asahi417's picture
Update README.md
828f80d verified
|
raw
history blame
3.22 kB
metadata
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:

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.