Delete voice_cloning_engine.py

voice_cloning_engine.py

@@ -1,377 +0,0 @@
-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