Add ai transcription guide

Signed-off-by: giraffekey <[email protected]>

definitions/20240819_definition_transcription.md

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+---
+title: "Transcription"
+description: "Transcription is the process of converting spoken language from audio or video recordings into written text, used in various fields like content creation, legal documentation, and research to create accessible and official records."
+date: 2024-08-19
+author: "Jacob Gaffke"
+---
+
+# Transcription
+
+## Definition
+
+Transcription is the process of converting spoken language (audio or video) into written text. This can be done manually by a person listening to the audio and typing out the words, or automatically using speech recognition software and AI models.
+
+## Context and Usage
+
+Transcription is commonly used in various fields such as content creation, legal documentation, journalism, academic research, and customer service. In content creation, transcription helps generate subtitles for videos, making them accessible to a wider audience. In legal settings, transcriptions are essential for creating official records of proceedings, depositions, and interviews. In academia, transcribing interviews and discussions is crucial for qualitative research analysis. With advancements in AI, automated transcription tools have become increasingly popular, offering fast and accurate conversions of speech to text, which can be further edited, analyzed, or published as needed.

definitions/20240819_definition_virtual environment.md

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+---
+title: "Virtual Environment"
+description: "A virtual environment is an isolated workspace in Python development that allows for managing specific project dependencies independently, preventing conflicts with other projects or the system-wide installation."
+date: 2024-08-19
+author: "Jacob Gaffke"
+---
+
+# Virtual Environment
+
+## Definition
+
+A virtual environment is an isolated workspace within a computer system that allows you to install and manage specific versions of Python packages and dependencies without affecting the system-wide Python installation or other projects.
+
+## Context and Usage
+
+Virtual environments are commonly used in Python development to create isolated environments for different projects, ensuring that each project can have its own dependencies, regardless of what other projects require. This is particularly important when working on multiple projects that might require different versions of the same package or library. By using virtual environments, developers can avoid conflicts between dependencies and ensure that their projects are portable and easy to reproduce on different systems. Virtual environments are created and managed using tools like `venv` or `virtualenv`, and they are activated whenever you work on a specific project to ensure the correct environment is used.