metadata
language:
- sv-SE
license: cc0-1.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
- sv
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300M - Swedish
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_8_0
type: mozilla-foundation/common_voice_8_0
args: sv-SE
metrics:
- name: Test WER
type: wer
value: 8.72
- name: Test CER
type: cer
value: 3.05
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: speech-recognition-community-v2/eval_data
type: speech-recognition-community-v2/eval_data
args: sv
metrics:
- name: Validation WER
type: wer
value: 19.67
- name: Validation CER
type: cer
value: 8.94
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: speech-recognition-community-v2/eval_data
type: speech-recognition-community-v2/eval_data
args: sv
metrics:
- name: Test WER
type: wer
value: 15.94
- name: Test CER
type: cer
value: 7.71
widget:
- example_title: Swedish
src: https://cdn-media.huggingface.co/speech_samples/cv_swedish_1.mp3
This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset. It achieves the following results on the evaluation set:
- Loss: 0.1595
- Wer: 0.1200
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: 0.00025
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 100.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.0418 | 5.49 | 500 | 3.0176 | 1.0 |
| 1.1819 | 10.98 | 1000 | 0.2562 | 0.2168 |
| 1.0032 | 16.48 | 1500 | 0.1746 | 0.1546 |
| 0.9077 | 21.97 | 2000 | 0.1600 | 0.1339 |
| 0.8687 | 27.47 | 2500 | 0.1647 | 0.1378 |
| 0.8081 | 32.96 | 3000 | 0.1608 | 0.1353 |
| 0.7923 | 38.46 | 3500 | 0.1534 | 0.1277 |
| 0.7349 | 43.95 | 4000 | 0.1546 | 0.1303 |
| 0.7199 | 49.45 | 4500 | 0.1617 | 0.1277 |
| 0.7028 | 54.94 | 5000 | 0.1572 | 0.1287 |
| 0.6912 | 60.44 | 5500 | 0.1560 | 0.1249 |
| 0.6492 | 65.93 | 6000 | 0.1542 | 0.1260 |
| 0.6407 | 71.43 | 6500 | 0.1605 | 0.1240 |
| 0.6222 | 76.92 | 7000 | 0.1577 | 0.1219 |
| 0.6039 | 82.42 | 7500 | 0.1645 | 0.1249 |
| 0.5928 | 87.91 | 8000 | 0.1590 | 0.1214 |
| 0.6022 | 93.4 | 8500 | 0.1597 | 0.1213 |
| 0.5814 | 98.9 | 9000 | 0.1599 | 0.1199 |
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0