---
language: "ar"
pipeline_tag: automatic-speech-recognition
tags:
- CTC
- Attention
- pytorch
- speechbrain
- Transformer
- hf-asr-leaderboard
license: "cc-by-nc-4.0"
datasets:
- commonvoice
metrics:
- wer
- cer
model-index:
- name: asafaya/hubert-large-arabic-ft
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0 ar
type: common_voice
args: ar
metrics:
- name: Test WER
type: wer
value: 17.68
- name: Test CER
type: cer
value: 5.49
- name: Validation WER
type: wer
value: 10.93
- name: Validation CER
type: cer
value: 3.13
---
# Arabic Hubert-Large - with CTC fine-tuned on Common Voice 8.0 (No LM)
This model is a fine-tuned version of [Arabic Hubert-Large](https://huggingface.co/asafaya/hubert-large-arabic). We finetuned this model on the Arabic CommonVoice dataset, acheiving a state of the art for commonvoice arabic test set WER of `17.68%` and CER of `5.49%`.
The original model was pre-trained on 2,000 hours of 16kHz sampled Arabic speech audio. When using the model make sure that your speech input is also sampled at 16Khz, see the original [paper](https://arxiv.org/abs/2106.07447) for more details on the model.
The performance of the model on CommonVoice Arabic 8.0 is the following:
| Valid WER | Valid CER | Test WER | Test CER |
|:---------:|:---------:|:--------:|:--------:|
| 10.93 | 3.13 | 17.68 | 5.49 |
This model is trained using [SpeechBrain](https://speechbrain.github.io).
# Usage
You can try the model using SpeechBrain as follows:
Install SpeechBrain and Transformers:
```
pip install speechbrain transformers
```
Then run the following code:
```python
from speechbrain.pretrained import EncoderASR
asr_model = EncoderASR.from_hparams(source="asafaya/hubert-large-arabic-ft", savedir="pretrained_models/asafaya/hubert-large-arabic-ft")
print(asr_model.transcribe_file("pretrained_models/asafaya/hubert-large-arabic-ft/example.wav"))
> وصلوا واحدا خلف الآخر
```
More about [SpeechBrain](https://speechbrain.github.io).
# License
This work is licensed under [CC BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
# Citation
# Acknowledgement
Model fine-tuning and data processing for in this work were performed at [KUACC](ai.ku.edu.tr/) Cluster.