---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: torgo_xlsr_finetune-M04-2
  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. -->

# torgo_xlsr_finetune-M04-2

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2407
- Wer: 1.2835

## 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

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 22.4872       | 0.88  | 500   | 3.2706          | 1.0    |
| 3.361         | 1.75  | 1000  | 2.8365          | 1.0    |
| 2.8532        | 2.63  | 1500  | 2.7444          | 1.0    |
| 2.5391        | 3.5   | 2000  | 2.1105          | 1.0824 |
| 1.6217        | 4.38  | 2500  | 1.7736          | 1.6424 |
| 1.1107        | 5.25  | 3000  | 1.5937          | 1.4918 |
| 0.8277        | 6.13  | 3500  | 1.5655          | 1.4729 |
| 0.6872        | 7.01  | 4000  | 1.6192          | 1.4671 |
| 0.5597        | 7.88  | 4500  | 1.6735          | 1.4176 |
| 0.4942        | 8.76  | 5000  | 1.5915          | 1.3847 |
| 0.4447        | 9.63  | 5500  | 1.8509          | 1.4506 |
| 0.3967        | 10.51 | 6000  | 1.7833          | 1.3929 |
| 0.3596        | 11.38 | 6500  | 2.0147          | 1.3776 |
| 0.3409        | 12.26 | 7000  | 1.8649          | 1.4    |
| 0.3169        | 13.13 | 7500  | 1.8252          | 1.3541 |
| 0.2962        | 14.01 | 8000  | 2.1108          | 1.3906 |
| 0.2934        | 14.89 | 8500  | 1.8004          | 1.3188 |
| 0.2564        | 15.76 | 9000  | 1.8681          | 1.3659 |
| 0.2447        | 16.64 | 9500  | 1.9341          | 1.3318 |
| 0.2248        | 17.51 | 10000 | 2.0251          | 1.3259 |
| 0.2234        | 18.39 | 10500 | 1.9982          | 1.2988 |
| 0.1955        | 19.26 | 11000 | 2.0277          | 1.3024 |
| 0.1882        | 20.14 | 11500 | 2.0001          | 1.2882 |
| 0.2022        | 21.02 | 12000 | 1.9842          | 1.2988 |
| 0.163         | 21.89 | 12500 | 1.9931          | 1.32   |
| 0.1732        | 22.77 | 13000 | 2.0577          | 1.2659 |
| 0.1522        | 23.64 | 13500 | 2.0511          | 1.2812 |
| 0.1367        | 24.52 | 14000 | 2.0308          | 1.2671 |
| 0.1393        | 25.39 | 14500 | 2.2392          | 1.2788 |
| 0.1407        | 26.27 | 15000 | 2.1329          | 1.2824 |
| 0.1244        | 27.15 | 15500 | 2.0721          | 1.2694 |
| 0.116         | 28.02 | 16000 | 2.1656          | 1.2824 |
| 0.125         | 28.9  | 16500 | 2.2338          | 1.2882 |
| 0.1063        | 29.77 | 17000 | 2.2407          | 1.2835 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2