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# whisper-small-ko-1159h

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1752
- Wer: 10.4449

## Model description

The model was trained to transcript the audio sources into Korean text.

## Intended uses & limitations

More information needed

## Training and evaluation data

I downloaded all data from AI-HUB (https://aihub.or.kr/). Two datasets, in particular, caught my attention: "Instruction Audio Set" and "Noisy Conversation Audio Set".
I intentionally gathered 796 hours of audio from the first dataset and 363 hours of audio from the second dataset (This includes statistics for the training data only, and excludes information about the validation data.).

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- 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: 100
- training_steps: 18483
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.0953        | 0.33  | 2053  | 0.2155          | 13.0432 |
| 0.0803        | 0.67  | 4106  | 0.1951          | 12.0399 |
| 0.0746        | 1.0   | 6159  | 0.1836          | 11.3995 |
| 0.0509        | 1.33  | 8212  | 0.1819          | 11.0396 |
| 0.0525        | 1.67  | 10265 | 0.1782          | 10.9039 |
| 0.0493        | 2.0   | 12318 | 0.1743          | 10.7255 |
| 0.034         | 2.33  | 14371 | 0.1784          | 10.7377 |
| 0.0326        | 2.67  | 16424 | 0.1765          | 10.5471 |
| 0.0293        | 3.0   | 18477 | 0.1752          | 10.4449 |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "whisper-small-ko-1159h", "results": []}]}
transformers pytorch tensorboard whisper automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

whisper-small-ko-1159h
======================


This model is a fine-tuned version of openai/whisper-small on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1752
* Wer: 10.4449


Model description
-----------------


The model was trained to transcript the audio sources into Korean text.


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


I downloaded all data from AI-HUB (URL Two datasets, in particular, caught my attention: "Instruction Audio Set" and "Noisy Conversation Audio Set".
I intentionally gathered 796 hours of audio from the first dataset and 363 hours of audio from the second dataset (This includes statistics for the training data only, and excludes information about the validation data.).


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* 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: 100
* training\_steps: 18483
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.13.1+cu117
* Datasets 2.11.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* 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: 100
* training\_steps: 18483
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.13.1+cu117
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* 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: 100
* training\_steps: 18483
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.13.1+cu117
* Datasets 2.11.0
* Tokenizers 0.13.2