---
language:
- sv-SE
license: cc0-1.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- sv
- generated_from_trainer
- robust-speech-event
- model_for_talk
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M-voxrex - Swedish
  results:
  - task: 
      name: Automatic Speech Recognition 
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 7
      type: mozilla-foundation/common_voice_7_0
      args: sv-SE
    metrics:
      - name: Test WER
        type: wer
        value: 18.89
      - name: Test CER
        type: cer
        value: 6.63
  - task: 
      name: Automatic Speech Recognition 
      type: automatic-speech-recognition
    dataset:
      name: Robust Speech Event - Dev Data
      type: speech-recognition-community-v2/dev_data
      args: sv
    metrics:
      - name: Test WER
        type: wer
        value: 30.65
      - name: Test CER
        type: cer
        value: 13.56
---

<!-- 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. -->

# 

This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex](https://huggingface.co/KBLab/wav2vec2-large-voxrex) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2201
- Wer: 0.1778

## 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: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.1522        | 1.45  | 500   | 3.1290          | 1.0    |
| 2.9576        | 2.91  | 1000  | 2.9633          | 1.0    |
| 1.9853        | 4.36  | 1500  | 0.8902          | 0.6104 |
| 1.5867        | 5.81  | 2000  | 0.4793          | 0.3664 |
| 1.4608        | 7.27  | 2500  | 0.3816          | 0.3095 |
| 1.3496        | 8.72  | 3000  | 0.3415          | 0.2783 |
| 1.3058        | 10.17 | 3500  | 0.3072          | 0.2519 |
| 1.2533        | 11.63 | 4000  | 0.2877          | 0.2381 |
| 1.2535        | 13.08 | 4500  | 0.2791          | 0.2320 |
| 1.2273        | 14.53 | 5000  | 0.2726          | 0.2282 |
| 1.2083        | 15.99 | 5500  | 0.2638          | 0.2212 |
| 1.1606        | 17.44 | 6000  | 0.2531          | 0.2174 |
| 1.1545        | 18.89 | 6500  | 0.2468          | 0.2109 |
| 1.1344        | 20.35 | 7000  | 0.2494          | 0.2050 |
| 1.1173        | 21.8  | 7500  | 0.2447          | 0.1980 |
| 1.1081        | 23.26 | 8000  | 0.2428          | 0.1998 |
| 1.1023        | 24.71 | 8500  | 0.2329          | 0.1951 |
| 1.0923        | 26.16 | 9000  | 0.2388          | 0.1962 |
| 1.0798        | 27.61 | 9500  | 0.2363          | 0.1944 |
| 1.0769        | 29.07 | 10000 | 0.2342          | 0.1913 |
| 1.0672        | 30.52 | 10500 | 0.2250          | 0.1875 |
| 1.0735        | 31.97 | 11000 | 0.2305          | 0.1874 |
| 1.0628        | 33.43 | 11500 | 0.2291          | 0.1851 |
| 1.0451        | 34.88 | 12000 | 0.2263          | 0.1856 |
| 1.0299        | 36.34 | 12500 | 0.2257          | 0.1834 |
| 1.0368        | 37.79 | 13000 | 0.2230          | 0.1808 |
| 1.0322        | 39.24 | 13500 | 0.2231          | 0.1833 |
| 1.0451        | 40.7  | 14000 | 0.2197          | 0.1817 |
| 1.0304        | 42.15 | 14500 | 0.2241          | 0.1813 |
| 1.0102        | 43.6  | 15000 | 0.2233          | 0.1795 |
| 1.0135        | 45.06 | 15500 | 0.2200          | 0.1794 |
| 1.014         | 46.51 | 16000 | 0.2207          | 0.1779 |
| 1.0071        | 47.96 | 16500 | 0.2205          | 0.1784 |
| 0.9729        | 49.42 | 17000 | 0.2204          | 0.1777 |


### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0