kotoba-tech/kotoba-whisper-bilingual-v1.0-faster

Whisper kotoba-whisper-bilingual-v1.0 model for CTranslate2

This repository contains the conversion of kotoba-tech/kotoba-whisper-bilingual-v1.0 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/datasets/japanese-asr/en_asr.esb_eval/resolve/main/sample.wav -O sample_en.wav
wget https://huggingface.co/datasets/japanese-asr/ja_asr.jsut_basic5000/resolve/main/sample.flac -O sample_ja.flac

Inference with the kotoba-whisper-bilingual-v1.0-faster.

from faster_whisper import WhisperModel

model = WhisperModel("kotoba-tech/kotoba-whisper-bilingual-v1.0-faster")

# Japanese ASR
segments, info = model.transcribe("sample_ja.flac", language="ja", task="transcribe", condition_on_previous_text=False)
for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))

# English ASR
segments, info = model.transcribe("sample_en.wav", language="en", task="transcribe", chunk_length=15, condition_on_previous_text=False)
for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))

# Japanese (speech) to English (text) Translation
segments, info = model.transcribe("sample_ja.flac", language="en", task="translate", chunk_length=15, condition_on_previous_text=False)
for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))

# English (speech) to Japanese (text) Translation
segments, info = model.transcribe("sample_en.wav", language="ja", task="translate", 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:

• 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-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.

More information

For more information about the kotoba-whisper-v2.0, refer to the original model card.