voice_cloning_engine.py

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