---
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: 0.20833333333333334
---

<!-- 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: 1.9825
- Accuracy: 0.2083

## 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: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.4542        | 1.0   | 48   | 2.4501          | 0.1354   |
| 2.499         | 2.0   | 96   | 2.4186          | 0.1042   |
| 2.4441        | 3.0   | 144  | 2.3464          | 0.1875   |
| 2.1364        | 4.0   | 192  | 2.2214          | 0.2083   |
| 1.9561        | 5.0   | 240  | 2.1193          | 0.1771   |
| 2.05          | 6.0   | 288  | 2.0221          | 0.1875   |
| 1.7704        | 7.0   | 336  | 2.0434          | 0.1771   |
| 1.8652        | 8.0   | 384  | 1.9728          | 0.1875   |
| 1.77          | 9.0   | 432  | 1.9415          | 0.2292   |
| 1.6381        | 10.0  | 480  | 2.0323          | 0.1562   |
| 1.6316        | 11.0  | 528  | 1.9657          | 0.2292   |
| 1.504         | 12.0  | 576  | 1.9644          | 0.1875   |
| 1.3872        | 13.0  | 624  | 1.9719          | 0.2292   |
| 1.3829        | 14.0  | 672  | 1.9794          | 0.1979   |
| 1.3251        | 15.0  | 720  | 1.9825          | 0.2083   |


### Framework versions

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