---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-Vocals-Kor
  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-Vocals-Kor

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: 0.4436
- Cer: 0.2135

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 12
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Cer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 10.0761       | 0.1181 | 300  | 3.5931          | 0.9861 |
| 2.8824        | 0.2361 | 600  | 1.9956          | 0.6491 |
| 1.1701        | 0.3542 | 900  | 0.8263          | 0.2735 |
| 0.8015        | 0.4723 | 1200 | 0.6946          | 0.2530 |
| 0.7235        | 0.5903 | 1500 | 0.6638          | 0.2380 |
| 0.6747        | 0.7084 | 1800 | 0.6288          | 0.2399 |
| 0.6528        | 0.8264 | 2100 | 0.5963          | 0.2382 |
| 0.6185        | 0.9445 | 2400 | 0.6014          | 0.2412 |
| 0.5861        | 1.0626 | 2700 | 0.5747          | 0.2388 |
| 0.5668        | 1.1806 | 3000 | 0.5561          | 0.2199 |
| 0.5628        | 1.2987 | 3300 | 0.5335          | 0.2235 |
| 0.5521        | 1.4168 | 3600 | 0.5489          | 0.2290 |
| 0.5309        | 1.5348 | 3900 | 0.4995          | 0.2125 |
| 0.5033        | 1.6529 | 4200 | 0.4905          | 0.2171 |
| 0.5018        | 1.7710 | 4500 | 0.4853          | 0.2129 |
| 0.5011        | 1.8890 | 4800 | 0.4901          | 0.2171 |
| 0.4907        | 2.0071 | 5100 | 0.4828          | 0.2135 |
| 0.4578        | 2.1251 | 5400 | 0.4855          | 0.2180 |
| 0.4552        | 2.2432 | 5700 | 0.4621          | 0.2216 |
| 0.4345        | 2.3613 | 6000 | 0.4669          | 0.2152 |
| 0.4332        | 2.4793 | 6300 | 0.4639          | 0.2171 |
| 0.4338        | 2.5974 | 6600 | 0.4517          | 0.2180 |
| 0.4181        | 2.7155 | 6900 | 0.4407          | 0.2117 |
| 0.4048        | 2.8335 | 7200 | 0.4394          | 0.2063 |
| 0.4003        | 2.9516 | 7500 | 0.4478          | 0.2100 |
| 0.3847        | 3.0697 | 7800 | 0.4478          | 0.2159 |
| 0.3634        | 3.1877 | 8100 | 0.4378          | 0.2145 |
| 0.3629        | 3.3058 | 8400 | 0.4386          | 0.2060 |
| 0.3603        | 3.4238 | 8700 | 0.4411          | 0.2127 |
| 0.361         | 3.5419 | 9000 | 0.4436          | 0.2135 |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.0.1+cu117
- Datasets 2.19.0
- Tokenizers 0.19.1