Features Architecture AI Models Dashboard

AI-Powered Emotional Connection for Partners

SoulSync creates personalized AI companions that mirror your partner's personality, voice, and communication style using cutting-edge transformer models.

AI Emotional Connection

System Architecture

Secure, scalable infrastructure for personalized AI companionship

graph TD A[Frontend] --> B[API Gateway] B --> C[Auth Service] C --> D[Consent Middleware] D --> E[Inference Service] E --> F[Text Personality Mirror] E --> G[Voice Cloning TTS] E --> H[Avatar Generation] D --> I[Batch Trainer] I --> J[Model Store] J --> F J --> G J --> H F --> K[Vector DB] G --> K H --> K

Distributed Inference

vLLM serving with tensor parallelism for low-latency responses

Consent Layer

Granular permission controls with immutable audit logs

Continual Learning

Nightly fine-tuning with LoRA adapters for personality evolution

AI Model Suite

Specialized transformer models for emotional presence and personality mirroring

Text Personality Mirror

Fine-tuned Mistral-7B with LoRA adapters for personalized responses

personality_mirror.py Transformer Model 
class PersonalityMirror(nn.Module):
    def __init__(self, base_model, lora_weights):
        super().__init__()
        self.base_model = AutoModelForCausalLM.from_pretrained(base_model)
        self.lora = PeftModel.from_pretrained(self.base_model, lora_weights)
        
    def forward(self, input_ids, attention_mask):
        outputs = self.lora(input_ids, attention_mask=attention_mask)
        return outputs.logits
        
    def generate(self, input_ids, **kwargs):
        # Apply personality-specific generation parameters
        kwargs.setdefault('temperature', 0.7)
        kwargs.setdefault('top_p', 0.9)
        return self.lora.generate(input_ids, **kwargs)

Voice Cloning

FastSpeech2 + HiFi-GAN with speaker embeddings

voice_cloning.py Speech Synthesis 
def clone_voice(text, speaker_embedding):
    # Text normalization and phonemization
    cleaned_text = clean_text(text)
    phonemes = phonemize(cleaned_text)
    
    # Generate mel-spectrogram with FastSpeech2
    mel = fastspeech2(phonemes, speaker_embedding)
    
    # Convert to waveform with HiFi-GAN
    audio = hifigan(mel)
    
    return audio

def extract_speaker_embedding(audio_sample):
    # Process 30s of audio to create speaker embedding
    preprocessed = preprocess_audio(audio_sample)
    embedding = speaker_encoder(preprocessed)
    return embedding

Avatar Generation

NeRF-based avatar rendering with audio-driven animation

avatar_generator.py 3D Rendering 
class NeRFAvatar:
    def __init__(self, model_path):
        self.model = load_nerf_model(model_path)
        self.animator = VisemeAnimator()
    
    def generate_frame(self, audio_frame, expression):
        # Extract viseme parameters from audio
        visemes = self.animator.get_visemes(audio_frame)
        
        # Combine with emotional expression
        params = combine_parameters(visemes, expression)
        
        # Render NeRF frame
        frame = self.model.render(params)
        return frame
        
    def generate_video(self, audio_frames, expressions):
        frames = []
        for i in range(len(audio_frames)):
            frame = self.generate_frame(audio_frames[i], expressions[i])
            frames.append(frame)
        return encode_video(frames)

Model Dashboard

Real-time monitoring and configuration of AI models

Personality Mirror

Current Configuration 
{
  "base_model": "mistralai/Mistral-7B",
  "lora_rank": 16,
  "temperature": 0.7,
  "top_p": 0.9,
  "training_data": "12,584 messages",
  "last_trained": "2025-08-14 03:45:21"
}

Voice Cloning

API Endpoint 
POST /v1/tts
Content-Type: application/json
Authorization: Bearer <token>

{
  "partner_id": "usr_abcd1234",
  "text": "Good morning! How did you sleep?",
  "style": "warm",
  "pitch": 0.2,
  "speed": 1.0
}

Consent & Privacy

consent_manager.py Privacy Layer 
class ConsentManager:
    def __init__(self, db):
        self.db = db
        
    def check_consent(self, user_id, action):
        consent = self.db.get_consent(user_id)
        if action == "voice" and not consent.voice:
            raise ConsentError("Voice cloning not permitted")
        if action == "avatar" and not consent.avatar:
            raise ConsentError("Avatar generation not permitted")
        return True
        
    def update_consent(self, user_id, new_consent):
        # Validate consent structure
        validate_consent_schema(new_consent)
        
        # Update database
        self.db.update_consent(user_id, new_consent)
        
        # If voice consent revoked, delete voice model
        if "voice" in new_consent and not new_consent.voice:
            delete_voice_model(user_id)
            
        # Log consent change
        audit_log(user_id, "consent_update", new_consent)

Experience SoulSync AI

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