AI Voice Cloning Tutorial: Create Any AI Voice with Kits.AI

This tutorial outlines the process of creating a high-quality AI voice model using Kits.AI. To achieve this, one needs 10 minutes of dry monophonic vocals, avoiding backing tracks, time-based effects, and harmonies. The quality of the voice model directly correlates with the quality of the input data, so clean recordings from a high-quality microphone in a lossless format are recommended. Kits.AI offers tools to extract vocals from master recordings and clean them up if necessary. The training process is straightforward: upload the data set to Kits.AI, and the platform will automatically train the voice model. Once trained, users can easily convert audio, experimenting with various settings to achieve the best sound. Kits.AI also provides a text-to-speech feature for additional versatility. The tutorial emphasizes the power of AI voice conversion and invites users to explore the potential of creating unlimited voices with Kits.AI.

Takeaways

• 🎙️ To train a high-quality voice model with Kits.AI, you need 10 minutes of clean, dry monophonic vocals without any backing tracks or time-based effects like reverb and delay.
• 🚫 Avoid including harmonies, doubling, or stereo effects in your data set to prevent misinterpretation by the voice model.
• 🎧 The quality of your voice model is directly related to the quality of your input data; use a high-quality microphone and lossless file format for best results.
• 🔊 Background noise, hum, and lossy compression artifacts can negatively impact the quality of your voice model.
• 🎼 Ensure your data set is as dry as possible and includes a wide range of pitches, vowels, and articulations to cover all sounds you want to convert.
• 💾 Use original recordings of your target voice, such as studio acappellas, for the best training data.
• 🧑‍💼 If studio acappellas are not available, use the Kits vocal separator tool to extract vocals from a master recording.
• 🔄 The vocal separator tool can also remove reverb, echo, and harmonies from your isolated vocals to clean them up.
• 📁 Compile around 10 minutes of good training data before uploading to Kits.AI to start training your voice model.
• 📚 Kits.AI can automatically train your model if you paste in YouTube links, isolating vocals and removing unwanted effects.
• 🔄 Experiment with the conversion string slider, dynamic slider, and pre-/post-processing effects to find the best sound for your converted audio.
• 📈 You can quickly test new models or conversion settings using demo audio without using up your conversion minutes.
• 🗣️ The text-to-speech feature allows you to type a phrase for your voice model to speak out loud, showcasing the power of AI voice conversion.

Q & A

What is the minimum duration of dry monophonic vocals required to train a high-quality voice model?

To train a high-quality voice model, you need 10 minutes of dry monophonic vocals.

What should be avoided in the data set when training a voice model to ensure quality?

The data set should avoid background noise, hum, lossy compression artifacts, harmony, doubling, stereo effects, reverb, and delay to ensure the best quality.

How does the quality of the input data affect the voice model?

The quality of the voice model is directly reflective of the input data. If the input consists of clean recordings from a high-quality microphone in a lossless file format, that quality will be reflected in the voice model.

What are the potential issues that can arise if the data set includes additional voices or effects?

Including additional voices or effects such as harmony, doubling, reverb, and delay can cause the voice model to misinterpret these as part of the original voice, leading to glitches and artifacts in the conversion.

What should the data set include to ensure a comprehensive training of the voice model?

The data set should include as many pitches, vowels, and articulations as possible to provide a good example for every sound the voice model will be used to convert.

What is the best source of training data for creating a voice model?

The best source of training data is original recordings of the target voice, such as studio acappellas.

How can one obtain vocal recordings if they do not have access to studio acappellas?

If studio acappellas are not available, the kits vocal separator tool can be used to extract vocals from a master recording by dropping a file or pasting a YouTube link.

What does the vocal separator tool do to the isolated vocals?

The vocal separator tool can remove backing vocals, reverb, and echo from the isolated vocals to clean them up for the training data.

How does one start the training process after compiling the training data?

After compiling the training data, one should head back to kits, upload the files, and start the training process.

What is the process for converting audio once the voice model is trained?

To convert audio, one should drop the input data, hit convert, and the converted audio will be ready for download within moments.

How can one experiment with different conversion settings?

One can experiment with the conversion string slider, dynamic slider, pre-processing effects, and post-processing effects to find the best sound. Demo audio can be used for testing without using up conversion minutes.

What additional feature is available for testing the voice model?

The text to speech feature allows one to type out a phrase for the voice model to speak out loud, providing another way to test the model's performance.

Outlines

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🎙️ Preparing a High-Quality Voice Model

To create an excellent voice model, you require 10 minutes of clean, dry monophonic vocals without any backing tracks or time-based effects like reverb and delay. Harmonies, doubling, or stereo effects should also be avoided. The data set should be recorded using a high-quality microphone in a lossless file format to ensure the voice model reflects this quality. Background noise, hum, or lossy compression can degrade the model's quality. It's crucial to prevent harmony or doubling in the data set, as these can be misinterpreted by the model, leading to glitches. Including a wide range of pitches, vowels, and articulations in the data set is beneficial. Original recordings of the target voice, such as studio acappellas, are the best source of training data. If these are unavailable, the kits vocal separator tool can be used to extract vocals from a master recording. The tool can also clean up vocals by removing reverb, echo, and backing vocals. Once a sufficient amount of clean training data is compiled, it can be uploaded to kits for training. The resulting voice model can then be used to convert audio, with the best results coming from dry monophonic input data. Users can experiment with conversion settings and use demo audio to test new models or settings without using their conversion minutes. Additionally, a text-to-speech feature allows users to input phrases for the voice model to vocalize.

