---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: w2v-V3
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_11_0
      type: common_voice_11_0
      config: ar
      split: test
      args: ar
    metrics:
    - name: Wer
      type: wer
      value: 0.16133249852681203
---

<!-- 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-V3

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_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1847
- Wer: 0.1613

## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 5000

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 1.566         | 0.0428 | 300  | 0.6246          | 0.5686 |
| 0.462         | 0.0856 | 600  | 0.5791          | 0.3623 |
| 0.4407        | 0.1284 | 900  | 0.4428          | 0.3232 |
| 0.4036        | 0.1712 | 1200 | 0.4119          | 0.3066 |
| 0.328         | 0.2139 | 1500 | 0.3693          | 0.2684 |
| 0.3151        | 0.2567 | 1800 | 0.3102          | 0.2462 |
| 0.2907        | 0.2995 | 2100 | 0.3221          | 0.2411 |
| 0.2553        | 0.3423 | 2400 | 0.3061          | 0.2430 |
| 0.2156        | 0.3851 | 2700 | 0.2857          | 0.2104 |
| 0.2034        | 0.4279 | 3000 | 0.2516          | 0.2025 |
| 0.2038        | 0.4707 | 3300 | 0.2395          | 0.1995 |
| 0.1751        | 0.5135 | 3600 | 0.2372          | 0.1875 |
| 0.1697        | 0.5563 | 3900 | 0.2063          | 0.1809 |
| 0.1501        | 0.5991 | 4200 | 0.2005          | 0.1775 |
| 0.1428        | 0.6418 | 4500 | 0.2024          | 0.1701 |
| 0.1211        | 0.6846 | 4800 | 0.1883          | 0.1642 |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0