---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-sinhala-original-split-part4-epoch30-final
  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. -->

# wav2vec2-large-xls-r-300m-sinhala-original-split-part4-epoch30-final

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: 0.1837
- Wer: 0.1334

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 6.0113        | 0.47  | 400   | 3.4040          | 1.0    |
| 1.3642        | 0.93  | 800   | 0.5373          | 0.6817 |
| 0.4891        | 1.4   | 1200  | 0.2962          | 0.4354 |
| 0.402         | 1.87  | 1600  | 0.2717          | 0.3688 |
| 0.3209        | 2.34  | 2000  | 0.2088          | 0.3262 |
| 0.2993        | 2.8   | 2400  | 0.1909          | 0.2587 |
| 0.2613        | 3.27  | 2800  | 0.1873          | 0.2502 |
| 0.2381        | 3.74  | 3200  | 0.1763          | 0.2310 |
| 0.2224        | 4.21  | 3600  | 0.1812          | 0.2142 |
| 0.2047        | 4.67  | 4000  | 0.1693          | 0.2089 |
| 0.1937        | 5.14  | 4400  | 0.1753          | 0.2085 |
| 0.1808        | 5.61  | 4800  | 0.1718          | 0.2153 |
| 0.1711        | 6.07  | 5200  | 0.1887          | 0.2206 |
| 0.1568        | 6.54  | 5600  | 0.1769          | 0.2111 |
| 0.161         | 7.01  | 6000  | 0.1701          | 0.2132 |
| 0.1391        | 7.48  | 6400  | 0.2001          | 0.2196 |
| 0.1447        | 7.94  | 6800  | 0.1749          | 0.2047 |
| 0.1237        | 8.41  | 7200  | 0.1833          | 0.2081 |
| 0.129         | 8.88  | 7600  | 0.1789          | 0.1993 |
| 0.1155        | 9.35  | 8000  | 0.1756          | 0.1838 |
| 0.1168        | 9.81  | 8400  | 0.1744          | 0.1913 |
| 0.1089        | 10.28 | 8800  | 0.1689          | 0.1793 |
| 0.1109        | 10.75 | 9200  | 0.1747          | 0.1785 |
| 0.0987        | 11.21 | 9600  | 0.1667          | 0.1769 |
| 0.0998        | 11.68 | 10000 | 0.1603          | 0.1715 |
| 0.094         | 12.15 | 10400 | 0.1649          | 0.1668 |
| 0.0942        | 12.62 | 10800 | 0.1654          | 0.1719 |
| 0.0912        | 13.08 | 11200 | 0.1840          | 0.1719 |
| 0.085         | 13.55 | 11600 | 0.1812          | 0.1778 |
| 0.0798        | 14.02 | 12000 | 0.1744          | 0.1704 |
| 0.0762        | 14.49 | 12400 | 0.1968          | 0.1702 |
| 0.078         | 14.95 | 12800 | 0.1897          | 0.1726 |
| 0.0717        | 15.42 | 13200 | 0.1795          | 0.1769 |
| 0.0753        | 15.89 | 13600 | 0.1940          | 0.1704 |
| 0.0718        | 16.36 | 14000 | 0.1944          | 0.1632 |
| 0.0671        | 16.82 | 14400 | 0.1731          | 0.1588 |
| 0.0656        | 17.29 | 14800 | 0.1999          | 0.1713 |
| 0.0626        | 17.76 | 15200 | 0.1844          | 0.1655 |
| 0.0617        | 18.22 | 15600 | 0.1920          | 0.1621 |
| 0.0613        | 18.69 | 16000 | 0.1856          | 0.1611 |
| 0.0576        | 19.16 | 16400 | 0.1794          | 0.1573 |
| 0.0592        | 19.63 | 16800 | 0.1949          | 0.1558 |
| 0.0551        | 20.09 | 17200 | 0.1850          | 0.1551 |
| 0.0526        | 20.56 | 17600 | 0.1869          | 0.1504 |
| 0.0521        | 21.03 | 18000 | 0.1891          | 0.1504 |
| 0.0497        | 21.5  | 18400 | 0.1909          | 0.1536 |
| 0.0475        | 21.96 | 18800 | 0.1768          | 0.1510 |
| 0.0455        | 22.43 | 19200 | 0.1963          | 0.1543 |
| 0.0472        | 22.9  | 19600 | 0.1837          | 0.1506 |
| 0.0474        | 23.36 | 20000 | 0.1842          | 0.1498 |
| 0.0412        | 23.83 | 20400 | 0.1817          | 0.1461 |
| 0.0421        | 24.3  | 20800 | 0.1831          | 0.1446 |
| 0.039         | 24.77 | 21200 | 0.1857          | 0.1447 |
| 0.0386        | 25.23 | 21600 | 0.1824          | 0.1415 |
| 0.0382        | 25.7  | 22000 | 0.1816          | 0.1397 |
| 0.0341        | 26.17 | 22400 | 0.1839          | 0.1423 |
| 0.0333        | 26.64 | 22800 | 0.1846          | 0.1416 |
| 0.0331        | 27.1  | 23200 | 0.1857          | 0.1436 |
| 0.0319        | 27.57 | 23600 | 0.1891          | 0.1396 |
| 0.0329        | 28.04 | 24000 | 0.1866          | 0.1356 |
| 0.031         | 28.5  | 24400 | 0.1864          | 0.1366 |
| 0.0296        | 28.97 | 24800 | 0.1860          | 0.1357 |
| 0.0298        | 29.44 | 25200 | 0.1836          | 0.1342 |
| 0.0278        | 29.91 | 25600 | 0.1837          | 0.1334 |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.1