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--- |
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license: apache-2.0 |
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base_model: openai/whisper-large-v3 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: whisper-large-v3-atcosim |
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results: [] |
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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-large-v3-atcosim |
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0573 |
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- Wer: 15.7807 |
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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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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 32 |
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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: 250 |
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- training_steps: 12500 |
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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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| 0.0031 | 8.33 | 1000 | 0.0372 | 54.8342 | |
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| 0.0005 | 16.67 | 2000 | 0.0415 | 20.1519 | |
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| 0.0024 | 25.0 | 3000 | 0.0392 | 10.2102 | |
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| 0.0 | 33.33 | 4000 | 0.0469 | 18.6609 | |
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| 0.0 | 41.67 | 5000 | 0.0493 | 17.3180 | |
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| 0.0 | 50.0 | 6000 | 0.0511 | 16.8179 | |
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| 0.0 | 58.33 | 7000 | 0.0526 | 16.4753 | |
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| 0.0 | 66.67 | 8000 | 0.0538 | 16.5725 | |
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| 0.0 | 75.0 | 9000 | 0.0550 | 15.9983 | |
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| 0.0 | 83.33 | 10000 | 0.0560 | 15.7205 | |
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| 0.0 | 91.67 | 11000 | 0.0568 | 15.7159 | |
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| 0.0 | 100.0 | 12000 | 0.0573 | 15.7807 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.14.1 |
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