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# korean-aihub-learning-3

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: 2.2854
- Wer: 0.7921

## 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: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 0.99  | 35   | 45.5713         | 1.0    |
| No log        | 1.99  | 70   | 24.4376         | 1.0    |
| 35.4145       | 2.99  | 105  | 18.3030         | 1.0    |
| 35.4145       | 3.99  | 140  | 12.6702         | 1.0    |
| 35.4145       | 4.99  | 175  | 7.4939          | 1.0    |
| 11.687        | 5.99  | 210  | 4.9592          | 1.0    |
| 11.687        | 6.99  | 245  | 4.6777          | 1.0    |
| 11.687        | 7.99  | 280  | 4.6597          | 1.0    |
| 4.8003        | 8.99  | 315  | 4.6777          | 1.0    |
| 4.8003        | 9.99  | 350  | 4.7003          | 1.0    |
| 4.8003        | 10.99 | 385  | 4.6129          | 1.0    |
| 4.6383        | 11.99 | 420  | 4.6209          | 1.0    |
| 4.6383        | 12.99 | 455  | 4.6035          | 1.0    |
| 4.6383        | 13.99 | 490  | 4.6166          | 1.0    |
| 4.577         | 14.99 | 525  | 4.6026          | 1.0    |
| 4.577         | 15.99 | 560  | 4.5337          | 1.0    |
| 4.577         | 16.99 | 595  | 4.5284          | 1.0    |
| 4.5124        | 17.99 | 630  | 4.5710          | 1.0    |
| 4.5124        | 18.99 | 665  | 4.5223          | 1.0    |
| 4.3818        | 19.99 | 700  | 4.4472          | 1.0    |
| 4.3818        | 20.99 | 735  | 4.4272          | 0.9977 |
| 4.3818        | 21.99 | 770  | 4.4160          | 0.9977 |
| 4.2796        | 22.99 | 805  | 4.3741          | 0.9988 |
| 4.2796        | 23.99 | 840  | 4.3087          | 1.0    |
| 4.2796        | 24.99 | 875  | 4.2336          | 1.0    |
| 4.0489        | 25.99 | 910  | 4.1352          | 0.9988 |
| 4.0489        | 26.99 | 945  | 4.0669          | 1.0    |
| 4.0489        | 27.99 | 980  | 3.8551          | 0.9988 |
| 3.6122        | 28.99 | 1015 | 3.6699          | 0.9919 |
| 3.6122        | 29.99 | 1050 | 3.4580          | 0.9781 |
| 3.6122        | 30.99 | 1085 | 3.1899          | 0.9434 |
| 2.8886        | 31.99 | 1120 | 3.0746          | 0.9550 |
| 2.8886        | 32.99 | 1155 | 2.8143          | 0.9353 |
| 2.8886        | 33.99 | 1190 | 2.7004          | 0.9122 |
| 2.0277        | 34.99 | 1225 | 2.5284          | 0.9076 |
| 2.0277        | 35.99 | 1260 | 2.4677          | 0.8972 |
| 2.0277        | 36.99 | 1295 | 2.3426          | 0.8568 |
| 1.2486        | 37.99 | 1330 | 2.2456          | 0.8822 |
| 1.2486        | 38.99 | 1365 | 2.3250          | 0.9238 |
| 0.7572        | 39.99 | 1400 | 2.2832          | 0.8557 |
| 0.7572        | 40.99 | 1435 | 2.2671          | 0.8406 |
| 0.7572        | 41.99 | 1470 | 2.3070          | 0.8857 |
| 0.4768        | 42.99 | 1505 | 2.2138          | 0.8476 |
| 0.4768        | 43.99 | 1540 | 2.2034          | 0.8799 |
| 0.4768        | 44.99 | 1575 | 2.2215          | 0.8487 |
| 0.3362        | 45.99 | 1610 | 2.3416          | 0.8834 |
| 0.3362        | 46.99 | 1645 | 2.3452          | 0.8383 |
| 0.3362        | 47.99 | 1680 | 2.2449          | 0.8360 |
| 0.257         | 48.99 | 1715 | 2.2249          | 0.8199 |
| 0.257         | 49.99 | 1750 | 2.3455          | 0.8106 |
| 0.257         | 50.99 | 1785 | 2.2537          | 0.8233 |
| 0.2116        | 51.99 | 1820 | 2.2501          | 0.8025 |
