🧹 Refactor model loading in vibevoice_server.py

🎯 What: Extracted inline model loading logic from `_load_model_sync` into distinct helper functions (`_init_processor`, `_init_model`, and `_load_voice_presets`).
💡 Why: This significantly reduces the complexity of `_load_model_sync`, making the code easier to read and maintain.
✅ Verification: Ran a syntax check (`python -m py_compile`), started the backend server with CPU inference, and verified the model initialized and correctly processed a text-to-speech request to the `/generate` endpoint without regressions.
✨ Result: Improved code modularity while preserving identical behavior.

Co-authored-by: LyAhn <27559362+LyAhn@users.noreply.github.com>

server/vibevoice_server.py

@@ -155,6 +155,54 @@ def _download_voices() -> None:
 
 # ── Background model loader ─────────────────────────────────────────────────────
 
+def _init_processor():
+    logger.info("Loading processor...")
+    from vibevoice.processor.vibevoice_streaming_processor import (
+        VibeVoiceStreamingProcessor,
+    )
+    return VibeVoiceStreamingProcessor.from_pretrained(MODEL_ID)
+
+
+def _init_model(device: str):
+    logger.info("Loading model on %s...", device)
+    load_dtype = torch.bfloat16 if device == "cuda" else torch.float32
+    attn_impl = "flash_attention_2" if device == "cuda" else "sdpa"
+
+    from vibevoice.modular.modeling_vibevoice_streaming_inference import (
+        VibeVoiceStreamingForConditionalGenerationInference,
+    )
+    try:
+        model = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained(
+            MODEL_ID,
+            torch_dtype=load_dtype,
+            device_map=device,
+            attn_implementation=attn_impl,
+        )
+    except Exception:
+        logger.warning("flash_attention_2 unavailable, falling back to sdpa")
+        model = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained(
+            MODEL_ID,
+            torch_dtype=load_dtype,
+            device_map=device,
+            attn_implementation="sdpa",
+        )
+
+    model.eval()
+    model.set_ddpm_inference_steps(num_steps=10)
+    return model
+
+
+def _load_voice_presets(device: str) -> dict[str, object]:
+    presets = {}
+    for name, filename in EN_VOICES.items():
+        path = VOICES_DIR / filename
+        if path.exists():
+            presets[name] = torch.load(
+                path, map_location=device, weights_only=False
+            )
+    return presets
+
+
 def _load_model_sync() -> None:
     global _processor, _model, _device, _model_status, _model_error, _voice_presets
 
@@ -174,44 +222,9 @@ def _load_model_sync() -> None:
             _device = _resolve_device()
             logger.info("Using device: %s", _device)
 
-            load_dtype = torch.bfloat16 if _device == "cuda" else torch.float32
+            _processor = _init_processor()
-            attn_impl = "flash_attention_2" if _device == "cuda" else "sdpa"
+            _model = _init_model(_device)
-
+            _voice_presets = _load_voice_presets(_device)
-            logger.info("Loading processor...")
-            from vibevoice.processor.vibevoice_streaming_processor import (
-                VibeVoiceStreamingProcessor,
-            )
-            _processor = VibeVoiceStreamingProcessor.from_pretrained(MODEL_ID)
-
-            logger.info("Loading model on %s...", _device)
-            from vibevoice.modular.modeling_vibevoice_streaming_inference import (
-                VibeVoiceStreamingForConditionalGenerationInference,
-            )
-            try:
-                _model = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained(
-                    MODEL_ID,
-                    torch_dtype=load_dtype,
-                    device_map=_device,
-                    attn_implementation=attn_impl,
-                )
-            except Exception:
-                logger.warning("flash_attention_2 unavailable, falling back to sdpa")
-                _model = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained(
-                    MODEL_ID,
-                    torch_dtype=load_dtype,
-                    device_map=_device,
-                    attn_implementation="sdpa",
-                )
-
-            _model.eval()
-            _model.set_ddpm_inference_steps(num_steps=10)
-
-            for name, filename in EN_VOICES.items():
-                path = VOICES_DIR / filename
-                if path.exists():
-                    _voice_presets[name] = torch.load(
-                        path, map_location=_device, weights_only=False
-                    )
 
             _model_status = "online"
             logger.info("Model ready on %s. Voices: %s", _device, list(_voice_presets.keys()))