---
base_model: facebook/w2v-bert-2.0
datasets:
- common_voice_17_0
library_name: transformers
license: mit
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: w2v-bert-2.0-slovak-colab-CV17.0
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: sk
      split: test
      args: sk
    metrics:
    - type: wer
      value: 0.1358878674797488
      name: Wer
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# w2v-bert-2.0-slovak-colab-CV17.0

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: 0.3823
- Wer: 0.1359

## 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: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 3.1601        | 1.6393  | 300  | 0.4869          | 0.2985 |
| 0.2141        | 3.2787  | 600  | 0.3886          | 0.2144 |
| 0.1323        | 4.9180  | 900  | 0.3180          | 0.1840 |
| 0.0754        | 6.5574  | 1200 | 0.3019          | 0.1750 |
| 0.0401        | 8.1967  | 1500 | 0.3717          | 0.1525 |
| 0.022         | 9.8361  | 1800 | 0.3408          | 0.1503 |
| 0.0083        | 11.4754 | 2100 | 0.3489          | 0.1413 |
| 0.0027        | 13.1148 | 2400 | 0.3681          | 0.1358 |
| 0.0011        | 14.7541 | 2700 | 0.3823          | 0.1359 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.1
- Datasets 2.19.1
- Tokenizers 0.20.1