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# subhadeep_whisper_base_finetune_teacher_babble_noise_libri_360_hours_100_epochs_batch_8

This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2491
- Wer: 13.5528

## 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
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.7942        | 1.98  | 100  | 0.2872          | 16.8523 |
| 0.1675        | 3.98  | 200  | 0.2003          | 13.5730 |
| 0.0819        | 5.98  | 300  | 0.1944          | 13.1208 |
| 0.0418        | 7.98  | 400  | 0.2070          | 13.0639 |
| 0.0264        | 9.98  | 500  | 0.2199          | 13.0289 |
| 0.0227        | 11.98 | 600  | 0.2310          | 13.3690 |
| 0.0218        | 13.98 | 700  | 0.2322          | 13.1870 |
| 0.02          | 15.98 | 800  | 0.2405          | 13.1466 |
| 0.0207        | 17.98 | 900  | 0.2496          | 13.4444 |
| 0.0226        | 19.98 | 1000 | 0.2491          | 13.5528 |


### 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": "subhadeep_whisper_base_finetune_teacher_babble_noise_libri_360_hours_100_epochs_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 

subhadeep\_whisper\_base\_finetune\_teacher\_babble\_noise\_libri\_360\_hours\_100\_epochs\_batch\_8
====================================================================================================


This model is a fine-tuned version of openai/URL on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2491
* Wer: 13.5528


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
* lr\_scheduler\_warmup\_ratio: 0.2
* num\_epochs: 100
* 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: 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
* lr\_scheduler\_warmup\_ratio: 0.2
* num\_epochs: 100
* 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: 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
* lr\_scheduler\_warmup\_ratio: 0.2
* num\_epochs: 100
* 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: 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
* lr\_scheduler\_warmup\_ratio: 0.2
* num\_epochs: 100
* 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