Faster Whisper Small

🚀 Whisper small model for CTranslate2

This repository offers the conversion of the openai/whisper-small model to the CTranslate2 model format. It enables seamless usage in CTranslate2 or related projects like faster-whisper, facilitating efficient automatic speech recognition.

🚀 Quick Start

The model can be effortlessly integrated into projects. Here's a simple example using the faster-whisper library:

from faster_whisper import WhisperModel

model = WhisperModel("small")

segments, info = model.transcribe("audio.mp3")
for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))

💻 Usage Examples

Basic Usage

from faster_whisper import WhisperModel

model = WhisperModel("small")

segments, info = model.transcribe("audio.mp3")
for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))

🔧 Technical Details

Conversion details

The original model was converted using the following command:

ct2-transformers-converter --model openai/whisper-small --output_dir faster-whisper-small \
    --copy_files tokenizer.json --quantization float16

Note that the model weights are saved in FP16. This data type can be modified when loading the model via the compute_type option in CTranslate2.

📚 Documentation

For more information about the original model, see its model card.

📄 License

This project is licensed under the MIT license.

Additional Information

• Supported Languages: en, zh, de, es, ru, ko, fr, ja, pt, tr, pl, ca, nl, ar, sv, it, id, hi, fi, vi, he, uk, el, ms, cs, ro, da, hu, ta, 'no', th, ur, hr, bg, lt, la, mi, ml, cy, sk, te, fa, lv, bn, sr, az, sl, kn, et, mk, br, eu, is, hy, ne, mn, bs, kk, sq, sw, gl, mr, pa, si, km, sn, yo, so, af, oc, ka, be, tg, sd, gu, am, yi, lo, uz, fo, ht, ps, tk, nn, mt, sa, lb, my, bo, tl, mg, as, tt, haw, ln, ha, ba, jw, su
• Tags: audio, automatic-speech-recognition
• Library Name: ctranslate2