---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: torgo_xlsr_finetune-F03-2
  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-F03-2

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: 1.5454
- Wer: 0.8555

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 25.1806       | 0.97  | 500   | 3.3512          | 1.0    |
| 3.3648        | 1.94  | 1000  | 3.1566          | 1.0    |
| 2.9865        | 2.91  | 1500  | 2.8249          | 1.0    |
| 2.8219        | 3.88  | 2000  | 2.7880          | 1.0    |
| 2.62          | 4.85  | 2500  | 2.4134          | 1.1793 |
| 2.0129        | 5.83  | 3000  | 1.7735          | 1.3777 |
| 1.3439        | 6.8   | 3500  | 1.4148          | 1.3656 |
| 0.9587        | 7.77  | 4000  | 1.3914          | 1.2437 |
| 0.7532        | 8.74  | 4500  | 1.2565          | 1.2957 |
| 0.6204        | 9.71  | 5000  | 1.2621          | 1.1074 |
| 0.5367        | 10.68 | 5500  | 1.3255          | 1.1199 |
| 0.4471        | 11.65 | 6000  | 1.2730          | 1.0789 |
| 0.3989        | 12.62 | 6500  | 1.2627          | 1.0258 |
| 0.3562        | 13.59 | 7000  | 1.3006          | 0.9754 |
| 0.3346        | 14.56 | 7500  | 1.2739          | 0.9598 |
| 0.2949        | 15.53 | 8000  | 1.3260          | 0.9238 |
| 0.2816        | 16.5  | 8500  | 1.3446          | 0.9152 |
| 0.2552        | 17.48 | 9000  | 1.3537          | 0.8848 |
| 0.2434        | 18.45 | 9500  | 1.3288          | 0.9258 |
| 0.2156        | 19.42 | 10000 | 1.3863          | 0.8812 |
| 0.2126        | 20.39 | 10500 | 1.3466          | 0.8867 |
| 0.1939        | 21.36 | 11000 | 1.4522          | 0.9113 |
| 0.1829        | 22.33 | 11500 | 1.5253          | 0.8922 |
| 0.179         | 23.3  | 12000 | 1.4589          | 0.8543 |
| 0.1684        | 24.27 | 12500 | 1.5436          | 0.8664 |
| 0.1516        | 25.24 | 13000 | 1.5324          | 0.8668 |
| 0.1472        | 26.21 | 13500 | 1.5561          | 0.8711 |
| 0.1399        | 27.18 | 14000 | 1.5400          | 0.8605 |
| 0.1405        | 28.16 | 14500 | 1.5626          | 0.8512 |
| 0.1349        | 29.13 | 15000 | 1.5454          | 0.8555 |


### Framework versions

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