---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_16_1
metrics:
- wer
model-index:
- name: w2v-bert-2.0-sr
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_16_1
      type: common_voice_16_1
      config: sr
      split: test
      args: sr
    metrics:
    - name: Wer
      type: wer
      value: 0.05344857999647204
---

<!-- 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-sr

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_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1469
- Wer: 0.0534

## 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
- gradient_accumulation_steps: 8
- 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: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.1994        | 1.89  | 300  | 0.1350          | 0.1078 |
| 0.2331        | 3.77  | 600  | 0.2306          | 0.1341 |
| 0.1879        | 5.66  | 900  | 0.1354          | 0.0766 |
| 0.1579        | 7.54  | 1200 | 0.1646          | 0.0958 |
| 0.1293        | 9.43  | 1500 | 0.1207          | 0.0713 |
| 0.1182        | 11.31 | 1800 | 0.1376          | 0.0737 |
| 0.1061        | 13.2  | 2100 | 0.1244          | 0.0580 |
| 0.1011        | 15.08 | 2400 | 0.1390          | 0.0602 |
| 0.0933        | 16.97 | 2700 | 0.1313          | 0.0524 |
| 0.0948        | 18.85 | 3000 | 0.1469          | 0.0534 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1