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--- |
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language: |
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- pt |
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license: mit |
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base_model: RodrigoLimaRFL/distil-whisper-nurc-sp-fine-tuned |
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tags: |
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- generated_from_trainer |
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datasets: |
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- nilc-nlp/CORAA-MUPE-ASR |
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metrics: |
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- wer |
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model-index: |
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- name: CORAA-MUPE-ASR distil-whisper fine-tuned |
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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: CORAA-MUPE-ASR |
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type: nilc-nlp/CORAA-MUPE-ASR |
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config: default |
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split: test |
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args: 'split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 15.273584751709397 |
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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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# CORAA-MUPE-ASR distil-whisper fine-tuned |
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This model is a fine-tuned version of [RodrigoLimaRFL/distil-whisper-nurc-sp-fine-tuned](https://huggingface.co/RodrigoLimaRFL/distil-whisper-nurc-sp-fine-tuned) on the CORAA-MUPE-ASR dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3488 |
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- Wer: 15.2736 |
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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: 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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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:------:|:----:|:---------------:|:-------:| |
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| 0.4451 | 0.0558 | 1000 | 0.4482 | 18.5598 | |
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| 0.4006 | 0.1116 | 2000 | 0.4095 | 17.3061 | |
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| 0.2992 | 0.1674 | 3000 | 0.3848 | 16.5660 | |
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| 0.2781 | 0.2232 | 4000 | 0.3609 | 15.5857 | |
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| 0.2839 | 0.2790 | 5000 | 0.3488 | 15.2736 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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