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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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model-index: |
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- name: miosipof/whisper-tiny-ft-balbus-sep28k-v1.1 |
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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: 0.7718583516139141 |
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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-tiny-ft-balbus-sep28k-v1.1 |
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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: 0.4870 |
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- Accuracy: 0.7719 |
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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-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: Use 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: linear |
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- lr_scheduler_warmup_ratio: 0.5 |
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- training_steps: 1000 |
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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 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:| |
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| 0.6991 | 0.1253 | 100 | 0.6929 | 0.4616 | |
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| 0.686 | 0.2506 | 200 | 0.6816 | 0.5577 | |
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| 0.6776 | 0.3759 | 300 | 0.6726 | 0.5631 | |
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| 0.6591 | 0.5013 | 400 | 0.6472 | 0.6244 | |
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| 0.6317 | 0.6266 | 500 | 0.6115 | 0.6802 | |
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| 0.5836 | 0.7519 | 600 | 0.5672 | 0.7104 | |
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| 0.5415 | 0.8772 | 700 | 0.5192 | 0.7499 | |
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| 0.4856 | 1.0025 | 800 | 0.4999 | 0.7667 | |
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| 0.4886 | 1.1278 | 900 | 0.4894 | 0.7715 | |
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| 0.4727 | 1.2531 | 1000 | 0.4870 | 0.7719 | |
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### Framework versions |
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- Transformers 4.48.0 |
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- Pytorch 2.2.0 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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