Reuse float arrays and Tensor/EValue objects across talker steps
instead of creating new ones each iteration. Eliminates ~7s of
GC overhead from thousands of JNI object allocations.
Same validated audio quality as before, no C++ pipeline needed.
Talker 35ms/step, CP 58ms/step, total 8.9s for 4.64s audio.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The native pipeline was adding zeros after trailing text tokens
instead of tts_eos_embed then tts_pad_embed. This caused the model
to mispronounce final words (e.g. "développement" → "devopment").
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Full talker+CP autoregressive loop in C++ via JNI.
Talker 20ms/step, CP 44ms/step, total 6.6s for 4.64s audio.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Pre-load all 15 CP heads at first CP call (eliminates lazy-load lag)
- Tested BigVGAN on GPU Adreno: no gain (+300ms vs CPU), kept on CPU
- Added headArgmaxOffset for future batch optimization
- Cancel previous pipeline on new run_pipeline intent
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Warmup forward() for talker+CP during init (avoids 7s DSP compilation
on first pipeline run)
- Cancel previous pipeline job before starting new one
- Use Dispatchers.IO for pipeline intent
First run after warmup: talker 19ms/step, CP 59ms/step → RTF ~1.9
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Kazeia Team