
# ast-finetuned-audioset-10-10-0.4593_ft_ESC-50_aug_0-1

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.
It achieves the following results on the evaluation set:
- Loss: 0.7391
- Accuracy: 0.9286
- Precision: 0.9449
- Recall: 0.9286
- F1: 0.9244

## Training and evaluation data

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):

**Gain**
- each audio sample is amplified/attenuated by a random factor between 0.5 and 1.5 with a 0.3 probability

**Noise**
- 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

**Speed adjust**
- duration of each audio sample is extended by a random amount between 0.5 and 1.5 with a 0.3 probability

**Pitch shift**
- 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

**Time masking**
- a random fraction of lenght of each audio sample in the range of (0,0.02] is erased with a 0.3 probability


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 9.9002        | 1.0   | 28   | 8.5662          | 0.0      | 0.0       | 0.0    | 0.0    |
| 5.7235        | 2.0   | 56   | 4.3990          | 0.0357   | 0.0238    | 0.0357 | 0.0286 |
| 2.4076        | 3.0   | 84   | 2.2972          | 0.4643   | 0.7405    | 0.4643 | 0.4684 |
| 1.4448        | 4.0   | 112  | 1.3975          | 0.7143   | 0.7340    | 0.7143 | 0.6863 |
| 0.8373        | 5.0   | 140  | 1.0468          | 0.8571   | 0.8524    | 0.8571 | 0.8448 |
| 0.7239        | 6.0   | 168  | 0.8518          | 0.8929   | 0.9164    | 0.8929 | 0.8766 |
| 0.6504        | 7.0   | 196  | 0.7391          | 0.9286   | 0.9449    | 0.9286 | 0.9244 |
| 0.535         | 8.0   | 224  | 0.6682          | 0.9286   | 0.9449    | 0.9286 | 0.9244 |
| 0.4237        | 9.0   | 252  | 0.6443          | 0.9286   | 0.9449    | 0.9286 | 0.9244 |
| 0.3709        | 10.0  | 280  | 0.6304          | 0.9286   | 0.9449    | 0.9286 | 0.9244 |

### Test results
|         Parameter        |        Value       |
|:------------------------:|:------------------:|
| test_loss                | 0.5829914808273315 |
| test_accuracy            | 0.9285714285714286 |
| test_precision           | 0.9446428571428571 |
| test_recall              | 0.9285714285714286 |
| test_f1                  | 0.930292723149866  |
| test_runtime (s)         | 4.1488             |
| test_samples_per_second  | 6.749              |
| test_steps_per_second    | 3.374              |
| epoch                    | 10.0               |

### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "bsd-3-clause", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "ast-finetuned-audioset-10-10-0.4593_ft_ESC-50_aug_0-1", "results": []}]}
transformers pytorch audio-spectrogram-transformer audio-classification generated_from_trainer arxiv:2103.12157 license:bsd-3-clause endpoints_compatible region:us
TAGS
#transformers #pytorch #audio-spectrogram-transformer #audio-classification #generated_from_trainer #arxiv-2103.12157 #license-bsd-3-clause #endpoints_compatible #region-us 

ast-finetuned-audioset-10-10-0.4593\_ft\_ESC-50\_aug\_0-1
=========================================================


This model is a fine-tuned version of MIT/ast-finetuned-audioset-10-10-0.4593 on a subset of ashraq/esc50 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7391
* Accuracy: 0.9286
* Precision: 0.9449
* Recall: 0.9286
* F1: 0.9244


Training and evaluation data
----------------------------


Training and evaluation data were augmented with audiomentations GitHub: 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:


Gain


* each audio sample is amplified/attenuated by a random factor between 0.5 and 1.5 with a 0.3 probability


Noise


* 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


Speed adjust


* duration of each audio sample is extended by a random amount between 0.5 and 1.5 with a 0.3 probability


Pitch shift


* 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


Time masking


* a random fraction of lenght of each audio sample in the range of (0,0.02] is erased with a 0.3 probability


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-06
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10


### Training results



### Test results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-06
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10 ### Training results ### Test results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #audio-spectrogram-transformer #audio-classification #generated_from_trainer #arxiv-2103.12157 #license-bsd-3-clause #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-06
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10 ### Training results ### Test results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #audio-spectrogram-transformer #audio-classification #generated_from_trainer #arxiv-2103.12157 #license-bsd-3-clause #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-06
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10### Training results### Test results### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* Datasets 2.10.1
* Tokenizers 0.13.2