Keywords

💡AI Voice Cloning

AI Voice Cloning refers to the process of using artificial intelligence to replicate a human voice. It involves training a voice model on a specific individual's voice to create a synthetic version that can be used for various purposes. In the context of the video, AI voice cloning is the main theme, as it guides the viewer through creating an AI voice with Kits.AI.

💡Dry Monophonic Vocals

Dry monophonic vocals are recordings of a single voice without any added effects or harmonies. They are 'dry' in the sense that they are not processed with effects like reverb or delay. This type of recording is crucial for training a voice model because it provides clean data for the AI to learn from. The script emphasizes the need for such vocals to train a high-quality voice model.

💡Training Data

Training data is the set of data used to teach a machine learning model to perform a specific task. In the video, it refers to the 10 minutes of dry monophonic vocals needed to train the AI voice model. The quality and characteristics of the training data directly affect the performance and accuracy of the resulting voice model.

💡High-Quality Microphone

A high-quality microphone is an essential tool for capturing clear and detailed audio recordings. It is mentioned in the script as a requirement for obtaining clean recordings for the training data set. The better the microphone, the higher the fidelity of the voice recordings, which in turn improves the quality of the voice model.

💡Lossless File Format

A lossless file format is a type of digital file format that retains all the original data from the recording, providing the highest quality audio without any compression artifacts. The script mentions that using a lossless file format for the training data ensures that the voice model reflects the quality of the original recordings.

💡Background Noise

Background noise refers to any unwanted sounds that are not part of the main recording. In the context of the video, background noise can negatively impact the quality of the voice model as it can introduce unwanted elements into the training data. The script advises avoiding background noise for the best results.

💡Harmony and Doubling

Harmony and doubling are audio techniques where multiple voices or instruments play the same or similar notes to create a fuller sound. The script warns against including these in the training data set because the voice model might misinterpret them as part of the original voice, leading to glitches in the AI voice conversion.

💡Vocal Separator Tool

The vocal separator tool is a feature within Kits.AI that allows users to extract the main vocal track from a master recording. It can also remove backing vocals, reverb, and echo to provide clean vocals for training the voice model. This tool is particularly useful when original recordings are not available.

💡Reverb and Delay

Reverberation (Reverb) and Delay are audio effects that simulate the persistence of sound in a particular space or the echo of a sound, respectively. The script mentions that these effects should be avoided in the training data as they can cause overlapping voices and negatively affect the voice model's performance.

💡Conversion String Slider

The conversion string slider is a control within the Kits.AI interface that allows users to adjust the parameters of the AI voice conversion process. The script suggests experimenting with this slider, along with other pre- and post-processing effects, to achieve the best sound for the converted audio.

💡Text-to-Speech Feature

The text-to-speech feature enables users to input text that the voice model will then speak aloud. This is a useful function for testing the voice model's capabilities and for creating audio content without the need for a voice recording. The script highlights this feature as a way to utilize the AI voice model.

💡Demo Audio

Demo audio refers to pre-recorded audio samples that can be used to test the AI voice model without using up the user's conversion minutes. The script mentions using demo audio to quickly test a new model or conversion settings, which helps users to refine their voice model before committing to a full conversion.

Highlights

To train a high-quality voice model, you need 10 minutes of dry monophonic vocals.

Avoid backing tracks, time-based effects like Reverb and Delay, and harmonies or stereo effects.

Clean recordings from a high-quality microphone in a lossless file format will reflect in the voice model quality.

Background noise, hum, and lossy compression artifacts can negatively impact the voice model quality.

Harmony or doubling in the data set may lead to glitches and artifacts in the voice model.

Reverb and Delay can cause overlapping voices, so ensure the data set is as dry as possible.

Include a variety of pitches, vowels, and articulations for a comprehensive voice model.

Original recordings of the target voice, like studio acappellas, are the best source of training data.

Use the Kits vocal separator tool to extract vocals from a master recording if studio acappellas are unavailable.

The vocal separator tool can remove backing vocals and clean up reverb and echo.

Once 10 minutes of good training data is compiled, upload the files to Kits to start training.

Kits can automatically isolate vocals, remove harmonies and Reverb, and train the model from YouTube links.

Dry monophonic input data will yield the best results in audio conversion.

Experiment with conversion settings using the dynamic slider and pre-/post-processing effects.

Demo audio allows for quick testing of new models or conversion settings without using convert minutes.

The text-to-speech feature enables typing out a phrase for the voice model to speak aloud.

AI voice conversion is a powerful tool for creators, offering unlimited voice possibilities with Kits.