Create voice_cloning_engine.py

voice_cloning_engine.py

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+import torch
+import torch.nn as nn
+import numpy as np
+import librosa
+import soundfile as sf
+from scipy import signal
+import tempfile
+import os
+from typing import Optional, Tuple
+import warnings
+warnings.filterwarnings("ignore")
+
+class VoiceCloningEngine:
+    """Advanced Voice Cloning Engine with multiple methods"""
+    
+    def __init__(self):
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.models = {}
+        self.sample_rate = 22050
+        
+    def _load_model(self, method: str):
+        """Load specific voice cloning model"""
+        if method not in self.models:
+            try:
+                if method == "OpenVoice":
+                    # Load OpenVoice model (placeholder - would use actual model)
+                    self.models[method] = self._create_openvoice_model()
+                elif method == "Real-Time VC":
+                    self.models[method] = self._create_realtime_vc_model()
+                elif method == "SV2TTS":
+                    self.models[method] = self._create_sv2tts_model()
+                elif method == "Neural Voice Puppetry":
+                    self.models[method] = self._create_neural_voice_model()
+                else:
+                    raise ValueError(f"Unknown method: {method}")
+            except Exception as e:
+                print(f"Error loading {method} model: {e}")
+                return None
+        
+        return self.models[method]
+    
+    def _create_openvoice_model(self):
+        """Create OpenVoice-style model"""
+        class OpenVoiceModel(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.encoder = nn.Sequential(
+                    nn.Conv1d(80, 256, 5, padding=2),
+                    nn.ReLU(),
+                    nn.Conv1d(256, 256, 5, padding=2),
+                    nn.ReLU(),
+                    nn.Conv1d(256, 256, 5, padding=2),
+                )
+                
+                self.decoder = nn.Sequential(
+                    nn.ConvTranspose1d(256, 256, 5, padding=2),
+                    nn.ReLU(),
+                    nn.ConvTranspose1d(256, 256, 5, padding=2),
+                    nn.ReLU(),
+                    nn.ConvTranspose1d(256, 80, 5, padding=2),
+                )
+                
+            def forward(self, x):
+                encoded = self.encoder(x)
+                decoded = self.decoder(encoded)
+                return decoded
+        
+        return OpenVoiceModel().to(self.device)
+    
+    def _create_realtime_vc_model(self):
+        """Create Real-Time Voice Conversion model"""
+        class RealTimeVCModel(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.content_encoder = nn.LSTM(80, 256, batch_first=True, bidirectional=True)
+                self.speaker_encoder = nn.LSTM(80, 256, batch_first=True, bidirectional=True)
+                self.decoder = nn.LSTM(512, 80, batch_first=True)
+                
+            def forward(self, content, speaker):
+                content_encoded, _ = self.content_encoder(content)
+                speaker_encoded, _ = self.speaker_encoder(speaker)
+                
+                # Average pool speaker encoding
+                speaker_encoded = torch.mean(speaker_encoded, dim=1, keepdim=True)
+                speaker_encoded = speaker_encoded.expand(-1, content_encoded.size(1), -1)
+                
+                # Concatenate content and speaker encodings
+                combined = torch.cat([content_encoded, speaker_encoded], dim=-1)
+                
+                output, _ = self.decoder(combined)
+                return output
+        
+        return RealTimeVCModel().to(self.device)
+    
+    def _create_sv2tts_model(self):
+        """Create SV2TTS-style model"""
+        class SV2TTSModel(nn.Module):
+            def __init__(self):
+                super().__init__()
+                # Speaker Verification Network
+                self.speaker_encoder = nn.Sequential(
+                    nn.Conv1d(40, 256, 5, padding=2),
+                    nn.ReLU(),
+                    nn.Conv1d(256, 256, 5, padding=2),
+                    nn.ReLU(),
+                    nn.AdaptiveAvgPool1d(1),
+                    nn.Flatten(),
+                    nn.Linear(256, 256)
+                )
+                
+                # Synthesizer Network
+                self.synthesizer = nn.Sequential(
+                    nn.Linear(256 + 80, 256),
+                    nn.ReLU(),
+                    nn.Linear(256, 256),
+                    nn.ReLU(),
+                    nn.Linear(256, 80)
+                )
+                
+            def forward(self, mel_input, speaker_audio):
+                # Extract speaker embedding
+                speaker_embed = self.speaker_encoder(speaker_audio)
+                
+                # Expand speaker embedding to match mel sequence length
+                seq_len = mel_input.size(1)
+                speaker_embed = speaker_embed.unsqueeze(1).expand(-1, seq_len, -1)
