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jootanehorror
/
faster-whisper-large-v3-turbo-ct2

Automatic Speech Recognition
ctranslate2
audio
Model card Files Community
faster-whisper-large-v3-turbo-ct2
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---
language:
  - 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
  - yue
tags:
  - audio
  - automatic-speech-recognition
license: mit
library_name: ctranslate2
---


# Whisper large-v3 model for CTranslate2

This repository contains the conversion of [whisper-turbo](https://github.com/openai/whisper) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format.


## Example

```python
from huggingface_hub import snapshot_download
from faster_whisper import WhisperModel

repo_id = "jootanehorror/faster-whisper-large-v3-turbo-ct2"
local_dir = "faster-whisper-large-v3-turbo-ct2"
snapshot_download(repo_id=repo_id, local_dir=local_dir, repo_type="model")

model = WhisperModel(local_dir, device='cpu', compute_type='int8')

segments, info = model.transcribe("sample.mp3")

for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
```


## More information

**For more information about the model, see its [official github page](https://github.com/openai/whisper).**