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# ft_0112_korean

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: 0.6163
- Cer: 0.1655

## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 66.0473       | 0.03  | 100   | 126.2500        | 1.0    |
| 39.2751       | 0.05  | 200   | 76.4439         | 1.0    |
| 24.2617       | 0.07  | 300   | 36.6274         | 1.0    |
| 10.2253       | 0.1   | 400   | 7.8025          | 1.0    |
| 4.9219        | 0.12  | 500   | 5.8257          | 1.0    |
| 4.7709        | 0.15  | 600   | 5.2597          | 1.0    |
| 4.7545        | 0.17  | 700   | 5.3516          | 1.0    |
| 4.701         | 0.2   | 800   | 5.2238          | 1.0    |
| 4.6753        | 0.23  | 900   | 5.1713          | 1.0    |
| 4.6339        | 0.25  | 1000  | 5.1546          | 1.0    |
| 4.6107        | 0.28  | 1100  | 5.0488          | 1.0    |
| 4.6086        | 0.3   | 1200  | 4.8149          | 1.0    |
| 4.5324        | 0.33  | 1300  | 4.7533          | 1.0    |
| 4.4797        | 0.35  | 1400  | 4.6892          | 1.0    |
| 4.4485        | 0.38  | 1500  | 4.5327          | 1.0    |
| 4.3794        | 0.4   | 1600  | 4.3797          | 0.9999 |
| 4.1549        | 0.42  | 1700  | 4.2075          | 0.9838 |
| 3.9647        | 0.45  | 1800  | 3.8729          | 0.9647 |
| 3.621         | 0.47  | 1900  | 3.3229          | 0.6854 |
| 3.3163        | 0.5   | 2000  | 2.9646          | 0.5646 |
| 3.0668        | 0.53  | 2100  | 2.7178          | 0.5608 |
| 2.8248        | 0.55  | 2200  | 2.4843          | 0.4937 |
| 2.7238        | 0.57  | 2300  | 2.3321          | 0.4736 |
| 2.614         | 0.6   | 2400  | 2.2513          | 0.4650 |
| 2.4994        | 0.62  | 2500  | 2.1655          | 0.4538 |
| 2.4431        | 0.65  | 2600  | 2.0785          | 0.4355 |
| 2.3307        | 0.68  | 2700  | 1.9603          | 0.4169 |
| 2.2495        | 0.7   | 2800  | 1.9026          | 0.4134 |
| 2.1647        | 0.72  | 2900  | 1.8152          | 0.4009 |
| 2.1075        | 0.75  | 3000  | 1.7521          | 0.3849 |
| 2.0577        | 0.78  | 3100  | 1.7004          | 0.3781 |
| 1.9935        | 0.8   | 3200  | 1.6226          | 0.3666 |
| 1.9391        | 0.82  | 3300  | 1.6097          | 0.3604 |
| 1.9295        | 0.85  | 3400  | 1.5416          | 0.3526 |
| 1.8759        | 0.88  | 3500  | 1.5227          | 0.3583 |
| 1.8316        | 0.9   | 3600  | 1.4791          | 0.3484 |
| 1.7531        | 0.93  | 3700  | 1.4472          | 0.3415 |
| 1.7413        | 0.95  | 3800  | 1.4178          | 0.3363 |
| 1.6609        | 0.97  | 3900  | 1.3587          | 0.3256 |
| 1.6986        | 1.0   | 4000  | 1.3396          | 0.3208 |
| 1.6189        | 1.02  | 4100  | 1.3253          | 0.3187 |
| 1.5853        | 1.05  | 4200  | 1.2929          | 0.3109 |
| 1.5153        | 1.07  | 4300  | 1.2691          | 0.3106 |
| 1.5259        | 1.1   | 4400  | 1.2500          | 0.3012 |
| 1.4916        | 1.12  | 4500  | 1.2151          | 0.2977 |
| 1.4113        | 1.15  | 4600  | 1.1796          | 0.2930 |
| 1.452         | 1.18  | 4700  | 1.1857          | 0.2928 |
| 1.3879        | 1.2   | 4800  | 1.1830          | 0.2915 |
| 1.4164        | 1.23  | 4900  | 1.1725          | 0.2920 |
| 1.4692        | 1.25  | 5000  | 1.1171          | 0.2794 |
| 1.346         | 1.27  | 5100  | 1.0858          | 0.2745 |
| 1.3964        | 1.3   | 5200  | 1.0644          | 0.2712 |
| 1.3359        | 1.32  | 5300  | 1.0585          | 0.2694 |
