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An alignment-path distillation framework for streaming automatic speech recognition (ASR) with large language models (LLMs) that extracts monotonic alignment paths from a non-streaming teacher's soft text-audio attention and uses them to construct interleaved training sequences.
Semantically enriched speech representations in FireRedTTS3 lead to unprecedented stability and fidelity in voice cloning and editing tasks.
Flow-matching transformers with latent multi-modal conditioning and self-reference can leapfrog existing virtual try-on methods in both visual fidelity and inference speed.
A single system now rivals or beats specialized models across ASR, voice activity detection, language ID, and punctuation, setting a new bar for industrial-grade speech processing.
