---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
  name: Waynehills-STT-doogie-server
---

<!-- 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. -->

# Waynehills-STT-doogie-server

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9322
- Wer: 1.0368

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.9017        | 0.51  | 100  | 3.9322          | 1.0368 |
| 1.9117        | 1.01  | 200  | 3.9322          | 1.0368 |
| 1.9099        | 1.52  | 300  | 3.9322          | 1.0368 |
| 1.8933        | 2.02  | 400  | 3.9322          | 1.0368 |
| 1.8659        | 2.53  | 500  | 3.9322          | 1.0368 |
| 1.936         | 3.03  | 600  | 3.9322          | 1.0368 |
| 1.8939        | 3.54  | 700  | 3.9322          | 1.0368 |
| 1.9037        | 4.04  | 800  | 3.9322          | 1.0368 |
| 1.9076        | 4.55  | 900  | 3.9322          | 1.0368 |
| 1.9136        | 5.05  | 1000 | 3.9322          | 1.0368 |
| 1.8875        | 5.56  | 1100 | 3.9322          | 1.0368 |
| 1.9003        | 6.06  | 1200 | 3.9322          | 1.0368 |
| 1.9138        | 6.57  | 1300 | 3.9322          | 1.0368 |
| 1.8942        | 7.07  | 1400 | 3.9322          | 1.0368 |
| 1.9035        | 7.58  | 1500 | 3.9322          | 1.0368 |
| 1.9076        | 8.08  | 1600 | 3.9322          | 1.0368 |
| 1.8997        | 8.59  | 1700 | 3.9322          | 1.0368 |
| 1.8958        | 9.09  | 1800 | 3.9322          | 1.0368 |
| 1.891         | 9.6   | 1900 | 3.9322          | 1.0368 |
| 1.9245        | 10.1  | 2000 | 3.9322          | 1.0368 |
| 1.9042        | 10.61 | 2100 | 3.9322          | 1.0368 |
| 1.9153        | 11.11 | 2200 | 3.9322          | 1.0368 |
| 1.892         | 11.62 | 2300 | 3.9322          | 1.0368 |
| 1.8937        | 12.12 | 2400 | 3.9322          | 1.0368 |
| 1.9036        | 12.63 | 2500 | 3.9322          | 1.0368 |
| 1.9162        | 13.13 | 2600 | 3.9322          | 1.0368 |
| 1.9014        | 13.64 | 2700 | 3.9322          | 1.0368 |
| 1.9083        | 14.14 | 2800 | 3.9322          | 1.0368 |
| 1.9003        | 14.65 | 2900 | 3.9322          | 1.0368 |
| 1.9015        | 15.15 | 3000 | 3.9322          | 1.0368 |
| 1.8851        | 15.66 | 3100 | 3.9322          | 1.0368 |
| 1.9062        | 16.16 | 3200 | 3.9322          | 1.0368 |
| 1.9279        | 16.67 | 3300 | 3.9322          | 1.0368 |
| 1.8795        | 17.17 | 3400 | 3.9322          | 1.0368 |
| 1.9126        | 17.68 | 3500 | 3.9322          | 1.0368 |
| 1.8688        | 18.18 | 3600 | 3.9322          | 1.0368 |
| 1.9234        | 18.69 | 3700 | 3.9322          | 1.0368 |
| 1.8872        | 19.19 | 3800 | 3.9322          | 1.0368 |
| 1.9096        | 19.7  | 3900 | 3.9322          | 1.0368 |
| 1.8854        | 20.2  | 4000 | 3.9322          | 1.0368 |
| 1.9168        | 20.71 | 4100 | 3.9322          | 1.0368 |
| 1.9145        | 21.21 | 4200 | 3.9322          | 1.0368 |
| 1.904         | 21.72 | 4300 | 3.9322          | 1.0368 |
| 1.8982        | 22.22 | 4400 | 3.9322          | 1.0368 |
| 1.8978        | 22.73 | 4500 | 3.9322          | 1.0368 |
| 1.9023        | 23.23 | 4600 | 3.9322          | 1.0368 |
| 1.8901        | 23.74 | 4700 | 3.9322          | 1.0368 |
| 1.9079        | 24.24 | 4800 | 3.9322          | 1.0368 |
| 1.8923        | 24.75 | 4900 | 3.9322          | 1.0368 |
| 1.9095        | 25.25 | 5000 | 3.9322          | 1.0368 |
| 1.909         | 25.76 | 5100 | 3.9322          | 1.0368 |
| 1.8871        | 26.26 | 5200 | 3.9322          | 1.0368 |
| 1.9046        | 26.77 | 5300 | 3.9322          | 1.0368 |
| 1.8877        | 27.27 | 5400 | 3.9322          | 1.0368 |
| 1.901         | 27.78 | 5500 | 3.9322          | 1.0368 |
| 1.9045        | 28.28 | 5600 | 3.9322          | 1.0368 |
| 1.907         | 28.79 | 5700 | 3.9322          | 1.0368 |
| 1.9075        | 29.29 | 5800 | 3.9322          | 1.0368 |
| 1.895         | 29.8  | 5900 | 3.9322          | 1.0368 |


### Framework versions

- Transformers 4.12.5
- Pytorch 1.10.0+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3