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on_boarding.md

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graph LR
    Experiment_Management["Experiment Management"]
    Data_Preprocessing["Data Preprocessing"]
    UIS_RNN_Core_Model["UIS-RNN Core Model"]
    Training_Module["Training Module"]
    Inference_Module["Inference Module"]
    Evaluation_Module["Evaluation Module"]
    Extension_Module["Extension Module"]
    Experiment_Management -- "initiates data preparation" --> Data_Preprocessing
    Experiment_Management -- "instantiates and configures" --> UIS_RNN_Core_Model
    Experiment_Management -- "orchestrates" --> Training_Module
    Experiment_Management -- "orchestrates" --> Inference_Module
    Experiment_Management -- "orchestrates" --> Evaluation_Module
    Data_Preprocessing -- "provides prepared data to" --> Training_Module
    Data_Preprocessing -- "provides prepared data to" --> Inference_Module
    Training_Module -- "trains and updates" --> UIS_RNN_Core_Model
    UIS_RNN_Core_Model -- "is trained and updated by" --> Training_Module
    Inference_Module -- "uses" --> UIS_RNN_Core_Model
    UIS_RNN_Core_Model -- "is used by" --> Inference_Module
    Inference_Module -- "provides predictions to" --> Evaluation_Module
    Extension_Module -- "may utilize" --> Data_Preprocessing
    click Experiment_Management href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/uis-rnn/Experiment_Management.md" "Details"
    click Data_Preprocessing href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/uis-rnn/Data_Preprocessing.md" "Details"
    click UIS_RNN_Core_Model href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/uis-rnn/UIS_RNN_Core_Model.md" "Details"
    click Inference_Module href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/uis-rnn/Inference_Module.md" "Details"
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Details

The uis-rnn project is structured around a core UIS-RNN Core Model for unsupervised speaker diarization. The Experiment Management component serves as the central orchestrator, handling experiment setup, data flow, and coordinating interactions between other modules. Data Preprocessing prepares input features for both training and inference. The Training Module and Inference Module manage the lifecycle of the UIS-RNN Core Model, from optimization to prediction. The Evaluation Module provides performance assessment, while the Extension Module offers specialized algorithms. This modular design facilitates clear data flow and component responsibilities, making it suitable for both detailed documentation and high-level diagram generation.

Experiment Management [Expand]

The primary entry point and orchestrator for diarization experiments, handling argument parsing and coordinating the overall workflow.

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Data Preprocessing [Expand]

Responsible for all data manipulation tasks, including feature extraction, sequence concatenation, and ensuring data integrity before model consumption.

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UIS-RNN Core Model [Expand]

Defines the fundamental neural network architecture (GRU-based) and the high-level UISRNN class that encapsulates the model's core logic.

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Training Module

Manages the training process of the UIS-RNN Core Model, including optimization, loss calculation, and batch processing.

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Inference Module [Expand]

Handles the prediction phase, utilizing the trained model to generate speaker diarization outputs, often employing beam search for optimal sequence prediction.

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Evaluation Module

Provides functionalities to assess the performance of the speaker diarization system by comparing predicted outputs against ground truth labels.

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Extension Module

Contains specialized or experimental algorithms that extend the core capabilities of the UIS-RNN library, such as advanced CRP alpha estimation.

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