---
base_model: facebook/w2v-bert-2.0
license: mit
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: w2v-bert-2.0-nonstudio_and_studioRecords_final
  results: []
---

<!-- 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-nonstudio_and_studioRecords_final

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1772
- Wer: 0.1266

## 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    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 1.055         | 0.4601 | 600   | 0.3683          | 0.4608 |
| 0.1734        | 0.9202 | 1200  | 0.2620          | 0.3546 |
| 0.1242        | 1.3804 | 1800  | 0.2115          | 0.3018 |
| 0.1075        | 1.8405 | 2400  | 0.2004          | 0.2889 |
| 0.0888        | 2.3006 | 3000  | 0.1870          | 0.2573 |
| 0.078         | 2.7607 | 3600  | 0.1724          | 0.2267 |
| 0.0664        | 3.2209 | 4200  | 0.1572          | 0.2244 |
| 0.0576        | 3.6810 | 4800  | 0.1746          | 0.2217 |
| 0.0522        | 4.1411 | 5400  | 0.1643          | 0.1796 |
| 0.0415        | 4.6012 | 6000  | 0.1781          | 0.1851 |
| 0.0398        | 5.0613 | 6600  | 0.1670          | 0.1714 |
| 0.0301        | 5.5215 | 7200  | 0.1531          | 0.1617 |
| 0.0296        | 5.9816 | 7800  | 0.1463          | 0.1590 |
| 0.0211        | 6.4417 | 8400  | 0.1566          | 0.1473 |
| 0.0206        | 6.9018 | 9000  | 0.1423          | 0.1468 |
| 0.0147        | 7.3620 | 9600  | 0.1443          | 0.1413 |
| 0.0136        | 7.8221 | 10200 | 0.1539          | 0.1418 |
| 0.0105        | 8.2822 | 10800 | 0.1611          | 0.1383 |
| 0.0079        | 8.7423 | 11400 | 0.1761          | 0.1351 |
| 0.0063        | 9.2025 | 12000 | 0.1814          | 0.1304 |
| 0.0043        | 9.6626 | 12600 | 0.1772          | 0.1266 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1