---
license: apache-2.0
tags:
- automatic-speech-recognition
- NbAiLab/NPSC
- generated_from_trainer
model-index:
- name: XLSR-1B-nynorsk-low
  results:
  - task:
      name: Automatic Speech Recognition 
      type: automatic-speech-recognition
    dataset:
      name: NPSC
      type: NbAiLab/NPSC
      args: 16K_mp3_nynorsk
    metrics:
       - name: Test (Nynorsk) WER
         type: wer
         value: 0.11319692134409612
       - name: Test (Nynorsk) CER
         type: cer
         value: 0.040263696587740365
---

<!-- 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. -->

# XLSR-1B-nynorsk-low

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the NBAILAB/NPSC - 16K_MP3_NYNORSK dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2909
- Wer: 0.1364

## 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: 2e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 60.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.8979        | 1.0   | 500   | 2.9413          | 1.0    |
| 1.2224        | 2.0   | 1000  | 1.0359          | 0.7802 |
| 0.8643        | 3.01  | 1500  | 0.7746          | 0.5969 |
| 0.8211        | 4.01  | 2000  | 0.4882          | 0.3710 |
| 0.5287        | 5.01  | 2500  | 0.4060          | 0.3085 |
| 0.4724        | 6.01  | 3000  | 0.3297          | 0.2517 |
| 0.4357        | 7.01  | 3500  | 0.3106          | 0.2342 |
| 0.376         | 8.02  | 4000  | 0.2776          | 0.2072 |
| 0.3286        | 9.02  | 4500  | 0.2888          | 0.2032 |
| 0.3731        | 10.02 | 5000  | 0.2691          | 0.1835 |
| 0.306         | 11.02 | 5500  | 0.2536          | 0.1835 |
| 0.3025        | 12.02 | 6000  | 0.2758          | 0.1809 |
| 0.3413        | 13.03 | 6500  | 0.2791          | 0.1823 |
| 0.2601        | 14.03 | 7000  | 0.2912          | 0.1759 |
| 0.2332        | 15.03 | 7500  | 0.2582          | 0.1694 |
| 0.2108        | 16.03 | 8000  | 0.2717          | 0.1660 |
| 0.2122        | 17.03 | 8500  | 0.2848          | 0.1647 |
| 0.2369        | 18.04 | 9000  | 0.2548          | 0.1646 |
| 0.1906        | 19.04 | 9500  | 0.2667          | 0.1627 |
| 0.1943        | 20.04 | 10000 | 0.2662          | 0.1623 |
| 0.18          | 21.04 | 10500 | 0.2769          | 0.1561 |
| 0.1654        | 22.04 | 11000 | 0.2661          | 0.1558 |
| 0.1515        | 23.05 | 11500 | 0.2870          | 0.1597 |
| 0.147         | 24.05 | 12000 | 0.2778          | 0.1551 |
| 0.1622        | 25.05 | 12500 | 0.2753          | 0.1541 |
| 0.1522        | 26.05 | 13000 | 0.2932          | 0.1521 |
| 0.1522        | 27.05 | 13500 | 0.2548          | 0.1513 |
| 0.1319        | 28.06 | 14000 | 0.2811          | 0.1532 |
| 0.1261        | 29.06 | 14500 | 0.2786          | 0.1521 |
| 0.1391        | 30.06 | 15000 | 0.2651          | 0.1461 |
| 0.1486        | 31.06 | 15500 | 0.2866          | 0.1494 |
| 0.1121        | 32.06 | 16000 | 0.2641          | 0.1478 |
| 0.1114        | 33.07 | 16500 | 0.2910          | 0.1478 |
| 0.101         | 34.07 | 17000 | 0.2884          | 0.1443 |
| 0.1135        | 35.07 | 17500 | 0.3029          | 0.1469 |
| 0.0972        | 36.07 | 18000 | 0.2870          | 0.1467 |
| 0.1178        | 37.07 | 18500 | 0.2745          | 0.1450 |
| 0.0885        | 38.08 | 19000 | 0.2836          | 0.1440 |
| 0.1144        | 39.08 | 19500 | 0.2761          | 0.1446 |
| 0.0997        | 40.08 | 20000 | 0.2806          | 0.1439 |
| 0.1012        | 41.08 | 20500 | 0.2878          | 0.1413 |
| 0.0902        | 42.08 | 21000 | 0.2832          | 0.1452 |
| 0.0804        | 43.09 | 21500 | 0.2911          | 0.1458 |
| 0.0762        | 44.09 | 22000 | 0.2708          | 0.1441 |
| 0.0758        | 45.09 | 22500 | 0.2804          | 0.1434 |
| 0.0874        | 46.09 | 23000 | 0.2831          | 0.1407 |
| 0.0895        | 47.09 | 23500 | 0.2913          | 0.1396 |
| 0.0975        | 48.1  | 24000 | 0.2956          | 0.1411 |
| 0.0758        | 49.1  | 24500 | 0.2920          | 0.1385 |
| 0.0704        | 50.1  | 25000 | 0.2788          | 0.1383 |
| 0.0707        | 51.1  | 25500 | 0.2822          | 0.1388 |
| 0.0664        | 52.1  | 26000 | 0.2876          | 0.1371 |
| 0.0692        | 53.11 | 26500 | 0.2815          | 0.1377 |
| 0.0799        | 54.11 | 27000 | 0.2806          | 0.1363 |
| 0.0611        | 55.11 | 27500 | 0.2878          | 0.1363 |
| 0.0759        | 56.11 | 28000 | 0.2900          | 0.1365 |
| 0.0801        | 57.11 | 28500 | 0.2881          | 0.1375 |
| 0.0644        | 58.12 | 29000 | 0.2898          | 0.1362 |
| 0.068         | 59.12 | 29500 | 0.2913          | 0.1369 |


### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 2.0.0
- Tokenizers 0.11.0