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---
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 |