---
base_model: oyemade/w2v-bert-2.0-yoruba-CV17.0
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: w2v-bert-2.0-yoruba-CV17.0
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: yo
      split: test
      args: yo
    metrics:
    - name: Wer
      type: wer
      value: 0.10649647551914651
---

<!-- 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-yoruba-CV17.0

This model is a fine-tuned version of [oyemade/w2v-bert-2.0-yoruba-CV17.0](https://huggingface.co/oyemade/w2v-bert-2.0-yoruba-CV17.0) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1095
- Wer: 0.1065

## 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: 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: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.3812        | 0.5102 | 100  | 0.3328          | 0.3070 |
| 0.2283        | 1.0204 | 200  | 0.2721          | 0.2807 |
| 0.1993        | 1.5306 | 300  | 0.3371          | 0.3481 |
| 0.2045        | 2.0408 | 400  | 0.3514          | 0.3314 |
| 0.2057        | 2.5510 | 500  | 0.3036          | 0.3086 |
| 0.2193        | 3.0612 | 600  | 0.2904          | 0.2847 |
| 0.1956        | 3.5714 | 700  | 0.2631          | 0.2534 |
| 0.1717        | 4.0816 | 800  | 0.1923          | 0.1995 |
| 0.1234        | 4.5918 | 900  | 0.1678          | 0.1732 |
| 0.0995        | 5.1020 | 1000 | 0.1280          | 0.1341 |
| 0.0614        | 5.6122 | 1100 | 0.1095          | 0.1065 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1