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Speech Recognition On Common Voice German

Métriques

Test WER

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
Test WER
Paper TitleRepository
ConformerCTC-L (no LM)6.68%NeMo: a toolkit for building AI applications using Neural Modules
wav2vec 2.0 XLS-R 1B + TEVR (5-gram)3.64%TEVR: Improving Speech Recognition by Token Entropy Variance Reduction
Whisper (Large v2)6.4%Robust Speech Recognition via Large-Scale Weak Supervision
wav2vec 2.0 XLS-R 1B + TEVR (4-gram)3.70%TEVR: Improving Speech Recognition by Token Entropy Variance Reduction
wav2vec 2.0 XLS-R 1B (5-gram)4.38%TEVR: Improving Speech Recognition by Token Entropy Variance Reduction
Conformer Transducer (no LM)6.28%Automatic Speech Recognition in German: A Detailed Error Analysis-
QuartzNet15x5DE (D37, 5-gram)6.6%Scribosermo: Fast Speech-to-Text models for German and other Languages
VoxPopuli (n-gram)7.8%VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation
ConformerCTC-L (4-gram)6.03%NeMo: a toolkit for building AI applications using Neural Modules
wav2vec 2.0 XLS-R (no LM)12.06%TEVR: Improving Speech Recognition by Token Entropy Variance Reduction
ConformerCTC-L (5-gram)4.05%Scribosermo: Fast Speech-to-Text models for German and other Languages
QuartzNet15x5DE (CV-only, 5-gram)7.7%Scribosermo: Fast Speech-to-Text models for German and other Languages
wav2vec 2.0 XLS-R 1B + TEVR (no LM)10.10%TEVR: Improving Speech Recognition by Token Entropy Variance Reduction
ConformerCTC-L (no LM)7.33%Scribosermo: Fast Speech-to-Text models for German and other Languages
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