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# subh_whisper_small_distil_att_loss_mozilla_epochs_50_batch_8

This model is a fine-tuned version of [rohitp1/dgx1_whisper_small_finetune_teacher_babble_noise_mozilla_40_epochs_batch_8](https://huggingface.co/rohitp1/dgx1_whisper_small_finetune_teacher_babble_noise_mozilla_40_epochs_batch_8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5208
- Wer: 100.0

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 512
- total_train_batch_size: 4096
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer   |
|:-------------:|:-----:|:----:|:---------------:|:-----:|
| 3.1278        | 4.39  | 150  | 0.4135          | 100.0 |
| 4.6475        | 8.78  | 300  | 0.4516          | 100.0 |
| 5.5834        | 13.17 | 450  | 0.4801          | 100.0 |
| 6.7365        | 17.55 | 600  | 0.4981          | 100.0 |
| 7.3081        | 21.94 | 750  | 0.5108          | 100.0 |
| 7.4849        | 26.33 | 900  | 0.5208          | 100.0 |


### Framework versions

- Transformers 4.29.2
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "subh_whisper_small_distil_att_loss_mozilla_epochs_50_batch_8", "results": []}]}
transformers pytorch tensorboard whisper automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us 

subh\_whisper\_small\_distil\_att\_loss\_mozilla\_epochs\_50\_batch\_8
======================================================================


This model is a fine-tuned version of rohitp1/dgx1\_whisper\_small\_finetune\_teacher\_babble\_noise\_mozilla\_40\_epochs\_batch\_8 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5208
* Wer: 100.0


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: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 512
* total\_train\_batch\_size: 4096
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.2
* num\_epochs: 50
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 512
* total\_train\_batch\_size: 4096
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.2
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 512
* total\_train\_batch\_size: 4096
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.2
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.2