---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec-read_aloud
  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. -->

# wav2vec-read_aloud

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: 0.1115
- Pcc Accuracy: 0.7918
- Pcc Fluency: 0.7940
- Pcc Total Score: 0.8472
- Pcc Content: 0.8160

## 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: 0.00055
- train_batch_size: 2
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 12
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 14

### Training results

| Training Loss | Epoch | Step | Validation Loss | Pcc Accuracy | Pcc Fluency | Pcc Total Score | Pcc Content |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:-----------:|:---------------:|:-----------:|
| 0.1483        | 1.94  | 500  | 0.1659          | 0.7256       | 0.6982      | 0.7616          | 0.7480      |
| 0.1338        | 3.89  | 1000 | 0.1369          | 0.7706       | 0.7680      | 0.8154          | 0.7835      |
| 0.124         | 5.83  | 1500 | 0.1754          | 0.6686       | 0.6459      | 0.7110          | 0.6823      |
| 0.1147        | 7.77  | 2000 | 0.1149          | 0.7838       | 0.7848      | 0.8368          | 0.8048      |
| 0.1024        | 9.72  | 2500 | 0.1135          | 0.7802       | 0.7819      | 0.8340          | 0.8048      |
| 0.0945        | 11.66 | 3000 | 0.1168          | 0.7891       | 0.7876      | 0.8418          | 0.8095      |
| 0.0945        | 13.61 | 3500 | 0.1115          | 0.7918       | 0.7940      | 0.8472          | 0.8160      |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.1