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---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
model-index:
- name: saq-20s_asr-scr_w2v2-base_003
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. -->
# saq-20s_asr-scr_w2v2-base_003
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4770
- Per: 0.1582
- Pcc: 0.6742
- Ctc Loss: 0.5610
- Mse Loss: 0.8995
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 1
- seed: 3333
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2226
- training_steps: 22260
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Per | Pcc | Ctc Loss | Mse Loss |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:--------:|:--------:|
| 16.8423 | 3.0 | 2226 | 4.5928 | 0.9983 | 0.6376 | 3.7671 | 0.8900 |
| 4.329 | 6.0 | 4452 | 4.5763 | 0.9983 | 0.6939 | 3.7500 | 0.9944 |
| 3.9518 | 9.0 | 6678 | 4.3570 | 0.9983 | 0.6962 | 3.7043 | 0.9024 |
| 3.4509 | 12.0 | 8904 | 3.2020 | 0.8036 | 0.6831 | 2.6097 | 0.7935 |
| 1.6554 | 15.0 | 11130 | 1.7792 | 0.2361 | 0.6712 | 0.9476 | 0.8436 |
| 0.9205 | 18.0 | 13356 | 1.6586 | 0.1900 | 0.6858 | 0.7026 | 0.9314 |
| 0.7081 | 21.0 | 15582 | 1.5955 | 0.1721 | 0.6716 | 0.6236 | 0.9429 |
| 0.5965 | 24.0 | 17808 | 1.5239 | 0.1634 | 0.6784 | 0.5880 | 0.9140 |
| 0.5351 | 27.0 | 20034 | 1.4992 | 0.1595 | 0.6782 | 0.5682 | 0.9115 |
| 0.5012 | 30.0 | 22260 | 1.4770 | 0.1582 | 0.6742 | 0.5610 | 0.8995 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.0.1
- Datasets 2.18.0
- Tokenizers 0.15.2
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