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# whisper-small-ar-12hrsdarijadata-April21-params1

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: 0.6189
- Wer: 57.5010
- Cer: 22.7156

## 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: 1e-05
- 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: 300
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     | Cer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 1.0603        | 0.38  | 250  | 0.8352          | 73.3652 | 36.2817 |
| 0.7041        | 0.75  | 500  | 0.6119          | 66.8656 | 29.7437 |
| 0.4434        | 1.13  | 750  | 0.5534          | 56.4001 | 24.9692 |
| 0.4344        | 1.51  | 1000 | 0.5341          | 55.8827 | 22.2788 |
| 0.3683        | 1.89  | 1250 | 0.5171          | 53.8931 | 23.3545 |
| 0.2964        | 2.26  | 1500 | 0.5245          | 53.2697 | 21.4447 |
| 0.2126        | 2.64  | 1750 | 0.5267          | 51.0545 | 19.9624 |
| 0.2095        | 3.02  | 2000 | 0.5428          | 55.1267 | 21.9697 |
| 0.1452        | 3.39  | 2250 | 0.5636          | 59.2386 | 23.4962 |
| 0.1123        | 3.77  | 2500 | 0.5598          | 54.0257 | 21.7816 |
| 0.077         | 4.15  | 2750 | 0.5809          | 53.5747 | 23.3614 |
| 0.0624        | 4.52  | 3000 | 0.5897          | 53.6941 | 22.1417 |
| 0.0492        | 4.9   | 3250 | 0.5973          | 54.0921 | 20.3922 |
| 0.0571        | 5.28  | 3500 | 0.6128          | 54.3043 | 21.2704 |
| 0.0637        | 5.66  | 3750 | 0.6139          | 54.6890 | 22.2114 |
| 0.0288        | 6.03  | 4000 | 0.6189          | 57.5010 | 22.7156 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.11.1.dev0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "whisper-small-ar-12hrsdarijadata-April21-params1", "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-ar-12hrsdarijadata-April21-params1
================================================


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: 0.6189
* Wer: 57.5010
* Cer: 22.7156


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: 1e-05
* 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: 300
* training\_steps: 4000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.1.dev0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* 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: 300
* training\_steps: 4000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.1.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: 1e-05
* 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: 300
* training\_steps: 4000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.1.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: 1e-05
* 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: 300
* training\_steps: 4000
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.1.dev0
* Tokenizers 0.13.3