---
language:
- ar
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper
- Arabic
- AR
- speech to text
- stt
- transcription
datasets:
- mozilla-foundation/common_voice_16_0
- BelalElhossany/mgb2_audios_transcriptions_non_overlap
- nadsoft/Jordan-Audio
metrics:
- wer
model-index:
- name: Whisper base arabic
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    metrics:
    - name: Wer
      type: wer
      value: 34.7
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper base arabic
It achieves the following results on the evaluation set:
- Loss: 0.44
- Wer: 34.7 

## Training and evaluation data
Train set:
- mozilla-foundation/common_voice_16_0 ar [train+validation]
- BelalElhossany/mgb2_audios_transcriptions_non_overlap
- nadsoft/Jordan-Audio

cross validation set:
600 samples in total from the 3 sets to save time during training as colab free tier was used to train the model. 
note: evaluate accuracy in the way you see fit. 


## Training procedure
removed arabic (حركات) from the texts.
trained the model on the combined dataset for 6 epochs, the best one being the fifth so the model is basically the 5th epoch.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 1
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500


### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4603        | 1     | 1437 | 0.4931          | 45.8857 |
| 0.2867        | 2     | 2874 | 0.4493          | 36.9973 |
| 0.2494        | 3     | 4311 | 0.4219          | 43.5553 |
| 0.1435        | 4     | 5748 | 0.4408          | 40.2351 |
| 0.1345        | 5     | 7185 | 0.4407          | 34.7081 |