---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-accents
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.39097744360902253
---

<!-- 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-accents

This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8429
- Accuracy: 0.3910

## 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: 0.7
- num_epochs: 14
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.5546        | 1.0   | 67   | 2.5463          | 0.1729   |
| 2.4756        | 2.0   | 134  | 2.4641          | 0.1654   |
| 2.3726        | 3.0   | 201  | 2.4065          | 0.2030   |
| 2.464         | 4.0   | 268  | 2.3753          | 0.2256   |
| 2.2215        | 5.0   | 335  | 2.3161          | 0.2481   |
| 2.346         | 6.0   | 402  | 2.2739          | 0.2556   |
| 1.8318        | 7.0   | 469  | 2.0260          | 0.3383   |
| 1.9612        | 8.0   | 536  | 1.8926          | 0.3684   |
| 1.7699        | 9.0   | 603  | 1.8646          | 0.3835   |
| 1.5864        | 10.0  | 670  | 2.0469          | 0.3083   |
| 1.5774        | 11.0  | 737  | 1.8156          | 0.3609   |
| 1.5087        | 12.0  | 804  | 1.8061          | 0.3609   |
| 1.2649        | 13.0  | 871  | 1.8970          | 0.3383   |
| 1.2179        | 14.0  | 938  | 1.8429          | 0.3910   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0