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# whisper-synthesized-turkish-4-hour-hlr

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: 0.4388
- Wer: 15.4240

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.7343        | 1.04  | 100  | 0.2580          | 16.7757 |
| 0.1568        | 2.08  | 200  | 0.2714          | 15.3129 |
| 0.091         | 3.12  | 300  | 0.3099          | 16.2573 |
| 0.0861        | 4.17  | 400  | 0.3946          | 22.9910 |
| 0.0967        | 5.21  | 500  | 0.4884          | 23.2132 |
| 0.0775        | 6.25  | 600  | 0.4263          | 19.9852 |
| 0.0692        | 7.29  | 700  | 0.4428          | 20.0099 |
| 0.052         | 8.33  | 800  | 0.4407          | 23.6761 |
| 0.0458        | 9.38  | 900  | 0.4760          | 19.7445 |
| 0.0326        | 10.42 | 1000 | 0.4847          | 18.6520 |
| 0.0281        | 11.46 | 1100 | 0.4936          | 20.2074 |
| 0.0221        | 12.5  | 1200 | 0.4655          | 19.3495 |
| 0.0123        | 13.54 | 1300 | 0.4657          | 17.5781 |
| 0.0105        | 14.58 | 1400 | 0.4493          | 16.2264 |
| 0.0042        | 15.62 | 1500 | 0.4396          | 15.5660 |
| 0.0029        | 16.67 | 1600 | 0.4412          | 15.7882 |
| 0.0011        | 17.71 | 1700 | 0.4400          | 15.8190 |
| 0.0005        | 18.75 | 1800 | 0.4400          | 15.4672 |
| 0.0003        | 19.79 | 1900 | 0.4389          | 15.4117 |
| 0.0002        | 20.83 | 2000 | 0.4388          | 15.4240 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "whisper-synthesized-turkish-4-hour-hlr", "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-synthesized-turkish-4-hour-hlr
======================================


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: 0.4388
* Wer: 15.4240


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: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 2000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 2000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* 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: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 2000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* 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: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 2000
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3