---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-lg-pt
  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-lg-pt

This model is a fine-tuned version of [Alvin-Nahabwe/wav2vec2-large-xls-r-300m-gn](https://huggingface.co/Alvin-Nahabwe/wav2vec2-large-xls-r-300m-gn) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2974
- Wer: 0.1465

## 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: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.3826        | 0.81  | 400   | 0.2260          | 0.2142 |
| 0.3513        | 1.61  | 800   | 0.2164          | 0.2289 |
| 0.3211        | 2.42  | 1200  | 0.1950          | 0.1895 |
| 0.2939        | 3.22  | 1600  | 0.1977          | 0.1969 |
| 0.2886        | 4.03  | 2000  | 0.1973          | 0.1957 |
| 0.2613        | 4.84  | 2400  | 0.1897          | 0.1825 |
| 0.2566        | 5.64  | 2800  | 0.1878          | 0.1753 |
| 0.2406        | 6.45  | 3200  | 0.1844          | 0.1713 |
| 0.2292        | 7.25  | 3600  | 0.1919          | 0.1706 |
| 0.2176        | 8.06  | 4000  | 0.1965          | 0.1681 |
| 0.2115        | 8.86  | 4400  | 0.1945          | 0.1746 |
| 0.1933        | 9.67  | 4800  | 0.2041          | 0.1712 |
| 0.1878        | 10.48 | 5200  | 0.2098          | 0.1718 |
| 0.1806        | 11.29 | 5600  | 0.2071          | 0.1666 |
| 0.1737        | 12.09 | 6000  | 0.2253          | 0.1655 |
| 0.1652        | 12.9  | 6400  | 0.2087          | 0.1627 |
| 0.1627        | 13.71 | 6800  | 0.2157          | 0.1666 |
| 0.1516        | 14.51 | 7200  | 0.2120          | 0.1687 |
| 0.1432        | 15.32 | 7600  | 0.2186          | 0.1715 |
| 0.1371        | 16.12 | 8000  | 0.2199          | 0.1681 |
| 0.1284        | 16.93 | 8400  | 0.2115          | 0.1647 |
| 0.1215        | 17.74 | 8800  | 0.2304          | 0.1568 |
| 0.115         | 18.55 | 9200  | 0.2322          | 0.1549 |
| 0.1122        | 19.35 | 9600  | 0.2427          | 0.1541 |
| 0.1041        | 20.16 | 10000 | 0.2512          | 0.1531 |
| 0.0999        | 20.96 | 10400 | 0.2526          | 0.1559 |
| 0.0929        | 21.77 | 10800 | 0.2591          | 0.1536 |
| 0.0877        | 22.58 | 11200 | 0.2645          | 0.1525 |
| 0.082         | 23.39 | 11600 | 0.2692          | 0.1494 |
| 0.0787        | 24.19 | 12000 | 0.2742          | 0.1530 |
| 0.0758        | 25.0  | 12400 | 0.2794          | 0.1484 |
| 0.0713        | 25.8  | 12800 | 0.2817          | 0.1493 |
| 0.0687        | 26.61 | 13200 | 0.2881          | 0.1491 |
| 0.065         | 27.42 | 13600 | 0.2945          | 0.1487 |
| 0.0619        | 28.22 | 14000 | 0.2955          | 0.1478 |
| 0.0592        | 29.03 | 14400 | 0.2965          | 0.1472 |
| 0.0569        | 29.84 | 14800 | 0.2974          | 0.1465 |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3