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---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- automatic-speech-recognition
- ./sample_speech.py
- generated_from_trainer
metrics:
- wer
model-index:
- name: ko-xlsr2
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. -->
# ko-xlsr2
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the ./SAMPLE_SPEECH.PY - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4239
- Cer: 0.1113
- Wer: 0.3038
## 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: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 1.7721 | 0.94 | 2000 | 1.1368 | 0.2903 | 0.6589 |
| 1.3501 | 1.89 | 4000 | 0.8561 | 0.2240 | 0.5451 |
| 1.2133 | 2.83 | 6000 | 0.7505 | 0.2003 | 0.4974 |
| 1.0981 | 3.77 | 8000 | 0.6768 | 0.1842 | 0.4686 |
| 1.0375 | 4.72 | 10000 | 0.6413 | 0.1707 | 0.4404 |
| 0.9927 | 5.66 | 12000 | 0.6106 | 0.1634 | 0.4246 |
| 0.9439 | 6.6 | 14000 | 0.5999 | 0.1613 | 0.4159 |
| 0.9059 | 7.55 | 16000 | 0.5740 | 0.1535 | 0.3985 |
| 0.8772 | 8.49 | 18000 | 0.5569 | 0.1478 | 0.3954 |
| 0.8483 | 9.43 | 20000 | 0.5407 | 0.1427 | 0.3784 |
| 0.81 | 10.37 | 22000 | 0.5283 | 0.1415 | 0.3744 |
| 0.793 | 11.32 | 24000 | 0.5179 | 0.1366 | 0.3663 |
| 0.7577 | 12.26 | 26000 | 0.5059 | 0.1359 | 0.3595 |
| 0.7379 | 13.2 | 28000 | 0.4969 | 0.1333 | 0.3532 |
| 0.7328 | 14.15 | 30000 | 0.4908 | 0.1308 | 0.3475 |
| 0.7119 | 15.09 | 32000 | 0.4887 | 0.1286 | 0.3478 |
| 0.7572 | 16.03 | 34000 | 0.5170 | 0.1327 | 0.3577 |
| 0.8198 | 16.98 | 36000 | 0.5839 | 0.1432 | 0.3825 |
| 0.8008 | 17.92 | 38000 | 0.5447 | 0.1376 | 0.3661 |
| 0.759 | 18.86 | 40000 | 0.4998 | 0.1337 | 0.3534 |
| 0.6907 | 19.81 | 42000 | 0.4710 | 0.1288 | 0.3412 |
| 0.659 | 20.75 | 44000 | 0.4578 | 0.1242 | 0.3325 |
| 0.6345 | 21.69 | 46000 | 0.4531 | 0.1221 | 0.3257 |
| 0.6242 | 22.64 | 48000 | 0.4498 | 0.1209 | 0.3218 |
| 0.6163 | 23.58 | 50000 | 0.4552 | 0.1194 | 0.3188 |
| 0.6121 | 24.52 | 52000 | 0.4633 | 0.1154 | 0.3137 |
| 0.6054 | 25.47 | 54000 | 0.4623 | 0.1176 | 0.3171 |
| 0.591 | 26.41 | 56000 | 0.4413 | 0.1146 | 0.3116 |
| 0.5713 | 27.35 | 58000 | 0.4338 | 0.1135 | 0.3093 |
| 0.5703 | 28.3 | 60000 | 0.4280 | 0.1121 | 0.3061 |
| 0.5576 | 29.24 | 62000 | 0.4248 | 0.1119 | 0.3047 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
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