---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_16_0
metrics:
- wer
model-index:
- name: w2v-bertkmr-test
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_16_0
      type: common_voice_16_0
      config: kmr
      split: test
      args: kmr
    metrics:
    - name: Wer
      type: wer
      value: 0.1570856537948175
---

<!-- 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-bertkmr-test

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: 0.2399
- Wer: 0.1571

## 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: 150
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 0.8   | 200  | 0.3476          | 0.3257 |
| 1.2561        | 1.6   | 400  | 0.2756          | 0.2669 |
| 0.1906        | 2.4   | 600  | 0.2484          | 0.2363 |
| 0.1906        | 3.2   | 800  | 0.2336          | 0.2177 |
| 0.1242        | 4.0   | 1000 | 0.2192          | 0.1919 |
| 0.0853        | 4.8   | 1200 | 0.2217          | 0.1879 |
| 0.0853        | 5.6   | 1400 | 0.2272          | 0.1786 |
| 0.0586        | 6.4   | 1600 | 0.2292          | 0.1695 |
| 0.0365        | 7.2   | 1800 | 0.2276          | 0.1613 |
| 0.0365        | 8.0   | 2000 | 0.2127          | 0.1626 |
| 0.0222        | 8.8   | 2200 | 0.2271          | 0.1568 |
| 0.0118        | 9.6   | 2400 | 0.2399          | 0.1571 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1