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# Whisper Tiny GPU test

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the NbAiLab/NCC_S3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9375
- Wer: 51.3703

## 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: 128
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 200
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 2.4574        | 0.1   | 200  | 1.4663          | 71.6504 |
| 1.9587        | 0.2   | 400  | 1.2581          | 64.7381 |
| 1.816         | 0.3   | 600  | 1.1672          | 60.9318 |
| 1.7199        | 0.4   | 800  | 1.1006          | 57.6736 |
| 1.6686        | 0.5   | 1000 | 1.0630          | 56.1815 |
| 1.621         | 0.6   | 1200 | 1.0273          | 55.4811 |
| 1.5846        | 0.7   | 1400 | 1.0017          | 53.9890 |
| 1.5482        | 0.8   | 1600 | 0.9773          | 53.0146 |
| 1.521         | 0.9   | 1800 | 0.9575          | 52.1011 |
| 1.4932        | 1.0   | 2000 | 0.9375          | 51.3703 |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["whisper-event", "generated_from_trainer"], "datasets": ["NbAiLab/NCC_S3"], "metrics": ["wer"], "model-index": [{"name": "Whisper Tiny GPU test", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "NbAiLab/NCC_S3", "type": "NbAiLab/NCC_S3", "config": "no", "split": "validation", "args": "no"}, "metrics": [{"type": "wer", "value": 51.37028014616322, "name": "Wer"}]}]}]}
transformers pytorch tensorboard whisper automatic-speech-recognition whisper-event generated_from_trainer dataset:NbAiLab/NCC_S3 license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #dataset-NbAiLab/NCC_S3 #license-apache-2.0 #model-index #endpoints_compatible #region-us 

Whisper Tiny GPU test
=====================


This model is a fine-tuned version of openai/whisper-tiny on the NbAiLab/NCC\_S3 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.9375
* Wer: 51.3703


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: 128
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant\_with\_warmup
* lr\_scheduler\_warmup\_steps: 200
* training\_steps: 2000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.13.0+cu117
* Datasets 2.7.1.dev0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-06
* train\_batch\_size: 128
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant\_with\_warmup
* lr\_scheduler\_warmup\_steps: 200
* training\_steps: 2000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.13.0+cu117
* Datasets 2.7.1.dev0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #dataset-NbAiLab/NCC_S3 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-06
* train\_batch\_size: 128
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant\_with\_warmup
* lr\_scheduler\_warmup\_steps: 200
* training\_steps: 2000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.13.0+cu117
* Datasets 2.7.1.dev0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #dataset-NbAiLab/NCC_S3 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-06
* train\_batch\_size: 128
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant\_with\_warmup
* lr\_scheduler\_warmup\_steps: 200
* training\_steps: 2000
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.13.0+cu117
* Datasets 2.7.1.dev0
* Tokenizers 0.13.2