---
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: AST-finetuned-on-shEMO_speech
  results: []
---

<!-- 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-on-shEMO_speech

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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6988
- Accuracy: 0.7967

## 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-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8657        | 1.0   | 75   | 0.7066          | 0.7867   |
| 0.6951        | 2.0   | 150  | 0.6622          | 0.7567   |
| 0.3368        | 3.0   | 225  | 0.5851          | 0.8433   |
| 0.1414        | 4.0   | 300  | 0.7233          | 0.79     |
| 0.1011        | 5.0   | 375  | 0.8763          | 0.7967   |
| 0.0438        | 6.0   | 450  | 0.9009          | 0.8067   |
| 0.0108        | 7.0   | 525  | 1.0540          | 0.83     |
| 0.0033        | 8.0   | 600  | 1.0177          | 0.81     |
| 0.0003        | 9.0   | 675  | 1.1074          | 0.84     |
| 0.0113        | 10.0  | 750  | 1.1107          | 0.8433   |
| 0.0002        | 11.0  | 825  | 1.1273          | 0.8367   |
| 0.0001        | 12.0  | 900  | 1.1634          | 0.8333   |
| 0.0001        | 13.0  | 975  | 1.1502          | 0.84     |
| 0.0045        | 14.0  | 1050 | 1.1541          | 0.84     |
| 0.0039        | 15.0  | 1125 | 1.1550          | 0.84     |


### Framework versions

- Transformers 4.34.1
- Pytorch 1.12.0+cu116
- Datasets 2.14.6
- Tokenizers 0.14.1