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

NbAiLab/wav2vec2-xlsr-300M-NPSC-LM

XLS-R-300M-LM - Norwegian

Scores without Language Model