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# wav2vec2-large-xls-r-300m-ur-BKK-35

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9856
- eval_wer: 0.4608
- eval_runtime: 52.457
- eval_samples_per_second: 7.549
- eval_steps_per_second: 0.953
- epoch: 9.33
- step: 448

## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-ur-BKK-35", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 




# wav2vec2-large-xls-r-300m-ur-BKK-35

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9856
- eval_wer: 0.4608
- eval_runtime: 52.457
- eval_samples_per_second: 7.549
- eval_steps_per_second: 0.953
- epoch: 9.33
- step: 448

## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2

# wav2vec2-large-xls-r-300m-ur-BKK-35

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9856
- eval_wer: 0.4608
- eval_runtime: 52.457
- eval_samples_per_second: 7.549
- eval_steps_per_second: 0.953
- epoch: 9.33
- step: 448 ## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 
 # wav2vec2-large-xls-r-300m-ur-BKK-35

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9856
- eval_wer: 0.4608
- eval_runtime: 52.457
- eval_samples_per_second: 7.549
- eval_steps_per_second: 0.953
- epoch: 9.33
- step: 448 ## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 
# wav2vec2-large-xls-r-300m-ur-BKK-35

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9856
- eval_wer: 0.4608
- eval_runtime: 52.457
- eval_samples_per_second: 7.549
- eval_steps_per_second: 0.953
- epoch: 9.33
- step: 448## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2