---
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-sv
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: sv-SE
      split: test
      args: sv-SE
    metrics:
    - name: Wer
      type: wer
      value: 0.10046931592103249
---

<!-- 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-sv

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: 0.1962
- Wer: 0.1005

## 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_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 2.075         | 0.7407 | 300  | 0.3441          | 0.3057 |
| 0.2837        | 1.4815 | 600  | 0.2995          | 0.2274 |
| 0.2081        | 2.2222 | 900  | 0.2443          | 0.1768 |
| 0.1579        | 2.9630 | 1200 | 0.2143          | 0.1493 |
| 0.1248        | 3.7037 | 1500 | 0.2165          | 0.1504 |
| 0.0934        | 4.4444 | 1800 | 0.1869          | 0.1284 |
| 0.0719        | 5.1852 | 2100 | 0.2072          | 0.1216 |
| 0.0573        | 5.9259 | 2400 | 0.1949          | 0.1195 |
| 0.0436        | 6.6667 | 2700 | 0.2025          | 0.1142 |
| 0.0317        | 7.4074 | 3000 | 0.2003          | 0.1097 |
| 0.0256        | 8.1481 | 3300 | 0.1942          | 0.1060 |
| 0.0169        | 8.8889 | 3600 | 0.1851          | 0.1030 |
| 0.0121        | 9.6296 | 3900 | 0.1962          | 0.1005 |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.1.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1