---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_16_1
metrics:
- wer
model-index:
- name: wav2vec2-turkish-300m-3
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_16_1
      type: common_voice_16_1
      config: tr
      split: test
      args: tr
    metrics:
    - name: Wer
      type: wer
      value: 0.2453349153273649
---

<!-- 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-turkish-300m-3

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_16_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2968
- Wer: 0.2453

## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 2.015         | 0.3652  | 500   | 0.5674          | 0.5880 |
| 0.5687        | 0.7305  | 1000  | 0.4913          | 0.5483 |
| 0.4727        | 1.0957  | 1500  | 0.4382          | 0.4868 |
| 0.3713        | 1.4609  | 2000  | 0.3941          | 0.4761 |
| 0.3653        | 1.8262  | 2500  | 0.3978          | 0.4609 |
| 0.3457        | 2.1914  | 3000  | 0.3570          | 0.4201 |
| 0.3108        | 2.5566  | 3500  | 0.3273          | 0.4045 |
| 0.2985        | 2.9218  | 4000  | 0.3559          | 0.4253 |
| 0.2768        | 3.2871  | 4500  | 0.3484          | 0.4288 |
| 0.2702        | 3.6523  | 5000  | 0.3422          | 0.3988 |
| 0.2626        | 4.0175  | 5500  | 0.3312          | 0.3875 |
| 0.246         | 4.3828  | 6000  | 0.3175          | 0.3735 |
| 0.2373        | 4.7480  | 6500  | 0.3126          | 0.3750 |
| 0.234         | 5.1132  | 7000  | 0.3289          | 0.3703 |
| 0.2225        | 5.4785  | 7500  | 0.3170          | 0.3700 |
| 0.2094        | 5.8437  | 8000  | 0.3127          | 0.3611 |
| 0.1961        | 6.2089  | 8500  | 0.3130          | 0.3604 |
| 0.1927        | 6.5741  | 9000  | 0.3167          | 0.3491 |
| 0.1963        | 6.9394  | 9500  | 0.2983          | 0.3451 |
| 0.1757        | 7.3046  | 10000 | 0.3044          | 0.3403 |
| 0.1732        | 7.6698  | 10500 | 0.2988          | 0.3407 |
| 0.1737        | 8.0351  | 11000 | 0.3128          | 0.3367 |
| 0.1686        | 8.4003  | 11500 | 0.2954          | 0.3296 |
| 0.1588        | 8.7655  | 12000 | 0.3226          | 0.3265 |
| 0.1481        | 9.1308  | 12500 | 0.2946          | 0.3172 |
| 0.1434        | 9.4960  | 13000 | 0.2981          | 0.3202 |
| 0.146         | 9.8612  | 13500 | 0.2936          | 0.3150 |
| 0.1352        | 10.2264 | 14000 | 0.2895          | 0.3091 |
| 0.1304        | 10.5917 | 14500 | 0.2932          | 0.3071 |
| 0.1253        | 10.9569 | 15000 | 0.2946          | 0.2997 |
| 0.12          | 11.3221 | 15500 | 0.2967          | 0.3065 |
| 0.1179        | 11.6874 | 16000 | 0.2856          | 0.3037 |
| 0.1185        | 12.0526 | 16500 | 0.2753          | 0.2973 |
| 0.1128        | 12.4178 | 17000 | 0.2954          | 0.2935 |
| 0.1054        | 12.7831 | 17500 | 0.2917          | 0.2916 |
| 0.1026        | 13.1483 | 18000 | 0.2878          | 0.2820 |
| 0.0981        | 13.5135 | 18500 | 0.2882          | 0.2863 |
| 0.0936        | 13.8787 | 19000 | 0.2758          | 0.2774 |
| 0.0911        | 14.2440 | 19500 | 0.2867          | 0.2811 |
| 0.0881        | 14.6092 | 20000 | 0.2952          | 0.2760 |
| 0.0809        | 14.9744 | 20500 | 0.2996          | 0.2772 |
| 0.0815        | 15.3397 | 21000 | 0.2806          | 0.2694 |
| 0.078         | 15.7049 | 21500 | 0.3050          | 0.2717 |
| 0.0727        | 16.0701 | 22000 | 0.2871          | 0.2682 |
| 0.0716        | 16.4354 | 22500 | 0.2935          | 0.2667 |
| 0.0672        | 16.8006 | 23000 | 0.2917          | 0.2632 |
| 0.0666        | 17.1658 | 23500 | 0.3075          | 0.2584 |
| 0.0654        | 17.5310 | 24000 | 0.3025          | 0.2580 |
| 0.0616        | 17.8963 | 24500 | 0.2952          | 0.2550 |
| 0.0609        | 18.2615 | 25000 | 0.3077          | 0.2567 |
| 0.0604        | 18.6267 | 25500 | 0.3040          | 0.2513 |
| 0.0549        | 18.9920 | 26000 | 0.3043          | 0.2481 |
| 0.0516        | 19.3572 | 26500 | 0.3036          | 0.2476 |
| 0.0543        | 19.7224 | 27000 | 0.2968          | 0.2453 |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.17.1
- Tokenizers 0.19.1