Restore KV=100 + fix as-is embeds + multi-segment support

- 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>

executorch-custom/jni_layer_tts.cpp

@@ -692,7 +692,7 @@ ExecuTorchJni::runTtsPipelineImpl(
     jint maxTokens)
 {
     static const int DIM=1024,VOCAB=3072,CB_SIZE=2048,NUM_CB=16;
-    static const int T_L=28,T_KV=8,T_HD=128,T_KV_LEN=64;
+    static const int T_L=28,T_KV=8,T_HD=128,T_KV_LEN=100;
     static const int C_L=5,C_KV=8,C_HD=128,C_KV_LEN=16;
     static const int CODEC_EOS=2150;
 
@@ -839,13 +839,13 @@ ExecuTorchJni::runTtsPipelineImpl(
         for(int i=0;i<NUM_CB;i++) allCodes.push_back(codes[i]);
         cb0Hist.push_back(curCb0);
 
-        // Next embed: pre-computed from Python (complete: codec_sum+text)
+        // Next embed: pre-computed from Python (already contains codec_sum+text)
-        // After exhausted: codec_sum(our codes) + pad
         float nextEmb[DIM]={};
         if(trIdx<nTrailing){
             memcpy(nextEmb,trailing.data()+trIdx*DIM,DIM*4);
             trIdx++;
         } else {
+            // After embeds exhausted: our codec_sum + pad
             const float*e0=codecEmb.data()+std::min(std::max(codes[0],0),VOCAB-1)*DIM;
             for(int k=0;k<DIM;k++) nextEmb[k]+=e0[k];
             for(int cb=0;cb<15;cb++){

kazeia-android/app/src/main/java/com/kazeia/tts/Qwen3TtsEngine.kt

@@ -68,7 +68,7 @@ class Qwen3TtsEngine(
         private const val CP_KV_LEN = 16  // max 16 past positions (17 total with current)
 
         // Talker .pte constants
-        private const val TALKER_PTE_KV_LEN = 64  // .pte talker KV window size (reduced from 100)
+        private const val TALKER_PTE_KV_LEN = 100  // must match .pte export (KV=64 caused quality loss)
 
         // Codec special token IDs (in talker's 3072 vocab space)
         private const val CODEC_EOS = 2150

scripts/export_talker_pte.py

@@ -11,7 +11,7 @@ warnings.filterwarnings('ignore')
 
 N_L = 28; N_H = 16; N_KV = 8; HD = 128; DIM = 1024; N_REP = 2
 VOCAB = 3072; FFN = 3072
-KV_LEN = 64  # Reduced from 100: saves 36% memcpy, sufficient for ~70 token generation
+KV_LEN = 100  # Must be >= prefill+maxGen. KV=64 caused quality loss (role tokens evicted)
 
 state = torch.load("/opt/Kazeia/models_qnn/qwen3-tts-export/qwen3_tts_talker.pth",
                     map_location="cpu", weights_only=False)

scripts/prepare_tts_segments.py

@@ -25,40 +25,29 @@ MODEL = "/home/alf/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-0.6B-Base
 import torch, numpy as np
 from qwen_tts import Qwen3TTSModel
 
-def split_sentences(text, max_tokens=60):
+def split_sentences(text, max_chars=120):
-    """Split text at sentence boundaries, keeping segments short."""
+    """Split text into SHORT segments (~40-50 tokens max). Each sentence separate."""
-    # Split at . ! ? ; and keep the punctuation
+    # Split at every sentence boundary
-    parts = re.split(r'(?<=[.!?;])\s+', text.strip())
+    parts = re.split(r'(?<=[.!?;:])\s+', text.strip())
 
-    segments = []
+    # Further split long sentences at commas
-    current = ""
-    for part in parts:
-        if current and len(current) + len(part) > 200:  # rough char limit
-            segments.append(current.strip())
-            current = part
-        else:
-            current = (current + " " + part).strip() if current else part
-    if current.strip():
-        segments.append(current.strip())
-
-    # If any segment is still too long, split at commas
     final = []
-    for seg in segments:
+    for part in parts:
-        if len(seg) > 250:
+        if len(part) > max_chars:
-            parts = re.split(r'(?<=,)\s+', seg)
+            subs = re.split(r'(?<=,)\s+', part)
-            sub = ""
+            current = ""
-            for p in parts:
+            for s in subs:
-                if sub and len(sub) + len(p) > 200:
+                if current and len(current) + len(s) > max_chars:
-                    final.append(sub.strip())
+                    final.append(current.strip())
-                    sub = p
+                    current = s
                 else:
-                    sub = (sub + " " + p).strip() if sub else p
+                    current = (current + " " + s).strip() if current else s
-            if sub.strip():
+            if current.strip():
-                final.append(sub.strip())
+                final.append(current.strip())
         else:
-            final.append(seg)
+            final.append(part)
 
-    return final if final else [text]
+    return [s for s in final if s.strip()] if final else [text]
 
 print(f"Text: '{TEXT[:80]}{'...' if len(TEXT)>80 else ''}'")
 segments = split_sentences(TEXT)