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--- |
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language: |
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- ar |
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license: apache-2.0 |
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base_model: openai/whisper-base |
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tags: |
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- whisper |
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- Arabic |
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- AR |
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- speech to text |
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- stt |
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- transcription |
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datasets: |
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- mozilla-foundation/common_voice_16_0 |
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- BelalElhossany/mgb2_audios_transcriptions_non_overlap |
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- nadsoft/Jordan-Audio |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper base arabic |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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metrics: |
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- name: Wer |
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type: wer |
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value: 34.7 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Whisper base arabic |
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It achieves the following results on the evaluation set: |
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- Loss: 0.44 |
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- Wer: 34.7 |
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## Training and evaluation data |
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Train set: |
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- mozilla-foundation/common_voice_16_0 ar [train+validation] |
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- BelalElhossany/mgb2_audios_transcriptions_non_overlap |
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- nadsoft/Jordan-Audio |
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cross validation set: |
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600 samples in total from the 3 sets to save time during training as colab free tier was used to train the model. |
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note: evaluate accuracy in the way you see fit. |
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## Training procedure |
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removed arabic (حركات) from the texts. |
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trained the model on the combined dataset for 6 epochs, the best one being the fifth so the model is basically the 5th epoch. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 1 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:| |
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| 0.4603 | 1 | 1437 | 0.4931 | 45.8857 | |
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| 0.2867 | 2 | 2874 | 0.4493 | 36.9973 | |
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| 0.2494 | 3 | 4311 | 0.4219 | 43.5553 | |
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| 0.1435 | 4 | 5748 | 0.4408 | 40.2351 | |
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| 0.1345 | 5 | 7185 | 0.4407 | 34.7081 | |
