uv = abs(uv) - s; // mirror
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，这一点在51吃瓜中也有详细论述
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JEPA-v0 matches baseline models when it comes to spotting fake audio. It scored 0.927, while Whisper scored 0.946 and Mimi scored 0.962. This task checks if a human throat and mouth actually made the sound. The encoder spends a lot of its processing power on speaker details like the shape of the vocal tract, the pitch, and specific spectral data. The model currently struggles when it has to attach meaning to those sounds. For example, in general captioning, it scored 0.478 compared to Whisper’s 0.625 and Mimi’s 0.583, and in speech recognition it scored 0.000 compared to Whisper’s 0.375 and Mimi’s 0.637.
0.0005 column is a very safe bet. dE00's JND is 2.0, so 0.05 is the one to go