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# wavLM-VLSP-vi-base

This model is a fine-tuned version of [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base-plus) on the PHONGDTD/VINDATAVLSP - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0390
- Wer: 0.9995
- Cer: 0.9414

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40.0
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3

{"tags": ["automatic-speech-recognition", "phongdtd/VinDataVLSP", "generated_from_trainer"], "model-index": [{"name": "wavLM-VLSP-vi-base", "results": []}]}
transformers pytorch tensorboard wavlm automatic-speech-recognition phongdtd/VinDataVLSP generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #phongdtd/VinDataVLSP #generated_from_trainer #endpoints_compatible #region-us 




# wavLM-VLSP-vi-base

This model is a fine-tuned version of microsoft/wavlm-base-plus on the PHONGDTD/VINDATAVLSP - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0390
- Wer: 0.9995
- Cer: 0.9414

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40.0
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3

# wavLM-VLSP-vi-base

This model is a fine-tuned version of microsoft/wavlm-base-plus on the PHONGDTD/VINDATAVLSP - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0390
- Wer: 0.9995
- Cer: 0.9414 ## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40.0
- mixed_precision_training: Native AMP ### Training results ### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #phongdtd/VinDataVLSP #generated_from_trainer #endpoints_compatible #region-us 
 # wavLM-VLSP-vi-base

This model is a fine-tuned version of microsoft/wavlm-base-plus on the PHONGDTD/VINDATAVLSP - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0390
- Wer: 0.9995
- Cer: 0.9414 ## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40.0
- mixed_precision_training: Native AMP ### Training results ### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #phongdtd/VinDataVLSP #generated_from_trainer #endpoints_compatible #region-us 
# wavLM-VLSP-vi-base

This model is a fine-tuned version of microsoft/wavlm-base-plus on the PHONGDTD/VINDATAVLSP - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0390
- Wer: 0.9995
- Cer: 0.9414## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40.0
- mixed_precision_training: Native AMP### Training results### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3