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# whisper-atcosim3

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) 
on the [atco2_atcosim](https://huggingface.co/datasets/luigisaetta/atco2_atcosim) 
dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0476
- Wer: 0.0198

## Model description

This model is a special ASR model, derived doing fine-tuning of OpenAI Whisper model on ATC conversations.
The base model is: [OpenAI Whisper Medium](https://huggingface.co/openai/whisper-medium)

## Intended uses & limitations

More information needed

## Training and evaluation data

The model has been trained on the [atco2_atcosim](https://huggingface.co/datasets/luigisaetta/atco2_atcosim)
dataset.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- training_steps: 600
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8218        | 0.2   | 50   | 0.3785          | 0.1451 |
| 0.1429        | 0.39  | 100  | 0.1213          | 0.0714 |
| 0.1155        | 0.59  | 150  | 0.0807          | 0.0517 |
| 0.0764        | 0.79  | 200  | 0.0652          | 0.0272 |
| 0.0724        | 0.98  | 250  | 0.0607          | 0.0393 |
| 0.0357        | 1.18  | 300  | 0.0569          | 0.0242 |
| 0.03          | 1.38  | 350  | 0.0553          | 0.0243 |
| 0.0325        | 1.57  | 400  | 0.0556          | 0.0228 |
| 0.03          | 1.77  | 450  | 0.0501          | 0.0242 |
| 0.0232        | 1.97  | 500  | 0.0485          | 0.0205 |
| 0.0143        | 2.16  | 550  | 0.0480          | 0.0194 |
| 0.0105        | 2.36  | 600  | 0.0476          | 0.0198 |


### Framework versions

- Transformers 4.29.0
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.11.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "whisper-atcosim3", "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-atcosim3
================


This model is a fine-tuned version of openai/whisper-medium
on the atco2\_atcosim
dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0476
* Wer: 0.0198


Model description
-----------------


This model is a special ASR model, derived doing fine-tuning of OpenAI Whisper model on ATC conversations.
The base model is: OpenAI Whisper Medium


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


The model has been trained on the atco2\_atcosim
dataset.


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 150
* training\_steps: 600
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.29.0
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 150
* training\_steps: 600
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.0
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.11.0
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: 1e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 150
* training\_steps: 600
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.0
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.11.0