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# whisper-medium-th-cv12

This model is a fine-tuned version of [biodatlab/whisper-medium-th-combined-v2](https://huggingface.co/biodatlab/whisper-medium-th-combined-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3122
- Wer: 23.0133

## 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: 6.25e-06
- train_batch_size: 32
- 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: 25
- training_steps: 250
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.8675        | 0.41  | 50   | 0.8984          | 28.2734 |
| 0.2598        | 0.82  | 100  | 0.3498          | 25.4960 |
| 0.1256        | 1.23  | 150  | 0.3226          | 24.8385 |
| 0.1198        | 1.64  | 200  | 0.3162          | 23.7955 |
| 0.1032        | 2.05  | 250  | 0.3122          | 23.0133 |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.1.dev0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "whisper-medium-th-cv12", "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-th-cv12
======================


This model is a fine-tuned version of biodatlab/whisper-medium-th-combined-v2 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3122
* Wer: 23.0133


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: 6.25e-06
* train\_batch\_size: 32
* 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: 25
* training\_steps: 250
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.12.1.dev0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6.25e-06
* train\_batch\_size: 32
* 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: 25
* training\_steps: 250
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.12.1.dev0
* Tokenizers 0.13.3
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: 6.25e-06
* train\_batch\_size: 32
* 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: 25
* training\_steps: 250
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.12.1.dev0
* Tokenizers 0.13.3