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# workstation_whisper_tiny_finetune_teacher_no_noise_libri_40_epochs_batch_8

This model is a fine-tuned version of [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4597
- Wer: 14.5197

## 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: 0.001
- 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: 200
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0539        | 76.86  | 1000 | 0.4647          | 15.6594 |
| 0.0006        | 153.79 | 2000 | 0.4597          | 14.5197 |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.11.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "workstation_whisper_tiny_finetune_teacher_no_noise_libri_40_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 

workstation\_whisper\_tiny\_finetune\_teacher\_no\_noise\_libri\_40\_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: 0.4597
* Wer: 14.5197


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: 0.001
* 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: 200
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.24.0
* Pytorch 1.12.1
* Datasets 2.7.1
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.001
* 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: 200
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 1.12.1
* Datasets 2.7.1
* Tokenizers 0.11.0
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: 0.001
* 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: 200
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 1.12.1
* Datasets 2.7.1
* Tokenizers 0.11.0