transformers tensorboard safetensors whisper automatic-speech-recognition hf-asr-leaderboard generated_from_trainer ko base_model:openai/whisper-large-v3 license:apache-2.0 endpoints_compatible region:us
---
language:
- ko
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
base_model: openai/whisper-large-v3
metrics:
- wer
model-index:
- name: whisper_finetune
  results: []
---

<!-- 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_finetune

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the aihub_100000 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4933
- Cer: 6.9924
- Wer: 28.6257

## 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-08
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Cer    | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 0.6435        | 0.14  | 1000 | 0.6061          | 7.0810 | 29.1317 |
| 0.515         | 0.28  | 2000 | 0.4933          | 6.9924 | 28.6257 |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.17.1
- Tokenizers 0.15.2
