---
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: 1.7114
- Wer: 1.0056

## 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: 16
- 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: 60

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0734        | 1.01  | 100  | 1.7114          | 1.0056 |
| 0.074         | 2.02  | 200  | 1.7114          | 1.0056 |
| 0.0707        | 3.03  | 300  | 1.7114          | 1.0056 |
| 0.0727        | 4.04  | 400  | 1.7114          | 1.0056 |
| 0.076         | 5.05  | 500  | 1.7114          | 1.0056 |
| 0.0713        | 6.06  | 600  | 1.7114          | 1.0056 |
| 0.0709        | 7.07  | 700  | 1.7114          | 1.0056 |
| 0.0721        | 8.08  | 800  | 1.7114          | 1.0056 |
| 0.0761        | 9.09  | 900  | 1.7114          | 1.0056 |
| 0.0724        | 10.1  | 1000 | 1.7114          | 1.0056 |
| 0.0749        | 11.11 | 1100 | 1.7114          | 1.0056 |
| 0.0746        | 12.12 | 1200 | 1.7114          | 1.0056 |
| 0.0756        | 13.13 | 1300 | 1.7114          | 1.0056 |
| 0.0712        | 14.14 | 1400 | 1.7114          | 1.0056 |
| 0.0704        | 15.15 | 1500 | 1.7114          | 1.0056 |
| 0.0698        | 16.16 | 1600 | 1.7114          | 1.0056 |
| 0.0715        | 17.17 | 1700 | 1.7114          | 1.0056 |
| 0.0743        | 18.18 | 1800 | 1.7114          | 1.0056 |
| 0.0743        | 19.19 | 1900 | 1.7114          | 1.0056 |
| 0.0745        | 20.2  | 2000 | 1.7114          | 1.0056 |
| 0.0702        | 21.21 | 2100 | 1.7114          | 1.0056 |
| 0.0717        | 22.22 | 2200 | 1.7114          | 1.0056 |
| 0.0673        | 23.23 | 2300 | 1.7114          | 1.0056 |
| 0.0701        | 24.24 | 2400 | 1.7114          | 1.0056 |
| 0.0754        | 25.25 | 2500 | 1.7114          | 1.0056 |
| 0.0677        | 26.26 | 2600 | 1.7114          | 1.0056 |
| 0.0751        | 27.27 | 2700 | 1.7114          | 1.0056 |
| 0.0828        | 28.28 | 2800 | 1.7114          | 1.0056 |
| 0.0714        | 29.29 | 2900 | 1.7114          | 1.0056 |
| 0.0735        | 30.3  | 3000 | 1.7114          | 1.0056 |
| 0.0724        | 31.31 | 3100 | 1.7114          | 1.0056 |
| 0.0777        | 32.32 | 3200 | 1.7114          | 1.0056 |
| 0.0747        | 33.33 | 3300 | 1.7114          | 1.0056 |
| 0.0724        | 34.34 | 3400 | 1.7114          | 1.0056 |
| 0.0717        | 35.35 | 3500 | 1.7114          | 1.0056 |
| 0.0723        | 36.36 | 3600 | 1.7114          | 1.0056 |
| 0.0797        | 37.37 | 3700 | 1.7114          | 1.0056 |
| 0.0693        | 38.38 | 3800 | 1.7114          | 1.0056 |
| 0.0748        | 39.39 | 3900 | 1.7114          | 1.0056 |
| 0.0739        | 40.4  | 4000 | 1.7114          | 1.0056 |
| 0.0701        | 41.41 | 4100 | 1.7114          | 1.0056 |
| 0.079         | 42.42 | 4200 | 1.7114          | 1.0056 |
| 0.0753        | 43.43 | 4300 | 1.7114          | 1.0056 |
| 0.0707        | 44.44 | 4400 | 1.7114          | 1.0056 |
| 0.0724        | 45.45 | 4500 | 1.7114          | 1.0056 |
| 0.0667        | 46.46 | 4600 | 1.7114          | 1.0056 |
| 0.077         | 47.47 | 4700 | 1.7114          | 1.0056 |
| 0.0716        | 48.48 | 4800 | 1.7114          | 1.0056 |
| 0.0731        | 49.49 | 4900 | 1.7114          | 1.0056 |
| 0.0741        | 50.51 | 5000 | 1.7114          | 1.0056 |
| 0.0705        | 51.52 | 5100 | 1.7114          | 1.0056 |
| 0.0736        | 52.53 | 5200 | 1.7114          | 1.0056 |
| 0.0741        | 53.54 | 5300 | 1.7114          | 1.0056 |
| 0.0721        | 54.55 | 5400 | 1.7114          | 1.0056 |
| 0.074         | 55.56 | 5500 | 1.7114          | 1.0056 |
| 0.071         | 56.57 | 5600 | 1.7114          | 1.0056 |
| 0.0723        | 57.58 | 5700 | 1.7114          | 1.0056 |
| 0.0725        | 58.59 | 5800 | 1.7114          | 1.0056 |
| 0.0746        | 59.6  | 5900 | 1.7114          | 1.0056 |


### Framework versions

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