---
license: apache-2.0
tags:
- generated_from_trainer
base_model: facebook/wav2vec2-xls-r-300m
datasets:
- zeroth_korean
metrics:
- wer
model-index:
- name: wav2vec2-large-xlrs-korean-v5
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: zeroth_korean
      type: zeroth_korean
      config: clean
      split: None
      args: clean
    metrics:
    - type: wer
      value: 0.2433368468604126
      name: Wer
---

<!-- 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-xlrs-korean-v5

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the zeroth_korean dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1300
- Wer: 0.2433

## 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: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 5.1453        | 1.4368  | 500   | 3.1530          | 1.0    |
| 2.4287        | 2.8736  | 1000  | 0.6084          | 0.8317 |
| 0.5556        | 4.3103  | 1500  | 0.3414          | 0.6165 |
| 0.3929        | 5.7471  | 2000  | 0.2729          | 0.5386 |
| 0.3211        | 7.1839  | 2500  | 0.2294          | 0.4794 |
| 0.281         | 8.6207  | 3000  | 0.2052          | 0.4298 |
| 0.2483        | 10.0575 | 3500  | 0.1911          | 0.4061 |
| 0.2243        | 11.4943 | 4000  | 0.1685          | 0.3873 |
| 0.2023        | 12.9310 | 4500  | 0.1627          | 0.3524 |
| 0.188         | 14.3678 | 5000  | 0.1572          | 0.3272 |
| 0.1784        | 15.8046 | 5500  | 0.1495          | 0.3131 |
| 0.1677        | 17.2414 | 6000  | 0.1424          | 0.2881 |
| 0.1533        | 18.6782 | 6500  | 0.1418          | 0.2709 |
| 0.1501        | 20.1149 | 7000  | 0.1387          | 0.2822 |
| 0.1402        | 21.5517 | 7500  | 0.1401          | 0.2697 |
| 0.1353        | 22.9885 | 8000  | 0.1367          | 0.2643 |
| 0.133         | 24.4253 | 8500  | 0.1337          | 0.2578 |
| 0.1254        | 25.8621 | 9000  | 0.1355          | 0.2560 |
| 0.1262        | 27.2989 | 9500  | 0.1339          | 0.2474 |
| 0.121         | 28.7356 | 10000 | 0.1300          | 0.2433 |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1