---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: w2v-bert-2.0-yoruba-colab-CV17.0-v2
  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: 0.5771575538197752
---

<!-- 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-colab-CV17.0-v2

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_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8450
- Wer: 0.5772

## 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_ratio: 0.15
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 2.7641        | 3.0769  | 200  | 1.0220          | 0.7877 |
| 0.6684        | 6.1538  | 400  | 0.9003          | 0.6490 |
| 0.4959        | 9.2308  | 600  | 0.9080          | 0.7072 |
| 0.359         | 12.3077 | 800  | 0.9788          | 0.6147 |
| 0.2047        | 15.3846 | 1000 | 1.0914          | 0.6017 |
| 0.0858        | 18.4615 | 1200 | 1.4604          | 0.5973 |
| 0.0426        | 21.5385 | 1400 | 1.5740          | 0.5988 |
| 0.0088        | 24.6154 | 1600 | 1.7418          | 0.5753 |
| 0.0017        | 27.6923 | 1800 | 1.8206          | 0.5779 |
| 0.001         | 30.7692 | 2000 | 1.8450          | 0.5772 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1