Self Training With Pseudo Labeling For Unlabeled Audio

via “semi-supervised and self-supervised learning with pseudo-labeling”

OpenMMLab detection toolbox with 300+ models.

Unique: Implements semi-supervised detection with pseudo-labeling where a teacher model generates labels on unlabeled data, and a student model is trained with both labeled and pseudo-labeled data; uses exponential moving average (EMA) teacher updates for stability and consistency regularization for improved robustness

vs others: More practical than fully self-supervised approaches because it leverages labeled data when available; more stable than naive pseudo-labeling because EMA teacher updates reduce label noise; better integrated than external semi-supervised frameworks because it's built into the training pipeline

via “self-supervised acoustic representation learning without labeled data”

feature-extraction model by undefined. 33,41,362 downloads.

Unique: Combines wav2vec2's contrastive learning (predicting masked frames from context) with BERT's masked language modeling on speech, creating a dual-objective pretraining approach that learns both acoustic and contextual patterns without labels — unlike supervised models requiring phoneme or speaker annotations

vs others: Eliminates annotation requirements compared to supervised acoustic models, while providing better generalization than single-objective self-supervised approaches (wav2vec2 alone) due to dual pretraining objectives

via “self-training with pseudo-labeling for unlabeled audio”

* ⭐ 08/2022: [MuLan: A Joint Embedding of Music Audio and Natural Language (MuLan)](https://arxiv.org/abs/2208.12415)

Unique: Integrates pseudo-labeling as middle stage between SSL pre-training and supervised fine-tuning in three-stage pipeline; specific pseudo-label generation and filtering mechanisms not disclosed, but represents systematic approach to leveraging unlabeled data in semi-supervised ASR

vs others: More systematic than ad-hoc pseudo-labeling by grounding in pre-trained representations; effectiveness vs alternatives depends on undisclosed pseudo-label quality control mechanisms

via “audio-based model training”