---
library_name: transformers
license: mit
base_model: distil-whisper/distil-large-v2
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
- f1
model-index:
- name: distil-large-v2_ADReSSo
  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: 0.7605633802816901
    - name: F1
      type: f1
      value: 0.7733333333333333
---

<!-- 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. -->

# distil-large-v2_ADReSSo

This model is a fine-tuned version of [distil-whisper/distil-large-v2](https://huggingface.co/distil-whisper/distil-large-v2) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7772
- Accuracy: 0.7606
- F1: 0.7733

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7           | 1.0   | 16   | 0.6865          | 0.5714   | 0.25   |
| 0.6796        | 2.0   | 32   | 0.6753          | 0.5      | 0.0    |
| 0.5951        | 3.0   | 48   | 0.5741          | 0.6905   | 0.6667 |
| 0.3828        | 4.0   | 64   | 1.2909          | 0.5476   | 0.24   |
| 0.1822        | 5.0   | 80   | 1.0336          | 0.7857   | 0.7907 |
| 0.2948        | 6.0   | 96   | 0.9771          | 0.7619   | 0.7222 |
| 0.064         | 7.0   | 112  | 0.9788          | 0.8333   | 0.8293 |
| 0.0007        | 8.0   | 128  | 1.0079          | 0.8333   | 0.8444 |
| 0.0003        | 9.0   | 144  | 1.0393          | 0.8333   | 0.8444 |
| 0.0002        | 10.0  | 160  | 1.0977          | 0.8571   | 0.8696 |
| 0.0001        | 11.0  | 176  | 1.1214          | 0.8571   | 0.8696 |
| 0.0001        | 12.0  | 192  | 1.1597          | 0.8571   | 0.8696 |
| 0.0001        | 13.0  | 208  | 1.1867          | 0.8571   | 0.8696 |
| 0.0001        | 14.0  | 224  | 1.2124          | 0.8571   | 0.8696 |
| 0.0001        | 15.0  | 240  | 1.2319          | 0.8571   | 0.8696 |
| 0.0001        | 16.0  | 256  | 1.2440          | 0.8571   | 0.8696 |
| 0.0001        | 17.0  | 272  | 1.2629          | 0.8571   | 0.8696 |
| 0.0001        | 18.0  | 288  | 1.2777          | 0.8571   | 0.8696 |
| 0.0001        | 19.0  | 304  | 1.2876          | 0.8571   | 0.8696 |
| 0.0           | 20.0  | 320  | 1.3026          | 0.8571   | 0.8696 |
| 0.0           | 21.0  | 336  | 1.3156          | 0.8571   | 0.8696 |
| 0.0           | 22.0  | 352  | 1.3261          | 0.8571   | 0.8696 |
| 0.0           | 23.0  | 368  | 1.3362          | 0.8571   | 0.8696 |
| 0.0           | 24.0  | 384  | 1.3445          | 0.8571   | 0.8696 |
| 0.0           | 25.0  | 400  | 1.3532          | 0.8571   | 0.8696 |
| 0.0           | 26.0  | 416  | 1.3604          | 0.8571   | 0.8696 |
| 0.0           | 27.0  | 432  | 1.3673          | 0.8571   | 0.8696 |
| 0.0           | 28.0  | 448  | 1.3746          | 0.8571   | 0.8696 |
| 0.0           | 29.0  | 464  | 1.3809          | 0.8571   | 0.8696 |
| 0.0           | 30.0  | 480  | 1.3878          | 0.8571   | 0.8696 |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.5.0+cu118
- Datasets 2.14.6
- Tokenizers 0.21.1