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WavLM is a research & education tool that provides large-scale self-supervised pre-training for full-stack speech processing. It learns universal representations from speech signals for various downstream tasks.
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WavLM is a novel pre-trained model designed to tackle full-stack downstream speech tasks by learning universal representations from complex speech signals. It jointly learns masked speech prediction and denoising during pre-training, which not only maintains speech content modeling capabilities but also enhances its potential for non-ASR tasks. The model incorporates gated relative position bias within its Transformer structure to better capture sequence ordering in speech. Furthermore, WavLM scales up its training dataset significantly, achieving state-of-the-art performance on benchmarks like SUPERB and delivering substantial improvements across various speech processing applications.
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