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wav2vec2-large-voxrex-npsc-nynorsk

Automatic Speech Recognition Transformers PyTorch TensorBoard
wav2vec2 generated_from_trainer NbAiLab/NPSC robust-speech-event no nn-NO hf-asr-leaderboard Eval Results Inference Endpoints
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metadata
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

wav2vec2-large-voxrex-npsc-nynorsk

This model is a fine-tuned version of 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