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---
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
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