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--- |
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license: apache-2.0 |
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base_model: facebook/hubert-base-ls960 |
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tags: |
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- audio-classification |
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- hubert |
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- esc50 |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: hubert-esc50-finetuned-v2 |
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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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# hubert-esc50-finetuned-v2 |
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This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the ESC-50 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9551 |
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- Accuracy: 0.85 |
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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: 5e-05 |
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- train_batch_size: 8 |
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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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- num_epochs: 100 |
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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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| 3.5337 | 1.0 | 200 | 3.4929 | 0.0775 | |
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| 3.1679 | 2.0 | 400 | 3.1355 | 0.1675 | |
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| 2.8042 | 3.0 | 600 | 2.8673 | 0.2075 | |
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| 2.5055 | 4.0 | 800 | 2.6202 | 0.2125 | |
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| 2.0268 | 5.0 | 1000 | 2.3768 | 0.3375 | |
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| 2.1337 | 6.0 | 1200 | 2.0171 | 0.4225 | |
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| 1.6061 | 7.0 | 1400 | 1.7294 | 0.5075 | |
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| 1.5169 | 8.0 | 1600 | 1.8017 | 0.5025 | |
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| 1.0634 | 9.0 | 1800 | 1.5051 | 0.5475 | |
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| 0.9651 | 10.0 | 2000 | 1.3431 | 0.635 | |
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| 0.8616 | 11.0 | 2200 | 1.3417 | 0.6375 | |
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| 0.6799 | 12.0 | 2400 | 1.2891 | 0.63 | |
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| 0.445 | 13.0 | 2600 | 1.2285 | 0.6575 | |
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| 0.2984 | 14.0 | 2800 | 1.2008 | 0.7125 | |
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| 0.5947 | 15.0 | 3000 | 1.3225 | 0.71 | |
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| 0.4194 | 16.0 | 3200 | 1.1032 | 0.775 | |
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| 0.3128 | 17.0 | 3400 | 1.8309 | 0.6625 | |
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| 0.237 | 18.0 | 3600 | 1.3349 | 0.7325 | |
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| 0.1701 | 19.0 | 3800 | 1.4491 | 0.7275 | |
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| 0.2618 | 20.0 | 4000 | 1.4919 | 0.7525 | |
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| 0.1336 | 21.0 | 4200 | 1.6088 | 0.7325 | |
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| 0.113 | 22.0 | 4400 | 1.3687 | 0.7725 | |
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| 0.0757 | 23.0 | 4600 | 1.4691 | 0.7875 | |
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| 0.0558 | 24.0 | 4800 | 1.8059 | 0.7525 | |
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| 0.1442 | 25.0 | 5000 | 1.7809 | 0.7475 | |
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| 0.1023 | 26.0 | 5200 | 1.8423 | 0.7875 | |
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| 0.0075 | 27.0 | 5400 | 1.7945 | 0.79 | |
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| 0.0054 | 28.0 | 5600 | 1.8221 | 0.7825 | |
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| 0.0584 | 29.0 | 5800 | 1.7593 | 0.785 | |
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| 0.07 | 30.0 | 6000 | 1.8601 | 0.7925 | |
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| 0.0827 | 31.0 | 6200 | 1.8467 | 0.7875 | |
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| 0.1128 | 32.0 | 6400 | 2.1020 | 0.765 | |
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| 0.2679 | 33.0 | 6600 | 2.0718 | 0.775 | |
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| 0.0647 | 34.0 | 6800 | 1.9542 | 0.7875 | |
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| 0.0376 | 35.0 | 7000 | 2.1877 | 0.7675 | |
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| 0.0019 | 36.0 | 7200 | 2.4088 | 0.745 | |
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| 0.1009 | 37.0 | 7400 | 2.2295 | 0.765 | |
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| 0.0039 | 38.0 | 7600 | 2.0022 | 0.7825 | |
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| 0.0006 | 39.0 | 7800 | 2.0640 | 0.795 | |
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| 0.0512 | 40.0 | 8000 | 2.3373 | 0.78 | |
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| 0.0282 | 41.0 | 8200 | 1.9908 | 0.795 | |
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| 0.0113 | 42.0 | 8400 | 2.3893 | 0.775 | |
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| 0.035 | 43.0 | 8600 | 2.3017 | 0.7775 | |
