---
license: apache-2.0
base_model: jadasdn/wav2vec2-3
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-4
  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. -->

# wav2vec2-4

This model is a fine-tuned version of [jadasdn/wav2vec2-3](https://huggingface.co/jadasdn/wav2vec2-3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8336
- Wer: 0.3531

## 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: 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_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.6872        | 0.5   | 500   | 0.3945          | 0.3562 |
| 0.5186        | 1.0   | 1000  | 0.4087          | 0.3701 |
| 0.5756        | 1.5   | 1500  | 0.4499          | 0.3770 |
| 0.4795        | 2.0   | 2000  | 0.4292          | 0.3754 |
| 0.379         | 2.5   | 2500  | 0.4430          | 0.3695 |
| 0.4879        | 3.0   | 3000  | 0.4530          | 0.3749 |
| 0.4527        | 3.5   | 3500  | 0.5052          | 0.3762 |
| 0.3305        | 4.0   | 4000  | 0.4820          | 0.3693 |
| 0.2707        | 4.5   | 4500  | 0.5045          | 0.3808 |
| 0.4131        | 5.0   | 5000  | 0.4771          | 0.3700 |
| 0.3045        | 5.5   | 5500  | 0.5130          | 0.3786 |
| 0.2459        | 6.0   | 6000  | 0.5071          | 0.3687 |
| 0.2674        | 6.5   | 6500  | 0.5515          | 0.3741 |
| 0.2224        | 7.0   | 7000  | 0.5358          | 0.3740 |
| 0.2228        | 7.5   | 7500  | 0.5648          | 0.3747 |
| 0.1992        | 8.0   | 8000  | 0.5644          | 0.3704 |
| 0.2089        | 8.5   | 8500  | 0.6098          | 0.3729 |
| 0.1795        | 9.0   | 9000  | 0.5837          | 0.3707 |
| 0.1584        | 9.5   | 9500  | 0.6143          | 0.3705 |
| 0.1741        | 10.0  | 10000 | 0.6294          | 0.3774 |
| 0.1461        | 10.5  | 10500 | 0.6406          | 0.3731 |
| 0.1448        | 11.0  | 11000 | 0.6352          | 0.3733 |
| 0.1318        | 11.5  | 11500 | 0.6338          | 0.3699 |
| 0.1396        | 12.0  | 12000 | 0.6440          | 0.3692 |
| 0.1226        | 12.5  | 12500 | 0.7047          | 0.3757 |
| 0.1232        | 13.0  | 13000 | 0.6815          | 0.3675 |
| 0.1168        | 13.5  | 13500 | 0.6607          | 0.3679 |
| 0.1128        | 14.0  | 14000 | 0.6650          | 0.3678 |
| 0.1046        | 14.5  | 14500 | 0.6944          | 0.3727 |
| 0.104         | 15.0  | 15000 | 0.7186          | 0.3652 |
| 0.097         | 15.5  | 15500 | 0.7224          | 0.3664 |
| 0.1022        | 16.0  | 16000 | 0.6928          | 0.3664 |
| 0.0987        | 16.5  | 16500 | 0.7248          | 0.3684 |
| 0.0905        | 17.0  | 17000 | 0.7157          | 0.3648 |
| 0.0863        | 17.5  | 17500 | 0.7410          | 0.3635 |
| 0.0829        | 18.0  | 18000 | 0.7629          | 0.3643 |
| 0.0788        | 18.5  | 18500 | 0.7371          | 0.3632 |
| 0.0799        | 19.0  | 19000 | 0.7554          | 0.3652 |
| 0.0744        | 19.5  | 19500 | 0.7886          | 0.3638 |
| 0.076         | 20.0  | 20000 | 0.7376          | 0.3631 |
| 0.0701        | 20.5  | 20500 | 0.7723          | 0.3586 |
| 0.0709        | 21.0  | 21000 | 0.7964          | 0.3613 |
| 0.0665        | 21.5  | 21500 | 0.7782          | 0.3576 |
| 0.066         | 22.0  | 22000 | 0.7885          | 0.3589 |
| 0.0621        | 22.5  | 22500 | 0.7906          | 0.3593 |
| 0.0614        | 23.0  | 23000 | 0.7737          | 0.3575 |
| 0.0626        | 23.5  | 23500 | 0.7903          | 0.3610 |
| 0.0595        | 24.0  | 24000 | 0.7937          | 0.3582 |
| 0.0572        | 24.5  | 24500 | 0.8263          | 0.3573 |
| 0.0553        | 25.0  | 25000 | 0.8125          | 0.3549 |
| 0.0529        | 25.5  | 25500 | 0.8154          | 0.3534 |
| 0.0524        | 26.0  | 26000 | 0.8018          | 0.3526 |
| 0.0513        | 26.5  | 26500 | 0.8160          | 0.3531 |
| 0.0491        | 27.0  | 27000 | 0.8117          | 0.3524 |
| 0.0456        | 27.5  | 27500 | 0.8263          | 0.3527 |
| 0.0479        | 28.0  | 28000 | 0.8368          | 0.3529 |
| 0.0459        | 28.5  | 28500 | 0.8325          | 0.3534 |
| 0.0427        | 29.0  | 29000 | 0.8331          | 0.3534 |
| 0.0458        | 29.5  | 29500 | 0.8315          | 0.3527 |
| 0.0436        | 30.0  | 30000 | 0.8336          | 0.3531 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0