kotoba-whisper-faster

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)

Scripts to re-run the experiment can be found bellow:

• whisper.cpp
• faster-whisper
• hf pipeline

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.