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# Whisper small 2 - BeaW

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Chat analysis dataset.

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- training_steps: 1
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.7.1+cu110
- Datasets 2.8.0
- Tokenizers 0.11.0

{"language": ["hi"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["logistics"], "model-index": [{"name": "Whisper small 2 - BeaW", "results": []}]}
transformers pytorch tensorboard whisper automatic-speech-recognition generated_from_trainer hi dataset:logistics license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #hi #dataset-logistics #license-apache-2.0 #endpoints_compatible #has_space #region-us 




# Whisper small 2 - BeaW

This model is a fine-tuned version of openai/whisper-small on the Chat analysis dataset.

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- training_steps: 1
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.7.1+cu110
- Datasets 2.8.0
- Tokenizers 0.11.0

# Whisper small 2 - BeaW

This model is a fine-tuned version of openai/whisper-small on the Chat analysis dataset. ## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- training_steps: 1
- mixed_precision_training: Native AMP ### Training results ### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.7.1+cu110
- Datasets 2.8.0
- Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #hi #dataset-logistics #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 # Whisper small 2 - BeaW

This model is a fine-tuned version of openai/whisper-small on the Chat analysis dataset. ## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- training_steps: 1
- mixed_precision_training: Native AMP ### Training results ### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.7.1+cu110
- Datasets 2.8.0
- Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #hi #dataset-logistics #license-apache-2.0 #endpoints_compatible #has_space #region-us 
# Whisper small 2 - BeaW

This model is a fine-tuned version of openai/whisper-small on the Chat analysis dataset.## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- training_steps: 1
- mixed_precision_training: Native AMP### Training results### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.7.1+cu110
- Datasets 2.8.0
- Tokenizers 0.11.0