Following this instruction :
1. Strictly adhere to PEP-8 standards, ensuring code is clean, well-structured, and follows Python's style guide for readability.
2. Use appropriate modules like `typing` for type hints, ensuring code clarity, maintainability, and leveraging Python's standard libraries effectively.
3. Focus on time, space, robustness, scalability; write optimized, efficient code that can handle large datasets and scale seamlessly.
4. Implement comprehensive error handling, catching exceptions gracefully, providing meaningful error messages, and ensuring code robustness under unexpected conditions.
5. Ensure the developer agent writes advanced, maintainable Python code, balancing performance and readability while adhering to best practices.


Problem Statement:
Objective: Develop a robust and scalable advanced API wrapper for torchaudio that simplifies the use of audio processing, transformation, and manipulation functions. The solution must ensure ease of scalability, improved performance, and maintainability, while offering flexibility for advanced users.

Key Challenges:
Ease of Use and Flexibility:

Users should be able to interact with both basic audio functionalities (loading, saving, metadata retrieval) and advanced transformations (e.g., noise addition, spectrogram computation) with minimal complexity.
The wrapper must abstract complexities without sacrificing the flexibility to customize operations for different use cases.
Scalability:

The API should scale seamlessly across various project sizes and use cases, supporting a wide range of input sizes and types (e.g., real-time audio, batch processing, etc.).
It should be designed to handle multiple concurrent audio processing tasks efficiently in larger workflows, integrating well with deep learning frameworks for model training and evaluation.
Robustness:

Ensure that the API is fault-tolerant, with appropriate error handling for various edge cases, such as invalid audio files, unsupported transformations, or incorrect parameter values.
Provide logging and debugging features to help users trace and resolve issues during audio processing.
Efficient Integration with Deep Learning:

The API should integrate efficiently with torch and torchaudio's tensors and autograd capabilities, supporting backpropagation for learning-based tasks (e.g., MFCC, spectrogram transformation).
Offer efficient memory management and GPU/CPU support to allow for faster execution and training in neural networks.
Extensibility:

The API should allow for easy extension, enabling users to integrate custom transformations or operations while maintaining a cohesive design structure.
Performance Optimization:

The wrapper should ensure optimal performance for different tasks, such as applying transformations, resampling, or augmentation, while maintaining minimal overhead.
Goals:
User-Friendly API Wrapper:

A clean and easy-to-use interface, allowing users to perform complex audio processing tasks with simple, high-level function calls.
Scalable Architecture:

A modular design to support scalability, from individual file processing to large-scale audio dataset manipulation in parallel.
Robust and Resilient:

Incorporate extensive error handling and input validation for reliable performance across a wide range of use cases.
Efficient for Deep Learning Applications:

Facilitate seamless integration with PyTorch’s deep learning pipeline for tasks such as speech recognition, audio classification, and more.
Extensible and Customizable:

Provide a flexible foundation for users to extend or modify audio transformations according to their specific requirements.


from torchaudio import (
    AudioMetaData,
    get_audio_backend,
    info,
    list_audio_backends,
    load,
    save,
    set_audio_backend,
    
)


from torchaudio.transforms import (
    AddNoise,AmplitudeToDB,ComputeDeltas,Convolve,Deemphasis,
    Fade,FFTConvolve,FrequencyMasking,GriffinLim,InverseMelScale,
    InverseSpectrogram,LFCC,Loudness,MelScale,MelSpectrogram,MFCC,
    MuLawDecoding,MuLawEncoding,PitchShift,Preemphasis,Resample,
    RNNTLoss,SlidingWindowCmn,SpecAugment,SpectralCentroid,
    Spectrogram,Speed,SpeedPerturbation,
    TimeMasking,TimeStretch,Vad,Vol,
)