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# whisper-large-v2_lora_int8

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1738

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7199        | 0.0   | 1    | 2.3895          |
| 0.1977        | 0.29  | 1000 | 0.1973          |
| 0.1777        | 0.58  | 2000 | 0.1846          |
| 0.1693        | 0.88  | 3000 | 0.1780          |
| 0.1579        | 1.17  | 4000 | 0.1753          |
| 0.157         | 1.46  | 5000 | 0.1738          |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "whisper-large-v2_lora_int8", "results": []}]}
transformers pytorch tensorboard whisper automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

whisper-large-v2\_lora\_int8
============================


This model is a fine-tuned version of openai/whisper-large-v2 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1738


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: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* training\_steps: 5000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* training\_steps: 5000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #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: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* training\_steps: 5000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3