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# dgx1_whisper_base_libri360_noisy_teacher_distil_epochs_50_batch_8

This model is a fine-tuned version of [rohitp1/subhadeep_whisper_base_finetune_teacher_babble_noise_libri_360_hours_100_epochs_batch_8](https://huggingface.co/rohitp1/subhadeep_whisper_base_finetune_teacher_babble_noise_libri_360_hours_100_epochs_batch_8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5761
- Wer: 10.6733

## 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: 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     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0423        | 1.48  | 150  | 0.1620          | 10.8902 |
| 0.0999        | 2.96  | 300  | 0.2030          | 10.7882 |
| 0.1577        | 4.45  | 450  | 0.2511          | 10.7937 |
| 0.2078        | 5.94  | 600  | 0.2966          | 10.7827 |
| 0.252         | 7.42  | 750  | 0.3321          | 10.7524 |
| 0.2841        | 8.91  | 900  | 0.3625          | 10.7588 |
| 0.3189        | 10.39 | 1050 | 0.3858          | 10.7772 |
| 0.341         | 11.88 | 1200 | 0.4090          | 10.7505 |
| 0.5277        | 13.36 | 1350 | 0.5461          | 11.1926 |
| 0.8342        | 14.85 | 1500 | 0.5250          | 10.8415 |
| 0.8278        | 16.33 | 1650 | 0.5543          | 10.7478 |
| 0.8255        | 17.82 | 1800 | 0.5481          | 10.6761 |
| 0.822         | 19.31 | 1950 | 0.5504          | 10.6650 |
| 0.8204        | 20.79 | 2100 | 0.5556          | 10.6650 |
| 0.8246        | 22.28 | 2250 | 0.5598          | 10.6586 |
| 0.8228        | 23.76 | 2400 | 0.5634          | 10.6770 |
| 0.8282        | 25.25 | 2550 | 0.5670          | 10.6706 |
| 0.8264        | 26.73 | 2700 | 0.5702          | 10.6752 |
| 0.8298        | 28.22 | 2850 | 0.5731          | 10.6908 |
| 0.8273        | 29.7  | 3000 | 0.5761          | 10.6733 |


### Framework versions

- Transformers 4.25.1
- 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": "dgx1_whisper_base_libri360_noisy_teacher_distil_epochs_50_batch_8", "results": []}]}
transformers pytorch whisper automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

dgx1\_whisper\_base\_libri360\_noisy\_teacher\_distil\_epochs\_50\_batch\_8
===========================================================================


This model is a fine-tuned version of rohitp1/subhadeep\_whisper\_base\_finetune\_teacher\_babble\_noise\_libri\_360\_hours\_100\_epochs\_batch\_8 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5761
* Wer: 10.6733


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: 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 4.25.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* 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 4.25.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #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: 5e-05
* 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 4.25.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.2