---
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
- int8
- quanto
- faster-whisper
license: apache-2.0
library_name: ctranslate2
---

# Model Card for Model ID

This model is quantized using the [Quanto Python Package](https://github.com/huggingface/quanto) and the [CTranslate2 Python Package](https://github.com/OpenNMT/CTranslate2).
From my early tests:
- Much less GPU memory required
- It seems that performance is on par with the original
- It seems that this combination is faster than just using the CTranslate2 int8 quantization.
Quantization method TBA. 
To use this model, use the faster_whisper module as stated in [the original faster-whisper model](https://huggingface.co/Systran/faster-whisper-large-v3) 

Any benchmark results are appreciated. I probably do not have time to do it myself.
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