Add timing instrumentation to pinpoint synthesis bottleneck

Wraps s3tokenizer, voice_encoder, and s3gen.inference with timing logs
so we can see exactly which step is consuming the missing ~33 seconds.

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

engine.py

@@ -1,4 +1,5 @@
 import logging
+import time
 import torch
 from typing import Optional, Tuple
 
@@ -44,6 +45,7 @@ def load_model() -> bool:
             _is_turbo = False
 
         _sample_rate = 24000
+        _patch_timing(chatterbox_model)
         logger.info("Model loaded successfully")
         return True
     except Exception:
@@ -51,6 +53,36 @@ def load_model() -> bool:
         return False
 
 
+def _patch_timing(model) -> None:
+    """Wrap key sub-model forward() calls with timing logs."""
+    def _wrap(obj, method_name, label):
+        original = getattr(obj, method_name)
+        def timed(*args, **kwargs):
+            t0 = time.monotonic()
+            result = original(*args, **kwargs)
+            if torch.cuda.is_available():
+                torch.cuda.synchronize()
+            logger.info(f"[timing] {label}: {time.monotonic() - t0:.3f}s")
+            return result
+        setattr(obj, method_name, timed)
+
+    try:
+        # S3 tokenizer — processes reference audio through a conformer
+        _wrap(model.s3tokenizer, "forward", "s3tokenizer (ref audio encoding)")
+    except AttributeError:
+        pass
+    try:
+        # Speaker/voice encoder — xvector embedding from reference audio
+        _wrap(model.voice_encoder, "forward", "voice_encoder (speaker embedding)")
+    except AttributeError:
+        pass
+    try:
+        # S3Gen decode: flow matching (token -> mel) + HiFiGAN (mel -> wav)
+        _wrap(model.s3gen, "inference", "s3gen.inference (flow+vocoder)")
+    except AttributeError:
+        pass
+
+
 def get_sample_rate() -> int:
     return _sample_rate