Add VibeVoice-ASR

vibevoice/processor/vibevoice_tokenizer_processor.py

@@ -13,80 +13,10 @@ import torch
 from transformers.feature_extraction_utils import FeatureExtractionMixin
 from transformers.utils import logging
 
+from .audio_utils import AudioNormalizer
+
 logger = logging.get_logger(__name__)
 
-
-class AudioNormalizer:
-    """
-    Audio normalization class for VibeVoice tokenizer.
-    
-    This class provides audio normalization to ensure consistent input levels
-    for the VibeVoice tokenizer while maintaining audio quality.
-    """
-    
-    def __init__(self, target_dB_FS: float = -25, eps: float = 1e-6):
-        """
-        Initialize the audio normalizer.
-        
-        Args:
-            target_dB_FS (float): Target dB FS level for the audio. Default: -25
-            eps (float): Small value to avoid division by zero. Default: 1e-6
-        """
-        self.target_dB_FS = target_dB_FS
-        self.eps = eps
-    
-    def tailor_dB_FS(self, audio: np.ndarray) -> tuple:
-        """
-        Adjust the audio to the target dB FS level.
-        
-        Args:
-            audio (np.ndarray): Input audio signal
-            
-        Returns:
-            tuple: (normalized_audio, rms, scalar)
-        """
-        rms = np.sqrt(np.mean(audio**2))
-        scalar = 10 ** (self.target_dB_FS / 20) / (rms + self.eps)
-        normalized_audio = audio * scalar
-        return normalized_audio, rms, scalar
-    
-    def avoid_clipping(self, audio: np.ndarray, scalar: Optional[float] = None) -> tuple:
-        """
-        Avoid clipping by scaling down if necessary.
-        
-        Args:
-            audio (np.ndarray): Input audio signal
-            scalar (float, optional): Explicit scaling factor
-            
-        Returns:
-            tuple: (normalized_audio, scalar)
-        """
-        if scalar is None:
-            max_val = np.max(np.abs(audio))
-            if max_val > 1.0:
-                scalar = max_val + self.eps
-            else:
-                scalar = 1.0
-        
-        return audio / scalar, scalar
-    
-    def __call__(self, audio: np.ndarray) -> np.ndarray:
-        """
-        Normalize the audio by adjusting to target dB FS and avoiding clipping.
-        
-        Args:
-            audio (np.ndarray): Input audio signal
-            
-        Returns:
-            np.ndarray: Normalized audio signal
-        """
-        # First adjust to target dB FS
-        audio, _, _ = self.tailor_dB_FS(audio)
-        # Then avoid clipping
-        audio, _ = self.avoid_clipping(audio)
-        return audio
-
-
 # Change from ProcessorMixin to FeatureExtractionMixin which is designed for single components
 class VibeVoiceTokenizerProcessor(FeatureExtractionMixin):
     """