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

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: 205.9549
- Pcc Accuracy: 0.8004
- Pcc Fluency: 0.7759
- Pcc Total Score: 0.8207
- Pcc Content: 0.7220

## 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: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Pcc Accuracy | Pcc Fluency | Pcc Total Score | Pcc Content |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:-----------:|:---------------:|:-----------:|
| 507.295       | 3.54  | 500  | 538.7184        | 0.2592       | 0.2368      | 0.2807          | 0.3206      |
| 267.4833      | 7.08  | 1000 | 374.0983        | 0.5787       | 0.5582      | 0.5900          | 0.5040      |
| 246.7156      | 10.62 | 1500 | 483.3237        | 0.6618       | 0.6387      | 0.6761          | 0.5837      |
| 269.7238      | 14.16 | 2000 | 446.4642        | 0.6964       | 0.6691      | 0.7131          | 0.6288      |
| 289.3261      | 17.7  | 2500 | 244.4726        | 0.7201       | 0.6928      | 0.7371          | 0.6482      |
| 249.89        | 21.24 | 3000 | 413.8036        | 0.7340       | 0.7052      | 0.7548          | 0.6796      |
| 235.8593      | 24.78 | 3500 | 251.3629        | 0.7472       | 0.7217      | 0.7676          | 0.6808      |
| 217.7143      | 28.32 | 4000 | 212.4162        | 0.7779       | 0.7547      | 0.7973          | 0.6948      |
| 123.7326      | 31.86 | 4500 | 362.4697        | 0.7782       | 0.7528      | 0.7987          | 0.7062      |
| 132.7905      | 35.4  | 5000 | 228.9714        | 0.7826       | 0.7603      | 0.8021          | 0.6987      |
| 111.7989      | 38.94 | 5500 | 189.2367        | 0.7985       | 0.7754      | 0.8188          | 0.7169      |
| 104.5979      | 42.48 | 6000 | 271.8181        | 0.7929       | 0.7692      | 0.8143          | 0.7192      |
| 115.256       | 46.02 | 6500 | 220.4324        | 0.8008       | 0.7753      | 0.8209          | 0.7230      |
| 86.3804       | 49.56 | 7000 | 205.9549        | 0.8004       | 0.7759      | 0.8207          | 0.7220      |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.17.1
- Tokenizers 0.15.2