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# demo_model

This model is a fine-tuned version of [hts98/whisper-medium-1113](https://huggingface.co/hts98/whisper-medium-1113) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5144
- eval_wer: 100.0055
- eval_runtime: 67.4062
- eval_samples_per_second: 3.397
- eval_steps_per_second: 0.341
- epoch: 3.04
- step: 280

## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 30
- training_steps: 500
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.7.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "demo_model", "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 




# demo_model

This model is a fine-tuned version of hts98/whisper-medium-1113 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5144
- eval_wer: 100.0055
- eval_runtime: 67.4062
- eval_samples_per_second: 3.397
- eval_steps_per_second: 0.341
- epoch: 3.04
- step: 280

## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 30
- training_steps: 500
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.7.0
- Tokenizers 0.13.3

# demo_model

This model is a fine-tuned version of hts98/whisper-medium-1113 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5144
- eval_wer: 100.0055
- eval_runtime: 67.4062
- eval_samples_per_second: 3.397
- eval_steps_per_second: 0.341
- epoch: 3.04
- step: 280 ## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 30
- training_steps: 500
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.7.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 # demo_model

This model is a fine-tuned version of hts98/whisper-medium-1113 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5144
- eval_wer: 100.0055
- eval_runtime: 67.4062
- eval_samples_per_second: 3.397
- eval_steps_per_second: 0.341
- epoch: 3.04
- step: 280 ## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 30
- training_steps: 500
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.7.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
# demo_model

This model is a fine-tuned version of hts98/whisper-medium-1113 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5144
- eval_wer: 100.0055
- eval_runtime: 67.4062
- eval_samples_per_second: 3.397
- eval_steps_per_second: 0.341
- epoch: 3.04
- step: 280## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 30
- training_steps: 500
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.7.0
- Tokenizers 0.13.3