wav2vec2-large-xlsr-53-fine-tune_korean_byAILAB2
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the zeroth_korean dataset. It achieves the following results on the evaluation set:
- Loss: 1.4929
- Wer: 0.9068
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 0.99 | 38 | 54.4059 | 1.0 |
| No log | 2.0 | 77 | 38.8388 | 1.0 |
| No log | 2.99 | 115 | 24.1740 | 1.0 |
| No log | 4.0 | 154 | 16.4733 | 1.0 |
| No log | 4.99 | 192 | 10.1900 | 1.0 |
| No log | 6.0 | 231 | 6.0076 | 1.0 |
| No log | 6.99 | 269 | 4.8990 | 1.0 |
| No log | 8.0 | 308 | 4.8442 | 1.0 |
| No log | 8.99 | 346 | 4.8284 | 1.0 |
| No log | 10.0 | 385 | 4.8316 | 1.0 |
| 16.886 | 10.99 | 423 | 4.8164 | 1.0 |
| 16.886 | 12.0 | 462 | 4.7815 | 1.0 |
| 16.886 | 12.99 | 500 | 4.7204 | 0.9989 |
| 16.886 | 14.0 | 539 | 4.6842 | 0.9989 |
| 16.886 | 14.99 | 577 | 4.6641 | 0.9994 |
| 16.886 | 16.0 | 616 | 4.6527 | 1.0 |
| 16.886 | 16.99 | 654 | 4.6745 | 0.9992 |
| 16.886 | 18.0 | 693 | 4.6591 | 1.0 |
| 16.886 | 18.99 | 731 | 4.6506 | 0.9997 |
| 16.886 | 20.0 | 770 | 4.6719 | 0.9967 |
| 4.4391 | 20.99 | 808 | 4.6067 | 0.9968 |
| 4.4391 | 22.0 | 847 | 4.5748 | 0.9968 |
| 4.4391 | 22.99 | 885 | 4.5166 | 0.9962 |
| 4.4391 | 24.0 | 924 | 4.3783 | 0.9926 |
| 4.4391 | 24.99 | 962 | 4.2711 | 0.9913 |
| 4.4391 | 26.0 | 1001 | 3.6515 | 1.0030 |
| 4.4391 | 26.99 | 1039 | 3.1057 | 1.0640 |
| 4.4391 | 28.0 | 1078 | 2.6593 | 1.0742 |
| 4.4391 | 28.99 | 1116 | 2.4071 | 1.0587 |
| 4.4391 | 30.0 | 1155 | 2.2041 | 1.0379 |
| 4.4391 | 30.99 | 1193 | 2.0495 | 1.0319 |
| 3.1722 | 32.0 | 1232 | 1.9754 | 1.0459 |
| 3.1722 | 32.99 | 1270 | 1.8658 | 0.9968 |
| 3.1722 | 34.0 | 1309 | 1.7887 | 0.9883 |
| 3.1722 | 34.99 | 1347 | 1.7560 | 0.9776 |
| 3.1722 | 36.0 | 1386 | 1.6987 | 0.9675 |
| 3.1722 | 36.99 | 1424 | 1.6513 | 0.9443 |
| 3.1722 | 38.0 | 1463 | 1.6187 | 0.9473 |
| 3.1722 | 38.99 | 1501 | 1.6210 | 0.9408 |
| 3.1722 | 40.0 | 1540 | 1.5957 | 0.9458 |
| 3.1722 | 40.99 | 1578 | 1.5673 | 0.9246 |
| 1.2364 | 42.0 | 1617 | 1.5748 | 0.9286 |
| 1.2364 | 42.99 | 1655 | 1.5333 | 0.9217 |
| 1.2364 | 44.0 | 1694 | 1.5138 | 0.9100 |
| 1.2364 | 44.99 | 1732 | 1.5244 | 0.9223 |
| 1.2364 | 46.0 | 1771 | 1.5041 | 0.9080 |
| 1.2364 | 46.99 | 1809 | 1.5151 | 0.9155 |
| 1.2364 | 48.0 | 1848 | 1.4955 | 0.9077 |
| 1.2364 | 48.99 | 1886 | 1.4924 | 0.9065 |
| 1.2364 | 49.35 | 1900 | 1.4929 | 0.9068 |
Framework versions
- Transformers 4.33.2
- Pytorch 1.12.1
- Datasets 2.14.5
- Tokenizers 0.13.3