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# kkkh_whisper_small_distillation_att_loss_libri360_epochs_50_batch_4_concat_dataset_try6

This model is a fine-tuned version of [rohitp1/kkkh_whisper_small_distillation_att_loss_libri360_epochs_100_batch_4_concat_dataset](https://huggingface.co/rohitp1/kkkh_whisper_small_distillation_att_loss_libri360_epochs_100_batch_4_concat_dataset) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7728
- Wer: 4.5237

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 256
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.0773        | 0.98  | 100  | 4.9642          | 4.4263 |
| 6.1998        | 1.97  | 200  | 5.0002          | 4.3767 |
| 6.298         | 2.95  | 300  | 5.1708          | 4.3390 |
| 6.4775        | 3.94  | 400  | 5.3493          | 4.3491 |
| 6.5852        | 4.92  | 500  | 5.1604          | 4.3813 |
| 6.3406        | 5.91  | 600  | 4.9918          | 4.6671 |
| 6.6247        | 6.89  | 700  | 5.5665          | 4.4796 |
| 6.0813        | 7.88  | 800  | 4.7728          | 4.5237 |


### Framework versions

- Transformers 4.27.3
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1

{"tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "kkkh_whisper_small_distillation_att_loss_libri360_epochs_50_batch_4_concat_dataset_try6", "results": []}]}
transformers pytorch whisper automatic-speech-recognition generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 

kkkh\_whisper\_small\_distillation\_att\_loss\_libri360\_epochs\_50\_batch\_4\_concat\_dataset\_try6
====================================================================================================


This model is a fine-tuned version of rohitp1/kkkh\_whisper\_small\_distillation\_att\_loss\_libri360\_epochs\_100\_batch\_4\_concat\_dataset on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 4.7728
* Wer: 4.5237


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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 256
* total\_train\_batch\_size: 2048
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine\_with\_restarts
* lr\_scheduler\_warmup\_ratio: 0.2
* num\_epochs: 50
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.3
* Pytorch 1.12.1
* Datasets 2.6.1
* Tokenizers 0.13.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 256
* total\_train\_batch\_size: 2048
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine\_with\_restarts
* lr\_scheduler\_warmup\_ratio: 0.2
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 1.12.1
* Datasets 2.6.1
* Tokenizers 0.13.1
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 256
* total\_train\_batch\_size: 2048
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine\_with\_restarts
* lr\_scheduler\_warmup\_ratio: 0.2
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 1.12.1
* Datasets 2.6.1
* Tokenizers 0.13.1