Chatterbox-TTS-Server

🖥️ Installation fixes across all platforms

• All platforms: Chatterbox is now installed with --no-deps across all installation paths (CPU, NVIDIA, cu128, ROCm). This eliminates ONNX source build failures, torch version conflicts, and CMake errors that affected many users. Chatterbox's dependencies (conformer, diffusers, transformers, s3tokenizer, etc.) are now listed explicitly in each requirements file with onnx==1.16.0 pinned to guarantee pre-built wheels.
• Apple Silicon / MPS: Fixed Turbo model crash ("Cannot convert a MPS Tensor to float64 dtype") by forcing float32 in s3tokenizer and voice_encoder. Fix applied in the chatterbox-v2 fork and also as an automatic post-install patch in start.py for users of other chatterbox versions. Thanks to @jonas3245 (#93).
• Docker CPU: New lightweight Dockerfile.cpu based on python:3.10-slim instead of the 4GB+ NVIDIA CUDA base image. docker-compose-cpu.yml now uses this smaller image. Removed deprecated version tags from all docker-compose files.
• config.yaml: Default device changed from cuda to auto for correct auto-detection on all hardware (CUDA, MPS, CPU).
• Python version: Python 3.10 is required — it is the only version with pre-built wheels for all dependencies (torch, torchvision, ONNX). Python 3.11+ may fail due to missing wheels. The Windows launcher's Portable Mode handles this automatically by using an embedded Python 3.10 runtime.
• Blackwell (CUDA 12.8): Fixed requirements-nvidia-cu128.txt to properly install PyTorch 2.9.0 with CUDA 12.8 (sm_120 support) for RTX 5060 Ti, 5070, 5070 Ti, 5080, and 5090 GPUs. The Dockerfile.cu128 now correctly installs chatterbox with --no-deps to prevent PyTorch downgrade.
• AMD ROCm: Fixed ROCm installation by switching to PyTorch's official ROCm 6.1 wheel index (torch==2.5.1+rocm6.1), which resolves the previous torch==2.6.0 / torchaudio==2.5.1 version conflict. A new requirements-rocm-init.txt installs the ROCm PyTorch stack before other dependencies. Both Dockerfile.rocm and start.py now use a two-step install to prevent pip from replacing ROCm torch wheels with CPU-only versions.
• Thanks to community contributors in issues #20, #23, #44, #58, #64, #79, #89, #92, #93, #98, #105, #107, #109, #113, #114, #121, and #122 for testing and reporting solutions.

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Hardware Compatibility Matrix

Hardware	Installation Option	Requirements File	Driver Requirement
CPU Only	--cpu	requirements.txt	None
NVIDIA RTX 20/30/40	--nvidia	requirements-nvidia.txt	525+
NVIDIA RTX 5090 / Blackwell (sm_120)	--nvidia-cu128	requirements-nvidia-cu128.txt (torch 2.9, CUDA 12.8)	570+
NVIDIA DGX Spark / GB10 (sm_121)	Docker only	requirements-nvidia-cu130.txt (torch 2.10, CUDA 13.0)	580+
AMD RX 6000/7000 (Linux)	--rocm	requirements-rocm.txt	ROCm 6.4+
AMD Strix Halo (Ryzen AI MAX+)	Docker only	requirements-strixhalo.txt (ROCm 7.2)	ROCm 7.2+
AMD RX 9000 series / RDNA4 (Linux)	Docker only	requirements-rdna4-init.txt (ROCm 7.2)	ROCm 7.2+
Apple Silicon (M1/M2/M3/M4)	Manual install	See Option 4	macOS 12.3+

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💻 Installation and Setup

This project uses specific dependency files to ensure a smooth installation for your hardware. You can choose between the automated launcher (recommended for most users) or manual installation (for advanced users).

1. Clone the Repository

git clone https://github.com/devnen/Chatterbox-TTS-Server.git
cd Chatterbox-TTS-Server

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🚀 Quick Start with Automated Launcher (Recommended)

The automated launcher handles virtual environment creation, hardware detection, dependency installation, and server startup - all in one step.

Windows

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# Skip menu and install NVIDIA CUDA 12.1 directly
python start.py --nvidia

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# Upgrade to l