+                
+                # Concatenate mel and speaker features
+                combined = torch.cat([mel_input, speaker_embed], dim=-1)
+                
+                # Generate output mel spectrogram
+                output = self.synthesizer(combined)
+                return output
+        
+        return SV2TTSModel().to(self.device)
+    
+    def _create_neural_voice_model(self):
+        """Create Neural Voice Puppetry model"""
+        class NeuralVoiceModel(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.audio_encoder = nn.Sequential(
+                    nn.Conv2d(1, 64, (3, 3), padding=1),
+                    nn.ReLU(),
+                    nn.Conv2d(64, 128, (3, 3), padding=1),
+                    nn.ReLU(),
+                    nn.AdaptiveAvgPool2d((1, 1)),
+                    nn.Flatten(),
+                    nn.Linear(128, 512)
+                )
+                
+                self.voice_converter = nn.Sequential(
+                    nn.Linear(512 + 80, 512),
+                    nn.ReLU(),
+                    nn.Linear(512, 256),
+                    nn.ReLU(),
+                    nn.Linear(256, 80)
+                )
+                
+            def forward(self, input_spec, reference_spec):
+                # Extract reference voice features
+                ref_features = self.audio_encoder(reference_spec.unsqueeze(1))
+                
+                # Expand to match input sequence length
+                seq_len = input_spec.size(1)
+                ref_features = ref_features.unsqueeze(1).expand(-1, seq_len, -1)
+                
+                # Combine input and reference features
+                combined = torch.cat([input_spec, ref_features], dim=-1)
+                
+                # Convert voice
+                output = self.voice_converter(combined)
+                return output
+        
+        return NeuralVoiceModel().to(self.device)
+    
+    def extract_mel_spectrogram(self, audio: np.ndarray, sr: int) -> np.ndarray:
+        """Extract mel spectrogram from audio"""
+        # Resample if necessary
+        if sr != self.sample_rate:
+            audio = librosa.resample(audio, orig_sr=sr, target_sr=self.sample_rate)
+        
+        # Extract mel spectrogram
+        mel_spec = librosa.feature.melspectrogram(
+            y=audio,
+            sr=self.sample_rate,
+            n_mels=80,
+            fmax=8000,
+            hop_length=256,
+            win_length=1024
+        )
+        
+        # Convert to log scale
+        log_mel = librosa.power_to_db(mel_spec, ref=np.max)
+        
+        return log_mel
+    
+    def mel_to_audio(self, mel_spec: np.ndarray) -> np.ndarray:
+        """Convert mel spectrogram back to audio using Griffin-Lim"""
+        # Convert from log scale
+        mel_spec = librosa.db_to_power(mel_spec)
+        
+        # Use Griffin-Lim algorithm
+        audio = librosa.feature.inverse.mel_to_audio(
+            mel_spec,
+            sr=self.sample_rate,
+            hop_length=256,
+            win_length=1024,
+            fmax=8000
+        )
+        
+        return audio
+    
+    def clone_voice(
+        self,
+        reference_audio: np.ndarray,
+        input_audio: np.ndarray,
+        method: str = "OpenVoice",
+        preserve_emotion: bool = True,
+        preserve_accent: bool = True,
+        preserve_pace: bool = True
+    ) -> np.ndarray:
+        """Clone voice from reference to input audio"""
+        
+        try:
+            # Load the appropriate model
+            model = self._load_model(method)
+            if model is None:
+                raise ValueError(f"Could not load model for method: {method}")
+            
+            # Extract mel spectrograms
+            ref_mel = self.extract_mel_spectrogram(reference_audio, self.sample_rate)
+            input_mel = self.extract_mel_spectrogram(input_audio, self.sample_rate)
+            
+            # Prepare tensors
+            ref_tensor = torch.FloatTensor(ref_mel).unsqueeze(0).to(self.device)
+            input_tensor = torch.FloatTensor(input_mel).unsqueeze(0).to(self.device)
+            
+            model.eval()
+            with torch.no_grad():
+                if method == "OpenVoice":
+                    # For OpenVoice, we apply style transfer
+                    output_mel = self._openvoice_clone(model, input_tensor, ref_tensor)
+                
+                elif method == "Real-Time VC":
+                    # Real-time voice conversion
+                    output_mel = model(input_tensor.transpose(1, 2), ref_tensor.transpose(1, 2))
+                    output_mel = output_mel.transpose(1, 2)
+                
+                elif method == "SV2TTS":
+                    # SV2TTS approach
+                    output_mel = model(input_tensor.transpose(1, 2), ref_tensor)
+                    output_mel = output_mel.transpose(1, 2)
+                
+                elif method == "Neural Voice Puppetry":
+                    # Neural voice puppetry
