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--- |
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library_name: transformers |
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language: |
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- ee |
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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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- generated_from_trainer |
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datasets: |
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- dodziraynard/ugspeechdata-ewe |
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metrics: |
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- wer |
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model-index: |
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- name: UG Speech Data ASR - Ewe nornmaliser |
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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: ugspeechdata-ewe |
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type: dodziraynard/ugspeechdata-ewe |
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metrics: |
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- name: Wer |
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type: wer |
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value: 38.34905660377358 |
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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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# UG Speech Data ASR - Ewe nornmaliser |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the ugspeechdata-ewe dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5273 |
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- Wer Ortho: 46.0461 |
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- Wer: 38.3491 |
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- Cer: 13.0384 |
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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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### 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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: constant_with_warmup |
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- lr_scheduler_warmup_steps: 50 |
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- training_steps: 4000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Cer | Validation Loss | Wer | Wer Ortho | |
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|:-------------:|:------:|:----:|:-------:|:---------------:|:-------:|:---------:| |
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| 0.5022 | 0.4785 | 400 | 15.0475 | 0.5773 | 44.5732 | 52.3734 | |
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| 0.4835 | 0.9569 | 800 | 13.6924 | 0.5142 | 40.5166 | 48.4899 | |
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| 0.3764 | 1.4354 | 1200 | 13.2187 | 0.4926 | 38.7241 | 47.1020 | |
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| 0.3624 | 1.9139 | 1600 | 12.8324 | 0.4770 | 37.8553 | 46.0811 | |
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| 0.3165 | 2.3923 | 2000 | 0.4770 | 45.1081 | 37.1660 | 12.5025 | |
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| 0.3058 | 2.8708 | 2400 | 0.4728 | 45.5634 | 37.5822 | 12.8574 | |
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| 0.2386 | 3.3493 | 2800 | 0.4945 | 45.8291 | 38.0272 | 12.8462 | |
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| 0.2334 | 3.8278 | 3200 | 0.4874 | 45.7743 | 38.0440 | 12.8868 | |
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| 0.1662 | 4.3062 | 3600 | 0.5242 | 46.6003 | 38.5020 | 12.9679 | |
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| 0.1615 | 4.7847 | 4000 | 0.5273 | 46.0461 | 38.3491 | 13.0384 | |
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### Framework versions |
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- Transformers 4.48.0 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 4.0.0 |
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- Tokenizers 0.21.4 |
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