---
language:
  - sv
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
- whisper-event
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Medium Sv
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_11_0
      type: common_voice_11_0
      config: sv-SE
      split: test
      args: sv-SE
    metrics:
    - name: Wer
      type: wer
      value: 10.712174146734748
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# openai/whisper-medium

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) trained on 
NST Swedish ASR and evaluated on Common Voice 11 testset. It achieves the following results on the evaluation set:
- Loss: 0.2636
- Wer: 10.7122

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0746        | 0.2   | 1000 | 0.2904          | 13.4695 |
| 0.0564        | 0.4   | 2000 | 0.3121          | 13.2384 |
| 0.0532        | 0.6   | 3000 | 0.2862          | 11.9726 |
| 0.0387        | 0.8   | 4000 | 0.2629          | 11.6931 |
| 0.0279        | 1.14  | 5000 | 0.2636          | 10.7122 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2