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# korean-aihub-learning-2

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9945
- Wer: 0.9533

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 0.99  | 35   | 46.3840         | 1.0    |
| No log        | 1.99  | 70   | 26.0949         | 1.0    |
| 37.1581       | 2.99  | 105  | 19.0168         | 1.0    |
| 37.1581       | 3.99  | 140  | 13.3294         | 1.0    |
| 37.1581       | 4.99  | 175  | 7.9410          | 1.0    |
| 12.5054       | 5.99  | 210  | 5.0323          | 1.0    |
| 12.5054       | 6.99  | 245  | 4.6242          | 1.0    |
| 12.5054       | 7.99  | 280  | 4.6206          | 1.0    |
| 4.8394        | 8.99  | 315  | 4.5820          | 1.0    |
| 4.8394        | 9.99  | 350  | 4.5629          | 1.0    |
| 4.8394        | 10.99 | 385  | 4.5385          | 1.0    |
| 4.6489        | 11.99 | 420  | 4.5627          | 1.0    |
| 4.6489        | 12.99 | 455  | 4.5276          | 1.0    |
| 4.6489        | 13.99 | 490  | 4.5292          | 1.0    |
| 4.5654        | 14.99 | 525  | 4.5179          | 1.0    |
| 4.5654        | 15.99 | 560  | 4.4928          | 1.0    |
| 4.5654        | 16.99 | 595  | 4.4791          | 1.0    |
| 4.521         | 17.99 | 630  | 4.4649          | 1.0    |
| 4.521         | 18.99 | 665  | 4.4588          | 1.0    |
| 4.3529        | 19.99 | 700  | 4.3632          | 1.0    |
| 4.3529        | 20.99 | 735  | 4.2990          | 1.0    |
| 4.3529        | 21.99 | 770  | 4.2326          | 0.9988 |
| 4.1301        | 22.99 | 805  | 4.0843          | 1.0    |
| 4.1301        | 23.99 | 840  | 3.9784          | 0.9975 |
| 4.1301        | 24.99 | 875  | 3.7876          | 1.0    |
| 3.7047        | 25.99 | 910  | 3.6109          | 0.9988 |
| 3.7047        | 26.99 | 945  | 3.4049          | 0.9828 |
| 3.7047        | 27.99 | 980  | 3.1913          | 0.9606 |
| 3.006         | 28.99 | 1015 | 3.0567          | 0.9508 |
| 3.006         | 29.99 | 1050 | 2.9945          | 0.9533 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "korean-aihub-learning-2", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

korean-aihub-learning-2
=======================


This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 2.9945
* Wer: 0.9533


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



### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1



### 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 ### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1