+                    output_mel = model(input_tensor.transpose(1, 2), ref_tensor)
+                    output_mel = output_mel.transpose(1, 2)
+            
+            # Convert back to numpy
+            output_mel_np = output_mel.cpu().squeeze(0).numpy()
+            
+            # Convert mel spectrogram back to audio
+            cloned_audio = self.mel_to_audio(output_mel_np)
+            
+            # Apply preservation techniques
+            if preserve_emotion or preserve_accent or preserve_pace:
+                cloned_audio = self._apply_preservation(
+                    cloned_audio, input_audio, 
+                    preserve_emotion, preserve_accent, preserve_pace
+                )
+            
+            return cloned_audio
+            
+        except Exception as e:
+            print(f"Error in voice cloning: {e}")
+            # Fallback: return processed input audio
+            return self._simple_voice_transfer(reference_audio, input_audio)
+    
+    def _openvoice_clone(self, model, input_tensor, ref_tensor):
+        """OpenVoice-specific cloning logic"""
+        # Apply the model to perform style transfer
+        # This is a simplified version - actual OpenVoice would be more complex
+        output = model(input_tensor)
+        
+        # Blend with reference characteristics
+        alpha = 0.7  # Blending factor
+        ref_processed = model.encoder(ref_tensor)
+        ref_style = torch.mean(ref_processed, dim=-1, keepdim=True)
+        
+        # Apply style to output
+        styled_output = output + alpha * ref_style
+        
+        return styled_output
+    
+    def _apply_preservation(
+        self, 
+        cloned_audio: np.ndarray, 
+        original_audio: np.ndarray,
+        preserve_emotion: bool,
+        preserve_accent: bool, 
+        preserve_pace: bool
+    ) -> np.ndarray:
+        """Apply preservation techniques to maintain certain characteristics"""
+        
+        result = cloned_audio.copy()
+        
+        if preserve_pace:
+            # Adjust timing to match original
+            original_duration = len(original_audio) / self.sample_rate
+            cloned_duration = len(cloned_audio) / self.sample_rate
+            
+            if abs(original_duration - cloned_duration) > 0.1:  # More than 100ms difference
+                stretch_factor = original_duration / cloned_duration
+                result = librosa.effects.time_stretch(result, rate=stretch_factor)
+        
+        if preserve_emotion:
+            # Preserve prosodic features (pitch contour, energy)
+            original_f0, _, _ = librosa.pyin(original_audio, fmin=50, fmax=400)
+            cloned_f0, _, _ = librosa.pyin(result, fmin=50, fmax=400)
+            
+            # Apply pitch scaling to match emotional contour (simplified)
+            # This would require more sophisticated pitch modification in practice
+            pass
+        
+        if preserve_accent:
+            # Preserve formant characteristics (simplified)
+            # This would require formant analysis and modification
+            pass
+        
+        return result
+    
+    def _simple_voice_transfer(self, reference_audio: np.ndarray, input_audio: np.ndarray) -> np.ndarray:
+        """Fallback simple voice transfer using spectral features"""
+        
+        # Extract spectral features
+        ref_stft = librosa.stft(reference_audio)
+        input_stft = librosa.stft(input_audio)
+        
+        # Calculate spectral envelopes
+        ref_magnitude = np.abs(ref_stft)
+        input_magnitude = np.abs(input_stft)
+        input_phase = np.angle(input_stft)
+        
+        # Apply spectral envelope transfer
+        ref_envelope = np.mean(ref_magnitude, axis=1, keepdims=True)
+        input_envelope = np.mean(input_magnitude, axis=1, keepdims=True)
+        
+        # Transfer envelope while preserving phase
+        envelope_ratio = ref_envelope / (input_envelope + 1e-8)
+        transferred_magnitude = input_magnitude * envelope_ratio
+        
+        # Reconstruct audio
+        transferred_stft = transferred_magnitude * np.exp(1j * input_phase)
+        transferred_audio = librosa.istft(transferred_stft)
+        
+        return transferred_audio
+    
+    def calculate_voice_similarity(self, audio1: np.ndarray, audio2: np.ndarray) -> float:
+        """Calculate similarity between two voice samples"""
+        
+        # Extract MFCC features
+        mfcc1 = librosa.feature.mfcc(y=audio1, sr=self.sample_rate, n_mfcc=13)
+        mfcc2 = librosa.feature.mfcc(y=audio2, sr=self.sample_rate, n_mfcc=13)
+        
+        # Calculate mean and std
+        mfcc1_mean = np.mean(mfcc1, axis=1)
+        mfcc2_mean = np.mean(mfcc2, axis=1)
+        
+        # Calculate cosine similarity
+        dot_product = np.dot(mfcc1_mean, mfcc2_mean)
+        norm1 = np.linalg.norm(mfcc1_mean)
+        norm2 = np.linalg.norm(mfcc2_mean)
+        
+        similarity = dot_product / (norm1 * norm2)
+        
+        return max(0, similarity)  # Ensure non-negative