
# whisper-medium-ko-normalized-1273h

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on a custom dataset for improving Korean speech recognition.
It achieves the following results on the evaluation set:
- Loss: 0.1254
- Wer: 0.0551

## Model description

The model was a fine-tuned version of `openai/whisper-medium` transcript the Korean audio sources into text.
It was trained on GCP's `a2-highgpu-1g` (a100-40G) for 26 hours with about $90.

## Intended uses & limitations

This model was trained to extend the performance of the original whisper model for Korean transcription task.

## 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". 
Following indicates the hours information for each dastset.

|dataset name| train_split (hours) | validation_split (hours)|
|---|---|---|
|Instruction Audio Set|910|105|
|Noisy Conversation Audio Set|363|76|

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 24
- 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
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.0588        | 1.0   | 8775  | 0.1225          | 0.0604 |
| 0.0287        | 2.0   | 17550 | 0.1186          | 0.0567 |
| 0.0148        | 3.0   | 26325 | 0.1254          | 0.0551 |



### Framework versions

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

## Evaluation Result for the dataset `google/fleurs`

The trained model is evaluated on the `test` split of subset `ko_kr` from the dataset `google/fleurs`.
Please note that the model was not trained on the `train` split from the dataset.

|model|Wer|
|---|---|
|openai/whisper|0.2469|
|this model|0.2189|

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "whisper-meidum-ko-normalized-1273h", "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-medium-ko-normalized-1273h
==================================


This model is a fine-tuned version of openai/whisper-medium on a custom dataset for improving Korean speech recognition.
It achieves the following results on the evaluation set:


* Loss: 0.1254
* Wer: 0.0551


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


The model was a fine-tuned version of 'openai/whisper-medium' transcript the Korean audio sources into text.
It was trained on GCP's 'a2-highgpu-1g' (a100-40G) for 26 hours with about $90.


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


This model was trained to extend the performance of the original whisper model for Korean transcription task.


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".
Following indicates the hours information for each dastset.


dataset name: Instruction Audio Set, train\_split (hours): 910, validation\_split (hours): 105
dataset name: Noisy Conversation Audio Set, train\_split (hours): 363, validation\_split (hours): 76


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


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 24
* 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
* num\_epochs: 3
* 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


Evaluation Result for the dataset 'google/fleurs'
-------------------------------------------------


The trained model is evaluated on the 'test' split of subset 'ko\_kr' from the dataset 'google/fleurs'.
Please note that the model was not trained on the 'train' split from the dataset.




### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 24
* 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
* num\_epochs: 3
* 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


Evaluation Result for the dataset 'google/fleurs'
-------------------------------------------------


The trained model is evaluated on the 'test' split of subset 'ko\_kr' from the dataset 'google/fleurs'.
Please note that the model was not trained on the 'train' split from the dataset.
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: 24
* 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
* num\_epochs: 3
* 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


Evaluation Result for the dataset 'google/fleurs'
-------------------------------------------------


The trained model is evaluated on the 'test' split of subset 'ko\_kr' from the dataset 'google/fleurs'.
Please note that the model was not trained on the 'train' split from the dataset.