---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-tr-colab
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice
      type: common_voice
      config: tr
      split: test
      args: tr
    metrics:
    - name: Wer
      type: wer
      value: 0.37473189663977124
---

<!-- 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-large-xls-r-300m-tr-colab

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 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4346
- Wer: 0.3747

## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.9005        | 4.26  | 400  | 0.6917          | 0.7251 |
| 0.4032        | 8.51  | 800  | 0.4781          | 0.5286 |
| 0.1863        | 12.77 | 1200 | 0.4682          | 0.4690 |
| 0.1323        | 17.02 | 1600 | 0.4664          | 0.4483 |
| 0.1014        | 21.28 | 2000 | 0.4500          | 0.4124 |
| 0.0749        | 25.53 | 2400 | 0.4510          | 0.3909 |
| 0.0568        | 29.79 | 2800 | 0.4346          | 0.3747 |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3