graph LR
Denoising_Model_Core["Denoising Model Core"]
Application_Entry_Configuration["Application Entry & Configuration"]
Data_Management_Augmentation["Data Management & Augmentation"]
Model_Operations_Evaluation["Model Operations & Evaluation"]
Audio_Processing_I_O["Audio Processing & I/O"]
Application_Entry_Configuration -- "initiates" --> Model_Operations_Evaluation
Application_Entry_Configuration -- "configures" --> Data_Management_Augmentation
Application_Entry_Configuration -- "configures" --> Audio_Processing_I_O
Data_Management_Augmentation -- "provides data to" --> Model_Operations_Evaluation
Denoising_Model_Core -- "processes audio for" --> Model_Operations_Evaluation
Denoising_Model_Core -- "processes audio for" --> Audio_Processing_I_O
Model_Operations_Evaluation -- "interacts with" --> Denoising_Model_Core
Model_Operations_Evaluation -- "consumes data from" --> Data_Management_Augmentation
Model_Operations_Evaluation -- "utilizes" --> Audio_Processing_I_O
Audio_Processing_I_O -- "interacts with" --> Denoising_Model_Core
Audio_Processing_I_O -- "supports" --> Model_Operations_Evaluation
click Denoising_Model_Core href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/denoiser/Denoising_Model_Core.md" "Details"
click Application_Entry_Configuration href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/denoiser/Application_Entry_Configuration.md" "Details"
click Data_Management_Augmentation href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/denoiser/Data_Management_Augmentation.md" "Details"
click Model_Operations_Evaluation href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/denoiser/Model_Operations_Evaluation.md" "Details"
click Audio_Processing_I_O href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/denoiser/Audio_Processing_I_O.md" "Details"
The denoiser project is structured around a core Denoising Model Core (an encoder-decoder deep learning model) that performs the primary audio denoising. The Application Entry & Configuration component acts as the central control, interpreting user commands and configurations to orchestrate various operational flows, including initiating training, batch enhancement, real-time processing, and model evaluation. It configures and directs data flow to other key components.
Data Management & Augmentation is responsible for preparing and augmenting audio datasets, providing the necessary input to the Model Operations & Evaluation component. This Model Operations & Evaluation component manages the entire lifecycle of the denoising model, encompassing training, loss computation, distributed execution, and performance assessment, and also handles the loading of pre-trained models. It interacts directly with the Denoising Model Core and consumes data from Data Management & Augmentation.
Finally, Audio Processing & I/O provides low-level signal manipulation, resampling, and handles audio device interactions. It supports both the Denoising Model Core and various operational modules, ensuring efficient audio input and output throughout the system. This architecture promotes clear separation of concerns, facilitating maintainability and scalability, with data flowing from configuration and data preparation through model operations and evaluation, leveraging the core denoising model and audio processing utilities.
Denoising Model Core [Expand]
an encoder-decoder deep learning model
Related Classes/Methods:
Application Entry & Configuration [Expand]
acts as the central orchestrator, interpreting user commands and configurations (leveraging Hydra) to direct control to specific operational flows: batch enhancement, real-time live processing, or model evaluation. It initiates the training process, which is then handled by the Model Operations & Evaluation component.
Related Classes/Methods:
Data Management & Augmentation [Expand]
responsible for preparing and augmenting audio datasets, feeding processed data to the model operations.
Related Classes/Methods:
Model Operations & Evaluation [Expand]
manages the complete lifecycle of the denoising model, including training, loss computation, distributed execution, and performance assessment, and also handles the loading of pre-trained models.
Related Classes/Methods:
Audio Processing & I/O [Expand]
provides essential low-level signal manipulation, resampling, and handles audio device interactions for both input and output, supporting the model core and various operational modules.
Related Classes/Methods: