---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- narad/ravdess
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-ravdess
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: RAVDESS
      type: narad/ravdess
      config: all
      split: train
      args: all
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8194444444444444
---

<!-- 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. -->

# distilhubert-finetuned-ravdess

This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co./ntu-spml/distilhubert) on the RAVDESS dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6720
- Accuracy: 0.8194

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.795         | 1.0   | 162  | 1.8129          | 0.25     |
| 1.1416        | 2.0   | 324  | 1.2499          | 0.5278   |
| 1.1677        | 3.0   | 486  | 0.9141          | 0.6875   |
| 0.5474        | 4.0   | 648  | 0.7662          | 0.75     |
| 0.4129        | 5.0   | 810  | 0.6744          | 0.7569   |
| 0.2396        | 6.0   | 972  | 0.6781          | 0.7986   |
| 0.0626        | 7.0   | 1134 | 0.7809          | 0.75     |
| 0.1198        | 8.0   | 1296 | 0.6404          | 0.8194   |
| 0.0187        | 9.0   | 1458 | 0.6750          | 0.8264   |
| 0.012         | 10.0  | 1620 | 0.6720          | 0.8194   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1
- Datasets 2.19.1
- Tokenizers 0.19.1