---
language:
- sv
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Tiny Swedish Fast
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_11_0 sv-SE
      type: mozilla-foundation/common_voice_11_0
      config: sv-SE
      split: test
      args: sv-SE
    metrics:
    - name: Wer
      type: wer
      value: 73.01634232878185
---

<!-- 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. -->

# Whisper Tiny Swedish Fast

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the mozilla-foundation/common_voice_11_0 sv-SE dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4344
- Wer: 73.0163

## 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: 128
- eval_batch_size: 64
- seed: 42
- 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      |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5547        | 6.01  | 1000 | 1.9244          | 113.4448 |
| 0.7244        | 12.02 | 2000 | 1.4593          | 81.0128  |
| 0.3583        | 18.03 | 3000 | 1.4019          | 74.3415  |
| 0.2157        | 25.01 | 4000 | 1.4249          | 73.8953  |
| 0.1897        | 31.02 | 5000 | 1.4344          | 73.0163  |


### Framework versions

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