---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2LugandaASR
  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: 0.23959817157435953
---

<!-- 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. -->

# wav2vec2LugandaASR

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: 0.2014
- Wer: 0.2396

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.8963        | 0.18  | 100  | 2.8825          | 1.0000 |
| 1.1814        | 0.36  | 200  | 0.3787          | 0.4585 |
| 0.3331        | 0.54  | 300  | 0.3166          | 0.3918 |
| 0.2939        | 0.72  | 400  | 0.2811          | 0.3483 |
| 0.2682        | 0.9   | 500  | 0.2652          | 0.3348 |
| 0.2389        | 1.08  | 600  | 0.2565          | 0.3207 |
| 0.2137        | 1.27  | 700  | 0.2452          | 0.3066 |
| 0.2062        | 1.45  | 800  | 0.2356          | 0.3092 |
| 0.2058        | 1.63  | 900  | 0.2346          | 0.2928 |
| 0.2055        | 1.81  | 1000 | 0.2252          | 0.2901 |
| 0.1979        | 1.99  | 1100 | 0.2215          | 0.2836 |
| 0.166         | 2.17  | 1200 | 0.2217          | 0.2811 |
| 0.1623        | 2.35  | 1300 | 0.2200          | 0.2685 |
| 0.1628        | 2.53  | 1400 | 0.2166          | 0.2707 |
| 0.1593        | 2.71  | 1500 | 0.2131          | 0.2634 |
| 0.1561        | 2.89  | 1600 | 0.2121          | 0.2661 |
| 0.146         | 3.07  | 1700 | 0.2128          | 0.2552 |
| 0.1339        | 3.25  | 1800 | 0.2119          | 0.2591 |
| 0.1314        | 3.43  | 1900 | 0.2090          | 0.2492 |
| 0.1296        | 3.62  | 2000 | 0.2058          | 0.2504 |
| 0.1304        | 3.8   | 2100 | 0.2057          | 0.2500 |
| 0.1276        | 3.98  | 2200 | 0.2028          | 0.2463 |
| 0.116         | 4.16  | 2300 | 0.2058          | 0.2461 |
| 0.1122        | 4.34  | 2400 | 0.2074          | 0.2443 |
| 0.1087        | 4.52  | 2500 | 0.2065          | 0.2411 |
| 0.1087        | 4.7   | 2600 | 0.2042          | 0.2412 |
| 0.11          | 4.88  | 2700 | 0.2014          | 0.2396 |


### Framework versions

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