---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_16_0
metrics:
- wer
model-index:
- name: wav2vec2-bert-cv16-mas-ex-cv16
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_16_0
      type: common_voice_16_0
      config: mn
      split: test
      args: mn
    metrics:
    - name: Wer
      type: wer
      value: 0.6611920817924734
---

<!-- 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. -->

# wav2vec2-bert-cv16-mas-ex-cv16

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_16_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7349
- Wer: 0.6612

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 700
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.3593        | 1.21  | 700  | 0.6050          | 0.5216 |
| 0.5443        | 2.43  | 1400 | 0.5665          | 0.4557 |
| 0.9415        | 3.64  | 2100 | 0.6099          | 0.5665 |
| 1.0953        | 4.85  | 2800 | 0.7349          | 0.6612 |
| 1.176         | 6.07  | 3500 | 0.7349          | 0.6612 |
| 1.1783        | 7.28  | 4200 | 0.7349          | 0.6612 |
| 1.1771        | 8.49  | 4900 | 0.7349          | 0.6612 |
| 1.1775        | 9.71  | 5600 | 0.7349          | 0.6612 |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.2.0
- Datasets 2.15.0
- Tokenizers 0.15.2