---
license: mit
base_model: arslanarjumand/wav2vec-reptiles
tags:
- generated_from_trainer
model-index:
- name: wav2vec-reptiles
  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-reptiles

This model is a fine-tuned version of [arslanarjumand/wav2vec-reptiles](https://huggingface.co/arslanarjumand/wav2vec-reptiles) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 180.5618
- Pcc Accuracy: 0.7344
- Pcc Fluency: 0.7572
- Pcc Total Score: 0.7949
- Pcc Content: 0.7727

## 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: 2.5e-05
- train_batch_size: 4
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.5
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Pcc Accuracy | Pcc Fluency | Pcc Total Score | Pcc Content |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:-----------:|:---------------:|:-----------:|
| 323.2938      | 2.13  | 500  | 333.4772        | 0.4645       | 0.5166      | 0.5181          | 0.4915      |
| 274.2192      | 4.27  | 1000 | 259.5493        | 0.5725       | 0.6371      | 0.6430          | 0.6182      |
| 287.9362      | 6.4   | 1500 | 291.9187        | 0.6475       | 0.6895      | 0.7121          | 0.6902      |
| 273.6328      | 8.54  | 2000 | 229.1164        | 0.6884       | 0.7243      | 0.7522          | 0.7285      |
| 211.4504      | 10.67 | 2500 | 223.4485        | 0.7087       | 0.7420      | 0.7727          | 0.7499      |
| 162.7622      | 12.81 | 3000 | 180.6950        | 0.7302       | 0.7557      | 0.7918          | 0.7695      |
| 194.6916      | 14.94 | 3500 | 180.5618        | 0.7344       | 0.7572      | 0.7949          | 0.7727      |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.17.1
- Tokenizers 0.15.1