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# wav2vec2_murad

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the cvbn dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2006
- eval_wer: 0.2084
- eval_runtime: 556.4634
- eval_samples_per_second: 8.985
- eval_steps_per_second: 0.562
- epoch: 12.32
- step: 28800

## 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: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 4.21.1
- Pytorch 1.11.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cvbn"], "model-index": [{"name": "wav2vec2_murad", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer dataset:cvbn license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-cvbn #license-apache-2.0 #endpoints_compatible #region-us 




# wav2vec2_murad

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2006
- eval_wer: 0.2084
- eval_runtime: 556.4634
- eval_samples_per_second: 8.985
- eval_steps_per_second: 0.562
- epoch: 12.32
- step: 28800

## 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: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 4.21.1
- Pytorch 1.11.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1

# wav2vec2_murad

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2006
- eval_wer: 0.2084
- eval_runtime: 556.4634
- eval_samples_per_second: 8.985
- eval_steps_per_second: 0.562
- epoch: 12.32
- step: 28800 ## 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: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 4.21.1
- Pytorch 1.11.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-cvbn #license-apache-2.0 #endpoints_compatible #region-us 
 # wav2vec2_murad

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2006
- eval_wer: 0.2084
- eval_runtime: 556.4634
- eval_samples_per_second: 8.985
- eval_steps_per_second: 0.562
- epoch: 12.32
- step: 28800 ## 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: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 4.21.1
- Pytorch 1.11.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-cvbn #license-apache-2.0 #endpoints_compatible #region-us 
# wav2vec2_murad

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2006
- eval_wer: 0.2084
- eval_runtime: 556.4634
- eval_samples_per_second: 8.985
- eval_steps_per_second: 0.562
- epoch: 12.32
- step: 28800## 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: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 4.21.1
- Pytorch 1.11.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1