Add transformers backend for MusicGen (Python 3.13+ compatible)

The generate_music() function now supports two backends:

1. audiocraft (preferred):
   - Original MusicGen implementation
   - Works on Python 3.12 and lower
   - Falls back to transformers if not available

2. transformers (fallback):
   - Uses HuggingFace transformers library
   - Works on Python 3.13+
   - No spacy/blis dependency issues

The function automatically:
- Tries audiocraft first (if available)
- Falls back to transformers if audiocraft fails or is not installed
- Provides clear error messages if neither backend is available

This allows MusicGen music generation to work on Python 3.13 without
the problematic audiocraft → spacy → thinc → blis dependency chain.

videogen

@@ -3586,44 +3586,103 @@ def generate_tts(text, output_path, voice_name="edge_female_us", custom_voice_id
 
 
 def generate_music(prompt, output_path, duration_seconds=10, model_size="medium", args=None):
-    """Generate music using MusicGen"""
-    if not MUSICGEN_AVAILABLE:
-        print("❌ MusicGen not available. Install with: pip install audiocraft")
-        return None
+    """Generate music using MusicGen
+    
+    Supports two backends:
+    1. audiocraft (preferred, but not compatible with Python 3.13)
+    2. transformers (works on Python 3.13+)
     
+    Automatically falls back to transformers if audiocraft is not available.
+    """
     print(f"🎵 Generating music with MusicGen ({model_size})...")
     print(f"  Prompt: {prompt}")
     print(f"  Duration: {duration_seconds}s")
     
-    try:
-        # Load model
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        model = MusicGen.get_pretrained(f"facebook/musicgen-{model_size}")
-        model.to(device)
-        
-        # Generate
-        model.set_generation_params(duration=duration_seconds)
-        wav = model.generate([prompt])
-        
-        # Save
-        audio_write(
-            output_path.replace('.wav', '').replace('.mp3', ''),
-            wav[0].cpu(),
-            model.sample_rate,
-            strategy="loudness",
-            loudness_compressor=True
-        )
-        
-        # Rename to desired output
-        generated_path = output_path.replace('.wav', '').replace('.mp3', '') + '.wav'
-        if generated_path != output_path and os.path.exists(generated_path):
-            os.rename(generated_path, output_path)
-        
-        print(f"  ✅ Saved music: {output_path}")
-        return output_path
-    except Exception as e:
-        print(f"❌ MusicGen failed: {e}")
-        return None
+    # Try audiocraft first (preferred method)
+    if MUSICGEN_AVAILABLE:
+        print("  Using audiocraft backend...")
+        try:
+            # Load model
+            device = "cuda" if torch.cuda.is_available() else "cpu"
+            model = MusicGen.get_pretrained(f"facebook/musicgen-{model_size}")
+            model.to(device)
+            
+            # Generate
+            model.set_generation_params(duration=duration_seconds)
+            wav = model.generate([prompt])
+            
+            # Save
+            audio_write(
+                output_path.replace('.wav', '').replace('.mp3', ''),
+                wav[0].cpu(),
+                model.sample_rate,
+                strategy="loudness",
+                loudness_compressor=True
+            )
+            
+            # Rename to desired output
+            generated_path = output_path.replace('.wav', '').replace('.mp3', '') + '.wav'
+            if generated_path != output_path and os.path.exists(generated_path):
+                os.rename(generated_path, output_path)
+            
+            print(f"  ✅ Saved music: {output_path}")
+            return output_path
+        except Exception as e:
+            print(f"  ⚠️ audiocraft failed: {e}")
+            print("  Trying transformers backend...")
+    
+    # Fallback to transformers (works on Python 3.13+)
+    if TRANSFORMERS_AVAILABLE:
+        print("  Using transformers backend (Python 3.13+ compatible)...")
+        try:
+            from transformers import AutoProcessor, MusicgenForConditionalGeneration
+            
+            device = "cuda" if torch.cuda.is_available() else "cpu"
+            dtype = torch.float16 if device == "cuda" else torch.float32
+            
+            # Map model size to model name
+            model_name_map = {
+                "small": "facebook/musicgen-small",
+                "medium": "facebook/musicgen-medium",
+                "large": "facebook/musicgen-large",
+            }
+            model_name = model_name_map.get(model_size, f"facebook/musicgen-{model_size}")
+            
+            print(f"  Loading model: {model_name}")
+            processor = AutoProcessor.from_pretrained(model_name)
+            model = MusicgenForConditionalGeneration.from_pretrained(
+                model_name,
+                torch_dtype=dtype
+            ).to(device)
+            
+            # Calculate tokens for duration (roughly 50 tokens per second)
+            max_tokens = int(duration_seconds * 50)
+            
+            # Generate
+            inputs = processor(text=[prompt], padding=True, return_tensors="pt").to(device)
+            audio_values = model.generate(**inputs, max_new_tokens=max_tokens)
+            
+            # Save
+            sampling_rate = model.config.audio_encoder.sampling_rate
+            audio_data = audio_values[0, 0].cpu().numpy()
+            
+            if SCIPY_AVAILABLE:
+                scipy.io.wavfile.write(output_path, rate=sampling_rate, data=audio_data)
+            else:
+                import numpy as np
+                sf.write(output_path, audio_data, sampling_rate)
+            
+            print(f"  ✅ Saved music: {output_path}")
+            return output_path
+        except Exception as e:
+            print(f"❌ transformers MusicGen failed: {e}")
+            return None
+    
+    print("❌ MusicGen not available.")
+    print("   Install one of:")
+    print("   - Python 3.12 or lower: pip install audiocraft")
+    print("   - Python 3.13+: pip install transformers scipy")
+    return None
 
 
 def get_audio_duration(audio_path):