---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- dataset/riksdagen
metrics:
- wer
model-index:
- name: whisper-small-sv
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: dataset/riksdagen audiofolder
      type: dataset/riksdagen
      config: test
      split: test
      args: audiofolder
    metrics:
    - name: Wer
      type: wer
      value: 0.2426515530366172
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: sv-SE
      split: test
      args:
        language: sv-SE
    metrics:
      - name: Test WER
        type: wer
        value: 0.2669
---

<!-- 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-small-sv

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co./openai/whisper-small) on the dataset/riksdagen audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3479
- Wer: 0.2427

## 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: 64
- 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: 100
- training_steps: 5000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5024        | 0.04  | 250  | 0.5073          | 0.2948 |
| 0.4684        | 0.08  | 500  | 0.4639          | 0.2784 |
| 0.4246        | 0.12  | 750  | 0.4396          | 0.2758 |
| 0.4132        | 0.17  | 1000 | 0.4222          | 0.2664 |
| 0.4021        | 0.21  | 1250 | 0.4101          | 0.2633 |
| 0.3871        | 0.25  | 1500 | 0.3982          | 0.2619 |
| 0.3813        | 0.29  | 1750 | 0.3895          | 0.2577 |
| 0.3878        | 0.33  | 2000 | 0.3827          | 0.2533 |
| 0.3704        | 0.37  | 2250 | 0.3770          | 0.2533 |
| 0.3516        | 0.42  | 2500 | 0.3714          | 0.2540 |
| 0.3792        | 0.46  | 2750 | 0.3675          | 0.2495 |
| 0.3476        | 0.5   | 3000 | 0.3636          | 0.2456 |
| 0.3522        | 0.54  | 3250 | 0.3611          | 0.2462 |
| 0.3545        | 0.58  | 3500 | 0.3560          | 0.2440 |
| 0.3426        | 0.62  | 3750 | 0.3543          | 0.2464 |
| 0.3437        | 0.66  | 4000 | 0.3524          | 0.2464 |
| 0.3562        | 0.71  | 4250 | 0.3507          | 0.2452 |
| 0.3555        | 0.75  | 4500 | 0.3491          | 0.2426 |
| 0.3397        | 0.79  | 4750 | 0.3483          | 0.2419 |
| 0.3516        | 0.83  | 5000 | 0.3479          | 0.2427 |


### Framework versions

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