#2 BigVGAN GPU experiment: ORT-QNN GPU EP loaded the v2_decoder_conv ONNX
model successfully (session creation 463 ms, no fallback warnings) but
per-phrase inference jumped to ~3.5 s vs ~2 s on CPU 8-thread. The GPU/CPU
memory transfer cost dominates for this conv-heavy decoder, and the
optimization went the wrong way. Comment block updated to record both the
HTP and GPU paths as tried-and-rejected so future passes don't re-walk the
same ground.
LLM streaming filter: extend the lookahead-based <think>…</think>
suppressor to also strip singleton special tokens (<|im_start|>,
<|im_end|>, <|endoftext|>). Previously the closing <|im_end|> at end of
the assistant's turn leaked into the SentenceStreamer and ended up as a
spurious sentence at the end of the TTS output. Same lookahead-buffer
trick handles split tokens.
Validated end-to-end: 'Bonjour, comment vas-tu ?' → "Bonjour ! Je vais
bien, merci. Comment vas-tu ?" → seg 0 "Bonjour !", seg 1 "Je vais bien,
merci." (no <|im_end|>), BigVGAN back to 1.8 s/phrase.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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