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# Whisper-medium-BTC

This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3875
- Wer: 6.5315

## 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: 3e-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 600
- training_steps: 800
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7348        | 2.01  | 50   | 0.7378          | 9.3091 |
| 0.5276        | 4.02  | 100  | 0.5290          | 8.6125 |
| 0.3585        | 7.0   | 150  | 0.3875          | 6.5315 |
| 0.2924        | 9.01  | 200  | 0.3548          | 6.6779 |
| 0.2506        | 11.02 | 250  | 0.3364          | 6.7888 |
| 0.1946        | 14.01 | 300  | 0.3262          | 7.1482 |
| 0.1411        | 16.02 | 350  | 0.3329          | 7.2104 |
| 0.1005        | 19.0  | 400  | 0.3422          | 7.5565 |
| 0.0535        | 21.01 | 450  | 0.3532          | 7.1793 |
| 0.0259        | 23.02 | 500  | 0.3456          | 7.5121 |
| 0.0137        | 26.0  | 550  | 0.3587          | 7.6541 |
| 0.0078        | 28.02 | 600  | 0.3591          | 7.3524 |
| 0.0041        | 30.02 | 650  | 0.3672          | 7.3035 |
| 0.0026        | 33.01 | 700  | 0.3962          | 7.3213 |
| 0.0022        | 35.02 | 750  | 0.3997          | 7.3524 |
| 0.0022        | 38.0  | 800  | 0.4025          | 7.3302 |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "Whisper-medium-BTC", "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-medium-BTC
==================


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


* Loss: 0.3875
* Wer: 6.5315


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: 3e-06
* train\_batch\_size: 32
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 600
* training\_steps: 800
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu117
* Datasets 2.9.1.dev0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-06
* train\_batch\_size: 32
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 600
* training\_steps: 800
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu117
* Datasets 2.9.1.dev0
* Tokenizers 0.13.2
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: 3e-06
* train\_batch\_size: 32
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 600
* training\_steps: 800
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu117
* Datasets 2.9.1.dev0
* Tokenizers 0.13.2
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: 3e-06
* train\_batch\_size: 32
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 600
* training\_steps: 800
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu117
* Datasets 2.9.1.dev0
* Tokenizers 0.13.2