metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-medium-ko-1195h
results: []
whisper-medium-ko-1195h
This model is a fine-tuned version of openai/whisper-medium on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1552
- Wer: 8.6411 (is improved against 10.4449 from jangmin/whisper-small-ko-1159h)
Model description
The model was trained to transcript the audio sources into Korean text.
Intended uses & limitations
More information needed
Training and evaluation data
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.).
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 59151
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0782 | 0.33 | 6572 | 0.1833 | 10.9268 |
| 0.07 | 0.67 | 13144 | 0.1680 | 10.3611 |
| 0.0605 | 1.0 | 19716 | 0.1600 | 9.9357 |
| 0.0345 | 1.33 | 26288 | 0.1573 | 9.4492 |
| 0.0365 | 1.67 | 32860 | 0.1518 | 9.3395 |
| 0.0339 | 2.0 | 39432 | 0.1478 | 8.9811 |
| 0.0176 | 2.33 | 46004 | 0.1596 | 9.1702 |
| 0.0159 | 2.67 | 52576 | 0.1572 | 8.6746 |
| 0.0141 | 3.0 | 59148 | 0.1552 | 8.6411 |
Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2