Speech Synthesis Model

README.md

flowchart TD
    subgraph Introduction
        A1[Purpose: Develop a speech synthesis model integrating user feedback]
    end
    
    subgraph Components_Overview
        B1[User Prompt: Text input from user]
        B2[Tokenizer: Converts text to tokens]
        B3[Model Weights: Parameters of the model]
        B4[TTS: Converts text tokens to audio]
        B5[GAN: Enhances or modifies audio]
        B6[WAV: Final audio output format]
        B7[Loss Function: Calculates error]
        B8[Evaluation Function: Assesses performance]
        B9[Gradient Ascent + Diffusion: Optimization techniques]
        B10[Training Data: Books and audiobooks]
    end
    
    subgraph Training_Process
        C1[Step 1: Tokenize input data]
        C2[Step 2: Initialize model weights]
        C3[Step 3: Train model with gradient ascent]
        C4[Step 4: Backup model weights]
        Tokenizer --> C1
        Book --> C1
        AudioBook --> C1
        C1 --> ModelWeights
        C2 --> ModelWeights
        C3 --> ModelWeights
        C4 --> ModelWeights
    end
    
    subgraph Inference_Process
        D1[Step 1: User provides a prompt]
        D2[Step 2: Process prompt through inference engine]
        D3[Step 3: Generate audio using TTS]
        D4[Step 4: Enhance audio using GAN]
        D5[Step 5: Produce final WAV output]
        UserPrompt --> D1
        D1 --> D2
        ModelWeights --> D2
        D2 --> TTSModule
        D2 --> GANModule
        TTSModule --> WAVOutput
        GANModule --> WAVOutput
        WAVOutput --> D2
    end
    
    subgraph Feedback_Loop
        E1[Step 1: Collect user feedback]
        E2[Step 2: Calculate loss using feedback]
        E3[Step 3: Evaluate model performance]
        E4[Step 4: Apply gradient ascent and diffusion]
        UserFeedback --> E1
        E1 --> LossFunction
        LossFunction --> EvalFunction
        EvalFunction --> GradientAscent
        GradientAscent --> ModelWeights
    end
    
    subgraph Advanced_Evaluation_Metrics
        F1[Integrate PESQ, MOS, WER for audio quality]
    end
    
    subgraph Scalability_and_Efficiency
        G1[Apply model pruning, quantization, distributed training]
    end
    
    subgraph Data_Augmentation
        H1[Use audio and text augmentation techniques]
    end
    
    subgraph Documentation_and_Visualization
        I1[Thoroughly document system architecture and processes]
    end
    
    Introduction --> Components_Overview
    Components_Overview --> Training_Process
    Components_Overview --> Inference_Process
    Components_Overview --> Feedback_Loop
    Components_Overview --> Advanced_Evaluation_Metrics
    Components_Overview --> Scalability_and_Efficiency
    Components_Overview --> Data_Augmentation
    Components_Overview --> Documentation_and_Visualization
    Training_Process --> ModelWeights
    Inference_Process --> ModelWeights
    Feedback_Loop --> ModelWeights
    Advanced_Evaluation_Metrics --> ModelWeights
    Scalability_and_Efficiency --> ModelWeights
    Data_Augmentation --> ModelWeights
    Documentation_and_Visualization --> ModelWeights

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