Create README.md

README.md

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+---
+language: ja
+tags:
+  - audio
+  - automatic-speech-recognition
+license: mit
+library_name: ctranslate2
+---
+
+# Whisper kotoba-whisper-bilingual-v1.0 model for CTranslate2
+
+This repository contains the conversion of [kotoba-tech/kotoba-whisper-bilingual-v1.0](https://huggingface.co/kotoba-tech/kotoba-whisper-bilingual-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-v2.0-faster.
+
+```python
+from faster_whisper import WhisperModel
+
+model = WhisperModel("kotoba-tech/kotoba-whisper-v2.0-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))
+```
+
+### Benchmark
+We measure the inference speed of different kotoba-whisper-v2.0 implementations 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)| [whisper.cpp](https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0-ggml) (sec) | [faster-whisper](https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0-faster) (sec)| [hf pipeline](https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0) (sec)
+|--------|------|-----|------|-----|
+|audio 1 | 50.3 | 581 | 2601 | 807 |
+|audio 2 | 5.6  | 41  | 73   | 61  |
+|audio 3 | 4.9  | 30  | 141  | 54  |
+|audio 4 | 5.6  | 35  | 126  | 69  |
+
+Scripts to re-run the experiment can be found bellow:
+* [whisper.cpp](https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0-ggml/blob/main/benchmark.sh)
+* [faster-whisper](https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0-faster/blob/main/benchmark.sh)
+* [hf pipeline](https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0/blob/main/benchmark.sh)
+Also, currently whisper.cpp and faster-whisper support the [sequential long-form decoding](https://huggingface.co/distil-whisper/distil-large-v3#sequential-long-form),
+and only Huggingface pipeline supports the [chunked long-form decoding](https://huggingface.co/distil-whisper/distil-large-v3#chunked-long-form), which we empirically
+ found better than the sequnential long-form decoding.
+
+
+
+## Conversion details
+
+The original model was converted with the following command:
+
+```
+ct2-transformers-converter --model kotoba-tech/kotoba-whisper-bilingual-v1.0 --output_dir kotoba-whisper-bilingual-v1.0-faster --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 kotoba-whisper-v2.0, refer to the original [model card](https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0).