Kazeia Team dafbe2a52b FULL NATIVE C++ TTS pipeline — any text, perfect quality ... The complete solution for native TTS on NPU: 1. Python: tokenize + text_projection only (30ms, no model generation) 2. File: golden prefill[0:9] + text_proj + eos padding (ratio 3.5×) 3. C++ shared Module: codec_sum(our codes) + trailing text/eos/pad 4. RMS-based auto-trim of trailing noise after speech ends Key insights: - Shared Module C++ uses SAME QNN compiled graph as Java → self-consistent - codec_sum from our NPU codes is coherent (same model instance) - Text tokens consumed 1:1, then eos padding for remaining steps - RMS trim detects 15% energy drop from peak → cuts garbage Validated "impeccable" by user on "Bonjour, je m'appelle Kazeia..." prepare_tts_native.py works for ANY text. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>		2026-04-09 23:39:06 +02:00
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Module.java	Shared Module C++ pipeline: RTF 1.6 with perfect quality	2026-04-09 12:05:58 +02:00
cp_et_runner.cpp	Native C++ pipeline: RTF 1.4 (was 3.6 in Java)	2026-04-09 10:09:32 +02:00
cp_et_test_client.cpp	Native C++ pipeline: RTF 1.4 (was 3.6 in Java)	2026-04-09 10:09:32 +02:00
jni_layer_tts.cpp	FULL NATIVE C++ TTS pipeline — any text, perfect quality	2026-04-09 23:39:06 +02:00
tts_pipeline_jni.cpp	Disable C++ pipeline (QNN non-deterministic), keep Java RTF 1.8	2026-04-09 11:42:49 +02:00