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| 0.006 | 44.0 | 8800 | 2.1261 | 0.7825 | |
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| 0.0556 | 45.0 | 9000 | 2.3122 | 0.775 | |
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| 0.0003 | 46.0 | 9200 | 2.1505 | 0.79 | |
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| 0.0115 | 47.0 | 9400 | 2.0387 | 0.805 | |
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| 0.0001 | 48.0 | 9600 | 2.1915 | 0.8 | |
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| 0.2299 | 49.0 | 9800 | 2.6715 | 0.76 | |
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| 0.0017 | 50.0 | 10000 | 2.7250 | 0.755 | |
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| 0.2944 | 51.0 | 10200 | 2.5766 | 0.79 | |
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| 0.1269 | 52.0 | 10400 | 2.3590 | 0.785 | |
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| 0.0941 | 53.0 | 10600 | 2.9789 | 0.755 | |
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| 0.0477 | 54.0 | 10800 | 2.7512 | 0.75 | |
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| 0.2068 | 55.0 | 11000 | 2.5162 | 0.7725 | |
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| 0.0004 | 56.0 | 11200 | 2.4355 | 0.7525 | |
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| 0.0657 | 57.0 | 11400 | 2.5043 | 0.7775 | |
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| 0.0002 | 58.0 | 11600 | 2.4236 | 0.785 | |
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| 0.0133 | 59.0 | 11800 | 2.4225 | 0.78 | |
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| 0.0 | 60.0 | 12000 | 2.3476 | 0.79 | |
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| 0.0159 | 61.0 | 12200 | 2.3234 | 0.7975 | |
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| 0.0002 | 62.0 | 12400 | 2.3763 | 0.78 | |
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| 0.0626 | 63.0 | 12600 | 2.0386 | 0.835 | |
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| 0.0112 | 64.0 | 12800 | 2.3345 | 0.81 | |
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| 0.0004 | 65.0 | 13000 | 2.3710 | 0.8075 | |
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| 0.0714 | 66.0 | 13200 | 2.0527 | 0.82 | |
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| 0.0008 | 67.0 | 13400 | 2.2063 | 0.8175 | |
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| 0.0001 | 68.0 | 13600 | 2.5772 | 0.795 | |
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| 0.0001 | 69.0 | 13800 | 2.4176 | 0.7975 | |
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| 0.0001 | 70.0 | 14000 | 2.1132 | 0.8125 | |
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| 0.0017 | 71.0 | 14200 | 2.2163 | 0.8125 | |
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| 0.2347 | 72.0 | 14400 | 2.0444 | 0.8275 | |
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| 0.0 | 73.0 | 14600 | 2.3745 | 0.8275 | |
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| 0.0001 | 74.0 | 14800 | 2.0128 | 0.8325 | |
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| 0.0037 | 75.0 | 15000 | 2.0867 | 0.8375 | |
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| 0.0 | 76.0 | 15200 | 2.2285 | 0.825 | |
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| 0.0001 | 77.0 | 15400 | 2.0214 | 0.8425 | |
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| 0.0001 | 78.0 | 15600 | 2.4193 | 0.82 | |
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| 0.0002 | 79.0 | 15800 | 2.4296 | 0.815 | |
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| 0.1198 | 80.0 | 16000 | 2.3698 | 0.8175 | |
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| 0.0001 | 81.0 | 16200 | 2.3521 | 0.82 | |
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| 0.0 | 82.0 | 16400 | 2.1241 | 0.8325 | |
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| 0.0001 | 83.0 | 16600 | 2.1642 | 0.8275 | |
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| 0.0005 | 84.0 | 16800 | 2.0545 | 0.835 | |
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| 0.0 | 85.0 | 17000 | 2.0386 | 0.8475 | |
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| 0.0003 | 86.0 | 17200 | 2.1348 | 0.83 | |
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| 0.0004 | 87.0 | 17400 | 2.2024 | 0.83 | |
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| 0.0 | 88.0 | 17600 | 2.1521 | 0.835 | |
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| 0.0001 | 89.0 | 17800 | 2.2244 | 0.83 | |
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| 0.0 | 90.0 | 18000 | 2.1535 | 0.8325 | |
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| 0.0 | 91.0 | 18200 | 2.2048 | 0.835 | |
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| 0.1711 | 92.0 | 18400 | 2.1023 | 0.83 | |
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| 0.0 | 93.0 | 18600 | 2.0534 | 0.845 | |
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| 0.0 | 94.0 | 18800 | 2.0220 | 0.845 | |
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| 0.0 | 95.0 | 19000 | 2.0061 | 0.845 | |
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| 0.0001 | 96.0 | 19200 | 1.9270 | 0.8475 | |
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| 0.0001 | 97.0 | 19400 | 1.9710 | 0.84 | |
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| 0.0001 | 98.0 | 19600 | 1.9561 | 0.845 | |
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| 0.0 | 99.0 | 19800 | 1.9567 | 0.845 | |
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| 0.0 | 100.0 | 20000 | 1.9551 | 0.85 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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