---
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-mas-ex
  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: 0.6300848379377855
---

<!-- 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-mas-ex

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: 0.7763
- Wer: 0.6301

## 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: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.424         | 0.12  | 300   | 1.3270          | 0.8863 |
| 1.2288        | 0.23  | 600   | 1.1525          | 0.8299 |
| 1.0443        | 0.35  | 900   | 0.9812          | 0.7729 |
| 1.0082        | 0.46  | 1200  | 0.9045          | 0.6852 |
| 0.8698        | 0.58  | 1500  | 0.9797          | 0.7063 |
| 0.8649        | 0.69  | 1800  | 0.9071          | 0.6724 |
| 0.8268        | 0.81  | 2100  | 0.8387          | 0.6716 |
| 0.8428        | 0.93  | 2400  | 0.8392          | 0.6623 |
| 0.6933        | 1.04  | 2700  | 0.7124          | 0.5966 |
| 0.6618        | 1.16  | 3000  | 0.7056          | 0.5688 |
| 0.6578        | 1.27  | 3300  | 0.7003          | 0.5708 |
| 0.6331        | 1.39  | 3600  | 0.6798          | 0.5578 |
| 0.5873        | 1.5   | 3900  | 0.6993          | 0.5453 |
| 0.6076        | 1.62  | 4200  | 0.6562          | 0.5268 |
| 0.5359        | 1.74  | 4500  | 0.6837          | 0.5735 |
| 0.6807        | 1.85  | 4800  | 0.6495          | 0.5272 |
| 0.5945        | 1.97  | 5100  | 0.6434          | 0.5058 |
| 0.5059        | 2.08  | 5400  | 0.6237          | 0.4855 |
| 0.5244        | 2.2   | 5700  | 0.6334          | 0.4749 |
| 0.5052        | 2.31  | 6000  | 0.6831          | 0.4976 |
| 0.5249        | 2.43  | 6300  | 0.6339          | 0.4919 |
| 0.5537        | 2.55  | 6600  | 0.6541          | 0.4990 |
| 0.6387        | 2.66  | 6900  | 0.8375          | 0.5829 |
| 0.669         | 2.78  | 7200  | 0.9152          | 0.6289 |
| 0.8881        | 2.89  | 7500  | 0.7704          | 0.6191 |
| 1.184         | 3.01  | 7800  | 0.8139          | 0.6866 |
| 1.0933        | 3.12  | 8100  | 0.7721          | 0.6518 |
| 1.3588        | 3.24  | 8400  | 0.7368          | 0.6152 |
| 1.4604        | 3.36  | 8700  | 0.7376          | 0.6158 |
| 1.2902        | 3.47  | 9000  | 0.7451          | 0.6188 |
| 1.3137        | 3.59  | 9300  | 0.7493          | 0.6194 |
| 1.3009        | 3.7   | 9600  | 0.7454          | 0.6164 |
| 1.3757        | 3.82  | 9900  | 0.7515          | 0.6289 |
| 1.2412        | 3.93  | 10200 | 0.7629          | 0.6237 |
| 1.2835        | 4.05  | 10500 | 0.7760          | 0.6351 |
| 1.3803        | 4.17  | 10800 | 0.7718          | 0.6273 |
| 1.325         | 4.28  | 11100 | 0.7763          | 0.6301 |
| 1.3798        | 4.4   | 11400 | 0.7763          | 0.6301 |
| 1.3421        | 4.51  | 11700 | 0.7763          | 0.6301 |
| 1.2834        | 4.63  | 12000 | 0.7763          | 0.6301 |
| 1.4757        | 4.74  | 12300 | 0.7763          | 0.6301 |
| 1.4171        | 4.86  | 12600 | 0.7763          | 0.6301 |
| 1.2838        | 4.97  | 12900 | 0.7763          | 0.6301 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.2