---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2LugandaASR20
  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: 0.23221005634102265
---

<!-- 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. -->

# wav2vec2LugandaASR20

This model is a fine-tuned version of [Gemmar/wav2vec2LugandaASR](https://huggingface.co/Gemmar/wav2vec2LugandaASR) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2393
- Wer: 0.2322

## 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: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1093        | 0.18  | 100  | 0.2134          | 0.2480 |
| 0.1141        | 0.36  | 200  | 0.2329          | 0.2724 |
| 0.1224        | 0.54  | 300  | 0.2560          | 0.2864 |
| 0.1345        | 0.72  | 400  | 0.2348          | 0.2716 |
| 0.1271        | 0.9   | 500  | 0.2339          | 0.2702 |
| 0.1232        | 1.08  | 600  | 0.2457          | 0.2806 |
| 0.1149        | 1.27  | 700  | 0.2372          | 0.2695 |
| 0.1129        | 1.45  | 800  | 0.2328          | 0.2718 |
| 0.1196        | 1.63  | 900  | 0.2326          | 0.2615 |
| 0.1185        | 1.81  | 1000 | 0.2249          | 0.2672 |
| 0.1159        | 1.99  | 1100 | 0.2202          | 0.2559 |
| 0.0933        | 2.17  | 1200 | 0.2302          | 0.2559 |
| 0.0947        | 2.35  | 1300 | 0.2306          | 0.2530 |
| 0.0941        | 2.53  | 1400 | 0.2325          | 0.2509 |
| 0.0946        | 2.71  | 1500 | 0.2233          | 0.2495 |
| 0.0949        | 2.89  | 1600 | 0.2320          | 0.2443 |
| 0.0883        | 3.07  | 1700 | 0.2383          | 0.2463 |
| 0.0783        | 3.25  | 1800 | 0.2386          | 0.2437 |
| 0.0753        | 3.43  | 1900 | 0.2329          | 0.2426 |
| 0.0772        | 3.62  | 2000 | 0.2317          | 0.2392 |
| 0.0774        | 3.8   | 2100 | 0.2308          | 0.2353 |
| 0.0764        | 3.98  | 2200 | 0.2293          | 0.2357 |
| 0.0666        | 4.16  | 2300 | 0.2446          | 0.2388 |
| 0.065         | 4.34  | 2400 | 0.2456          | 0.2359 |
| 0.0643        | 4.52  | 2500 | 0.2446          | 0.2345 |
| 0.0652        | 4.7   | 2600 | 0.2430          | 0.2325 |
| 0.0669        | 4.88  | 2700 | 0.2393          | 0.2322 |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3