Train an AI voice model for free without needing a GPU or complex installations. Just use Google Collab and some creative coding to train your own voice model online. It’s like capturing lightning in a bottle – the power of technology at your fingertips! Get ready to bring your favorite characters and voices to life without breaking the bank. 🎤✨
In this video, I’ll be demonstrating how to train AI voice models for free online without the need for a fancy GPU, just a stable internet connection. Specifically, I’ll be focusing on training AI voice models using RVC, which is described as the gold standard for cloning and voice conversion. Most voice models you see online have been trained using this tool.
Benefits of Training Using RVC
Let’s take a look at some key takeaways to understand the advantages of training AI voice models using RVC.
| Key Takeaways |
|---|
| 1. RVC is considered the gold standard for voice conversion. |
| 2. Models trained using RVC can clone voices of various characters, including popular celebrities. |
| 3. A notebook called RVC V2 will be used to demonstrate the training process. |
The traditional method to train an AI voice model involves installing RVC on your computer and necessitates having Nvidia Cuda to accomplish the task. However, running RVC in Google Collab proved to be problematic due to disruptions caused by the free plan’s restrictions on graphical interfaces. By learning to run RVC using code within the notebook, you can bypass this issue and train your model without interruptions.
Considerations Before Getting Started
Before diving into the training process, it is important to consider a few factors to ensure a smooth experience.
- When training your model, you might need to run code periodically to avoid inactivity-related disruptions.
- Overtraining your voice model, as determined by the number of epochs, can lead to disconnection messages due to inactivity.
As the notebook for training and running RVC is made available, we have the opportunity to run RVC on computers that lack a GPU, thus eliminating dependency on high-end hardware. What’s important to note here is that the primary aim of this approach is to enable training your voice model without having a GPU at your disposal.
Unveiling the Training Process
To gain a comprehensive understanding of the training process, we will provide a detailed outline of the sequential steps involved.
| Steps |
|---|
| 1. Naming the experiment and choosing the algorithm version. |
| 2. Preprocessing and feature extraction of audio data. |
| 3. Instruction on training the voice model using specific parameters such as dataset size and batch size. |
| 4. Exploring the implications of setting frequency, save frequency, and total epochs regarding model training. |
Uploading and Pre-Processing Audio
Before initiating the training process, you will need to upload your audio samples into Google Drive. It’s essential to adhere to the instructions provided within the notebook, including creating and organizing the files accordingly for a seamless setup.
Key Steps for Uploading Audio
- Naming the experiment and setting the version.
- Conducting pre-processing for feature extraction of audio data.
- Initiating the model training process with specified parameters.
- Exploring the implications of setting frequencies, including their necessity and relevance.
The training process we are undertaking involves training an AI voice model for a specific source voice, in this case, "gura." As the training is in progress, the notebook is organized in a sequential manner to guide users through each stage effectively.
Wrapping Up and Exporting the Model
Upon successfully training the model, the next steps involve the finalization of the model and exporting it to Google Drive. Notably, the training process does require careful consideration of specific limitations.
| Precautions |
|---|
| * Vigilance against possible disconnections or interruptions due to inactivity. |
| * The significance of saving the model files onto Google Drive to safeguard the overall progress. |
| * Understanding the necessity of exporting the trained model for future revisions. |
Final Steps and Continuous Training
Once the training process is complete, users can proceed with exporting the trained model to Google Drive, ensuring its accessibility for future reference. Moreover, opening the collab notebook and continuing the training process is an option worth exploring for users who wish to perform further training on the model.
You can refer to the uploaded training data and the approach outlined above to resume training or initiate new training sessions. Additionally, the option to continue training will be available based on specific performance and quality assessment requirements.
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