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--- |
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license: apache-2.0 |
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base_model: openai/whisper-small |
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tags: |
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- audio |
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- automatic-speech-recognition |
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metrics: |
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- wer |
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widget: |
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- example_title: Sample 1 |
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src: sample_ar_1.mp3 |
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- example_title: Sample 2 |
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src: sample_ar_2.mp3 |
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model-index: |
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- name: whisper-small-ar-v2 |
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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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dataset: |
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name: common_voice_16_1 |
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type: common_voice_16_1 |
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config: ar |
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split: test |
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args: ar |
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metrics: |
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- name: Wer |
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type: wer |
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value: 47.726437288634024 |
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language: |
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- ar |
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library_name: transformers |
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pipeline_tag: automatic-speech-recognition |
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datasets: |
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- mozilla-foundation/common_voice_16_1 |
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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-small-ar-v2 |
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This model is for Arabic automatic speech recognition (ASR). It is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Arabic portion of the [mozilla-foundation/common_voice_16_1](https://huggingface.co/datasets/mozilla-foundation/common_voice_16_1) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4007 |
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- Wer: 47.7264 |
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## Model description |
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Whisper model fine-tuned on Arabic data, following the [official tutorial](https://huggingface.co/blog/fine-tune-whisper). |
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## Intended uses & limitations |
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It is recommended to fine-tune and evaluate on your data before using it. |
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## Training and evaluation data |
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Training Data: CommonVoice (v16.1) Arabic train + validation splits |
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Validation Data: CommonVoice (v16.1) Arabic test split |
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## Training procedure |
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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: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 8000 |
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- mixed_precision_training: Native AMP |
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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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| 0.2742 | 0.82 | 1000 | 0.3790 | 275.2463 | |
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| 0.1625 | 1.65 | 2000 | 0.3353 | 228.5252 | |
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| 0.1002 | 2.47 | 3000 | 0.3311 | 238.8858 | |
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| 0.0751 | 3.3 | 4000 | 0.3354 | 158.1532 | |
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| 0.0601 | 4.12 | 5000 | 0.3576 | 48.9285 | |
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| 0.0612 | 4.95 | 6000 | 0.3575 | 47.8937 | |
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| 0.0383 | 5.77 | 7000 | 0.3819 | 46.9085 | |
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| 0.0234 | 6.6 | 8000 | 0.4007 | 47.7264 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
