VoiceStudio System Requirements: Hardware & Software Specs for macOS, Windows, and Linux

VoiceStudio requires 8 GB RAM, 10 GB free disk space, and runs on Windows 10 x64, macOS 13.3 (Apple Silicon), or Linux x86_64 with glibc 2.39+, with GPU acceleration optional but recommended for faster voice cloning and inference.

VoiceStudio is a cross-platform desktop application for voice cloning, voice design, video dubbing, and speech-to-text. Because it runs entirely locally—no cloud dependencies—your hardware directly determines performance. This guide breaks down the VoiceStudio system requirements documented in the repository's README.md and shows how to verify your setup before installation.

---

Operating System Requirements

VoiceStudio supports three platforms with specific minimum versions:

• Windows 10 x64 — modern Windows releases with 64-bit architecture
• macOS 13.3 — Apple Silicon (M1/M2/M3) required; Intel Macs not supported
• Linux x86_64 — distributions with glibc 2.39 or newer

The codebase enforces these constraints through platform detection in backend/main.py. Running on older operating systems will trigger warnings or installation failures during the Python environment setup phase.

For containerized deployments, the deploy/Dockerfile defines the base image with necessary system libraries, making Docker an alternative when host OS requirements cannot be met directly.

---

Memory and Storage Specifications

Component	Minimum	Recommended
RAM	8 GB	16 GB or more
Disk Space	10 GB free	20 GB+ on SSD

The 10 GB minimum covers the application binaries, managed Python environment, and default voice model downloaded on first launch. Users working with multiple voice engines or large datasets should budget 20 GB+ on an SSD—mechanical drives significantly slow model loading and audio processing.

Memory pressure is most acute during voice cloning and video dubbing workflows, where audio tensors and video frames reside simultaneously in RAM. The application will attempt to offload operations to disk when memory-constrained, but this degrades real-time performance.

---

GPU Requirements and Acceleration Options

VoiceStudio operates in CPU-only mode by default, making GPU acceleration entirely optional but strongly recommended for production use.

Configuration	VRAM Requirement	Notes
CPU-only	None	Functional for all features; slower inference
NVIDIA CUDA	≥ 4 GB minimum, ≥ 8 GB recommended	Best performance on Windows/Linux
Apple Silicon MPS	Unified memory architecture	Native acceleration on macOS
AMD ROCm	≥ 8 GB	Linux-only, requires explicit enablement

The backend automatically detects available hardware through PyTorch's device inspection:

import torch

if torch.cuda.is_available():
    print(f"CUDA GPU detected: {torch.cuda.get_device_name(0)}")
else:
    print("No CUDA GPU – falling back to CPU")

Systems with limited VRAM automatically offload operations to CPU when tensor allocation fails. Windows users with AMD/Radeon graphics must use CPU inference—ROCm is not supported on Windows in this codebase.

For engine-specific GPU optimization strategies, see docs/performance.md in the repository.

---

Python Environment

VoiceStudio manages its own Python installation—no manual Python setup required. On first launch, the application:

1. Downloads Python 3.11+ via uv (the fast Python package installer)
2. Creates an isolated virtual environment
3. Installs dependencies including PyTorch, FFmpeg bindings, and ML inference engines

Supported Python versions: 3.11 and 3.12

Advanced users can inspect the environment configuration in backend/main.py, specifically the dependency resolution logic triggered during initialization.

---

Verifying Your System Compatibility

Before full installation, run VoiceStudio's built-in diagnostic tool to validate hardware and software prerequisites:

# From the repository root

uv run python backend/main.py --diagnose --deep

The --deep flag performs comprehensive checks including:

• CUDA driver version and compute capability
• Available system RAM and disk space
• Audio subsystem accessibility
• FFmpeg and yt-dlp binary presence

For the standard desktop experience, launch via:

bun run desktop   # triggers automatic environment setup on first run

---

Additional Dependencies

VoiceStudio relies on native tools for media processing. Platform-specific installation guides in docs/install/ detail how to obtain:

• FFmpeg — audio/video codec operations
• yt-dlp — media download functionality (optional)

These are not bundled with the application and must be present in PATH or explicitly configured. The Docker image in deploy/Dockerfile pre-installs these dependencies for containerized deployments.

---

Summary

• VoiceStudio system requirements center on 8 GB RAM, 10 GB disk, and modern 64-bit operating systems (Windows 10+, macOS 13.3 Apple Silicon, Linux glibc 2.39+)
• GPU acceleration is optional — NVIDIA CUDA (≥4 GB VRAM) or Apple MPS recommended for production workloads
• Python 3.11+ installs automatically; no manual environment management needed
• Self-diagnosis via backend/main.py --diagnose --deep validates readiness before full deployment
• Platform-specific setup details reside in docs/install/ with performance tuning in docs/performance.md

---

Frequently Asked Questions

Does VoiceStudio work on Intel Macs?

No. The VoiceStudio system requirements specify macOS 13.3 with Apple Silicon (M1, M2, or M3 chips). Intel Macs are not supported due to PyTorch MPS acceleration and ARM-optimized dependencies in the codebase.

Can I run VoiceStudio without a GPU?

Yes. CPU-only mode is fully functional for all features including voice cloning and speech-to-text. Performance is reduced—real-time processing may lag—but no hardware capabilities are artificially restricted. The backend automatically detects GPU absence and configures PyTorch accordingly.

How much VRAM do I need for large voice models?

8 GB VRAM is the practical minimum for large engine variants. The repository's docs/performance.md documents specific engine VRAM requirements; models exceeding available memory trigger automatic CPU offloading in backend/main.py's tensor allocation logic.

Is Docker a viable alternative to native installation?

Yes. The deploy/Dockerfile defines a complete runtime environment with system dependencies, GPU libraries, and Python pre-configured. Docker deployment bypasses host OS version constraints and simplifies reproducible setups across Linux distributions, though container overhead slightly increases latency for audio I/O operations.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}