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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_002
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_002
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: 1.5036
- Per: 0.1541
- Pcc: 0.6677
- Ctc Loss: 0.5422
- Mse Loss: 0.9427
## 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: 2222
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:--------:|:--------:|
| 16.9349 | 3.0 | 2226 | 4.6257 | 0.9983 | 0.6397 | 3.7753 | 0.9152 |
| 4.358 | 6.0 | 4452 | 4.3728 | 0.9983 | 0.6743 | 3.7449 | 0.7973 |
| 3.976 | 9.0 | 6678 | 4.2399 | 0.9983 | 0.6928 | 3.6699 | 0.8195 |
| 2.9839 | 12.0 | 8904 | 2.3433 | 0.3730 | 0.6740 | 1.5100 | 0.8973 |
| 1.2641 | 15.0 | 11130 | 1.7650 | 0.2095 | 0.6732 | 0.7985 | 0.9498 |
| 0.8466 | 18.0 | 13356 | 1.5664 | 0.1818 | 0.6642 | 0.6611 | 0.8872 |
| 0.6752 | 21.0 | 15582 | 1.5958 | 0.1708 | 0.6690 | 0.6012 | 0.9664 |
| 0.5802 | 24.0 | 17808 | 1.7719 | 0.1651 | 0.6737 | 0.5668 | 1.1474 |
| 0.5266 | 27.0 | 20034 | 1.6479 | 0.1577 | 0.6707 | 0.5482 | 1.0587 |
| 0.4851 | 30.0 | 22260 | 1.5036 | 0.1541 | 0.6677 | 0.5422 | 0.9427 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.2
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