whisper-medium-ko-normalized-1273h
This model is a fine-tuned version of openai/whisper-medium on a custom dataset for improving Korean speech recognition. It achieves the following results on the evaluation set:
- Loss: 0.1254
- Wer: 0.0551
Model description
The model was a fine-tuned version of openai/whisper-medium transcript the Korean audio sources into text.
It was trained on GCP's a2-highgpu-1g (a100-40G) for 26 hours with about $90.
Intended uses & limitations
This model was trained to extend the performance of the original whisper model for Korean transcription task.
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". Following indicates the hours information for each dastset.
| dataset name | train_split (hours) | validation_split (hours) |
|---|---|---|
| Instruction Audio Set | 910 | 105 |
| Noisy Conversation Audio Set | 363 | 76 |
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 24
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0588 | 1.0 | 8775 | 0.1225 | 0.0604 |
| 0.0287 | 2.0 | 17550 | 0.1186 | 0.0567 |
| 0.0148 | 3.0 | 26325 | 0.1254 | 0.0551 |
Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2
Evaluation Result for the dataset google/fleurs
The trained model is evaluated on the test split of subset ko_kr from the dataset google/fleurs.
Please note that the model was not trained on the train split from the dataset.
| model | Wer |
|---|---|
| openai/whisper | 0.2469 |
| this model | 0.2189 |
