---
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: 0.22405586116204554
  - 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: WER
        type: wer
        value: 26.69
---

<!-- 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: 0.2917
- Wer: 0.2241

## 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: 20000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.5023        | 0.04  | 250   | 0.5072          | 0.2949 |
| 0.4678        | 0.08  | 500   | 0.4632          | 0.2780 |
| 0.4233        | 0.12  | 750   | 0.4384          | 0.2749 |
| 0.4113        | 0.17  | 1000  | 0.4205          | 0.2673 |
| 0.3994        | 0.21  | 1250  | 0.4079          | 0.2649 |
| 0.3841        | 0.25  | 1500  | 0.3947          | 0.2609 |
| 0.3775        | 0.29  | 1750  | 0.3854          | 0.2564 |
| 0.383         | 0.33  | 2000  | 0.3781          | 0.2540 |
| 0.3651        | 0.37  | 2250  | 0.3721          | 0.2532 |
| 0.3456        | 0.42  | 2500  | 0.3651          | 0.2517 |
| 0.3719        | 0.46  | 2750  | 0.3612          | 0.2481 |
| 0.3399        | 0.5   | 3000  | 0.3561          | 0.2437 |
| 0.3428        | 0.54  | 3250  | 0.3522          | 0.2465 |
| 0.3442        | 0.58  | 3500  | 0.3451          | 0.2399 |
| 0.3315        | 0.62  | 3750  | 0.3431          | 0.2417 |
| 0.3299        | 0.66  | 4000  | 0.3404          | 0.2428 |
| 0.3417        | 0.71  | 4250  | 0.3373          | 0.2395 |
| 0.3399        | 0.75  | 4500  | 0.3332          | 0.2390 |
| 0.3222        | 0.79  | 4750  | 0.3310          | 0.2385 |
| 0.3319        | 0.83  | 5000  | 0.3291          | 0.2372 |
| 0.3188        | 0.87  | 5250  | 0.3265          | 0.2359 |
| 0.3197        | 0.91  | 5500  | 0.3240          | 0.2378 |
| 0.3099        | 0.96  | 5750  | 0.3215          | 0.2342 |
| 0.3132        | 1.0   | 6000  | 0.3195          | 0.2374 |
| 0.286         | 1.04  | 6250  | 0.3179          | 0.2348 |
| 0.2765        | 1.08  | 6500  | 0.3166          | 0.2354 |
| 0.2795        | 1.12  | 6750  | 0.3153          | 0.2324 |
| 0.2825        | 1.16  | 7000  | 0.3145          | 0.2316 |
| 0.2865        | 1.21  | 7250  | 0.3144          | 0.2329 |
| 0.2703        | 1.25  | 7500  | 0.3126          | 0.2326 |
| 0.2792        | 1.29  | 7750  | 0.3121          | 0.2324 |
| 0.2749        | 1.33  | 8000  | 0.3106          | 0.2325 |
| 0.2762        | 1.37  | 8250  | 0.3093          | 0.2315 |
| 0.2813        | 1.41  | 8500  | 0.3080          | 0.2302 |
| 0.2755        | 1.45  | 8750  | 0.3078          | 0.2321 |
| 0.2779        | 1.5   | 9000  | 0.3062          | 0.2305 |
| 0.2764        | 1.54  | 9250  | 0.3059          | 0.2336 |
| 0.2763        | 1.58  | 9500  | 0.3041          | 0.2310 |
| 0.2723        | 1.62  | 9750  | 0.3027          | 0.2292 |
| 0.2756        | 1.66  | 10000 | 0.3026          | 0.2301 |
| 0.2663        | 1.7   | 10250 | 0.3008          | 0.2262 |
| 0.269         | 1.75  | 10500 | 0.3006          | 0.2280 |
| 0.2682        | 1.79  | 10750 | 0.3002          | 0.2291 |
| 0.2721        | 1.83  | 11000 | 0.2994          | 0.2267 |
| 0.2681        | 1.87  | 11250 | 0.2987          | 0.2288 |
| 0.278         | 1.91  | 11500 | 0.2978          | 0.2296 |
| 0.2625        | 1.95  | 11750 | 0.2978          | 0.2278 |
| 0.2583        | 1.99  | 12000 | 0.2967          | 0.2259 |
| 0.2403        | 2.04  | 12250 | 0.2976          | 0.2276 |
| 0.2414        | 2.08  | 12500 | 0.2972          | 0.2264 |
| 0.251         | 2.12  | 12750 | 0.2969          | 0.2256 |
| 0.2404        | 2.16  | 13000 | 0.2968          | 0.2253 |
| 0.2473        | 2.2   | 13250 | 0.2966          | 0.2253 |
| 0.2444        | 2.24  | 13500 | 0.2965          | 0.2262 |
| 0.2512        | 2.29  | 13750 | 0.2962          | 0.2253 |
| 0.2417        | 2.33  | 14000 | 0.2950          | 0.2280 |
| 0.2445        | 2.37  | 14250 | 0.2950          | 0.2256 |
| 0.2461        | 2.41  | 14500 | 0.2949          | 0.2262 |
| 0.2496        | 2.45  | 14750 | 0.2944          | 0.2261 |
| 0.2422        | 2.49  | 15000 | 0.2942          | 0.2248 |
| 0.2415        | 2.53  | 15250 | 0.2940          | 0.2252 |
| 0.2465        | 2.58  | 15500 | 0.2932          | 0.2269 |
| 0.2508        | 2.62  | 15750 | 0.2931          | 0.2245 |
| 0.2339        | 2.66  | 16000 | 0.2930          | 0.2257 |
| 0.2441        | 2.7   | 16250 | 0.2923          | 0.2247 |
| 0.2444        | 2.74  | 16500 | 0.2921          | 0.2246 |
| 0.2416        | 2.78  | 16750 | 0.2918          | 0.2264 |
| 0.2425        | 2.83  | 17000 | 0.2916          | 0.2251 |
| 0.2404        | 2.87  | 17250 | 0.2916          | 0.2234 |
| 0.2456        | 2.91  | 17500 | 0.2911          | 0.2238 |
| 0.2384        | 2.95  | 17750 | 0.2908          | 0.2252 |
| 0.244         | 2.99  | 18000 | 0.2905          | 0.2251 |
| 0.2197        | 3.03  | 18250 | 0.2919          | 0.2239 |
| 0.2194        | 3.08  | 18500 | 0.2919          | 0.2237 |
| 0.2294        | 3.12  | 18750 | 0.2919          | 0.2243 |
| 0.2225        | 3.16  | 19000 | 0.2918          | 0.2252 |
| 0.2229        | 3.2   | 19250 | 0.2919          | 0.2242 |
| 0.2153        | 3.24  | 19500 | 0.2917          | 0.2241 |
| 0.2137        | 3.28  | 19750 | 0.2917          | 0.2239 |
| 0.2194        | 3.32  | 20000 | 0.2917          | 0.2241 |


### Framework versions

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