---
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: 390.7498
- Pcc Accuracy: 0.3249
- Pcc Fluency: 0.3309
- Pcc Total Score: 0.3483
- Pcc Content: 0.3289

## 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: 4
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:-----------:|:---------------:|:-----------:|
| 295.2316      | 1.07  | 500  | 461.7437        | -0.1108      | -0.1167     | -0.1176         | -0.1085     |
| 597.6253      | 2.13  | 1000 | 457.3701        | -0.0901      | -0.0972     | -0.0963         | -0.0889     |
| 398.6829      | 3.2   | 1500 | 451.0632        | -0.0516      | -0.0600     | -0.0562         | -0.0518     |
| 268.3367      | 4.27  | 2000 | 442.0888        | -0.0001      | -0.0087     | -0.0019         | -0.0014     |
| 672.6757      | 5.34  | 2500 | 430.7292        | 0.0784       | 0.0684      | 0.0804          | 0.0751      |
| 356.5551      | 6.4   | 3000 | 420.3665        | 0.1491       | 0.1413      | 0.1562          | 0.1459      |
| 439.0877      | 7.47  | 3500 | 418.4001        | 0.1997       | 0.1944      | 0.2109          | 0.1975      |
| 486.5083      | 8.54  | 4000 | 403.9485        | 0.2559       | 0.2552      | 0.2724          | 0.2560      |
| 282.6038      | 9.61  | 4500 | 398.6847        | 0.2874       | 0.2885      | 0.3064          | 0.2882      |
| 397.4536      | 10.67 | 5000 | 394.2357        | 0.3064       | 0.3098      | 0.3274          | 0.3088      |
| 347.7374      | 11.74 | 5500 | 392.6132        | 0.3168       | 0.3217      | 0.3392          | 0.3201      |
| 343.9868      | 12.81 | 6000 | 391.1635        | 0.3226       | 0.3286      | 0.3460          | 0.3265      |
| 704.0403      | 13.87 | 6500 | 390.9952        | 0.3244       | 0.3304      | 0.3477          | 0.3284      |
| 333.3059      | 14.94 | 7000 | 390.7498        | 0.3249       | 0.3309      | 0.3483          | 0.3289      |


### Framework versions

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