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Self-host Breeze TTS 2

Real-time TTS with voice cloning and voice design

ReleaseOpen CoreApache-2.0
Updated2 weeks ago
LanguagePython
Setup difficultyAdvanced
Stars
1 090
Port
7860 (Streaming and batch TTS API server)

What is Breeze TTS 2?

An open-weight text-to-speech system for real-time interaction that leads the Artificial Analysis leaderboard among open models. Self-host via Docker, pip, or Python. Three synthesis modes: clone a speaker from reference audio, design a voice from a text description, or direct a cloned voice with instructions. Inline vocal events add laughter, sighs, and more. A streaming HTTP API delivers 24 kHz PCM on port 7860; a batch endpoint processes up to 128 texts. Requires an NVIDIA GPU with 12 GB VRAM (eager) or 24 GB (fast path). Weights on Hugging Face under a non-commercial license; code is Apache-2.0. Commercial use needs a paid BreezeBlue subscription.

Should you self-host Breeze TTS 2?

Breeze TTS 2 scores 54/100 on selfhost.directory (how scoring works) and ranks #35 of 125 in AI. It has 1 090 GitHub stars.

There was no stable release in the past 12 months, so check the repository's recent activity before you rely on it.

Setup is advanced: plan for several services and some ongoing maintenance. It is Apache-2.0-licensed and written mostly in Python.

Its closest alternative on selfhost.directory is ComfyUI (93/100, 143 752 stars). It scores higher overall, so compare the two before you pick. See Breeze TTS 2 vs ComfyUI side by side.

Features

  • Batch API
  • Bilingual support
  • Fast Inference Modes
  • GPU-Efficient
  • Hugging Face Hub Integration
  • Model Digest Verification
  • Real-time Streaming
  • Single-Concurrency API
  • Streaming REST API
  • Ultra-Low Latency
  • Vocal Events
  • Voice Clone
  • Voice Design
  • Voice Direction

Breeze TTS 2 README

How to configure Breeze TTS 2

  • BREEZE_IMAGEbreeze-pytorch-infer:latestDocker image tag to build

    Docker image tag to build

  • FLASH_ATTN_CUDA_ARCHS90CUDA architecture for flash attention compilation (default 90 for H100/Hopper, 80 for A100)

    CUDA architecture for flash attention compilation (default 90 for H100/Hopper, 80 for A100)

    values: 80, 90

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