README.md

metadata

language: ar
pipeline_tag: automatic-speech-recognition
tags:
  - CTC
  - Attention
  - pytorch
  - Transformer
license: cc-by-nc-4.0
datasets:
  - MGB-3
  - egyptian-arabic-conversational-speech-corpus
metrics:
  - wer
model-index:
  - name: omarxadel/hubert-large-arabic-egyptian
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        metrics:
          - name: Test WER
            type: wer
            value: 25.9
          - name: Validation WER
            type: wer
            value: 23.5

Arabic Hubert-Large - with CTC fine-tuned on MGB-3 and Egyptian Arabic Conversational Speech Corpus (No LM)

This model is a fine-tuned version of Arabic Hubert-Large. We finetuned this model on the MGB-3 and Egyptian Arabic Conversational Speech Corpus datasets, acheiving a state of the art for Egyptian Arabic with WER of 25.9%.

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 for more details on the model.

The performance of the model on the datasets is the following:

Valid WER	Test WER
23.55	25.59

Acknowledgement

Model fine-tuning and data processing for this work were performed as a part of a Graduation Project from Faculty of Engineering, Alexandria University, CCE Program.