Open-source ElevenLabs alternatives, compared.
Twelve open models that clone voices, stream in real time, and read long-form — every one with standardized voice samples so you can hear the difference before you commit. Self-host them free, or use them hosted.
Why people look for an alternative
ElevenLabs makes excellent voices, but the model is a subscription: you buy a monthly credit allowance, and what you don’t use expires. Open-source TTS has closed most of the quality gap — the models below are the proof, and you can listen for yourself.
The 12 best open-source alternatives
Chatterbox
Best overall alternativeResemble AI's MIT-licensed model is the closest like-for-like swap: zero-shot voice cloning from seconds of audio, an emotion-exaggeration dial ElevenLabs doesn't have, and blind-test results where listeners frequently prefer it over commercial systems.
Kokoro-82M
Best on a budgetAt 82M parameters Kokoro tops quality-per-dollar charts. No cloning, but its preset voices are clean and consistent — and it's small enough to run almost anywhere.
XTTS v2
Best multilingual cloningThe community classic: clone a voice from ~6 seconds and speak it in 17 languages, including cross-language cloning. Mind the Coqui Public Model License if you use it commercially.
F5-TTS
Best one-shot cloningA single short reference clip is enough. F5-TTS's flow-matching design makes cloning fast, stable, and surprisingly faithful — a favorite for dubbing and narration pipelines.
GPT-SoVITS
Best few-shot cloningThe biggest community in open voice cloning. Zero-shot works from seconds of audio, but give it one minute of a voice and its few-shot training produces clones with fidelity zero-shot models can't match.
Sesame CSM-1B
Best for voice assistantsThe open model behind Sesame's viral 'Maya' demo. It conditions speech on conversation context, so replies carry the hesitations and tone shifts of a real back-and-forth.
Orpheus TTS
Best for conversationBuilt on Llama 3B, Orpheus nails conversational intonation and supports inline emotive tags — <laugh>, <sigh> — plus low-latency streaming for voice agents.
CosyVoice 2
Best for real-time agentsAlibaba's streaming model answers in ~150ms with quality nearly identical to offline synthesis, with instruction control over emotion and dialect. The pick for interactive voice.
VibeVoice
Best for long-formMicrosoft's VibeVoice generates up to ~90 minutes of continuous audio with up to four speakers in one pass — podcast-scale output no commercial API matches today.
Dia
Best for dialogueWrite a screenplay with [S1]/[S2] tags and Dia performs the whole scene — two voices, laughs, coughs and all — in a single generation.
Higgs Audio v2
Best expressivenessBoson AI's LLM-based model wins most emotion benchmarks against leading commercial systems and handles multi-speaker dialogue and even humming.
Bark
Best for creative audioSuno's Bark goes beyond speech: laughter, music, sound effects and nonverbal chaos from inline cues. Less control, more character.
ElevenLabs alternatives FAQ
What is the best open-source alternative to ElevenLabs?
For most people it's Chatterbox — MIT-licensed, zero-shot voice cloning, and quality that holds up in blind tests against commercial systems. For pure narration on small hardware, Kokoro is hard to beat. For multilingual cloning, XTTS v2 or F5-TTS.
Is there a free ElevenLabs alternative?
Every model on this page is open source and free to self-host if you have the GPU and the patience. You can listen to standardized samples for each one right here before you pick.
Can open-source TTS clone voices like ElevenLabs?
Yes. Chatterbox, GPT-SoVITS, XTTS v2, F5-TTS, OpenVoice v2 and others clone from a few seconds of reference audio. Quality is competitive — and unlike a closed API, you can inspect exactly how your voice data is used.
What's the catch with self-hosting these models?
Setup and hardware. Each model has its own Python environment, weights, and quirks, and most want a CUDA GPU. If you'd rather skip that, OpenSpeech Cloud offers hosted, pay-per-minute access to every model in this directory.
How do I compare these models fairly?
Every model in this directory reads the same three scripts — neutral, emotional, and numbers — so you're comparing voices, not cherry-picked demos. You can also pit them head-to-head in the Arena.