fix(demo): add MPS and CPU support for ASR inference demo

- Add MPS device choice and auto-detect MPS availability
- Change default attention implementation to 'auto' with smart fallback
- Auto-detect flash_attention_2 availability on CUDA, fallback to sdpa
- Use sdpa for MPS and CPU devices (flash_attention_2 not supported)
- Use float32 dtype for MPS/CPU devices for better compatibility

Fixes #206

demo/vibevoice_asr_inference_from_file.py

@@ -30,14 +30,14 @@ class VibeVoiceASRBatchInference:
         model_path: str, 
         device: str = "cuda", 
         dtype: torch.dtype = torch.bfloat16,
-        attn_implementation: str = "flash_attention_2"
+        attn_implementation: str = "sdpa"
     ):
         """
         Initialize the ASR batch inference pipeline.
         
         Args:
             model_path: Path to the pretrained model
-            device: Device to run inference on
+            device: Device to run inference on (cuda, mps, cpu, auto)
             dtype: Data type for model weights
             attn_implementation: Attention implementation to use ('flash_attention_2', 'sdpa', 'eager')
         """
@@ -442,8 +442,8 @@ def main():
     parser.add_argument(
         "--device", 
         type=str, 
-        default="cuda" if torch.cuda.is_available() else "cpu",
-        choices=["cuda", "cpu", "auto"],
+        default="cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu"),
+        choices=["cuda", "cpu", "mps", "auto"],
         help="Device to run inference on"
     )
     parser.add_argument(
@@ -473,12 +473,27 @@ def main():
     parser.add_argument(
         "--attn_implementation",
         type=str,
-        default="flash_attention_2",
-        help="Attention implementation to use (default: flash_attention_2)"
+        default="auto",
+        choices=["flash_attention_2", "sdpa", "eager", "auto"],
+        help="Attention implementation to use. 'auto' will select the best available for your device (flash_attention_2 for CUDA, sdpa for MPS/CPU)"
     )
     
     args = parser.parse_args()
     
+    # Auto-detect best attention implementation based on device
+    if args.attn_implementation == "auto":
+        if args.device == "cuda" and torch.cuda.is_available():
+            try:
+                import flash_attn
+                args.attn_implementation = "flash_attention_2"
+            except ImportError:
+                print("flash_attn not installed, falling back to sdpa")
+                args.attn_implementation = "sdpa"
+        else:
+            # MPS and CPU don't support flash_attention_2
+            args.attn_implementation = "sdpa"
+        print(f"Auto-detected attention implementation: {args.attn_implementation}")
+    
     # Collect audio files
     audio_files = []
     concatenated_audio = None  # For storing concatenated dataset audio
@@ -514,10 +529,18 @@ def main():
         print(f"\nConcatenated dataset audios: {len(concatenated_audio)} audio(s)")
     
     # Initialize model
+    # Handle MPS device and dtype
+    if args.device == "mps":
+        model_dtype = torch.float32  # MPS works better with float32
+    elif args.device == "cpu":
+        model_dtype = torch.float32
+    else:
+        model_dtype = torch.bfloat16
+    
     asr = VibeVoiceASRBatchInference(
         model_path=args.model_path,
         device=args.device,
-        dtype=torch.bfloat16 if args.device != "cpu" else torch.float32,
+        dtype=model_dtype,
         attn_implementation=args.attn_implementation
     )