---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_7_0
metrics:
- wer
model-index:
- name: w2v-bert-2.0-luganda-CV-train-validation-7.0
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_7_0
      type: common_voice_7_0
      config: lg
      split: test
      args: lg
    metrics:
    - name: Wer
      type: wer
      value: 0.18224972173115955
---

<!-- 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. -->

# w2v-bert-2.0-luganda-CV-train-validation-7.0

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_7_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2122
- Wer: 0.1822

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.31          | 1.89  | 300  | 0.2527          | 0.2845 |
| 0.112         | 3.77  | 600  | 0.2177          | 0.2326 |
| 0.0698        | 5.66  | 900  | 0.2085          | 0.2127 |
| 0.0426        | 7.55  | 1200 | 0.2024          | 0.1941 |
| 0.0272        | 9.43  | 1500 | 0.2122          | 0.1822 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.2.1+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2