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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_11_0 |
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metrics: |
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- wer |
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model-index: |
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- name: whisper_small-fa_v02 |
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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: mozilla-foundation/common_voice_11_0 fa |
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type: mozilla-foundation/common_voice_11_0 |
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config: fa |
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split: test |
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metrics: |
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- name: Wer |
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type: wer |
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value: 30.9315 |
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language: |
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- fa |
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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-fa_v02 |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 fa dataset. We also did data augmentation using audiomentations library. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2291 |
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- Wer: 30.3423 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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You can Find the notebooks [here](https://github.com/mohammadh128/Persian_ASR). |
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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: 8 |
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- eval_batch_size: 8 |
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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: 5000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Step | Training Loss | Validation Loss | Wer | |
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|:----:|:-------------:|:---------------:|:-------:| |
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| 500 | 1.770700 | 0.476709 | 52.29181| |
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| 1000 | 0.762300 | 0.368512 | 41.83410| |
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| 1500 | 0.645000 | 0.323680 | 37.57881| |
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| 2000 | 0.601900 | 0.297370 | 36.43209| |
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| 2500 | 0.529700 | 0.276422 | 33.52608| |
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| 3000 | 0.523200 | 0.260825 | 31.94485| |
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| 3500 | 0.488400 | 0.249957 | 33.11771| |
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| 4000 | 0.464800 | 0.241462 | 30.34238| |
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| 4500 | 0.440500 | 0.233215 | 31.04969| |
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| 5000 | 0.440500 | 0.229116 | 30.73605| |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.3 |