| 1.2769        | 1.35  | 5400  | 1.0290          | 0.2614 |
| 1.2741        | 1.38  | 5500  | 1.0356          | 0.2604 |
| 1.2257        | 1.4   | 5600  | 1.0167          | 0.2607 |
| 1.2416        | 1.43  | 5700  | 1.0074          | 0.2558 |
| 1.2376        | 1.45  | 5800  | 0.9889          | 0.2524 |
| 1.2048        | 1.48  | 5900  | 0.9649          | 0.2464 |
| 1.1335        | 1.5   | 6000  | 0.9580          | 0.2488 |
| 1.1946        | 1.52  | 6100  | 0.9503          | 0.2471 |
| 1.1926        | 1.55  | 6200  | 0.9467          | 0.2494 |
| 1.1451        | 1.57  | 6300  | 0.9202          | 0.2408 |
| 1.1426        | 1.6   | 6400  | 0.9018          | 0.2359 |
| 1.1569        | 1.62  | 6500  | 0.9216          | 0.2362 |
| 1.1093        | 1.65  | 6600  | 0.9433          | 0.2414 |
| 1.1258        | 1.68  | 6700  | 0.8986          | 0.2291 |
| 1.1024        | 1.7   | 6800  | 0.8838          | 0.2305 |
| 1.0567        | 1.73  | 6900  | 0.8916          | 0.2298 |
| 1.0928        | 1.75  | 7000  | 0.8855          | 0.2294 |
| 1.0526        | 1.77  | 7100  | 0.8592          | 0.2237 |
| 1.0236        | 1.8   | 7200  | 0.8433          | 0.2209 |
| 1.0454        | 1.82  | 7300  | 0.8382          | 0.2214 |
| 1.0252        | 1.85  | 7400  | 0.8252          | 0.2173 |
| 1.0404        | 1.88  | 7500  | 0.8190          | 0.2148 |
| 1.0326        | 1.9   | 7600  | 0.8067          | 0.2155 |
| 1.0008        | 1.93  | 7700  | 0.8081          | 0.2161 |
| 0.9814        | 1.95  | 7800  | 0.8061          | 0.2152 |
| 0.9664        | 1.98  | 7900  | 0.8147          | 0.2155 |
| 1.0032        | 2.0   | 8000  | 0.8232          | 0.2128 |
| 0.9274        | 2.02  | 8100  | 0.7951          | 0.2118 |
| 0.9115        | 2.05  | 8200  | 0.7857          | 0.2105 |
| 0.9339        | 2.08  | 8300  | 0.7722          | 0.2069 |
| 0.8553        | 2.1   | 8400  | 0.7603          | 0.2070 |
| 0.8671        | 2.12  | 8500  | 0.7927          | 0.2099 |
| 0.9067        | 2.15  | 8600  | 0.7511          | 0.2013 |
| 0.8507        | 2.17  | 8700  | 0.7763          | 0.2029 |
| 0.899         | 2.2   | 8800  | 0.7579          | 0.2026 |
| 0.8061        | 2.23  | 8900  | 0.7561          | 0.2014 |
| 0.8191        | 2.25  | 9000  | 0.7590          | 0.2024 |
| 0.8084        | 2.27  | 9100  | 0.7394          | 0.1972 |
| 0.8163        | 2.3   | 9200  | 0.7404          | 0.1941 |
| 0.8189        | 2.33  | 9300  | 0.7340          | 0.1955 |
| 0.8639        | 2.35  | 9400  | 0.7331          | 0.1950 |
| 0.8218        | 2.38  | 9500  | 0.7347          | 0.1959 |
| 0.8221        | 2.4   | 9600  | 0.7098          | 0.1922 |
| 0.7725        | 2.42  | 9700  | 0.7264          | 0.1923 |
| 0.7882        | 2.45  | 9800  | 0.7079          | 0.1875 |
| 0.7786        | 2.48  | 9900  | 0.7131          | 0.1913 |
| 0.7734        | 2.5   | 10000 | 0.7079          | 0.1912 |
| 0.7834        | 2.52  | 10100 | 0.6944          | 0.1896 |
| 0.78          | 2.55  | 10200 | 0.6980          | 0.1879 |
| 0.7602        | 2.58  | 10300 | 0.7076          | 0.1894 |
| 0.7415        | 2.6   | 10400 | 0.6946          | 0.1857 |
| 0.7791        | 2.62  | 10500 | 0.7025          | 0.1887 |
| 0.7357        | 2.65  | 10600 | 0.6949          | 0.1885 |
| 0.7102        | 2.67  | 10700 | 0.6978          | 0.1895 |
| 0.7395        | 2.7   | 10800 | 0.6893          | 0.1859 |
| 0.7301        | 2.73  | 10900 | 0.6847          | 0.1857 |
