---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-cs-colab
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: cs
      split: test
      args: cs
    metrics:
    - name: Wer
      type: wer
      value: 0.13424044564175153
---

<!-- 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-cs-colab

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1447
- Wer: 0.1342

## 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: 5e-05
- 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: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 3.3706        | 0.5485  | 500   | 3.4060          | 1.0    |
| 3.2801        | 1.0971  | 1000  | 3.2557          | 1.0    |
| 0.7687        | 1.6456  | 1500  | 0.4183          | 0.3893 |
| 0.515         | 2.1942  | 2000  | 0.2665          | 0.2576 |
| 0.4229        | 2.7427  | 2500  | 0.2231          | 0.2245 |
| 0.345         | 3.2913  | 3000  | 0.2010          | 0.2019 |
| 0.3364        | 3.8398  | 3500  | 0.1865          | 0.1874 |
| 0.2881        | 4.3884  | 4000  | 0.1759          | 0.1741 |
| 0.304         | 4.9369  | 4500  | 0.1684          | 0.1693 |
| 0.2849        | 5.4855  | 5000  | 0.1695          | 0.1683 |
| 0.2492        | 6.0340  | 5500  | 0.1634          | 0.1596 |
| 0.2419        | 6.5826  | 6000  | 0.1606          | 0.1569 |
| 0.2282        | 7.1311  | 6500  | 0.1566          | 0.1520 |
| 0.2233        | 7.6796  | 7000  | 0.1549          | 0.1506 |
| 0.2063        | 8.2282  | 7500  | 0.1537          | 0.1489 |
| 0.2045        | 8.7767  | 8000  | 0.1541          | 0.1499 |
| 0.2127        | 9.3253  | 8500  | 0.1545          | 0.1446 |
| 0.1954        | 9.8738  | 9000  | 0.1509          | 0.1422 |
| 0.1931        | 10.4224 | 9500  | 0.1532          | 0.1419 |
| 0.2022        | 10.9709 | 10000 | 0.1472          | 0.1404 |
| 0.1868        | 11.5195 | 10500 | 0.1472          | 0.1373 |
| 0.1852        | 12.0680 | 11000 | 0.1469          | 0.1377 |
| 0.1817        | 12.6166 | 11500 | 0.1473          | 0.1374 |
| 0.1941        | 13.1651 | 12000 | 0.1443          | 0.1360 |
| 0.1632        | 13.7137 | 12500 | 0.1464          | 0.1342 |
| 0.1705        | 14.2622 | 13000 | 0.1451          | 0.1348 |
| 0.1725        | 14.8108 | 13500 | 0.1447          | 0.1342 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.20.1