---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-may23-luganda-colab
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice
      type: common_voice
      config: lg
      split: test
      args: lg
    metrics:
    - name: Wer
      type: wer
      value: 0.502121009153829
---

<!-- 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-may23-luganda-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: 0.7210
- Wer: 0.5021

## 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: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0539        | 7.77  | 400  | 0.6641          | 0.5738 |
| 0.0725        | 15.53 | 800  | 0.6735          | 0.5932 |
| 0.058         | 23.3  | 1200 | 0.6754          | 0.5751 |
| 0.0517        | 31.07 | 1600 | 0.6591          | 0.5901 |
| 0.0437        | 38.83 | 2000 | 0.7140          | 0.5658 |
| 0.0366        | 46.6  | 2400 | 0.7154          | 0.5602 |
| 0.0295        | 54.37 | 2800 | 0.6942          | 0.5140 |
| 0.0251        | 62.14 | 3200 | 0.7095          | 0.5204 |
| 0.0191        | 69.9  | 3600 | 0.7459          | 0.5267 |
| 0.0157        | 77.67 | 4000 | 0.6825          | 0.5155 |
| 0.0126        | 85.44 | 4400 | 0.7197          | 0.5135 |
| 0.0098        | 93.2  | 4800 | 0.7210          | 0.5021 |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3