---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: tun_wav2vec7
  results: []
---

<!-- 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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/myriam-charfeddine5/huggingface/runs/g060p3bj)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/myriam-charfeddine5/huggingface/runs/g060p3bj)
# tun_wav2vec7

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2586
- Wer: 0.5945
- Cer: 0.1592

## 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: 0.0001
- train_batch_size: 10
- 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_steps: 70
- num_epochs: 100

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 2.0634        | 5.4545  | 300  | 1.3331          | 0.9794 | 0.3126 |
| 0.7466        | 10.9091 | 600  | 1.1736          | 0.8522 | 0.2637 |
| 0.496         | 16.3636 | 900  | 1.0844          | 0.8076 | 0.2344 |
| 0.3864        | 21.8182 | 1200 | 1.5553          | 0.7766 | 0.2436 |
| 0.2908        | 27.2727 | 1500 | 1.1381          | 0.7045 | 0.2089 |
| 0.2241        | 32.7273 | 1800 | 1.4912          | 0.7182 | 0.2236 |
| 0.175         | 38.1818 | 2100 | 1.1890          | 0.6426 | 0.1910 |
| 0.1483        | 43.6364 | 2400 | 1.1582          | 0.6323 | 0.1667 |
| 0.1237        | 49.0909 | 2700 | 1.2264          | 0.6357 | 0.1722 |
| 0.1676        | 54.5455 | 3000 | 1.1899          | 0.6632 | 0.1726 |
| 0.1437        | 60.0    | 3300 | 1.2421          | 0.6323 | 0.1793 |
| 0.1261        | 65.4545 | 3600 | 1.2693          | 0.6117 | 0.1663 |
| 0.0787        | 70.9091 | 3900 | 1.2301          | 0.6014 | 0.1601 |
| 0.0952        | 76.3636 | 4200 | 1.2063          | 0.6186 | 0.1659 |
| 0.1385        | 81.8182 | 4500 | 1.2732          | 0.6082 | 0.1642 |
| 0.0659        | 87.2727 | 4800 | 1.3254          | 0.6048 | 0.1692 |
| 0.058         | 92.7273 | 5100 | 1.2216          | 0.5979 | 0.1575 |
| 0.1454        | 98.1818 | 5400 | 1.2586          | 0.5945 | 0.1592 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1