language: ja
tags:
- audio
- automatic-speech-recognition
license: mit
library_name: ctranslate2
Whisper kotoba-whisper-v2.0 model for CTranslate2
This repository contains the conversion of kotoba-tech/kotoba-whisper-v2.0 to the CTranslate2 model format.
This model can be used in CTranslate2 or projects based on CTranslate2 such as faster-whisper.
Example
Install library and download sample audio.
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.
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 (sec) | faster-whisper (sec) | hf pipeline (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:
Also, currently whisper.cpp and faster-whisper support the sequential long-form decoding, and only Huggingface pipeline supports the chunked long-form decoding, 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-v2.0 --output_dir kotoba-whisper-v2.0-faster \
--copy_files tokenizer.json preprocessor_config.json --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.
More information
For more information about the kotoba-whisper-v2.0, refer to the original model card.