---
license: apache-2.0
base_model: jadasdn/wav2vec2-1
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-2
  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. -->

# wav2vec2-2

This model is a fine-tuned version of [jadasdn/wav2vec2-1](https://huggingface.co/jadasdn/wav2vec2-1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8353
- Wer: 0.3593

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.4516        | 0.5   | 500   | 0.4973          | 0.3748 |
| 0.4624        | 1.0   | 1000  | 0.4486          | 0.3958 |
| 0.5211        | 1.5   | 1500  | 0.5173          | 0.3916 |
| 0.5317        | 2.0   | 2000  | 0.4713          | 0.3992 |
| 0.4277        | 2.5   | 2500  | 0.4859          | 0.3888 |
| 0.4495        | 3.0   | 3000  | 0.4962          | 0.3862 |
| 0.3712        | 3.5   | 3500  | 0.5237          | 0.3899 |
| 0.3855        | 4.0   | 4000  | 0.4975          | 0.3850 |
| 0.3254        | 4.5   | 4500  | 0.5405          | 0.3897 |
| 0.331         | 5.0   | 5000  | 0.5255          | 0.3950 |
| 0.2907        | 5.5   | 5500  | 0.5646          | 0.3852 |
| 0.2949        | 6.0   | 6000  | 0.5782          | 0.3965 |
| 0.2521        | 6.5   | 6500  | 0.5563          | 0.3879 |
| 0.2663        | 7.0   | 7000  | 0.5627          | 0.3829 |
| 0.2342        | 7.5   | 7500  | 0.6145          | 0.3872 |
| 0.2374        | 8.0   | 8000  | 0.5860          | 0.3883 |
| 0.2099        | 8.5   | 8500  | 0.6920          | 0.3810 |
| 0.2133        | 9.0   | 9000  | 0.6354          | 0.3895 |
| 0.1887        | 9.5   | 9500  | 0.6618          | 0.3813 |
| 0.1924        | 10.0  | 10000 | 0.6522          | 0.3850 |
| 0.1728        | 10.5  | 10500 | 0.6324          | 0.3813 |
| 0.1797        | 11.0  | 11000 | 0.6637          | 0.3882 |
| 0.163         | 11.5  | 11500 | 0.6806          | 0.3799 |
| 0.1623        | 12.0  | 12000 | 0.6801          | 0.3811 |
| 0.149         | 12.5  | 12500 | 0.6723          | 0.3832 |
| 0.1493        | 13.0  | 13000 | 0.7032          | 0.3888 |
| 0.1389        | 13.5  | 13500 | 0.7294          | 0.3793 |
| 0.1383        | 14.0  | 14000 | 0.7311          | 0.3800 |
| 0.127         | 14.5  | 14500 | 0.7088          | 0.3773 |
| 0.127         | 15.0  | 15000 | 0.7352          | 0.3775 |
| 0.1159        | 15.5  | 15500 | 0.7886          | 0.3792 |
| 0.114         | 16.0  | 16000 | 0.7582          | 0.3802 |
| 0.1103        | 16.5  | 16500 | 0.7662          | 0.3717 |
| 0.1088        | 17.0  | 17000 | 0.7855          | 0.3704 |
| 0.1021        | 17.5  | 17500 | 0.7326          | 0.3717 |
| 0.104         | 18.0  | 18000 | 0.7518          | 0.3723 |
| 0.096         | 18.5  | 18500 | 0.7468          | 0.3743 |
| 0.0914        | 19.0  | 19000 | 0.7906          | 0.3741 |
| 0.0881        | 19.5  | 19500 | 0.7879          | 0.3740 |
| 0.0908        | 20.0  | 20000 | 0.8111          | 0.3676 |
| 0.0832        | 20.5  | 20500 | 0.8114          | 0.3681 |
| 0.0848        | 21.0  | 21000 | 0.8178          | 0.3651 |
| 0.0762        | 21.5  | 21500 | 0.8212          | 0.3686 |
| 0.0728        | 22.0  | 22000 | 0.8142          | 0.3673 |
| 0.074         | 22.5  | 22500 | 0.8177          | 0.3666 |
| 0.0691        | 23.0  | 23000 | 0.8323          | 0.3662 |
| 0.0689        | 23.5  | 23500 | 0.8020          | 0.3678 |
| 0.0643        | 24.0  | 24000 | 0.8145          | 0.3653 |
| 0.0647        | 24.5  | 24500 | 0.8376          | 0.3594 |
| 0.0654        | 25.0  | 25000 | 0.8307          | 0.3608 |
| 0.061         | 25.5  | 25500 | 0.8432          | 0.3600 |
| 0.0573        | 26.0  | 26000 | 0.8361          | 0.3629 |
| 0.0583        | 26.5  | 26500 | 0.8363          | 0.3625 |
| 0.054         | 27.0  | 27000 | 0.8277          | 0.3625 |
| 0.058         | 27.5  | 27500 | 0.8354          | 0.3614 |
| 0.0531        | 28.0  | 28000 | 0.8363          | 0.3595 |
| 0.0522        | 28.5  | 28500 | 0.8429          | 0.3588 |
| 0.0503        | 29.0  | 29000 | 0.8267          | 0.3595 |
| 0.0504        | 29.5  | 29500 | 0.8401          | 0.3597 |
| 0.0511        | 30.0  | 30000 | 0.8353          | 0.3593 |


### Framework versions

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