[dev-fp16] Only convert T3 to fp16, leave s3gen/ve in fp32

s3gen.speaker_encoder (CAMPPlus xvector) hardcodes float32 inputs in
its inference() method, causing dtype mismatch when weights are fp16.
T3 (the autoregressive GPT-2-medium LLM) has no such constraint and
is the token-generation bottleneck that benefits most from fp16.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

engine.py

@@ -51,17 +51,16 @@ def load_model() -> bool:
 
         _sample_rate = 24000
 
-        # Convert weights to fp16. Done once at load time so the warmup
+        # Convert T3 (the autoregressive LLM) to fp16 for faster token generation.
-        # covers the right dtypes and there's no per-call casting overhead.
+        # s3gen and ve are left in fp32 — s3gen.speaker_encoder (CAMPPlus xvector)
+        # hardcodes float32 inputs in its inference() method and errors on fp16 weights.
         if torch.cuda.is_available():
             try:
-                for attr in ("t3", "s3gen", "ve"):
+                if hasattr(chatterbox_model, "t3"):
-                    m = getattr(chatterbox_model, attr, None)
+                    chatterbox_model.t3.half()
-                    if m is not None:
+                    logger.info("T3 converted to fp16")
-                        m.half()
-                logger.info("Model converted to fp16")
             except Exception:
-                logger.warning("fp16 conversion failed, running in fp32", exc_info=True)
+                logger.warning("T3 fp16 conversion failed, running in fp32", exc_info=True)
 
         logger.info("Model loaded successfully")
         return True