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--- |
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library_name: transformers |
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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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- balbus-classifier |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: miosipof/whisper-small-ft-balbus-sep28k-v1.6 |
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results: |
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- task: |
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name: Audio Classification |
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type: audio-classification |
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dataset: |
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name: Apple dataset |
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type: balbus-classifier |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8100908806016922 |
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- name: Precision |
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type: precision |
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value: 0.8183656957928802 |
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- name: Recall |
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type: recall |
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value: 0.7261306532663316 |
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- name: F1 |
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type: f1 |
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value: 0.7694941042221377 |
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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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# miosipof/whisper-small-ft-balbus-sep28k-v1.6 |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co./openai/whisper-small) on the Apple dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1091 |
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- Accuracy: 0.8101 |
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- Precision: 0.8184 |
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- Recall: 0.7261 |
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- F1: 0.7695 |
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- Roc-auc: 0.8006 |
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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: 2e-06 |
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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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- gradient_accumulation_steps: 2 |
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- total_train_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_ratio: 0.5 |
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- training_steps: 1200 |
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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 | Accuracy | Precision | Recall | F1 | Roc-auc | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:| |
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| 0.1683 | 0.2506 | 200 | 0.1682 | 0.5730 | 0.7364 | 0.0341 | 0.0652 | 0.5123 | |
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| 0.1494 | 0.5013 | 400 | 0.1446 | 0.7084 | 0.6603 | 0.6838 | 0.6718 | 0.7056 | |
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| 0.1212 | 0.7519 | 600 | 0.1236 | 0.7629 | 0.6917 | 0.8245 | 0.7523 | 0.7699 | |
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| 0.1088 | 1.0025 | 800 | 0.1107 | 0.8062 | 0.8337 | 0.6945 | 0.7578 | 0.7936 | |
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| 0.0955 | 1.2531 | 1000 | 0.1106 | 0.8081 | 0.8036 | 0.7416 | 0.7713 | 0.8006 | |
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| 0.0997 | 1.5038 | 1200 | 0.1091 | 0.8101 | 0.8184 | 0.7261 | 0.7695 | 0.8006 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.2.0 |
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- Datasets 3.2.0 |
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- Tokenizers 0.20.3 |
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