# Glossary

• Vocal lines that contribute to the sound of the lead vocals in a song.

• In the field of digital audio, it defines the dynamic range of each sample.

• This determines the difference between the quietest & loudest sound.

• Basically, higher bit depths represent more accurately the loudness of an audio.

• The amount of data processed per certain unit of time, usually in kilobits per second (KBPS).

• Higher bitrate equals a higher quality.

• You can think of it as video resolution (240, 480, 1080, etc.).

• It stands for Digital Audio Workstation, and it's any software used for making and mixing music.

• For basic audio editing, we recommend Audacity.

• For professional mixing, FL Studio.

• In RVC, these are files of a model that generate over the course of training, that can be very useful.

• The rate at which they're saved is determined by the save frequency value (or save rate or similar names). For newbies, it's recommended use a value of 15.

• They are divided by two types:

  • Weights:
    • These are actual models.
    • They're organized with this format: modelname_epoch_step.pth
    • Example: Tyler_e60_s120.pth
  • G and D:
    • Named G_ and D_, followed by the step number & .pth.
    • Example: G_70.pth and D_70.pth
    • These allow you to resume training, if G and D's numbers match.

• It stands for Graphics Processing Unit. It's designed to rapidly manipulate and alter memory to accelerate creation of images.
• In AI training, is used for quick parallel independent computations, which increases the speed substantially.
• Basically the speed at which RVC/UVR will work will depend on how good your GPU is.

• Running locally is a process that involves running apps in your own machine, using its resources.
• Done by users with a powerful GPU.
• The opposite of cloud-based.

• Any software or application that's stored, managed, and available through the provider's virtual servers, and is accessed through a web browser.
• Used by users with a weak GPU, which can't do local running.
• The opposite of local running.

• A technology developed by NVIDIA, that uses the power of graphics cards to perform calculations that require great processing power.
• It's especially useful for AI tools, such as RVC and UVR, which greatly optimizes the process.
• CUDA is downloaded automatically as a part of the NVIDIA driver.
• Which Nvidia driver you use might affect performance, Studio Drivers can help AI, but this is mostly for other types of AIs such as Stable Diffusion, rather than RVC.

• Further improving an AI model, training it with a another dataset.

• It's a copy of a main GitHub project. It aims to make a different version of the project with improvements, changes & new features.

• Gradio is an open-source Python packag that makes it easy for developers to create user-friendly web interfaces for machine learning models and other applications, such as RVC.

• It deploys the program on a Local URL, which is the one running locally on the machine, and a Public Share Link, which is a tunnel that exposes the Local URL. The Public Share Link is used, for example, in Google Colabs, powered by their Share API. Sometimes, the Share API goes down, you can check its status.

Of course. Here is a standalone section about Jupyter Notebooks, written in the same glossary style.

• An interactive, web-based document that lets you combine runnable code, explanatory text, and media (like images and charts) into a single file.
• It's organized into individual cells. Code cells can be run one by one to perform tasks, while text cells (using Markdown) are used for documentation and instructions.
• This cell-based format is perfect for creating step-by-step guides and running AI applications, which is why it's the standard interface for platforms like Google Colab, Kaggle, and Lightning.AI.
• Jupyter Notebook files are saved with the .ipynb file extension.

• Google Colaboratory, also known as Google Colab or just Colab, is a cloud-based platform by Google to run Jupyter Notebooks.
• It requires a Google Account.
• It doesn't Web User Interfaces in the Free Tier, meaning it needs encrypted Jupyter Notebooks) and has ban risk like Kaggle.
• It's free tier offers max 4 hours a day of T4 GPU, but it's random. Once you exhaust it, you'll have to wait 12 - 24 hours or get a paid tier.
• Learn how to bypass their limitations here.

