Merge branch 'main' into channels

backend/main.py

@@ -47,7 +47,7 @@ app.add_middleware(
 @app.get("/")
 async def root():
     """Root endpoint."""
-    return {"message": "voicebox API", "version": "0.1.1"}
+    return {"message": "voicebox API", "version": "0.1.3"}
 
 
 @app.get("/health", response_model=models.HealthResponse)
@@ -58,10 +58,14 @@ async def health():
     import os
     
     tts_model = tts.get_tts_model()
-    gpu_available = torch.cuda.is_available()
-    
+
+    # Check for GPU availability (CUDA or MPS)
+    has_cuda = torch.cuda.is_available()
+    has_mps = hasattr(torch.backends, 'mps') and torch.backends.mps.is_available()
+    gpu_available = has_cuda or has_mps
+
     vram_used = None
-    if gpu_available:
+    if has_cuda:
         vram_used = torch.cuda.memory_allocated() / 1024 / 1024  # MB
     
     # Check if model is loaded - use the same logic as model status endpoint
@@ -1172,13 +1176,22 @@ async def get_active_tasks():
 # STARTUP & SHUTDOWN
 # ============================================
 
+def _get_gpu_status() -> str:
+    """Get GPU availability status."""
+    if torch.cuda.is_available():
+        return f"CUDA ({torch.cuda.get_device_name(0)})"
+    elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
+        return "MPS (Apple Silicon)"
+    return "None (CPU only)"
+
+
 @app.on_event("startup")
 async def startup_event():
     """Run on application startup."""
     print("voicebox API starting up...")
     database.init_db()
     print(f"Database initialized at {database._db_path}")
-    print(f"GPU available: {torch.cuda.is_available()}")
+    print(f"GPU available: {_get_gpu_status()}")
 
 
 @app.on_event("shutdown")

backend/voicebox-server.spec

@@ -4,15 +4,16 @@ from PyInstaller.utils.hooks import collect_submodules
 from PyInstaller.utils.hooks import copy_metadata
 
 datas = []
-hiddenimports = ['backend', 'backend.main', 'backend.config', 'backend.database', 'backend.models', 'backend.profiles', 'backend.history', 'backend.tts', 'backend.transcribe', 'backend.utils.audio', 'backend.utils.cache', 'backend.utils.progress', 'backend.utils.hf_progress', 'backend.utils.validation', 'torch', 'transformers', 'fastapi', 'uvicorn', 'sqlalchemy', 'librosa', 'soundfile', 'qwen_tts', 'qwen_tts.inference', 'qwen_tts.inference.qwen3_tts_model', 'qwen_tts.inference.qwen3_tts_tokenizer', 'qwen_tts.core', 'qwen_tts.cli']
+hiddenimports = ['backend', 'backend.main', 'backend.config', 'backend.database', 'backend.models', 'backend.profiles', 'backend.history', 'backend.tts', 'backend.transcribe', 'backend.utils.audio', 'backend.utils.cache', 'backend.utils.progress', 'backend.utils.hf_progress', 'backend.utils.validation', 'torch', 'transformers', 'fastapi', 'uvicorn', 'sqlalchemy', 'librosa', 'soundfile', 'qwen_tts', 'qwen_tts.inference', 'qwen_tts.inference.qwen3_tts_model', 'qwen_tts.inference.qwen3_tts_tokenizer', 'qwen_tts.core', 'qwen_tts.cli', 'pkg_resources.extern']
 datas += collect_data_files('qwen_tts')
 datas += copy_metadata('qwen-tts')
 hiddenimports += collect_submodules('qwen_tts')
+hiddenimports += collect_submodules('jaraco')
 
 
 a = Analysis(
     ['server.py'],
-    pathex=['/Users/jamespine/Projects/voice/Qwen3-TTS'],
+    pathex=['C:\\Users\\ijame\\Projects\\voice\\Qwen3-TTS'],
     binaries=[],
     datas=datas,
     hiddenimports=hiddenimports,