_ja_speech.wav
+make -j && ./main -m models/ggml-kotoba-whisper-v2.0.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
+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.
+
+### Quantized Model
+To use the quantized model, download the quantized GGML weights:
+
+```bash
+wget https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0-ggml/resolve/main/ggml-kotoba-whisper-v2.0-q5_0.bin -P ./models
+```
+
+Run inference on the sample audio:
+```bash
+make -j && ./main -m models/ggml-kotoba-whisper-v2.0-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.
+
+### Conversion details
+The original model was converted with the following command:
+
+```
+# clone OpenAI whisper and whisper.cpp
+git clone https://github.com/openai/whisper
+git clone https://github.com/ggerganov/whisper.cpp
+
+# get the models
+cd whisper.cpp/models
+git clone https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0
+
+# convert to ggml
+python3 ./convert-h5-to-ggml.py ./kotoba-whisper-v2.0/ ../../whisper .
+mv ggml-model.bin ggml-kotoba-whisper-v2.0
+
+# quantize ggml model
+cd ../
+./quantize models/ggml-kotoba-whisper-v2.0.bin models/ggml-kotoba-whisper-v2.0-q5_0.bin q5_0
+```
+
+## Model Details
+
+For more information about the kotoba-whisper-v2.0, refer to the original [model card](https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0).