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# hubert-large-ami-shard-experiment-colab

This model is a fine-tuned version of [facebook/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: nan
- eval_wer: 1.0
- eval_runtime: 6.0682
- eval_samples_per_second: 16.479
- eval_steps_per_second: 2.142
- epoch: 1.02
- step: 1000

## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "hubert-large-ami-shard-experiment-colab", "results": []}]}
transformers pytorch tensorboard hubert automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 




# hubert-large-ami-shard-experiment-colab

This model is a fine-tuned version of facebook/hubert-large-ls960-ft on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: nan
- eval_wer: 1.0
- eval_runtime: 6.0682
- eval_samples_per_second: 16.479
- eval_steps_per_second: 2.142
- epoch: 1.02
- step: 1000

## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3

# hubert-large-ami-shard-experiment-colab

This model is a fine-tuned version of facebook/hubert-large-ls960-ft on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: nan
- eval_wer: 1.0
- eval_runtime: 6.0682
- eval_samples_per_second: 16.479
- eval_steps_per_second: 2.142
- epoch: 1.02
- step: 1000 ## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 # hubert-large-ami-shard-experiment-colab

This model is a fine-tuned version of facebook/hubert-large-ls960-ft on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: nan
- eval_wer: 1.0
- eval_runtime: 6.0682
- eval_samples_per_second: 16.479
- eval_steps_per_second: 2.142
- epoch: 1.02
- step: 1000 ## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
# hubert-large-ami-shard-experiment-colab

This model is a fine-tuned version of facebook/hubert-large-ls960-ft on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: nan
- eval_wer: 1.0
- eval_runtime: 6.0682
- eval_samples_per_second: 16.479
- eval_steps_per_second: 2.142
- epoch: 1.02
- step: 1000## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3