

# XLS-R-300M-LM - Norwegian

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the Norwegian [NPSC](https://huggingface.co/datasets/NbAiLab/NPSC) dataset.
 
### Scores without Language Model
Without using a language model, it achieves the following scores on the NPSC Eval set
It achieves the following results on the evaluation set without a language model:
- WER: 0.2110
- CER: 0.0622

### Scores with Language Model
A 5-gram KenLM was added to boost the models performance. The language model was created on a corpus mainly consisting of online newspapers, public reports and Wikipedia data. After this we are getting these values.

- WER: 0.1540
- CER: 0.0548

## Team
The model is developed by Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen. Name in alphabetic order.

## Model description
This current version is based on checkpoint 8500 of [NbAiLab/wav2vec2-xlsr-300M-NPSC-OH](https://huggingface.co/NbAiLab/wav2vec2-xlsr-300M-NPSC-OH).

## Intended uses & limitations
Demo version only. The model will be updated later this week.

## Training and evaluation data
The model is trained and evaluated on [NPSC](https://huggingface.co/datasets/NbAiLab/NPSC). Unfortunately there is no Norwegian test data in Common Voice, and currently the model is only evaluated on the validation set of NPSC..

## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 2000
- num_epochs: 30.0 (But interrupted after 8500 steps, approx 6 epochs)
- mixed_precision_training: Native AMP



{"language": ["nb-NO"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", false, "nb-NO", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "XLS-R-300M-LM - Norwegian", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "NPSC", "type": "NbAiLab/NPSC"}, "metrics": [{"type": "wer", "value": 15.4, "name": "Eval WER"}, {"type": "cer", "value": 5.48, "name": "Eval CER"}]}]}]}
transformers pytorch wav2vec2 automatic-speech-recognition dataset:NbAiLab/NPSC license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us 



# XLS-R-300M-LM - Norwegian

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Norwegian NPSC dataset.
 
### Scores without Language Model
Without using a language model, it achieves the following scores on the NPSC Eval set
It achieves the following results on the evaluation set without a language model:
- WER: 0.2110
- CER: 0.0622

### Scores with Language Model
A 5-gram KenLM was added to boost the models performance. The language model was created on a corpus mainly consisting of online newspapers, public reports and Wikipedia data. After this we are getting these values.

- WER: 0.1540
- CER: 0.0548

## Team
The model is developed by Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen. Name in alphabetic order.

## Model description
This current version is based on checkpoint 8500 of NbAiLab/wav2vec2-xlsr-300M-NPSC-OH.

## Intended uses & limitations
Demo version only. The model will be updated later this week.

## Training and evaluation data
The model is trained and evaluated on NPSC. Unfortunately there is no Norwegian test data in Common Voice, and currently the model is only evaluated on the validation set of NPSC..

## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 2000
- num_epochs: 30.0 (But interrupted after 8500 steps, approx 6 epochs)
- mixed_precision_training: Native AMP



# XLS-R-300M-LM - Norwegian

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Norwegian NPSC dataset. ### Scores without Language Model
Without using a language model, it achieves the following scores on the NPSC Eval set
It achieves the following results on the evaluation set without a language model:
- WER: 0.2110
- CER: 0.0622 ### Scores with Language Model
A 5-gram KenLM was added to boost the models performance. The language model was created on a corpus mainly consisting of online newspapers, public reports and Wikipedia data. After this we are getting these values.

- WER: 0.1540
- CER: 0.0548 ## Team
The model is developed by Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen. Name in alphabetic order. ## Model description
This current version is based on checkpoint 8500 of NbAiLab/wav2vec2-xlsr-300M-NPSC-OH. ## Intended uses & limitations
Demo version only. The model will be updated later this week. ## Training and evaluation data
The model is trained and evaluated on NPSC. Unfortunately there is no Norwegian test data in Common Voice, and currently the model is only evaluated on the validation set of NPSC.. ## Training procedure ### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 2000
- num_epochs: 30.0 (But interrupted after 8500 steps, approx 6 epochs)
- mixed_precision_training: Native AMP
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 # XLS-R-300M-LM - Norwegian

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Norwegian NPSC dataset. ### Scores without Language Model
Without using a language model, it achieves the following scores on the NPSC Eval set
It achieves the following results on the evaluation set without a language model:
- WER: 0.2110
- CER: 0.0622 ### Scores with Language Model
A 5-gram KenLM was added to boost the models performance. The language model was created on a corpus mainly consisting of online newspapers, public reports and Wikipedia data. After this we are getting these values.

- WER: 0.1540
- CER: 0.0548 ## Team
The model is developed by Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen. Name in alphabetic order. ## Model description
This current version is based on checkpoint 8500 of NbAiLab/wav2vec2-xlsr-300M-NPSC-OH. ## Intended uses & limitations
Demo version only. The model will be updated later this week. ## Training and evaluation data
The model is trained and evaluated on NPSC. Unfortunately there is no Norwegian test data in Common Voice, and currently the model is only evaluated on the validation set of NPSC.. ## Training procedure ### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 2000
- num_epochs: 30.0 (But interrupted after 8500 steps, approx 6 epochs)
- mixed_precision_training: Native AMP
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us 
# XLS-R-300M-LM - Norwegian

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Norwegian NPSC dataset.### Scores without Language Model
Without using a language model, it achieves the following scores on the NPSC Eval set
It achieves the following results on the evaluation set without a language model:
- WER: 0.2110
- CER: 0.0622### Scores with Language Model
A 5-gram KenLM was added to boost the models performance. The language model was created on a corpus mainly consisting of online newspapers, public reports and Wikipedia data. After this we are getting these values.

- WER: 0.1540
- CER: 0.0548## Team
The model is developed by Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen. Name in alphabetic order.## Model description
This current version is based on checkpoint 8500 of NbAiLab/wav2vec2-xlsr-300M-NPSC-OH.## Intended uses & limitations
Demo version only. The model will be updated later this week.## Training and evaluation data
The model is trained and evaluated on NPSC. Unfortunately there is no Norwegian test data in Common Voice, and currently the model is only evaluated on the validation set of NPSC..## Training procedure