---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Model_S_D_Wav2Vec2
  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. -->

# Model_S_D_Wav2Vec2

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0464
- Wer: 0.2319
- Cer: 0.0598

## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 3.5768        | 0.85  | 400   | 0.6152          | 0.5812 | 0.1905 |
| 0.3226        | 1.71  | 800   | 0.1026          | 0.3195 | 0.0722 |
| 0.1827        | 2.56  | 1200  | 0.0725          | 0.2048 | 0.0454 |
| 0.129         | 3.41  | 1600  | 0.0671          | 0.2393 | 0.0525 |
| 0.1075        | 4.26  | 2000  | 0.0556          | 0.2312 | 0.0497 |
| 0.0924        | 5.12  | 2400  | 0.0572          | 0.2040 | 0.0478 |
| 0.076         | 5.97  | 2800  | 0.0596          | 0.1472 | 0.0346 |
| 0.0695        | 6.82  | 3200  | 0.0608          | 0.2274 | 0.0510 |
| 0.0707        | 7.68  | 3600  | 0.0490          | 0.2665 | 0.0660 |
| 0.0597        | 8.53  | 4000  | 0.0509          | 0.2442 | 0.0593 |
| 0.0557        | 9.38  | 4400  | 0.0501          | 0.2533 | 0.0610 |
| 0.0503        | 10.23 | 4800  | 0.0519          | 0.2534 | 0.0622 |
| 0.0471        | 11.09 | 5200  | 0.0512          | 0.2585 | 0.0638 |
| 0.0417        | 11.94 | 5600  | 0.0497          | 0.2522 | 0.0610 |
| 0.0415        | 12.79 | 6000  | 0.0508          | 0.2547 | 0.0629 |
| 0.0372        | 13.65 | 6400  | 0.0497          | 0.2580 | 0.0643 |
| 0.0364        | 14.5  | 6800  | 0.0448          | 0.2498 | 0.0600 |
| 0.034         | 15.35 | 7200  | 0.0522          | 0.2419 | 0.0593 |
| 0.0306        | 16.2  | 7600  | 0.0510          | 0.2433 | 0.0560 |
| 0.0345        | 17.06 | 8000  | 0.0503          | 0.2610 | 0.0657 |
| 0.0266        | 17.91 | 8400  | 0.0462          | 0.2434 | 0.0620 |
| 0.0273        | 18.76 | 8800  | 0.0507          | 0.2456 | 0.0622 |
| 0.0216        | 19.62 | 9200  | 0.0466          | 0.2214 | 0.0531 |
| 0.0208        | 20.47 | 9600  | 0.0497          | 0.2396 | 0.0598 |
| 0.0201        | 21.32 | 10000 | 0.0470          | 0.2332 | 0.0559 |
| 0.0174        | 22.17 | 10400 | 0.0418          | 0.2346 | 0.0590 |
| 0.0198        | 23.03 | 10800 | 0.0472          | 0.2386 | 0.0602 |
| 0.0149        | 23.88 | 11200 | 0.0490          | 0.2446 | 0.0638 |
| 0.0133        | 24.73 | 11600 | 0.0497          | 0.2430 | 0.0632 |
| 0.0118        | 25.59 | 12000 | 0.0498          | 0.2368 | 0.0620 |
| 0.0106        | 26.44 | 12400 | 0.0453          | 0.2309 | 0.0590 |
| 0.0104        | 27.29 | 12800 | 0.0452          | 0.2296 | 0.0583 |
| 0.0085        | 28.14 | 13200 | 0.0467          | 0.2352 | 0.0604 |
| 0.0081        | 29.0  | 13600 | 0.0470          | 0.2310 | 0.0592 |
| 0.0079        | 29.85 | 14000 | 0.0464          | 0.2319 | 0.0598 |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 1.18.3
- Tokenizers 0.13.3