feat: use xpu as default if it exists

.gitignore

@@ -169,9 +169,14 @@ tags
 .ruff_cache
 
 # our proj
+/inputs/
 /output/
 /outputs/
 /checkpoint/
 /checkpoints/
 exp
 .gradio/
+
+*~
+*swp
+*swo

demo/inference_from_file.py

@@ -16,17 +16,17 @@ logger = logging.get_logger(__name__)
 
 class VoiceMapper:
     """Maps speaker names to voice file paths"""
-    
+
     def __init__(self):
         self.setup_voice_presets()
 
         # change name according to our preset wav file
         new_dict = {}
         for name, path in self.voice_presets.items():
-            
+
             if '_' in name:
                 name = name.split('_')[0]
-            
+
             if '-' in name:
                 name = name.split('-')[-1]
 
@@ -37,21 +37,21 @@ class VoiceMapper:
     def setup_voice_presets(self):
         """Setup voice presets by scanning the voices directory."""
         voices_dir = os.path.join(os.path.dirname(__file__), "voices")
-        
+
         # Check if voices directory exists
         if not os.path.exists(voices_dir):
             print(f"Warning: Voices directory not found at {voices_dir}")
             self.voice_presets = {}
             self.available_voices = {}
             return
-        
+
         # Scan for all WAV files in the voices directory
         self.voice_presets = {}
-        
+
         # Get all .wav files in the voices directory
-        wav_files = [f for f in os.listdir(voices_dir) 
+        wav_files = [f for f in os.listdir(voices_dir)
                     if f.lower().endswith('.wav') and os.path.isfile(os.path.join(voices_dir, f))]
-        
+
         # Create dictionary with filename (without extension) as key
         for wav_file in wav_files:
             # Remove .wav extension to get the name
@@ -59,16 +59,16 @@ class VoiceMapper:
             # Create full path
             full_path = os.path.join(voices_dir, wav_file)
             self.voice_presets[name] = full_path
-        
+
         # Sort the voice presets alphabetically by name for better UI
         self.voice_presets = dict(sorted(self.voice_presets.items()))
-        
+
         # Filter out voices that don't exist (this is now redundant but kept for safety)
         self.available_voices = {
             name: path for name, path in self.voice_presets.items()
             if os.path.exists(path)
         }
-        
+
         print(f"Found {len(self.available_voices)} voice files in {voices_dir}")
         print(f"Available voices: {', '.join(self.available_voices.keys())}")
 
@@ -77,13 +77,13 @@ class VoiceMapper:
         # First try exact match
         if speaker_name in self.voice_presets:
             return self.voice_presets[speaker_name]
-        
+
         # Try partial matching (case insensitive)
         speaker_lower = speaker_name.lower()
         for preset_name, path in self.voice_presets.items():
             if preset_name.lower() in speaker_lower or speaker_lower in preset_name.lower():
                 return path
-        
+
         # Default to first voice if no match found
         default_voice = list(self.voice_presets.values())[0]
         print(f"Warning: No voice preset found for '{speaker_name}', using default voice: {default_voice}")
@@ -99,25 +99,25 @@ def parse_txt_script(txt_content: str) -> Tuple[List[str], List[str]]:
     lines = txt_content.strip().split('\n')
     scripts = []
     speaker_numbers = []
-    
+
     # Pattern to match "Speaker X:" format where X is a number
     speaker_pattern = r'^Speaker\s+(\d+):\s*(.*)$'
-    
+
     current_speaker = None
     current_text = ""
-    
+
     for line in lines:
         line = line.strip()
         if not line:
             continue
-            
+
         match = re.match(speaker_pattern, line, re.IGNORECASE)
         if match:
             # If we have accumulated text from previous speaker, save it
             if current_speaker and current_text:
                 scripts.append(f"Speaker {current_speaker}: {current_text.strip()}")
                 speaker_numbers.append(current_speaker)
-            
+
             # Start new speaker
             current_speaker = match.group(1).strip()
             current_text = match.group(2).strip()
@@ -127,12 +127,12 @@ def parse_txt_script(txt_content: str) -> Tuple[List[str], List[str]]:
                 current_text += " " + line
             else:
                 current_text = line
-    
+
     # Don't forget the last speaker
     if current_speaker and current_text:
         scripts.append(f"Speaker {current_speaker}: {current_text.strip()}")
         speaker_numbers.append(current_speaker)
-    
+
     return scripts, speaker_numbers
 
 
@@ -144,7 +144,7 @@ def parse_args():
         default="microsoft/VibeVoice-1.5b",
         help="Path to the HuggingFace model directory",
     )
-    
+
     parser.add_argument(
         "--txt_path",
         type=str,
@@ -167,7 +167,7 @@ def parse_args():
     parser.add_argument(
         "--device",
         type=str,
-        default=("cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")),
+        default=("cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else ("xpu" if torch.xpu.is_available() else "cpu"))),
         help="Device for inference: cuda | mps | cpu",
     )
     parser.add_argument(
@@ -176,7 +176,7 @@ def parse_args():
         default=1.3,
         help="CFG (Classifier-Free Guidance) scale for generation (default: 1.3)",
     )
-    
+
     return parser.parse_args()
 
 def main():
@@ -196,44 +196,44 @@ def main():
 
