metadata

language:
  - sv
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
  - hf-asr-leaderboard
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