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# dgx1_distil_w2v2_base_mozilla_12_to_6_batch_16_epoch_20_continue

This model is a fine-tuned version of [rohitp1/dgx2_distil_w2v2_base_mozilla_12_to_6_batch_16_epoch_30](https://huggingface.co/rohitp1/dgx2_distil_w2v2_base_mozilla_12_to_6_batch_16_epoch_30) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 33.4149
- eval_wer: 0.9868
- eval_runtime: 768.2877
- eval_samples_per_second: 7.81
- eval_steps_per_second: 7.81
- epoch: 13.23
- 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: 16
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 128
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 20
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "model-index": [{"name": "dgx1_distil_w2v2_base_mozilla_12_to_6_batch_16_epoch_20_continue", "results": []}]}
transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 




# dgx1_distil_w2v2_base_mozilla_12_to_6_batch_16_epoch_20_continue

This model is a fine-tuned version of rohitp1/dgx2_distil_w2v2_base_mozilla_12_to_6_batch_16_epoch_30 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 33.4149
- eval_wer: 0.9868
- eval_runtime: 768.2877
- eval_samples_per_second: 7.81
- eval_steps_per_second: 7.81
- epoch: 13.23
- 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: 16
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 128
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 20
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2

# dgx1_distil_w2v2_base_mozilla_12_to_6_batch_16_epoch_20_continue

This model is a fine-tuned version of rohitp1/dgx2_distil_w2v2_base_mozilla_12_to_6_batch_16_epoch_30 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 33.4149
- eval_wer: 0.9868
- eval_runtime: 768.2877
- eval_samples_per_second: 7.81
- eval_steps_per_second: 7.81
- epoch: 13.23
- 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: 16
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 128
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 20
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 
 # dgx1_distil_w2v2_base_mozilla_12_to_6_batch_16_epoch_20_continue

This model is a fine-tuned version of rohitp1/dgx2_distil_w2v2_base_mozilla_12_to_6_batch_16_epoch_30 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 33.4149
- eval_wer: 0.9868
- eval_runtime: 768.2877
- eval_samples_per_second: 7.81
- eval_steps_per_second: 7.81
- epoch: 13.23
- 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: 16
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 128
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 20
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 
# dgx1_distil_w2v2_base_mozilla_12_to_6_batch_16_epoch_20_continue

This model is a fine-tuned version of rohitp1/dgx2_distil_w2v2_base_mozilla_12_to_6_batch_16_epoch_30 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 33.4149
- eval_wer: 0.9868
- eval_runtime: 768.2877
- eval_samples_per_second: 7.81
- eval_steps_per_second: 7.81
- epoch: 13.23
- 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: 16
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 128
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 20
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2