- prepare_tts_native.py: auto-splits long text at sentence/comma
boundaries, max 15 tokens per segment
- Multi-segment format: each segment gets fresh KV cache
- Formula: target_len = n_tokens × 3.2 + 5 per segment
- Tested on Edouard Baer monologue: 28 segments, 102s audio
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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>
- KV_LEN restored to 100 (KV=64 caused quality loss from evicted role tokens)
- C++ uses pre-computed embeds as-is (no double codec_sum)
- Multi-segment format support in Kotlin (detects n_segments header)
- prepare_tts_segments.py: splits text + generates per-segment embeds
- Quality issue: Python-captured embeds differ from original working file
(original was likely captured on-device, not from Python model.forward)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Kazeia Team