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# 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
{"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"}]}]}]}
transformers pytorch tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer ko dataset:Bingsu/zeroth-korean base_model:openai/whisper-large-v2 license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #ko #dataset-Bingsu/zeroth-korean #base_model-openai/whisper-large-v2 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 

whisper-large-v2-Ko
===================


This model is a fine-tuned version of 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



### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.12.1+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2



### 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 ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.12.1+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #ko #dataset-Bingsu/zeroth-korean #base_model-openai/whisper-large-v2 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
 ### 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 ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.12.1+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #ko #dataset-Bingsu/zeroth-korean #base_model-openai/whisper-large-v2 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
### 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### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.12.1+cu113
* Datasets 2.10.1
* Tokenizers 0.13.2