---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: output2
  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. -->

# output2

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7711
- Wer: 0.3693

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.9858        | 0.5   | 500   | 0.8322          | 0.6842 |
| 0.7867        | 1.0   | 1000  | 0.6777          | 0.6137 |
| 0.6252        | 1.5   | 1500  | 0.6082          | 0.5503 |
| 0.5833        | 2.0   | 2000  | 0.5441          | 0.5066 |
| 0.4611        | 2.5   | 2500  | 0.5498          | 0.4922 |
| 0.4511        | 3.0   | 3000  | 0.5262          | 0.4654 |
| 0.37          | 3.5   | 3500  | 0.5422          | 0.4554 |
| 0.375         | 4.0   | 4000  | 0.6414          | 0.4659 |
| 0.3149        | 4.5   | 4500  | 0.5149          | 0.4353 |
| 0.3103        | 5.0   | 5000  | 0.5329          | 0.4423 |
| 0.2735        | 5.5   | 5500  | 0.9014          | 0.4359 |
| 0.2711        | 6.0   | 6000  | 3.1838          | 0.4374 |
| 0.26          | 6.5   | 6500  | 0.5987          | 0.4288 |
| 0.2451        | 7.0   | 7000  | 0.5245          | 0.4206 |
| 0.2184        | 7.5   | 7500  | 0.5627          | 0.4138 |
| 0.2115        | 8.0   | 8000  | 0.6408          | 0.4245 |
| 0.187         | 8.5   | 8500  | 0.5788          | 0.4093 |
| 0.1955        | 9.0   | 9000  | 0.5591          | 0.4214 |
| 0.1725        | 9.5   | 9500  | 0.5812          | 0.4135 |
| 0.1758        | 10.0  | 10000 | 0.5863          | 0.4051 |
| 0.1592        | 10.5  | 10500 | 0.6263          | 0.4116 |
| 0.1576        | 11.0  | 11000 | 0.5829          | 0.4028 |
| 0.1427        | 11.5  | 11500 | 0.6378          | 0.4016 |
| 0.1476        | 12.0  | 12000 | 0.5706          | 0.3988 |
| 0.1289        | 12.5  | 12500 | 0.6381          | 0.4104 |
| 0.1366        | 13.0  | 13000 | 0.6326          | 0.3975 |
| 0.1183        | 13.5  | 13500 | 0.6256          | 0.3916 |
| 0.1225        | 14.0  | 14000 | 0.6376          | 0.3971 |
| 0.1083        | 14.5  | 14500 | 0.6493          | 0.3905 |
| 0.1134        | 15.0  | 15000 | 0.6686          | 0.3951 |
| 0.1003        | 15.5  | 15500 | 0.6983          | 0.3967 |
| 0.104         | 16.0  | 16000 | 0.6324          | 0.3927 |
| 0.0928        | 16.5  | 16500 | 0.6482          | 0.3907 |
| 0.0944        | 17.0  | 17000 | 0.6790          | 0.3912 |
| 0.0925        | 17.5  | 17500 | 0.6877          | 0.3902 |
| 0.0847        | 18.0  | 18000 | 0.6572          | 0.3845 |
| 0.0808        | 18.5  | 18500 | 0.6551          | 0.3910 |
| 0.0836        | 19.0  | 19000 | 0.6832          | 0.3859 |
| 0.0757        | 19.5  | 19500 | 0.7594          | 0.3905 |
| 0.0751        | 20.0  | 20000 | 0.6960          | 0.3880 |
| 0.0715        | 20.5  | 20500 | 0.7244          | 0.3840 |
| 0.07          | 21.0  | 21000 | 0.7233          | 0.3848 |
| 0.0654        | 21.5  | 21500 | 0.7428          | 0.3833 |
| 0.0657        | 22.0  | 22000 | 0.7014          | 0.3842 |
| 0.0641        | 22.5  | 22500 | 0.7357          | 0.3796 |
| 0.0624        | 23.0  | 23000 | 0.7338          | 0.3796 |
| 0.0575        | 23.5  | 23500 | 0.7375          | 0.3804 |
| 0.0578        | 24.0  | 24000 | 0.7386          | 0.3782 |
| 0.0542        | 24.5  | 24500 | 0.7405          | 0.3758 |
| 0.0509        | 25.0  | 25000 | 0.7719          | 0.3774 |
| 0.0495        | 25.5  | 25500 | 0.7505          | 0.3763 |
| 0.0521        | 26.0  | 26000 | 0.7345          | 0.3742 |
| 0.0477        | 26.5  | 26500 | 0.7776          | 0.3740 |
| 0.0442        | 27.0  | 27000 | 0.7742          | 0.3738 |
| 0.0473        | 27.5  | 27500 | 0.7695          | 0.3719 |
| 0.0452        | 28.0  | 28000 | 0.7737          | 0.3705 |
| 0.0425        | 28.5  | 28500 | 0.7937          | 0.3702 |
| 0.0415        | 29.0  | 29000 | 0.7970          | 0.3713 |
| 0.0432        | 29.5  | 29500 | 0.7714          | 0.3700 |
| 0.041         | 30.0  | 30000 | 0.7711          | 0.3693 |


### Framework versions

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