---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- dataset/riksdagen
metrics:
- wer
model-index:
- name: whisper-tiny-sv
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: dataset/riksdagen audiofolder
      type: dataset/riksdagen
      config: audiofolder
      split: train
      args: audiofolder
    metrics:
    - name: Wer
      type: wer
      value: 0.3700987201570632
---

<!-- 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-sv

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the dataset/riksdagen audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6435
- Wer: 0.3701

## 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: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 5000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0032        | 0.08  | 250  | 1.0075          | 0.5063 |
| 0.8983        | 0.17  | 500  | 0.8945          | 0.4649 |
| 0.8227        | 0.25  | 750  | 0.8336          | 0.4491 |
| 0.777         | 0.33  | 1000 | 0.7931          | 0.4314 |
| 0.7728        | 0.42  | 1250 | 0.7640          | 0.4217 |
| 0.7141        | 0.5   | 1500 | 0.7407          | 0.4134 |
| 0.7208        | 0.58  | 1750 | 0.7225          | 0.4023 |
| 0.6911        | 0.66  | 2000 | 0.7083          | 0.3942 |
| 0.6924        | 0.75  | 2250 | 0.6948          | 0.3911 |
| 0.6702        | 0.83  | 2500 | 0.6849          | 0.3884 |
| 0.663         | 0.91  | 2750 | 0.6766          | 0.3769 |
| 0.6548        | 1.0   | 3000 | 0.6686          | 0.3759 |
| 0.638         | 1.08  | 3250 | 0.6627          | 0.3728 |
| 0.6222        | 1.16  | 3500 | 0.6574          | 0.3733 |
| 0.6323        | 1.25  | 3750 | 0.6528          | 0.3691 |
| 0.6192        | 1.33  | 4000 | 0.6498          | 0.3688 |
| 0.633         | 1.41  | 4250 | 0.6469          | 0.3677 |
| 0.6229        | 1.5   | 4500 | 0.6451          | 0.3681 |
| 0.6246        | 1.58  | 4750 | 0.6439          | 0.3706 |
| 0.6214        | 1.66  | 5000 | 0.6435          | 0.3701 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.12.0a0+8a1a93a
- Datasets 2.7.1
- Tokenizers 0.13.2