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# Whisper Base Pashto - Augmented

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the google/fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8723
- Wer: 57.6120
- Cer: 26.6468

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 30
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     | Cer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 0.9708        | 2.38  | 100  | 0.8821          | 64.0133 | 27.3253 |
| 0.7477        | 4.75  | 200  | 0.8062          | 59.9576 | 26.4079 |
| 0.6229        | 7.14  | 300  | 0.7855          | 58.3081 | 26.3193 |
| 0.4833        | 9.52  | 400  | 0.7870          | 57.5288 | 24.8855 |
| 0.4084        | 11.89 | 500  | 0.7980          | 56.5224 | 25.2214 |
| 0.3323        | 14.28 | 600  | 0.8201          | 56.6662 | 25.3317 |
| 0.283         | 16.66 | 700  | 0.8406          | 57.7406 | 26.8674 |
| 0.2598        | 19.05 | 800  | 0.8538          | 57.2866 | 26.0386 |
| 0.2235        | 21.42 | 900  | 0.8697          | 58.2703 | 26.6819 |
| 0.2202        | 23.8  | 1000 | 0.8723          | 57.6120 | 26.6468 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2

{"language": ["ps"], "license": "apache-2.0", "tags": ["whisper-event", "generated_from_trainer"], "datasets": ["google/fleurs"], "metrics": ["wer"], "model-index": [{"name": "Whisper Base Pashto - Augmented", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "google/fleurs", "type": "google/fleurs", "config": "ps_af", "split": "test", "args": "ps_af"}, "metrics": [{"type": "wer", "value": 57.611985472154956, "name": "Wer"}]}]}]}
transformers pytorch tensorboard whisper automatic-speech-recognition whisper-event generated_from_trainer ps dataset:google/fleurs license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #ps #dataset-google/fleurs #license-apache-2.0 #model-index #endpoints_compatible #region-us 

Whisper Base Pashto - Augmented
===============================


This model is a fine-tuned version of openai/whisper-base on the google/fleurs dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8723
* Wer: 57.6120
* Cer: 26.6468


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
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 30
* training\_steps: 1000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.8.1.dev0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 30
* training\_steps: 1000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.8.1.dev0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #ps #dataset-google/fleurs #license-apache-2.0 #model-index #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
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 30
* training\_steps: 1000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.8.1.dev0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #ps #dataset-google/fleurs #license-apache-2.0 #model-index #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
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 30
* training\_steps: 1000
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.8.1.dev0
* Tokenizers 0.13.2