---
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
datasets:
- Nooon/Donate_a_cry
metrics:
- accuracy
model-index:
- name: ast-finetuned-cry
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: DonateACry
      type: Nooon/Donate_a_cry
      config: train
      split: train
      args: train
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5454545454545454
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# ast-finetuned-cry

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 the DonateACry dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6404
- Accuracy: 0.5455

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- 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: 0.05
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6297        | 1.0   | 11   | 1.6891          | 0.3636   |
| 1.1137        | 2.0   | 22   | 1.3156          | 0.4545   |
| 0.5047        | 3.0   | 33   | 1.3955          | 0.4545   |
| 0.2062        | 4.0   | 44   | 1.4002          | 0.6364   |
| 0.0613        | 5.0   | 55   | 1.6693          | 0.5455   |
| 0.0142        | 6.0   | 66   | 1.3452          | 0.6364   |
| 0.0053        | 7.0   | 77   | 1.6914          | 0.5455   |
| 0.0038        | 8.0   | 88   | 1.6689          | 0.5455   |
| 0.0027        | 9.0   | 99   | 1.6357          | 0.5455   |
| 0.002         | 10.0  | 110  | 1.6404          | 0.5455   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1