---
library_name: transformers
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
- f1
model-index:
- name: AST_EmoRecog_Model_v4
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# AST_EmoRecog_Model_v4

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 [IEMOCAP](https://sail.usc.edu/iemocap/) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4615
- Accuracy: 0.5159
- Recall: 0.4007
- Precision: 0.4956
- F1: 0.4090

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.4443        | 1.0   | 377  | 1.3359          | 0.4695   | 0.3408 | 0.4793    | 0.3099 |
| 1.1556        | 2.0   | 754  | 1.2506          | 0.5266   | 0.3877 | 0.6026    | 0.3970 |
| 0.8988        | 3.0   | 1131 | 1.2633          | 0.5279   | 0.4175 | 0.5148    | 0.4208 |
| 0.6187        | 4.0   | 1508 | 1.3426          | 0.5279   | 0.4031 | 0.5425    | 0.4153 |
| 0.3944        | 5.0   | 1885 | 1.4266          | 0.5206   | 0.4021 | 0.5256    | 0.4152 |
| 0.2555        | 6.0   | 2262 | 1.4615          | 0.5159   | 0.4007 | 0.4956    | 0.4090 |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0