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# whisper-medium-1113

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4282
- eval_wer: 100.0222
- eval_runtime: 126.5405
- eval_samples_per_second: 4.678
- eval_steps_per_second: 0.395
- epoch: 6.06
- step: 1200

## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000

### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "whisper-medium-1113", "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-medium-1113

This model is a fine-tuned version of openai/whisper-medium on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4282
- eval_wer: 100.0222
- eval_runtime: 126.5405
- eval_samples_per_second: 4.678
- eval_steps_per_second: 0.395
- epoch: 6.06
- step: 1200

## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000

### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.12.1

# whisper-medium-1113

This model is a fine-tuned version of openai/whisper-medium on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4282
- eval_wer: 100.0222
- eval_runtime: 126.5405
- eval_samples_per_second: 4.678
- eval_steps_per_second: 0.395
- epoch: 6.06
- step: 1200 ## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000 ### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 # whisper-medium-1113

This model is a fine-tuned version of openai/whisper-medium on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4282
- eval_wer: 100.0222
- eval_runtime: 126.5405
- eval_samples_per_second: 4.678
- eval_steps_per_second: 0.395
- epoch: 6.06
- step: 1200 ## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000 ### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
# whisper-medium-1113

This model is a fine-tuned version of openai/whisper-medium on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4282
- eval_wer: 100.0222
- eval_runtime: 126.5405
- eval_samples_per_second: 4.678
- eval_steps_per_second: 0.395
- epoch: 6.06
- step: 1200## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.12.1