---
base_model: wetdog/TUT-urban-acoustic-scenes-2018-development-16bit
tags:
- audio-classification
- generated_from_trainer
datasets:
- acoustic-scenes
metrics:
- accuracy
model-index:
- name: ast-finetuned-audioset-10-10-0.4593-TUT-acoustic-scenes
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: TUT-urban-acoustic-scenes-2018-development-16bit
      type: acoustic-scenes
      args: 'split: train'
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.715647339158062
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# ast-finetuned-audioset-10-10-0.4593-TUT-acoustic-scenes

This model is a fine-tuned version of [wetdog/TUT-urban-acoustic-scenes-2018-development-16bit](https://huggingface.co/wetdog/TUT-urban-acoustic-scenes-2018-development-16bit) on the TUT-urban-acoustic-scenes-2018-development-16bit dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8055
- Accuracy: 0.7156

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4664        | 1.12  | 500  | 1.3147          | 0.6136   |
| 0.6605        | 2.23  | 1000 | 0.8055          | 0.7156   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3