---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab-full
  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: 0.30497395567357777
---

<!-- 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-turkish-colab-full

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: 0.3991
- Wer: 0.3050

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.9196        | 3.67  | 400  | 0.6539          | 0.6524 |
| 0.3908        | 7.34  | 800  | 0.4486          | 0.4502 |
| 0.1859        | 11.01 | 1200 | 0.4015          | 0.3799 |
| 0.1228        | 14.68 | 1600 | 0.4080          | 0.3741 |
| 0.0956        | 18.35 | 2000 | 0.3930          | 0.3468 |
| 0.0757        | 22.02 | 2400 | 0.4163          | 0.3355 |
| 0.0573        | 25.69 | 2800 | 0.3983          | 0.3115 |
| 0.0463        | 29.36 | 3200 | 0.3991          | 0.3050 |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3