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---
language:
- sv-SE
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300M - Swedish - CV8
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: sv-SE
metrics:
- name: Test WER
type: wer
value: 17.1
- name: Test CER
type: cer
value: 5.7
- 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: 26.92
- name: Test CER
type: cer
value: 12.53
---
<!-- 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 [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
**Without LM**:
- Wer: 0.2465
- Cer: 0.0717
**With LM**:
- Wer: 0.1710
- Cer: 0.0569
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.3224 | 1.37 | 500 | 3.2676 | 1.0 |
| 2.9319 | 2.74 | 1000 | 2.9287 | 1.0000 |
| 2.1173 | 4.11 | 1500 | 1.1478 | 0.8788 |
| 1.6973 | 5.48 | 2000 | 0.6749 | 0.6547 |
| 1.5865 | 6.85 | 2500 | 0.5500 | 0.5634 |
| 1.5094 | 8.22 | 3000 | 0.4840 | 0.5430 |
| 1.4644 | 9.59 | 3500 | 0.4844 | 0.4142 |
| 1.4061 | 10.96 | 4000 | 0.4356 | 0.3808 |
| 1.3584 | 12.33 | 4500 | 0.4192 | 0.3698 |
| 1.3438 | 13.7 | 5000 | 0.3980 | 0.3584 |
| 1.3332 | 15.07 | 5500 | 0.3896 | 0.3572 |
| 1.3025 | 16.44 | 6000 | 0.3835 | 0.3487 |
| 1.2979 | 17.81 | 6500 | 0.3781 | 0.3417 |
| 1.2736 | 19.18 | 7000 | 0.3734 | 0.3270 |
| 1.2415 | 20.55 | 7500 | 0.3637 | 0.3316 |
| 1.2255 | 21.92 | 8000 | 0.3546 | 0.3147 |
| 1.2193 | 23.29 | 8500 | 0.3524 | 0.3196 |
| 1.2104 | 24.66 | 9000 | 0.3403 | 0.3097 |
| 1.1965 | 26.03 | 9500 | 0.3508 | 0.3093 |
| 1.1976 | 27.4 | 10000 | 0.3419 | 0.3071 |
| 1.182 | 28.77 | 10500 | 0.3364 | 0.2963 |
| 1.158 | 30.14 | 11000 | 0.3338 | 0.2932 |
| 1.1414 | 31.51 | 11500 | 0.3376 | 0.2940 |
| 1.1402 | 32.88 | 12000 | 0.3370 | 0.2891 |
| 1.1213 | 34.25 | 12500 | 0.3201 | 0.2874 |
| 1.1207 | 35.62 | 13000 | 0.3261 | 0.2826 |
| 1.1074 | 36.98 | 13500 | 0.3117 | 0.2786 |
| 1.0818 | 38.36 | 14000 | 0.3194 | 0.2776 |
| 1.0889 | 39.73 | 14500 | 0.3188 | 0.2738 |
| 1.0672 | 41.1 | 15000 | 0.3196 | 0.2773 |
| 1.0838 | 42.47 | 15500 | 0.3130 | 0.2739 |
| 1.0553 | 43.83 | 16000 | 0.3165 | 0.2704 |
| 1.0786 | 45.21 | 16500 | 0.3108 | 0.2706 |
| 1.0546 | 46.57 | 17000 | 0.3102 | 0.2677 |
| 1.0425 | 47.94 | 17500 | 0.3115 | 0.2679 |
| 1.0398 | 49.31 | 18000 | 0.3131 | 0.2666 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu113
- Datasets 1.18.1.dev0
- Tokenizers 0.10.3
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