Update README.md

README.md

@@ -1,46 +1,44 @@
 ---
+language: "ar"
+pipeline_tag: automatic-speech-recognition
 tags:
-- generated_from_trainer
+- CTC
+- Attention
+- pytorch
+- Transformer
+license: "cc-by-nc-4.0"
+datasets:
+- MGB-3
+- egyptian-arabic-conversational-speech-corpus
+metrics:
+- wer
 model-index:
-- name: hubert-large-arabic-egyptian
-  results: []
+- 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
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# Arabic Hubert-Large - with CTC fine-tuned on MGB-3 and Egyptian Arabic Conversational Speech Corpus (No LM)
 
-# hubert-large-arabic-egyptian
+This model is a fine-tuned version of [Arabic Hubert-Large](https://huggingface.co/asafaya/hubert-large-arabic). 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%`. 
 
-This model was trained from scratch on an unknown dataset.
+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.
 
-## Model description
+The performance of the model on CommonVoice Arabic 8.0 is the following:
 
-More information needed
+| Valid WER | Test WER |
+|:---------:|:--------:|
+|   23.55   |  25.59   | 
 
-## Intended uses & limitations
+# Acknowledgement
 
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 3.0
-
-### Framework versions
-
-- Transformers 4.20.1
-- Pytorch 1.11.0+cu113
-- Datasets 2.3.2
-- Tokenizers 0.12.1
+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.