AI Voice Cloning Tutorial: Create Any AI Voice with Kits.AI
TLDRThe AI Voice Cloning Tutorial from Kits.AI guides users on creating high-quality voice models using 10 minutes of clean, dry monophonic vocals. It emphasizes the importance of using high-quality, lossless recordings from a good microphone to ensure the best results. The tutorial advises against including background noise, hum, or lossy compression artifacts, as these can degrade the voice model's quality. It also warns against using harmonies or doubling in the dataset, which can cause misinterpretation and glitches. The ideal data source is original recordings like studio acappellas. If these are unavailable, the Kits vocal separator tool can extract vocals from a master recording. The tool can also clean up vocals by removing reverb, delay, and harmonies. Once the training data is compiled, users can upload it to Kits to train their voice model. The tutorial also covers how to convert audio using the trained model, suggesting experimentation with various settings for optimal results. The Kits platform offers an automatic training feature that uses YouTube links to isolate vocals and train the model. The summary highlights the potential of AI voice conversion as a powerful tool for creators.
Takeaways
- 🎙️ To train a high-quality voice model, you need 10 minutes of dry monophonic vocals.
- 🚫 Avoid using backing tracks, time-based effects like reverb and delay, and harmonies or stereo effects.
- 🌰 A good example of suitable data is a clean recording from a high-quality microphone in a lossless file format.
- ❌ Background noise, hum, and lossy compression can negatively impact the voice model quality.
- 🔊 Ensure your dataset is free from harmony or doubling to prevent misinterpretation by the voice model.
- 🎶 Reverb and delay can cause overlapping voices, so keep your dataset as dry as possible.
- 📈 Include a wide range of pitches, vowels, and articulations in your dataset for comprehensive training.
- 🎼 If you lack studio acappellas, use the Kits vocal separator tool to extract vocals from a master recording.
- 🔄 The vocal separator tool can also remove reverb, echo, and harmonies to clean up your vocals.
- 💾 Compile around 10 minutes of good training data before uploading to Kits for training.
- 🔧 Experiment with conversion settings like the dynamic slider and pre-/post-processing effects for optimal results.
- 📚 Use demo audio to quickly test new models or conversion settings without using up your conversion minutes.
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 with Kits.AI?
-You should avoid backing tracks, time-based effects like reverb and delay, harmonies, doubling, and stereo effects in the data set.
How does the quality of the input data set affect the voice model's output quality?
-The quality of the voice model's output is directly related to the quality of the input data set. Clean recordings from a high-quality microphone in a lossless file format will be reflected in the voice model's quality.
What can be the impact of background noise, hum, or lossy compression artifacts on the voice model?
-Background noise, hum, and lossy compression artifacts can negatively impact the quality of the voice model and may introduce glitches and artifacts.
Why should harmony or doubling be avoided in the data set?
-Harmony or doubling should be avoided because the voice model might misinterpret these additional voices as part of the original, leading to glitches and artifacts later on.
What is the best source of training data for a voice model?
-The best source of training data is original recordings of the target voice, such as studio acappellas.
How can the Kits vocal separator tool be used?
-The Kits vocal separator tool can be used to extract vocals from a master recording by dropping a file or pasting a YouTube link, which will isolate the main vocal from the backing track.
What can be done if the isolated vocals have reverb, delay, or harmonies?
-If the isolated vocals have reverb, delay, or harmonies, you can use the vocal separator tool to select 'remove backing vocals' and 'remove Reverb and Echo' to clean them up.
How much training data is needed before starting the training process on Kits?
-You need to compile around 10 minutes of good training data before starting the training process on Kits.
What type of input data is recommended for the best conversion results when using the voice model?
-Dry monophonic input data is recommended for the best conversion results.
How can users experiment with conversion settings?
-Users can experiment with the conversion string slider, dynamic slider, pre-processing effects, and post-processing effects to find the best sound. They can also use demo audio to test new models or conversion settings without using their conversion minutes.
What additional feature can be used to test the voice model?
-The text-to-speech feature can be used to type out a phrase for the voice model to speak out loud, providing another way to test the model.
Outlines
🎤 Training a High-Quality Voice Model
To create a high-quality voice model, one requires 10 minutes of dry monophonic vocals, free from backing tracks and time-based effects like reverb and delay. Harmonies, doubling, and stereo effects should also be avoided. Kits, the platform mentioned, captures every detail from the data set and uses it to produce a realistic conversion. The quality of the voice model is directly related to the quality of the input data, which should be clean recordings from a high-quality microphone in a lossless file format. Background noise, hum, and lossy compression artifacts can negatively impact the model's quality. It's advised to avoid harmony or doubling in the data set to prevent misinterpretation by the model. The data set should include a wide range of pitches, vowels, and articulations to ensure the model can accurately convert every sound. Original recordings of the target voice, such as studio acappellas, are the best source of training data. If studio acappellas are not available, 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 harmonies. Once around 10 minutes of good training data is compiled, it can be uploaded to Kits for training. The process is automated, and once the model is trained, users can easily convert audio with dry monophonic input data. Users can experiment with conversion settings and use demo audio to test new models or settings without using up their conversion minutes. The text-to-speech feature allows users to input a phrase for the voice model to vocalize.
Mindmap
Keywords
💡AI Voice Cloning
💡Dry Monophonic Vocals
💡Training Data
💡High-Quality Microphone
💡Lossless File Format
💡Background Noise
💡Vocal Separator Tool
💡Reverb and Delay
💡Harmonies and Doubling
💡Conversion String Slider
💡Text to Speech Feature
Highlights
To train a high-quality voice model, you need 10 minutes of dry monophonic vocals without backing tracks or time-based effects.
The quality of the voice model is directly related to the quality of the input data.
Background noise, hum, and lossy compression artifacts can negatively impact the voice model's quality.
Harmony or doubling in the data set can lead to misinterpretation and glitches in the voice model.
Including a wide range of pitches, vowels, and articulations in the data set improves the voice model's versatility.
Original recordings of the target voice, like studio acappellas, are the best source of training data.
If studio acappellas are not available, the Kits vocal separator tool can extract vocals from a master recording.
The vocal separator tool can also remove reverb, delay, and harmonies to clean up the vocals.
Once 10 minutes of good training data is compiled, upload the files to Kits to start training.
Training voice models can also be done by pasting YouTube links for Kits to automatically extract and process vocals.
After training, converting audio is straightforward with dry monophonic input data.
Experimentation with conversion settings such as the dynamic slider and pre-/post-processing effects can optimize the output.
Demo audio can be used to test new models or conversion settings without using up conversion minutes.
The text-to-speech feature allows typing a phrase for the voice model to vocalize.
AI voice conversion is a powerful tool for creators, offering the ability to put unlimited voices at their fingertips.
Kits provides an easy-to-use platform for voice model training and audio conversion.
Avoiding reverb and delay in the data set is crucial for creating a clean and glitch-free voice model.
Lossless file formats from high-quality microphones ensure the best possible voice model quality.
The voice model uses detailed data from the dataset to create realistic audio conversions.
If the model encounters a sound it hasn't trained on, it may result in scratchiness and glitches.