---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- arrow
model-index:
- name: whisper-tiny-kor-16k-hf-ep100
  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-tiny-kor-16k-hf-ep100

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the arrow dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9175
- Cer: 29.8227

## 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: 5e-05
- train_batch_size: 128
- 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: 500
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Cer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.1166        | 7.94  | 1000  | 0.6351          | 34.2349 |
| 0.0024        | 15.87 | 2000  | 0.7071          | 33.1900 |
| 0.0015        | 23.81 | 3000  | 0.7484          | 29.1958 |
| 0.0009        | 31.75 | 4000  | 0.7972          | 29.5301 |
| 0.0008        | 39.68 | 5000  | 0.8082          | 29.1122 |
| 0.0003        | 47.62 | 6000  | 0.8581          | 29.5182 |
| 0.0           | 55.56 | 7000  | 0.8756          | 29.6316 |
| 0.0           | 63.49 | 8000  | 0.8922          | 29.6316 |
| 0.0004        | 71.43 | 9000  | 0.8693          | 29.5958 |
| 0.0           | 79.37 | 10000 | 0.8930          | 29.9003 |
| 0.0           | 87.3  | 11000 | 0.9081          | 29.7212 |
| 0.0           | 95.24 | 12000 | 0.9175          | 29.8227 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.0