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Although reinforcement learning (RL) post-training repairs the localized segmental errors of zero-shot text-to-speech (TTS), arriving at a working recipe still relies on tedious manual tuning, and whether LLM agents can take over this research pipeline is unclear. We investigate this question with AgenticTTS-Forge, a c...
Ruby-ASR is presented, which refines the conventional target into a span-bound orthographic--lexical-reading sequence and shows that refining the recognition target can improve lexical-reading recovery without sacrificing readable orthographic transcription.
This work proposes Hikari, a policy-free, end-to-end model for simultaneous speech-to-text translation and streaming transcription, and presents a supervised fine-tuning strategy that trains the model to recover from delays, significantly improving the quality-latency trade-off.
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