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# whisper-tiny-fluers_V2_telugu_Augmentation_full_datset_V2_e5

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3374
- Wer: 61.2000

## 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: 1.5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer      |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2926        | 0.09  | 300  | 1.3993          | 129.5    |
| 1.0948        | 0.18  | 600  | 1.2674          | 109.1500 |
| 0.6591        | 0.28  | 900  | 0.5519          | 81.55    |
| 0.5326        | 0.37  | 1200 | 0.4361          | 72.55    |
| 0.4737        | 0.46  | 1500 | 0.4036          | 69.2000  |
| 0.4239        | 0.55  | 1800 | 0.3793          | 63.6     |
| 0.4011        | 0.64  | 2100 | 0.3625          | 62.2500  |
| 0.3687        | 0.73  | 2400 | 0.3651          | 62.5     |
| 0.3712        | 0.83  | 2700 | 0.3491          | 59.9     |
| 0.3686        | 0.92  | 3000 | 0.3438          | 60.6500  |
| 0.3381        | 1.01  | 3300 | 0.3391          | 58.25    |
| 0.3483        | 1.1   | 3600 | 0.3385          | 60.0500  |
| 0.341         | 1.19  | 3900 | 0.3374          | 61.2000  |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.12.1
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "whisper-tiny-fluers_V2_telugu_Augmentation_full_datset_V2_e5", "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-tiny-fluers\_V2\_telugu\_Augmentation\_full\_datset\_V2\_e5
===================================================================


This model is a fine-tuned version of openai/whisper-tiny on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3374
* Wer: 61.2000


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: 1.5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 4000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.12.1
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1.5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 4000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.12.1
* 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: 1.5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 4000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.12.1
* Datasets 2.11.0
* Tokenizers 0.13.3