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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-meidum-ko-normalized-1273h
results: []
---
# whisper-medium-ko-normalized-1273h
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/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|
|