---
language:
- ko
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- Bingsu/zeroth-korean
metrics:
- wer
pipeline_tag: automatic-speech-recognition
base_model: openai/whisper-large-v2
model-index:
- name: whisper-large-v2-Ko
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Bingsu/zeroth-korean
      type: Bingsu/zeroth-korean
    metrics:
    - type: wer
      value: 2.9
      name: Wer
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: google/fleurs
      type: google/fleurs
      config: ko_kr
      split: test
    metrics:
    - type: wer
      value: 20.66
      name: WER
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# whisper-large-v2-Ko

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0617
- Wer: **2.9**

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

***** train metrics *****                    
  epoch                    =        50.0     
  train_loss               =      0.0234     
  train_runtime            = 16:20:18.00     
  train_samples            =       22262     
  train_samples_per_second =      19.042     
  train_steps_per_second   =       0.085

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 7
- total_train_batch_size: 224
- total_eval_batch_size: 112
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0299        | 10.0  | 1000 | 0.0745          | 0.0447 |
| 0.0085        | 20.0  | 2000 | 0.0608          | 0.0353 |
| 0.0036        | 30.0  | 3000 | 0.0593          | 0.0302 |
| 0.0013        | 40.0  | 4000 | 0.0609          | 0.0282 |
| 0.0008        | 50.0  | 5000 | 0.0617          | 0.0290 |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2