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# whisper-small-vinzalo-v1-50k

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1210

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- training_steps: 50000

### Training results



### Framework versions

- Transformers 4.27.2
- Pytorch 2.1.0a0+git4ab1588
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["whisper-event", "generated_from_trainer"], "model-index": [{"name": "whisper-small-vinzalo-v1-50k", "results": []}]}
transformers pytorch tensorboard whisper automatic-speech-recognition whisper-event generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 




# whisper-small-vinzalo-v1-50k

This model is a fine-tuned version of openai/whisper-small on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1210

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- training_steps: 50000

### Training results



### Framework versions

- Transformers 4.27.2
- Pytorch 2.1.0a0+git4ab1588
- Datasets 2.10.1
- Tokenizers 0.13.2

# whisper-small-vinzalo-v1-50k

This model is a fine-tuned version of openai/whisper-small on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1210 ## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- training_steps: 50000 ### Training results ### Framework versions

- Transformers 4.27.2
- Pytorch 2.1.0a0+git4ab1588
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 # whisper-small-vinzalo-v1-50k

This model is a fine-tuned version of openai/whisper-small on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1210 ## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- training_steps: 50000 ### Training results ### Framework versions

- Transformers 4.27.2
- Pytorch 2.1.0a0+git4ab1588
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
# whisper-small-vinzalo-v1-50k

This model is a fine-tuned version of openai/whisper-small on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1210## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- training_steps: 50000### Training results### Framework versions

- Transformers 4.27.2
- Pytorch 2.1.0a0+git4ab1588
- Datasets 2.10.1
- Tokenizers 0.13.2