whisper-tiny-sv / README.md
Martin Malmsten
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: whisper-tiny-sv
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: dataset/riksdagen audiofolder
          type: dataset/riksdagen
          config: null
          split: None
          args: audiofolder
        metrics:
          - name: Wer
            type: wer
            value: 0.3700987201570632

whisper-tiny-sv

This model is a fine-tuned version of openai/whisper-tiny on the dataset/riksdagen audiofolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6435
  • Wer: 0.3701

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: 1e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • total_train_batch_size: 128
  • total_eval_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • training_steps: 5000

Training results

Training Loss Epoch Step Validation Loss Wer
1.0032 0.08 250 1.0075 0.5063
0.8983 0.17 500 0.8945 0.4649
0.8227 0.25 750 0.8336 0.4491
0.777 0.33 1000 0.7931 0.4314
0.7728 0.42 1250 0.7640 0.4217
0.7141 0.5 1500 0.7407 0.4134
0.7208 0.58 1750 0.7225 0.4023
0.6911 0.66 2000 0.7083 0.3942
0.6924 0.75 2250 0.6948 0.3911
0.6702 0.83 2500 0.6849 0.3884
0.663 0.91 2750 0.6766 0.3769
0.6548 1.0 3000 0.6686 0.3759
0.638 1.08 3250 0.6627 0.3728
0.6222 1.16 3500 0.6574 0.3733
0.6323 1.25 3750 0.6528 0.3691
0.6192 1.33 4000 0.6498 0.3688
0.633 1.41 4250 0.6469 0.3677
0.6229 1.5 4500 0.6451 0.3681
0.6246 1.58 4750 0.6439 0.3706
0.6214 1.66 5000 0.6435 0.3701

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.12.0a0+8a1a93a
  • Datasets 2.7.1
  • Tokenizers 0.13.2