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# wav2vec2-large-xls-r-300m-dansk-CV-80

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for Danish, using the [mozilla-foundation/common_voice_8_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) dataset.

It achieves the following results on the evaluation set:
- eval_loss: 0.6394
- eval_wer: 0.3682
- eval_runtime: 104.0466
- eval_samples_per_second: 13.359
- eval_steps_per_second: 1.672
- epoch: 21.28
- step: 2000

## Model description

ASR Danish model

## Intended uses & limitations

More information needed

## Training and evaluation data

Danish subset of [mozilla-foundation/common_voice_8_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0)

## 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 4.16.1
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-dansk-CV-80", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer dataset:common_voice license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 




# wav2vec2-large-xls-r-300m-dansk-CV-80

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m for Danish, using the mozilla-foundation/common_voice_8_0 dataset.

It achieves the following results on the evaluation set:
- eval_loss: 0.6394
- eval_wer: 0.3682
- eval_runtime: 104.0466
- eval_samples_per_second: 13.359
- eval_steps_per_second: 1.672
- epoch: 21.28
- step: 2000

## Model description

ASR Danish model

## Intended uses & limitations

More information needed

## Training and evaluation data

Danish subset of mozilla-foundation/common_voice_8_0

## 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 4.16.1
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0

# wav2vec2-large-xls-r-300m-dansk-CV-80

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m for Danish, using the mozilla-foundation/common_voice_8_0 dataset.

It achieves the following results on the evaluation set:
- eval_loss: 0.6394
- eval_wer: 0.3682
- eval_runtime: 104.0466
- eval_samples_per_second: 13.359
- eval_steps_per_second: 1.672
- epoch: 21.28
- step: 2000 ## Model description

ASR Danish model ## Intended uses & limitations

More information needed ## Training and evaluation data

Danish subset of mozilla-foundation/common_voice_8_0 ## 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 4.16.1
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 
 # wav2vec2-large-xls-r-300m-dansk-CV-80

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m for Danish, using the mozilla-foundation/common_voice_8_0 dataset.

It achieves the following results on the evaluation set:
- eval_loss: 0.6394
- eval_wer: 0.3682
- eval_runtime: 104.0466
- eval_samples_per_second: 13.359
- eval_steps_per_second: 1.672
- epoch: 21.28
- step: 2000 ## Model description

ASR Danish model ## Intended uses & limitations

More information needed ## Training and evaluation data

Danish subset of mozilla-foundation/common_voice_8_0 ## 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 4.16.1
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 
# wav2vec2-large-xls-r-300m-dansk-CV-80

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m for Danish, using the mozilla-foundation/common_voice_8_0 dataset.

It achieves the following results on the evaluation set:
- eval_loss: 0.6394
- eval_wer: 0.3682
- eval_runtime: 104.0466
- eval_samples_per_second: 13.359
- eval_steps_per_second: 1.672
- epoch: 21.28
- step: 2000## Model description

ASR Danish model## Intended uses & limitations

More information needed## Training and evaluation data

Danish subset of mozilla-foundation/common_voice_8_0## 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 4.16.1
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0