• A cloud-based is a cloud-based platform by Google to run Jupyter Notebooks.
• It's focused on data science and machine learning.
• It doesn't Web User Interfaces, meaning it needs encrypted Jupyter Notebooks) and has ban risk like Google Colab.
• If you have trouble verifying your phone number, Contact Kaggle.
• It requires a phone number verification to access the internet (which is needed to run most Jupyter Notebooks).
• The free tier provides a limited quota of 30 free GPU hours per week of either T4x2 (2 T4 GPUs at once) or P100, which you can swap at any time, using either will consume the quota, which resets weekly.

• A cloud-based platform designed for developing and running AI applications in persistent environments called "Studios.", and one of the options is via (but not limited to) running Jupyter Notebooks.
• It requires a phone number verification to full access.
• It features persistent storage, meaning your files are saved between sessions.
• It allows Web User Interfaces on the Free tier, avoiding any encryption or ban risk unlike Google Colab or Kaggle.
• Studios Auto Sleep (stop running) after 10 minutes of inactivity (such as closing the site or not running anything in the background) in the Free tier.
• Users inactive for a 6 month period and do not have an active paid subscription will be scheduled for deletion. Users will be notified 30 days before the scheduled deletion, with several reminders sent during this period.
• Free tier users get free 15 monthly credits that can be used on CPUs or GPUs. Be sure to monitor your usage and stop the Studio when not in use.
• "free studios" are considerated the free CPU studios, which have a 4 hour limit session, after that they will become paid CPU studios using your credits unless you restart them. Unlike GPU Studios which directly use credits.
• In Free tier you can change GPU/CPUs at any time, but if you used all free 15 credits monthly only on a specific computing, you'd have:
  • GPUs (Powerful, Recommended):
    • T4 (16GB VRAM): 75 hours
    • L4 (24GB VRAM): 31 hours
    • L40S (48GB VRAM): 15 hours
  • CPUs:
    • Default (CPU) (4 cores, 16GB RAM): unlimited (first 4 hours are free and you'd need to restart, or keep paying, and if you pay you'd have 45 hours monthly)
    • Large (CPU): (8 cores, 32GB RAM): 29 hours
    • X-Large (CPU): (16 cores, 64GB RAM): 15 hours
• You can check the pricing for more info.

• In the context of AI, it's using an AI model to complete any task.

• For this, using the GPU is more convenient as it's faster. Though normally you can still use CPU, which takes longer.

• For example, in RVC is when a voice model is used to transform an audio, to make it sound like the model.

• Audio formats that don't compress (lose) the original quality.
• They're recommended for RVC, as the more quality an audio has, the more accurate results they'll offer.
• The main ones are WAV & FLAC:
  • FLAC:
    • Its algorithm compresses the data without losing quality.
    • It's recommended over WAV since it's space-efficient.
  • WAV:
    • Doesn't do any kind of compression. It's purely the original data.
    • Therefore it has a much bigger file size.
    • It's more accurate to describe it as an uncompressed format

• Audio formats that compress (lose) the original quality. They're built to be space-efficient.

• So by getting rid of some data (in this case, quality), they achieve a smaller file size.

• Common lossy formats are MP3, OGG, OPUS, M4A, etc.

• Localtunnel is a tunnel made to expose a local url (like http://localhost:3000).

• It's used in Google Colabs to expose the Local URL so that users on Cloud can access the program.

• In the field of AI, is the process where an AI model is fed with its dataset & learns from it.

• It refers to a computer's specifications. Hardware like GPU, CPU, RAM, etc.

• The performance of the hardware of a computer directly correlates to the performance of all its software.

• Doing inference on an AI model without explicitly training on it.

• It's faster but with less quality, and you won't be able to save the model.

• For example, in TTS you do inference by cloning a voice with an audio, a data it hasn't seen before.

• Different from making a dataset & doing the long training process, based on lots of criteria such as epochs.

• In some cases you can do it on GPU, some in CPU.

• It is a shorter way to say optimizer.

• A optimizer is an algorithm used to minimize the loss function during the training of neural networks. It helps adjust the model's weights and biases.