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--- |
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language: |
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- nb-NO |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- generated_from_trainer |
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- false |
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- nb-NO |
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- robust-speech-event |
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- model_for_talk |
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- hf-asr-leaderboard |
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datasets: |
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- NbAiLab/NPSC |
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model-index: |
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- name: XLS-R-300M-LM - Norwegian |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: NPSC |
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type: NbAiLab/NPSC |
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metrics: |
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- name: Eval WER |
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type: wer |
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value: 15.4 |
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- name: Eval CER |
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type: cer |
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value: 5.48 |
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--- |
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# XLS-R-300M-LM - Norwegian
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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.
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### Scores without Language Model
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Without using a language model, it achieves the following scores on the NPSC Eval set
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It achieves the following results on the evaluation set without a language model:
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- WER: 0.2110
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- CER: 0.0622
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### Scores with Language Model
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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.
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- WER: 0.1540
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- CER: 0.0548
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## Team
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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.
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## Model description
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This current version is based on checkpoint 8500 of [NbAiLab/wav2vec2-xlsr-300M-NPSC-OH](https://huggingface.co/NbAiLab/wav2vec2-xlsr-300M-NPSC-OH).
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## Intended uses & limitations
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Demo version only. The model will be updated later this week.
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## Training and evaluation data
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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..
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 7.5e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 32
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 2000
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- num_epochs: 30.0 (But interrupted after 8500 steps, approx 6 epochs)
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- mixed_precision_training: Native AMP
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