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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- speech_commands |
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metrics: |
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- accuracy |
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model-index: |
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- name: wav2vec2-speechcommonds-kws |
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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: speech_commands |
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type: speech_commands |
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config: v0.02 |
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split: test |
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args: v0.02 |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9854975457385096 |
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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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# wav2vec2-speechcommonds-kws |
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This model was trained from scratch on the speech_commands dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0613 |
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- Accuracy: 0.9855 |
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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: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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: 0.1 |
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- num_epochs: 5 |
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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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| 1.0871 | 1.0 | 741 | 0.3374 | 0.9810 | |
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| 0.5168 | 2.0 | 1482 | 0.1022 | 0.9866 | |
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| 0.4113 | 3.0 | 2223 | 0.0766 | 0.9853 | |
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| 0.3622 | 4.0 | 2964 | 0.0670 | 0.9859 | |
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| 0.3454 | 5.0 | 3705 | 0.0613 | 0.9855 | |
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### Framework versions |
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- Transformers 4.36.1 |
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- Pytorch 2.1.1 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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