pytorch tensorboard wav2vec2 automatic-speech-recognition transformers generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xlsr-korean-demo-colab
  results: []
---

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# wav2vec2-large-xlsr-korean-demo-colab

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4534
- Wer: 0.3272

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 17.4809       | 0.65  | 400   | 4.6145          | 1.0    |
| 4.4863        | 1.29  | 800   | 4.3819          | 1.0    |
| 4.2921        | 1.94  | 1200  | 4.1163          | 0.9970 |
| 2.7971        | 2.59  | 1600  | 1.5376          | 0.8379 |
| 1.5061        | 3.24  | 2000  | 1.0354          | 0.7299 |
| 1.1123        | 3.88  | 2400  | 0.7909          | 0.6418 |
| 0.9037        | 4.53  | 2800  | 0.6345          | 0.5698 |
| 0.779         | 5.18  | 3200  | 0.5909          | 0.5571 |
| 0.6834        | 5.83  | 3600  | 0.5339          | 0.5063 |
| 0.6287        | 6.47  | 4000  | 0.5326          | 0.4954 |
| 0.5518        | 7.12  | 4400  | 0.4930          | 0.4607 |
| 0.5315        | 7.77  | 4800  | 0.4577          | 0.4451 |
| 0.4867        | 8.41  | 5200  | 0.4547          | 0.4382 |
| 0.4543        | 9.06  | 5600  | 0.4581          | 0.4371 |
| 0.4089        | 9.71  | 6000  | 0.4387          | 0.4258 |
| 0.3893        | 10.36 | 6400  | 0.4300          | 0.4100 |
| 0.3751        | 11.0  | 6800  | 0.4265          | 0.4137 |
| 0.3333        | 11.65 | 7200  | 0.4294          | 0.4011 |
| 0.3039        | 12.3  | 7600  | 0.4187          | 0.3912 |
| 0.2974        | 12.94 | 8000  | 0.4079          | 0.3805 |
| 0.2658        | 13.59 | 8400  | 0.4273          | 0.3864 |
| 0.2676        | 14.24 | 8800  | 0.4103          | 0.3734 |
| 0.2466        | 14.89 | 9200  | 0.4122          | 0.3701 |
| 0.2282        | 15.53 | 9600  | 0.4176          | 0.3650 |
| 0.2186        | 16.18 | 10000 | 0.4199          | 0.3632 |
| 0.2132        | 16.83 | 10400 | 0.4159          | 0.3671 |
| 0.1962        | 17.48 | 10800 | 0.4321          | 0.3641 |
| 0.1922        | 18.12 | 11200 | 0.4300          | 0.3535 |
| 0.1827        | 18.77 | 11600 | 0.4244          | 0.3596 |
| 0.1709        | 19.42 | 12000 | 0.4191          | 0.3518 |
| 0.157         | 20.06 | 12400 | 0.4308          | 0.3496 |
| 0.147         | 20.71 | 12800 | 0.4360          | 0.3457 |
| 0.1502        | 21.36 | 13200 | 0.4329          | 0.3431 |
| 0.1448        | 22.01 | 13600 | 0.4334          | 0.3432 |
| 0.1407        | 22.65 | 14000 | 0.4392          | 0.3440 |
| 0.1342        | 23.3  | 14400 | 0.4418          | 0.3399 |
| 0.1325        | 23.95 | 14800 | 0.4360          | 0.3383 |
| 0.1183        | 24.6  | 15200 | 0.4521          | 0.3359 |
| 0.1174        | 25.24 | 15600 | 0.4426          | 0.3322 |
| 0.1137        | 25.89 | 16000 | 0.4438          | 0.3356 |
| 0.1129        | 26.54 | 16400 | 0.4547          | 0.3347 |
| 0.1077        | 27.18 | 16800 | 0.4482          | 0.3300 |
| 0.0999        | 27.83 | 17200 | 0.4491          | 0.3281 |
| 0.0978        | 28.48 | 17600 | 0.4533          | 0.3281 |
| 0.0997        | 29.13 | 18000 | 0.4542          | 0.3283 |
| 0.0908        | 29.77 | 18400 | 0.4534          | 0.3272 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.12.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
