---
language:
- tr
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17
model-index:
- name: 'Wav2Vec2-XLS-TR '
  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-XLS-TR 

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the Common Voice 17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5348

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss |
|:-------------:|:-------:|:-----:|:---------------:|
| 21.9538       | 0.3446  | 500   | 12.1320         |
| 7.2139        | 0.6892  | 1000  | 5.5372          |
| 4.9229        | 1.0338  | 1500  | 4.4389          |
| 4.0539        | 1.3784  | 2000  | 3.7210          |
| 3.5019        | 1.7229  | 2500  | 3.3480          |
| 3.2272        | 2.0675  | 3000  | 3.1302          |
| 2.9163        | 2.4121  | 3500  | 2.5875          |
| 1.902         | 2.7567  | 4000  | 1.5471          |
| 1.2034        | 3.1013  | 4500  | 1.1319          |
| 0.9389        | 3.4459  | 5000  | 1.0412          |
| 0.7599        | 3.7905  | 5500  | 0.8486          |
| 0.6418        | 4.1351  | 6000  | 0.7179          |
| 0.569         | 4.4797  | 6500  | 0.7109          |
| 0.5248        | 4.8243  | 7000  | 0.6470          |
| 0.4752        | 5.1688  | 7500  | 0.6298          |
| 0.4461        | 5.5134  | 8000  | 0.6198          |
| 0.4187        | 5.8580  | 8500  | 0.6224          |
| 0.3935        | 6.2026  | 9000  | 0.6116          |
| 0.3756        | 6.5472  | 9500  | 0.5536          |
| 0.3597        | 6.8918  | 10000 | 0.5263          |
| 0.3483        | 7.2364  | 10500 | 0.5179          |
| 0.3283        | 7.5810  | 11000 | 0.5054          |
| 0.3204        | 7.9256  | 11500 | 0.5200          |
| 0.3031        | 8.2702  | 12000 | 0.4984          |
| 0.2986        | 8.6147  | 12500 | 0.4846          |
| 0.2936        | 8.9593  | 13000 | 0.4984          |
| 0.2789        | 9.3039  | 13500 | 0.4888          |
| 0.2724        | 9.6485  | 14000 | 0.4654          |
| 0.2718        | 9.9931  | 14500 | 0.4553          |
| 0.2533        | 10.3377 | 15000 | 0.4506          |
| 0.2498        | 10.6823 | 15500 | 0.4983          |
| 0.2501        | 11.0269 | 16000 | 0.4835          |
| 0.2384        | 11.3715 | 16500 | 0.4749          |
| 0.2371        | 11.7161 | 17000 | 0.4877          |
| 0.2319        | 12.0606 | 17500 | 0.4806          |
| 0.2235        | 12.4052 | 18000 | 0.4874          |
| 0.2218        | 12.7498 | 18500 | 0.4565          |
| 0.2193        | 13.0944 | 19000 | 0.4733          |
| 0.2154        | 13.4390 | 19500 | 0.4747          |
| 0.2157        | 13.7836 | 20000 | 0.4694          |
| 0.2067        | 14.1282 | 20500 | 0.4848          |
| 0.2044        | 14.4728 | 21000 | 0.5092          |
| 0.2023        | 14.8174 | 21500 | 0.4752          |
| 0.1975        | 15.1620 | 22000 | 0.4852          |
| 0.1903        | 15.5065 | 22500 | 0.4891          |
| 0.1922        | 15.8511 | 23000 | 0.4825          |
| 0.1901        | 16.1957 | 23500 | 0.4836          |
| 0.1862        | 16.5403 | 24000 | 0.4838          |
| 0.1842        | 16.8849 | 24500 | 0.4897          |
| 0.1837        | 17.2295 | 25000 | 0.4920          |
| 0.1782        | 17.5741 | 25500 | 0.4937          |
| 0.1775        | 17.9187 | 26000 | 0.4868          |
| 0.1738        | 18.2633 | 26500 | 0.5107          |
| 0.1739        | 18.6079 | 27000 | 0.4943          |
| 0.1722        | 18.9524 | 27500 | 0.4740          |
| 0.1691        | 19.2970 | 28000 | 0.4965          |
| 0.1684        | 19.6416 | 28500 | 0.4834          |
| 0.1646        | 19.9862 | 29000 | 0.5187          |
| 0.1637        | 20.3308 | 29500 | 0.4979          |
| 0.1625        | 20.6754 | 30000 | 0.4945          |
| 0.1608        | 21.0200 | 30500 | 0.5149          |
| 0.1567        | 21.3646 | 31000 | 0.5030          |
| 0.1571        | 21.7092 | 31500 | 0.5013          |
| 0.1589        | 22.0538 | 32000 | 0.5269          |
| 0.1524        | 22.3983 | 32500 | 0.5191          |
| 0.1491        | 22.7429 | 33000 | 0.5200          |
| 0.1521        | 23.0875 | 33500 | 0.5206          |
| 0.149         | 23.4321 | 34000 | 0.5214          |
| 0.1505        | 23.7767 | 34500 | 0.5255          |
| 0.1516        | 24.1213 | 35000 | 0.5168          |
| 0.1479        | 24.4659 | 35500 | 0.5431          |
| 0.1476        | 24.8105 | 36000 | 0.5352          |
| 0.1433        | 25.1551 | 36500 | 0.5281          |
| 0.1464        | 25.4997 | 37000 | 0.5207          |
| 0.1444        | 25.8442 | 37500 | 0.5254          |
| 0.1466        | 26.1888 | 38000 | 0.5163          |
| 0.1424        | 26.5334 | 38500 | 0.5200          |
| 0.1375        | 26.8780 | 39000 | 0.5188          |
| 0.1428        | 27.2226 | 39500 | 0.5315          |
| 0.1376        | 27.5672 | 40000 | 0.5323          |
| 0.139         | 27.9118 | 40500 | 0.5370          |
| 0.1437        | 28.2564 | 41000 | 0.5426          |
| 0.138         | 28.6010 | 41500 | 0.5263          |
| 0.1382        | 28.9456 | 42000 | 0.5286          |
| 0.139         | 29.2901 | 42500 | 0.5302          |
| 0.139         | 29.6347 | 43000 | 0.5334          |
| 0.1356        | 29.9793 | 43500 | 0.5348          |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1