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# dgx2_whisper_base_teacher_student_distill_att_loss_libri360_epochs_50_batch_8

This model is a fine-tuned version of [rohitp1/subh_whisper_base_teacher_student_distill_att_loss_libri360_epochs_100_batch_4_ckpt1500](https://huggingface.co/rohitp1/subh_whisper_base_teacher_student_distill_att_loss_libri360_epochs_100_batch_4_ckpt1500) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1009
- Wer: 7.2700

## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 1
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5913        | 0.98  | 100  | 0.6243          | 6.3702 |
| 0.6028        | 1.97  | 200  | 0.6798          | 6.6119 |
| 0.6546        | 2.95  | 300  | 0.7314          | 6.9170 |
| 0.7386        | 3.94  | 400  | 1.3793          | 7.0099 |
| 1.2318        | 4.92  | 500  | 0.9823          | 7.0099 |
| 1.2304        | 5.91  | 600  | 1.1009          | 7.2700 |


### Framework versions

- Transformers 4.28.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "dgx2_whisper_base_teacher_student_distill_att_loss_libri360_epochs_50_batch_8", "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 

dgx2\_whisper\_base\_teacher\_student\_distill\_att\_loss\_libri360\_epochs\_50\_batch\_8
=========================================================================================


This model is a fine-tuned version of rohitp1/subh\_whisper\_base\_teacher\_student\_distill\_att\_loss\_libri360\_epochs\_100\_batch\_4\_ckpt1500 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.1009
* Wer: 7.2700


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: 0.0005
* train\_batch\_size: 8
* eval\_batch\_size: 1
* 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 4.28.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 8
* eval\_batch\_size: 1
* 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 4.28.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.2
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: 0.0005
* train\_batch\_size: 8
* eval\_batch\_size: 1
* 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 4.28.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.2