Wav2Vec 2.0 is a cutting-edge framework designed for self-supervised learning of speech representations. By leveraging large amounts of unlabeled speech data, it trains models to understand and represent speech in a meaningful way without requiring manually labeled data. This framework utilizes advanced techniques to encode the raw audio signal into compact and informative representations, enabling applications such as speech recognition, speaker identification, and speech synthesis. Wav2Vec 2.0 offers a powerful tool for researchers and practitioners in the field of speech processing, allowing them to develop high-performance speech-based applications with minimal reliance on labeled data.
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