     # Initialize voice mapper
     voice_mapper = VoiceMapper()
-    
+
     # Check if txt file exists
     if not os.path.exists(args.txt_path):
         print(f"Error: txt file not found: {args.txt_path}")
         return
-    
+
     # Read and parse txt file
     print(f"Reading script from: {args.txt_path}")
     with open(args.txt_path, 'r', encoding='utf-8') as f:
         txt_content = f.read()
-    
+
     # Parse the txt content to get speaker numbers
     scripts, speaker_numbers = parse_txt_script(txt_content)
-    
+
     if not scripts:
         print("Error: No valid speaker scripts found in the txt file")
         return
-    
+
     print(f"Found {len(scripts)} speaker segments:")
     for i, (script, speaker_num) in enumerate(zip(scripts, speaker_numbers)):
         print(f"  {i+1}. Speaker {speaker_num}")
         print(f"     Text preview: {script[:100]}...")
-    
+
     # Map speaker numbers to provided speaker names
     speaker_name_mapping = {}
     speaker_names_list = args.speaker_names if isinstance(args.speaker_names, list) else [args.speaker_names]
     for i, name in enumerate(speaker_names_list, 1):
         speaker_name_mapping[str(i)] = name
-    
+
     print(f"\nSpeaker mapping:")
     for speaker_num in set(speaker_numbers):
         mapped_name = speaker_name_mapping.get(speaker_num, f"Speaker {speaker_num}")
         print(f"  Speaker {speaker_num} -> {mapped_name}")
-    
+
     # Map speakers to voice files using the provided speaker names
     voice_samples = []
     actual_speakers = []
-    
+
     # Get unique speaker numbers in order of first appearance
     unique_speaker_numbers = []
     seen = set()
@@ -241,18 +241,18 @@ def main():
         if speaker_num not in seen:
             unique_speaker_numbers.append(speaker_num)
             seen.add(speaker_num)
-    
+
     for speaker_num in unique_speaker_numbers:
         speaker_name = speaker_name_mapping.get(speaker_num, f"Speaker {speaker_num}")
         voice_path = voice_mapper.get_voice_path(speaker_name)
         voice_samples.append(voice_path)
         actual_speakers.append(speaker_name)
         print(f"Speaker {speaker_num} ('{speaker_name}') -> Voice: {os.path.basename(voice_path)}")
-    
+
     # Prepare data for model
     full_script = '\n'.join(scripts)
-    full_script = full_script.replace("’", "'")        
+    full_script = full_script.replace("’", "'")
-    
+
     print(f"Loading processor & model from {args.model_path}")
     processor = VibeVoiceProcessor.from_pretrained(args.model_path)
 
@@ -314,7 +314,7 @@ def main():
 
     if hasattr(model.model, 'language_model'):
        print(f"Language model attention: {model.model.language_model.config._attn_implementation}")
-       
+
     # Prepare inputs for the model
     inputs = processor(
         text=[full_script], # Wrap in list for batch processing
@@ -344,7 +344,7 @@ def main():
     )
     generation_time = time.time() - start_time
     print(f"Generation time: {generation_time:.2f} seconds")
-    
+
     # Calculate audio duration and additional metrics
     if outputs.speech_outputs and outputs.speech_outputs[0] is not None:
         # Assuming 24kHz sample rate (common for speech synthesis)
@@ -352,17 +352,17 @@ def main():
         audio_samples = outputs.speech_outputs[0].shape[-1] if len(outputs.speech_outputs[0].shape) > 0 else len(outputs.speech_outputs[0])
         audio_duration = audio_samples / sample_rate
         rtf = generation_time / audio_duration if audio_duration > 0 else float('inf')
-        
+
         print(f"Generated audio duration: {audio_duration:.2f} seconds")
         print(f"RTF (Real Time Factor): {rtf:.2f}x")
     else:
         print("No audio output generated")
-    
+
     # Calculate token metrics
     input_tokens = inputs['input_ids'].shape[1]  # Number of input tokens
     output_tokens = outputs.sequences.shape[1]  # Total tokens (input + generated)
     generated_tokens = output_tokens - input_tokens
-    
+
     print(f"Prefilling tokens: {input_tokens}")
     print(f"Generated tokens: {generated_tokens}")
     print(f"Total tokens: {output_tokens}")
@@ -371,13 +371,13 @@ def main():
     txt_filename = os.path.splitext(os.path.basename(args.txt_path))[0]
     output_path = os.path.join(args.output_dir, f"{txt_filename}_generated.wav")
     os.makedirs(args.output_dir, exist_ok=True)
-    
+
     processor.save_audio(
         outputs.speech_outputs[0], # First (and only) batch item
         output_path=output_path,
     )
     print(f"Saved output to {output_path}")
-    
+
     # Print summary
     print("\n" + "="*50)
     print("GENERATION SUMMARY")
@@ -393,7 +393,7 @@ def main():
     print(f"Generation time: {generation_time:.2f} seconds")
     print(f"Audio duration: {audio_duration:.2f} seconds")
     print(f"RTF (Real Time Factor): {rtf:.2f}x")
-    
+
     print("="*50)
 
 if __name__ == "__main__":