metadata

datasets:
  - coscan-speech2
license: cc0-1.0
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: wav2vec2-large-voxrex-swedish-coscan-no-region
    results:
      - dataset:
          name: Coscan Speech
          type: NbAiLab/coscan-speech2
        metrics:
          - name: Test Accuracy on Coscan Speech
            type: accuracy
            value: 0.6155107552811807
          - name: Validation Accuracy on Coscan Speech
            type: accuracy
            value: 0.8773432861141742
          - name: Test F1 (micro) on Coscan Speech
            type: f1
            value: 0.6155107552811807
          - name: Validation F1 (micro) on Coscan Speech
            type: f1
            value: 0.8773432861141742
        task:
          name: Audio Classification
          type: audio-classification
tags:
  - generated_from_trainer

wav2vec2-large-voxrex-swedish-coscan-no-region

This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex-swedish on the coscan-speech2 dataset. It achieves the following results on the evaluation set:

Loss: 1.0151
Accuracy: 0.8773
F1: 0.8773
Precision: 0.8773
Recall: 0.8773

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: 3e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.1651	1.0	6468	0.5657	0.8650	0.8650	0.8650	0.8650
0.1217	2.0	12936	0.9411	0.8487	0.8487	0.8487	0.8487
0.0013	3.0	19404	0.9991	0.8617	0.8617	0.8617	0.8617
0.0652	4.0	25872	1.0151	0.8773	0.8773	0.8773	0.8773
0.0001	5.0	32340	1.1031	0.8700	0.8700	0.8700	0.8700

Classification report on Coscan Speech (test set)

                         precision    recall  f1-score   support

Bergen og Ytre Vestland       0.65      0.97      0.78      1809
     Hedmark og Oppland       0.12      0.06      0.08      2302
               Nordland       0.97      0.47      0.63      2195
           Oslo-området       0.78      0.42      0.55      6957
               Sunnmøre       0.94      0.71      0.81      2636
         Sør-Vestlandet       0.96      0.46      0.62      2860
              Sørlandet       0.62      0.81      0.70      2490
                  Troms       0.67      1.00      0.80      2867
              Trøndelag       0.52      0.94      0.67      2666
         Voss og omland       0.70      0.71      0.71      2641
         Ytre Oslofjord       0.20      0.49      0.29      1678

               accuracy                           0.62     31101
              macro avg       0.65      0.64      0.60     31101
           weighted avg       0.68      0.62      0.61     31101

Framework versions

Transformers 4.22.0.dev0
Pytorch 1.10.1+cu102
Datasets 2.4.1.dev0
Tokenizers 0.12.1