transformers tensorboard safetensors whisper automatic-speech-recognition whisper-event generated_from_trainer ko dataset:mozilla-foundation/common_voice_16_0 base_model:openai/whisper-base license:apache-2.0 model-index endpoints_compatible region:us
---
language:
- ko
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
model-index:
- name: Whisper Base Korean
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_16_0 ko
      type: mozilla-foundation/common_voice_16_0
      config: ko
      split: test
      args: ko
    metrics:
    - name: Wer
      type: wer
      value: 45.5026455026455
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper Base Korean

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_16_0 ko dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6687
- Wer: 45.5026

## 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: 1e-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer     |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.0149        | 133.0  | 1000  | 0.6687          | 45.5026 |
| 0.0048        | 266.0  | 2000  | 0.7148          | 47.7633 |
| 0.0024        | 399.0  | 3000  | 0.7484          | 48.4848 |
| 0.0014        | 533.0  | 4000  | 0.7774          | 49.0139 |
| 0.0009        | 666.0  | 5000  | 0.8037          | 48.8215 |
| 0.0006        | 799.0  | 6000  | 0.8269          | 49.4468 |
| 0.0004        | 933.0  | 7000  | 0.8482          | 49.3987 |
| 0.0003        | 1066.0 | 8000  | 0.8662          | 54.6417 |
| 0.0003        | 1199.0 | 9000  | 0.8800          | 49.9278 |
| 0.0003        | 1333.0 | 10000 | 0.8856          | 49.8316 |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0
