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# dgx1_whisper_small_libri360_noisy_teacher_interleaved_dataset_distil_epochs_50_batch_4_try2

This model is a fine-tuned version of [rohitp1/dgx2_whisper_small_finetune_teacher_babble_noise_libri_360_hours_50_epochs_batch_8](https://huggingface.co/rohitp1/dgx2_whisper_small_finetune_teacher_babble_noise_libri_360_hours_50_epochs_batch_8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8330
- Wer: 8.9519

## 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: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 256
- total_train_batch_size: 1024
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0247        | 0.74  | 150  | 0.0998          | 8.9666 |
| 0.0927        | 1.48  | 300  | 0.1621          | 8.8958 |
| 0.1626        | 2.22  | 450  | 0.2224          | 8.9059 |
| 0.2571        | 2.95  | 600  | 0.3091          | 8.9059 |
| 0.4578        | 3.69  | 750  | 0.4165          | 8.9225 |
| 0.5417        | 4.43  | 900  | 0.5238          | 8.9519 |
| 0.7906        | 5.17  | 1050 | 0.8330          | 8.9519 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "dgx1_whisper_small_libri360_noisy_teacher_interleaved_dataset_distil_epochs_50_batch_4_try2", "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 

dgx1\_whisper\_small\_libri360\_noisy\_teacher\_interleaved\_dataset\_distil\_epochs\_50\_batch\_4\_try2
========================================================================================================


This model is a fine-tuned version of rohitp1/dgx2\_whisper\_small\_finetune\_teacher\_babble\_noise\_libri\_360\_hours\_50\_epochs\_batch\_8 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8330
* Wer: 8.9519


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: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 256
* total\_train\_batch\_size: 1024
* 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: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 256
* total\_train\_batch\_size: 1024
* 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 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 256
* total\_train\_batch\_size: 1024
* 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