dafbe2a52b
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> |
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| .. | ||
| cp_et_runner.cpp | ||
| export_cp_pte.py | ||
| export_talker_pte.py | ||
| prepare_tts_embeds.py | ||
| prepare_tts_native.py | ||
| prepare_tts_segments.py | ||
| qc_schema_serialize_patched.py | ||
| test_cp_et_quality.py | ||