This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:
The model was trained to transcript the audio sources into Korean text.
More information needed
I downloaded all data from AI-HUB (https://aihub.or.kr/). Two datasets, in particular, caught my attention: "Instruction Audio Set" and "Noisy Conversation Audio Set". I intentionally gathered 796 hours of audio from the first dataset and 363 hours of audio from the second dataset (This includes statistics for the training data only, and excludes information about the validation data.).
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0953 | 0.33 | 2053 | 0.2155 | 13.0432 |
| 0.0803 | 0.67 | 4106 | 0.1951 | 12.0399 |
| 0.0746 | 1.0 | 6159 | 0.1836 | 11.3995 |
| 0.0509 | 1.33 | 8212 | 0.1819 | 11.0396 |
| 0.0525 | 1.67 | 10265 | 0.1782 | 10.9039 |
| 0.0493 | 2.0 | 12318 | 0.1743 | 10.7255 |
| 0.034 | 2.33 | 14371 | 0.1784 | 10.7377 |
| 0.0326 | 2.67 | 16424 | 0.1765 | 10.5471 |
| 0.0293 | 3.0 | 18477 | 0.1752 | 10.4449 |
pytorch_model.bin 967 MBtraining_args.bin 3.7 kB