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metadata
language:
  - sv
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
  - babelbox/babelbox_voice
  - google/fleurs
model-index:
  - name: Whisper Medium Swedish
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0
          type: mozilla-foundation/common_voice_11_0
          config: sv-SE
          split: test
        metrics:
          - name: Wer
            type: wer
            value: 9.89

Whisper Medium Swedish

This model is a fine-tuned version of Whisper Medium Nordic on the mozilla-foundation/common_voice_11_0 (train+validation), the babelbox/babelbox_voice (NST SV - train split) and the google/fleurs (sv_se - train+validation+test) datasets. It achieves the following results on the evaluation set:

  • eval_loss: 0.2483
  • eval_wer: 9.8914
  • eval_runtime: 2924.8709
  • eval_samples_per_second: 1.733
  • eval_steps_per_second: 0.108

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: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 250
  • training_steps: 5000
  • mixed_precision_training: Native AMP

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.1+cu117
  • Datasets 2.7.1.dev0
  • Tokenizers 0.13.2

WandB run

https://wandb.ai/pn-aa/whisper/runs/z2lzjx4x?workspace=user-emilio_marinone