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--- |
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license: bsd-3-clause |
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tags: |
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- generated_from_trainer |
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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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base_model: MIT/ast-finetuned-audioset-10-10-0.4593 |
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model-index: |
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- name: ast-finetuned-audioset-10-10-0.4593_ft_ESC-50_aug_0-1 |
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results: [] |
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--- |
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# ast-finetuned-audioset-10-10-0.4593_ft_ESC-50_aug_0-1 |
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This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on a subset of [ashraq/esc50](https://huggingface.co/datasets/ashraq/esc50) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7391 |
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- Accuracy: 0.9286 |
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- Precision: 0.9449 |
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- Recall: 0.9286 |
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- F1: 0.9244 |
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## Training and evaluation data |
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Training and evaluation data were augmented with audiomentations [GitHub: iver56/audiomentations](https://github.com/iver56/audiomentations) library and the following augmentation methods have been performed based on previous experiments [Elliott et al.: Tiny transformers for audio classification at the edge](https://arxiv.org/pdf/2103.12157.pdf): |
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**Gain** |
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- each audio sample is amplified/attenuated by a random factor between 0.5 and 1.5 with a 0.3 probability |
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**Noise** |
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- a random amount of Gaussian noise with a relative amplitude between 0.001 and 0.015 is added to each audio sample with a 0.5 probability |
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**Speed adjust** |
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- duration of each audio sample is extended by a random amount between 0.5 and 1.5 with a 0.3 probability |
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**Pitch shift** |
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- pitch of each audio sample is shifted by a random amount of semitones selected from the closed interval [-4,4] with a 0.3 probability |
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**Time masking** |
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- a random fraction of lenght of each audio sample in the range of (0,0.02] is erased with a 0.3 probability |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 8 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 9.9002 | 1.0 | 28 | 8.5662 | 0.0 | 0.0 | 0.0 | 0.0 | |
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| 5.7235 | 2.0 | 56 | 4.3990 | 0.0357 | 0.0238 | 0.0357 | 0.0286 | |
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| 2.4076 | 3.0 | 84 | 2.2972 | 0.4643 | 0.7405 | 0.4643 | 0.4684 | |
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| 1.4448 | 4.0 | 112 | 1.3975 | 0.7143 | 0.7340 | 0.7143 | 0.6863 | |
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| 0.8373 | 5.0 | 140 | 1.0468 | 0.8571 | 0.8524 | 0.8571 | 0.8448 | |
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| 0.7239 | 6.0 | 168 | 0.8518 | 0.8929 | 0.9164 | 0.8929 | 0.8766 | |
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| 0.6504 | 7.0 | 196 | 0.7391 | 0.9286 | 0.9449 | 0.9286 | 0.9244 | |
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| 0.535 | 8.0 | 224 | 0.6682 | 0.9286 | 0.9449 | 0.9286 | 0.9244 | |
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| 0.4237 | 9.0 | 252 | 0.6443 | 0.9286 | 0.9449 | 0.9286 | 0.9244 | |
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| 0.3709 | 10.0 | 280 | 0.6304 | 0.9286 | 0.9449 | 0.9286 | 0.9244 | |
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### Test results |
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| Parameter | Value | |
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|:------------------------:|:------------------:| |
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| test_loss | 0.5829914808273315 | |
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| test_accuracy | 0.9285714285714286 | |
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| test_precision | 0.9446428571428571 | |
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| test_recall | 0.9285714285714286 | |
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| test_f1 | 0.930292723149866 | |
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| test_runtime (s) | 4.1488 | |
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| test_samples_per_second | 6.749 | |
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| test_steps_per_second | 3.374 | |
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| epoch | 10.0 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 2.0.0 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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