---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: LugandaASRwav2Vec300M
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_13_0
      type: common_voice_13_0
      config: lg
      split: validation
      args: lg
    metrics:
    - name: Wer
      type: wer
      value: 0.22313171042840438
---

<!-- 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. -->

# LugandaASRwav2Vec300M

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_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1741
- Wer: 0.2231

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 24
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.4394        | 0.14  | 100  | 2.9784          | 1.0    |
| 2.8739        | 0.27  | 200  | 2.7056          | 1.0000 |
| 1.2203        | 0.41  | 300  | 0.5656          | 0.7264 |
| 0.4507        | 0.54  | 400  | 0.3978          | 0.5258 |
| 0.3657        | 0.68  | 500  | 0.3314          | 0.4416 |
| 0.3131        | 0.81  | 600  | 0.2996          | 0.4049 |
| 0.2886        | 0.95  | 700  | 0.2823          | 0.3766 |
| 0.2535        | 1.08  | 800  | 0.2517          | 0.3317 |
| 0.2279        | 1.22  | 900  | 0.2407          | 0.3190 |
| 0.2209        | 1.36  | 1000 | 0.2296          | 0.3077 |
| 0.2075        | 1.49  | 1100 | 0.2228          | 0.2931 |
| 0.1983        | 1.63  | 1200 | 0.2139          | 0.2809 |
| 0.1902        | 1.76  | 1300 | 0.2093          | 0.2688 |
| 0.1931        | 1.9   | 1400 | 0.2019          | 0.2666 |
| 0.1741        | 2.03  | 1500 | 0.1951          | 0.2521 |
| 0.1481        | 2.17  | 1600 | 0.1934          | 0.2435 |
| 0.1423        | 2.3   | 1700 | 0.1912          | 0.2409 |
| 0.1413        | 2.44  | 1800 | 0.1841          | 0.2368 |
| 0.1361        | 2.58  | 1900 | 0.1813          | 0.2310 |
| 0.1337        | 2.71  | 2000 | 0.1775          | 0.2279 |
| 0.1358        | 2.85  | 2100 | 0.1756          | 0.2247 |
| 0.133         | 2.98  | 2200 | 0.1741          | 0.2231 |


### Framework versions

- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3