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# wav2vec2-large-xls-r-300m-spanish-custom

This model was trained from scratch on the common_voice dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2245
- eval_wer: 0.2082
- eval_runtime: 801.6784
- eval_samples_per_second: 18.822
- eval_steps_per_second: 2.354
- epoch: 0.76
- step: 8400

## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 10
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0

{"tags": ["generated_from_trainer", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-spanish-custom", "results": []}]}
transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer robust-speech-event dataset:common_voice endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #dataset-common_voice #endpoints_compatible #region-us 




# wav2vec2-large-xls-r-300m-spanish-custom

This model was trained from scratch on the common_voice dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2245
- eval_wer: 0.2082
- eval_runtime: 801.6784
- eval_samples_per_second: 18.822
- eval_steps_per_second: 2.354
- epoch: 0.76
- step: 8400

## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 10
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0

# wav2vec2-large-xls-r-300m-spanish-custom

This model was trained from scratch on the common_voice dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2245
- eval_wer: 0.2082
- eval_runtime: 801.6784
- eval_samples_per_second: 18.822
- eval_steps_per_second: 2.354
- epoch: 0.76
- step: 8400 ## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 10
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #dataset-common_voice #endpoints_compatible #region-us 
 # wav2vec2-large-xls-r-300m-spanish-custom

This model was trained from scratch on the common_voice dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2245
- eval_wer: 0.2082
- eval_runtime: 801.6784
- eval_samples_per_second: 18.822
- eval_steps_per_second: 2.354
- epoch: 0.76
- step: 8400 ## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 10
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #dataset-common_voice #endpoints_compatible #region-us 
# wav2vec2-large-xls-r-300m-spanish-custom

This model was trained from scratch on the common_voice dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2245
- eval_wer: 0.2082
- eval_runtime: 801.6784
- eval_samples_per_second: 18.822
- eval_steps_per_second: 2.354
- epoch: 0.76
- step: 8400## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 10
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0