The guide discusses various methods for removing background noise from audio to improve the quality of speech-to-text (STT) transcription, highlighting three main approaches: AI-powered online tools, professional desktop software, and Python programming. Each method is suited to different needs depending on the user's technical expertise and the volume of audio files processed. Online tools provide quick, automatic noise removal without setup but are limited in batch processing, while desktop software like Audacity and Adobe Audition offers precise manual control through spectral editing. Python libraries, such as noisereduce, enable automated noise reduction across multiple files and integrate with transcription services like AssemblyAI. The guide emphasizes understanding when noise removal enhances transcription accuracy, particularly in recordings with low signal-to-noise ratios, and advises caution against over-processing clean audio to avoid speech distortion. The tutorial provides a step-by-step process for building a complete noise reduction and transcription pipeline using Python, offering a scalable solution for handling noisy audio recordings effectively.