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# workstation_whisper_small_distil_libri360_12_to_6_batch_8_epoch_100

This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 7.0953
- eval_wer: 101.0579
- eval_runtime: 672.9494
- eval_samples_per_second: 8.033
- eval_steps_per_second: 8.033
- epoch: 17.98
- step: 900

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 512
- total_train_batch_size: 4096
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 100
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.11.0

{"tags": ["generated_from_trainer"], "model-index": [{"name": "workstation_whisper_small_distil_libri360_12_to_6_batch_8_epoch_100", "results": []}]}
transformers pytorch whisper automatic-speech-recognition generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 




# workstation_whisper_small_distil_libri360_12_to_6_batch_8_epoch_100

This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 7.0953
- eval_wer: 101.0579
- eval_runtime: 672.9494
- eval_samples_per_second: 8.033
- eval_steps_per_second: 8.033
- epoch: 17.98
- step: 900

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 512
- total_train_batch_size: 4096
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 100
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.11.0

# workstation_whisper_small_distil_libri360_12_to_6_batch_8_epoch_100

This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 7.0953
- eval_wer: 101.0579
- eval_runtime: 672.9494
- eval_samples_per_second: 8.033
- eval_steps_per_second: 8.033
- epoch: 17.98
- step: 900 ## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 512
- total_train_batch_size: 4096
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 100
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.11.0
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 
 # workstation_whisper_small_distil_libri360_12_to_6_batch_8_epoch_100

This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 7.0953
- eval_wer: 101.0579
- eval_runtime: 672.9494
- eval_samples_per_second: 8.033
- eval_steps_per_second: 8.033
- epoch: 17.98
- step: 900 ## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 512
- total_train_batch_size: 4096
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 100
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.11.0
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 
# workstation_whisper_small_distil_libri360_12_to_6_batch_8_epoch_100

This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 7.0953
- eval_wer: 101.0579
- eval_runtime: 672.9494
- eval_samples_per_second: 8.033
- eval_steps_per_second: 8.033
- epoch: 17.98
- step: 900## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 512
- total_train_batch_size: 4096
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 100
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.11.0