---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_16_0
metrics:
- wer
model-index:
- name: w2v-bert-2.0-krd-colab-CV16.0
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_16_0
      type: common_voice_16_0
      config: ckb
      split: test
      args: ckb
    metrics:
    - name: Wer
      type: wer
      value: 0.23061901252763448
---

<!-- 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-krd-colab-CV16.0

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_16_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2704
- Wer: 0.2306

## 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    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 2.283         | 0.7979 | 300  | 0.3271          | 0.3871 |
| 0.2931        | 1.5957 | 600  | 0.2957          | 0.3468 |
| 0.2358        | 2.3936 | 900  | 0.2746          | 0.3299 |
| 0.1842        | 3.1915 | 1200 | 0.2473          | 0.2846 |
| 0.1532        | 3.9894 | 1500 | 0.2257          | 0.2632 |
| 0.1198        | 4.7872 | 1800 | 0.2403          | 0.2600 |
| 0.1027        | 5.5851 | 2100 | 0.2239          | 0.2513 |
| 0.0837        | 6.3830 | 2400 | 0.2310          | 0.2591 |
| 0.0678        | 7.1809 | 2700 | 0.2295          | 0.2402 |
| 0.0527        | 7.9787 | 3000 | 0.2428          | 0.2334 |
| 0.0374        | 8.7766 | 3300 | 0.2448          | 0.2347 |
| 0.0298        | 9.5745 | 3600 | 0.2704          | 0.2306 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu118
- Datasets 2.19.2
- Tokenizers 0.19.1