| 0.2116        | 52.99 | 1855 | 2.3180          | 0.8649 |
| 0.2116        | 53.99 | 1890 | 2.1855          | 0.8106 |
| 0.1787        | 54.99 | 1925 | 2.2140          | 0.8014 |
| 0.1787        | 55.99 | 1960 | 2.3140          | 0.8453 |
| 0.1787        | 56.99 | 1995 | 2.2140          | 0.8025 |
| 0.1498        | 57.99 | 2030 | 2.3381          | 0.8314 |
| 0.1498        | 58.99 | 2065 | 2.2591          | 0.8256 |
| 0.1372        | 59.99 | 2100 | 2.2538          | 0.7979 |
| 0.1372        | 60.99 | 2135 | 2.2052          | 0.7933 |
| 0.1372        | 61.99 | 2170 | 2.2370          | 0.8233 |
| 0.129         | 62.99 | 2205 | 2.2331          | 0.7898 |
| 0.129         | 63.99 | 2240 | 2.3022          | 0.8002 |
| 0.129         | 64.99 | 2275 | 2.3514          | 0.7956 |
| 0.1075        | 65.99 | 2310 | 2.3303          | 0.8279 |
| 0.1075        | 66.99 | 2345 | 2.2747          | 0.8025 |
| 0.1075        | 67.99 | 2380 | 2.2899          | 0.8152 |
| 0.0979        | 68.99 | 2415 | 2.3299          | 0.8164 |
| 0.0979        | 69.99 | 2450 | 2.1819          | 0.7945 |
| 0.0979        | 70.99 | 2485 | 2.2141          | 0.8222 |
| 0.0973        | 71.99 | 2520 | 2.3683          | 0.8395 |
| 0.0973        | 72.99 | 2555 | 2.2235          | 0.8199 |
| 0.0973        | 73.99 | 2590 | 2.2474          | 0.8048 |
| 0.0814        | 74.99 | 2625 | 2.3116          | 0.7968 |
| 0.0814        | 75.99 | 2660 | 2.2494          | 0.7945 |
| 0.0814        | 76.99 | 2695 | 2.2441          | 0.7968 |
| 0.0745        | 77.99 | 2730 | 2.2489          | 0.7864 |
| 0.0745        | 78.99 | 2765 | 2.2568          | 0.7921 |
| 0.0741        | 79.99 | 2800 | 2.2598          | 0.7875 |
| 0.0741        | 80.99 | 2835 | 2.3131          | 0.8002 |
| 0.0741        | 81.99 | 2870 | 2.2719          | 0.7898 |
| 0.0662        | 82.99 | 2905 | 2.2901          | 0.7875 |
| 0.0662        | 83.99 | 2940 | 2.3092          | 0.7979 |
| 0.0662        | 84.99 | 2975 | 2.3361          | 0.8048 |
| 0.0556        | 85.99 | 3010 | 2.3308          | 0.8152 |
| 0.0556        | 86.99 | 3045 | 2.3106          | 0.8164 |
| 0.0556        | 87.99 | 3080 | 2.3363          | 0.8002 |
| 0.0504        | 88.99 | 3115 | 2.3588          | 0.7910 |
| 0.0504        | 89.99 | 3150 | 2.3528          | 0.7956 |
| 0.0504        | 90.99 | 3185 | 2.3201          | 0.7794 |
| 0.0496        | 91.99 | 3220 | 2.3386          | 0.7991 |
| 0.0496        | 92.99 | 3255 | 2.3423          | 0.7956 |
| 0.0496        | 93.99 | 3290 | 2.3312          | 0.7956 |
| 0.0468        | 94.99 | 3325 | 2.3362          | 0.7968 |
| 0.0468        | 95.99 | 3360 | 2.2962          | 0.7887 |
| 0.0468        | 96.99 | 3395 | 2.2864          | 0.7841 |
| 0.0475        | 97.99 | 3430 | 2.2870          | 0.7898 |
| 0.0475        | 98.99 | 3465 | 2.2866          | 0.7898 |
| 0.0411        | 99.99 | 3500 | 2.2854          | 0.7921 |


### 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-3", "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-3
=======================


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: 2.2854
* Wer: 0.7921


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: 100
* 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: 100
* 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: 100
* 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: 100
* 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