RUNLOCALAIv38
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RUNLOCALAI · v38
← Home·/apps·Voice / transcription

OpenedAI-Speech

Fully offline

Drop-in OpenAI TTS-compatible server. Self-hosted, talks to local voice models.

Editorial verdict: “Best 'drop-in local TTS for OpenAI clients'. Bridge solution for existing pipelines.”

Voice / transcription
Free
AGPL-3.0
★ 4.2 / 5
GitHub ★ 1,500
↗ GitHub

Compatibility at a glance

Which runtime + OS combos this app works against. Source of truth for "will it run on my setup?"

§ Runtimes supported
openai-compat
§ OS / platform
linuxmacoswindows
§ Hardware + model hint
Minimum VRAM
4 GB
Recommended starter model
Piper voices or Coqui XTTS-v2
→ Build the rest of the stack with /stack-builder→ Pick a GPU for this app

What it is

For teams already wired to OpenAI’s TTS API, OpenedAI-Speech is the most direct path to fully offline voice synthesis. It accepts the same request format as OpenAI’s `/v1/audio/speech` endpoint, then routes to local models like Piper (fast, low-footprint) or Coqui XTTS-v2 (higher quality, needs ~4 GB VRAM). Setup requires Docker Compose and manual model path configuration, so it’s not a one-click install. Once running, any client that speaks OpenAI TTS—Home Assistant, custom chatbots, automation scripts—works without code changes. The AGPL-3.0 license is fine for personal use, but teams building proprietary products should check compatibility.

✓ Strengths

  • +True drop-in for OpenAI TTS API
  • +Multiple voice backends (Piper fast, XTTS quality)
  • +Docker compose works first try

△ Caveats

  • −Setup leans technical (docker-compose, model paths)
  • −Smaller user community than Whisper-side tooling

About the Voice / transcription category

Transcription, speech-to-text, or text-to-speech.

§ Other voice / transcription apps
Buzz

Best open-source Whisper desktop app. Cross-platform, free, less polish than MacWhisper.

MacWhisper

Best Whisper desktop app on macOS. Pay once, transcribe locally forever.

Where to go from here

Stack Builder →

Pre-filled with this app's recommended use case + budget tier. Get the full rig + runtime + model picks.

Back to /apps →

The full directory — filter by category, runtime, OS, privacy posture, or VRAM.

Runtimes (/tools) →

What this app talks to: Ollama, vLLM, llama.cpp, MLX, LM Studio. The upstream layer.

Community benchmarks →

Did this app work for you on a specific rig? Submit the benchmark — it powers the model + hardware pages.