readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  - automatic-speech-recognition
  - NbAiLab/NPSC
  - robust-speech-event
  - "no"
  - nn-NO
  - hf-asr-leaderboard
  datasets:
  - NbAiLab/NPSC
  language:
  - nn-NO
  model-index:
  - name: wav2vec2-large-voxrex-npsc-nynorsk
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: NPSC
        type: NbAiLab/NPSC
        args: 16K_mp3_nynorsk
      metrics:
      - name: Test (Nynorsk) WER
        type: wer
        value: 0.12220762155059132
      - name: Test (Nynorsk) CER
        type: cer
        value: 0.04195612578778549
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # wav2vec2-large-voxrex-npsc-nynorsk
  
  This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex](https://huggingface.co/KBLab/wav2vec2-large-voxrex) on the NBAILAB/NPSC - 16K_MP3_NYNORSK dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4142
  - Wer: 0.1576
  
  ## 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: 7.5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - 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: 2000
  - num_epochs: 40.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 3.086         | 2.17  | 500  | 3.0773          | 1.0    |
  | 2.8532        | 4.35  | 1000 | 2.8393          | 1.0    |
  | 0.9738        | 6.52  | 1500 | 0.7283          | 0.4890 |
  | 0.6763        | 8.7   | 2000 | 0.5340          | 0.3662 |
  | 0.5303        | 10.87 | 2500 | 0.4521          | 0.3140 |
  | 0.4765        | 13.04 | 3000 | 0.4181          | 0.2853 |
  | 0.4219        | 15.22 | 3500 | 0.4156          | 0.2934 |
  | 0.3564        | 17.39 | 4000 | 0.3925          | 0.2509 |
  | 0.3282        | 19.57 | 4500 | 0.3824          | 0.2420 |
  | 0.3118        | 21.74 | 5000 | 0.3636          | 0.2354 |
  | 0.2919        | 23.91 | 5500 | 0.3615          | 0.2281 |
  | 0.2961        | 26.09 | 6000 | 0.3548          | 0.2255 |
  | 0.284         | 28.26 | 6500 | 0.3526          | 0.2209 |
  | 0.2566        | 30.43 | 7000 | 0.3526          | 0.2205 |
  | 0.2422        | 32.61 | 7500 | 0.3569          | 0.2173 |
  | 0.2472        | 34.78 | 8000 | 0.3592          | 0.2166 |
  | 0.2337        | 36.96 | 8500 | 0.3625          | 0.2172 |
  | 0.2315        | 39.13 | 9000 | 0.3580          | 0.2155 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.10.3
  