ANVILis a standalone software solution, uniquely crafted to bring the power of machine learning into the hands of audio enthusiasts and creators. ANVIL is free software during this introductory period and a companion application to MACE.
A full comprehensive introductory guide and further information can be found at:
It operates as a pre-packaged Python environment, boasting a user-friendly interface that simplifies the complex processes of audio data management and model training. With ANVIL, users can effortlessly create audio datasets and develop models capable of generating audio. These models are also compatible withMACE, further enhancing their utility.
A Commitment to Local Processing: Empowering Users with Direct Model Training
In a deliberate move away from cloud-based services, ANVIL is engineered to function locally on users’ machines. The vision of ANVIL is to empower users by enabling them to train their own models directly. This approach not only avoids the need for subscription fees associated with cloud computing but also aligns with our belief in harnessing the growing capabilities of average CPUs and GPUs. As hardware technology advances, we are confident that local machines will increasingly be able to handle the computational demands of sophisticated audio model training, making ANVILan even more powerful tool in the hands of our users.
Requirements
Navigating the Technical Landscape: Understanding ANVIL’s Requirements
As we strive to make ANVIL a versatile and powerful tool in the realm of audio machine learning, it’s important for users to understand the current technical requirements and our ongoing efforts towards broader compatibility.
Windows and NVIDIA Graphics Card: The Current Optimal Setup
At present, ANVIL is tailored for Windows operating systems. The software leverages the Pytorch machine learning environment, which requires CUDA support. Consequently, an NVIDIA graphics card is recommended for optimal performance. This setup ensures that users can fully utilize ANVIL’s capabilities for efficient and effective audio model training.
CPU Mode: Ensuring Universal Compatibility
Recognizing the diverse hardware configurations of our users, ANVIL also includes a CPU mode. While this mode ensures universal compatibility, it’s important to note that CPU processing is significantly slower compared to using a graphics card. We are committed to adapting ANVIL to various chip architectures and keeping pace with the evolving landscape of machine learning applications.
Mac Version: Harnessing the Power of Silicon Chips
A Mac version of ANVIL is currently in development. The aim is to harness the capabilities of Apple’s silicon chips (M1, M2, and M3), which show promise in machine learning tasks when coupled with Metal Performance Shaders (MPS). This development marks a step towards expanding ANVIL’s reach and versatility.
AMD Graphics Cards on Windows: A Future Possibility
For users with AMD graphics cards on Windows, we acknowledge the current limitation due to Pytorch’s lack of support in this setup. However, we are closely monitoring the progress of ROCm support for Windows. We will attempt to integrate this feature into ANVIL as soon as it becomes feasible, aiming to open up more possibilities for our users.
Linux Users: A Potential Future Release
Lastly, for Linux enthusiasts, it’s worth noting that Linux currently supports ROCm/Pytorch. This compatibility opens up the potential for a Linux version of ANVIL, which we may pursue as an additional offering in the future.
