---
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](https://huggingface.co/marinone94/whisper-medium-nordic) on the [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (train+validation), the [babelbox/babelbox_voice](https://huggingface.co/datasets/babelbox/babelbox_voice) (NST SV - train split) and the [google/fleurs](https://huggingface.co/datasets/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