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---
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: 182.3516
- Pcc Accuracy: 0.6684
- Pcc Fluency: 0.6499
- Pcc Total Score: 0.7110
- Pcc Content: 0.6788
## 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: 5.5e-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: 0.4
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pcc Accuracy | Pcc Fluency | Pcc Total Score | Pcc Content |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:-----------:|:---------------:|:-----------:|
| 2719.4074 | 0.97 | 500 | 2790.7349 | 0.1171 | 0.1116 | 0.1218 | 0.1245 |
| 386.8535 | 1.93 | 1000 | 361.3293 | 0.1481 | 0.1332 | 0.1511 | 0.1445 |
| 273.8093 | 2.9 | 1500 | 304.4040 | 0.2869 | 0.2915 | 0.3062 | 0.2849 |
| 280.8214 | 3.87 | 2000 | 277.9273 | 0.4065 | 0.4344 | 0.4465 | 0.4131 |
| 264.1531 | 4.84 | 2500 | 265.5385 | 0.5012 | 0.5234 | 0.5490 | 0.5117 |
| 211.6362 | 5.8 | 3000 | 226.9335 | 0.5675 | 0.5768 | 0.6171 | 0.5817 |
| 217.8737 | 6.77 | 3500 | 218.1019 | 0.6089 | 0.5984 | 0.6525 | 0.6194 |
| 180.3319 | 7.74 | 4000 | 201.4108 | 0.6296 | 0.6142 | 0.6721 | 0.6395 |
| 174.7695 | 8.7 | 4500 | 201.3474 | 0.6427 | 0.6297 | 0.6872 | 0.6542 |
| 182.4466 | 9.67 | 5000 | 189.6567 | 0.6566 | 0.6333 | 0.6957 | 0.6619 |
| 184.7177 | 10.64 | 5500 | 182.7654 | 0.6628 | 0.6405 | 0.7033 | 0.6713 |
| 174.6915 | 11.61 | 6000 | 181.2284 | 0.6635 | 0.6479 | 0.7077 | 0.6755 |
| 187.671 | 12.57 | 6500 | 180.5753 | 0.6676 | 0.6486 | 0.7099 | 0.6773 |
| 166.4409 | 13.54 | 7000 | 181.2506 | 0.6682 | 0.6493 | 0.7105 | 0.6781 |
| 176.7043 | 14.51 | 7500 | 182.3516 | 0.6684 | 0.6499 | 0.7110 | 0.6788 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.1
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