| 0.7492        | 2.75  | 11000 | 0.7063          | 0.1863 |
| 0.7372        | 2.77  | 11100 | 0.6917          | 0.1857 |
| 0.7474        | 2.8   | 11200 | 0.6843          | 0.1845 |
| 0.6727        | 2.83  | 11300 | 0.6628          | 0.1775 |
| 0.7342        | 2.85  | 11400 | 0.6729          | 0.1797 |
| 0.6599        | 2.88  | 11500 | 0.6631          | 0.1797 |
| 0.7209        | 2.9   | 11600 | 0.6658          | 0.1795 |
| 0.7222        | 2.92  | 11700 | 0.6741          | 0.1807 |
| 0.7124        | 2.95  | 11800 | 0.6722          | 0.1828 |
| 0.7304        | 2.98  | 11900 | 0.6606          | 0.1782 |
| 0.7234        | 3.0   | 12000 | 0.6499          | 0.1753 |
| 0.6857        | 3.02  | 12100 | 0.6547          | 0.1751 |
| 0.6238        | 3.05  | 12200 | 0.6615          | 0.1771 |
| 0.6495        | 3.08  | 12300 | 0.6499          | 0.1764 |
| 0.6219        | 3.1   | 12400 | 0.6558          | 0.1752 |
| 0.6684        | 3.12  | 12500 | 0.6479          | 0.1752 |
| 0.6455        | 3.15  | 12600 | 0.6574          | 0.1741 |
| 0.6414        | 3.17  | 12700 | 0.6489          | 0.1755 |
| 0.6619        | 3.2   | 12800 | 0.6527          | 0.1754 |
| 0.6303        | 3.23  | 12900 | 0.6462          | 0.1743 |
| 0.6525        | 3.25  | 13000 | 0.6505          | 0.1731 |
| 0.6347        | 3.27  | 13100 | 0.6432          | 0.1713 |
| 0.6206        | 3.3   | 13200 | 0.6495          | 0.1746 |
| 0.6445        | 3.33  | 13300 | 0.6328          | 0.1706 |
| 0.6097        | 3.35  | 13400 | 0.6329          | 0.1689 |
| 0.6151        | 3.38  | 13500 | 0.6473          | 0.1730 |
| 0.5948        | 3.4   | 13600 | 0.6413          | 0.1714 |
| 0.5949        | 3.42  | 13700 | 0.6377          | 0.1712 |
| 0.6402        | 3.45  | 13800 | 0.6295          | 0.1692 |
| 0.6607        | 3.48  | 13900 | 0.6287          | 0.1694 |
| 0.6219        | 3.5   | 14000 | 0.6357          | 0.1704 |
| 0.61          | 3.52  | 14100 | 0.6392          | 0.1715 |
| 0.5974        | 3.55  | 14200 | 0.6315          | 0.1687 |
| 0.5839        | 3.58  | 14300 | 0.6359          | 0.1689 |
| 0.6017        | 3.6   | 14400 | 0.6316          | 0.1673 |
| 0.6091        | 3.62  | 14500 | 0.6284          | 0.1686 |
| 0.6565        | 3.65  | 14600 | 0.6304          | 0.1684 |
| 0.6179        | 3.67  | 14700 | 0.6259          | 0.1661 |
| 0.5813        | 3.7   | 14800 | 0.6310          | 0.1672 |
| 0.5802        | 3.73  | 14900 | 0.6250          | 0.1667 |
| 0.6035        | 3.75  | 15000 | 0.6284          | 0.1666 |
| 0.5569        | 3.77  | 15100 | 0.6203          | 0.1651 |
| 0.5712        | 3.8   | 15200 | 0.6207          | 0.1660 |
| 0.546         | 3.83  | 15300 | 0.6246          | 0.1661 |
| 0.5602        | 3.85  | 15400 | 0.6206          | 0.1656 |
| 0.591         | 3.88  | 15500 | 0.6179          | 0.1650 |
| 0.5972        | 3.9   | 15600 | 0.6164          | 0.1653 |
| 0.6168        | 3.92  | 15700 | 0.6174          | 0.1660 |
| 0.5957        | 3.95  | 15800 | 0.6164          | 0.1657 |
| 0.5754        | 3.98  | 15900 | 0.6163          | 0.1657 |
| 0.5686        | 4.0   | 16000 | 0.6163          | 0.1655 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.13.0
- Tokenizers 0.15.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "ft_0112_korean", "results": []}]}
transformers tensorboard safetensors wav2vec2 automatic-speech-recognition generated_from_trainer base_model:facebook/wav2vec2-xls-r-300m license:apache-2.0 endpoints_compatible region:us