higgs-tts-3

Higgs TTS 3 is built for voice chat: it speaks, not just reads. It turns model responses into expressive conversational speech across 100+ languages, with zero-shot voice cloning and inline control over emotion, style, prosody, pauses, and sound effects.

[!TIP] Released for research and non-commercial use under the Boson Higgs TTS 3 Research and Non-Commercial License. Production, hosted APIs, embedding in a product/service, or reselling the model requires a separate commercial license. Prohibited: voice cloning without consent, impersonation, fraud, election deception, biometric surveillance, or any unlawful use.

[!TIP] Free for digital creators — including monetized content. Under the license's Creator Use Grant, creators may use Higgs TTS 3 to make and monetize podcasts, videos, and social posts for free. The one requirement is to credit Boson AI's Higgs Audio — either in the audio or prominently in the accompanying text (e.g., the video description or show notes). Suggested credit: "This audio was created with Boson AI's Higgs Audio — https://www.boson.ai/higgs-audio". See the Creator Use section below.

Higgs autoregressive decoder consumes interleaved text and audio tokens. Audio is encoded by the Higgs Tokenizer into 8 codebooks at 25 fps, staggered via a delay pattern, then mapped to backbone hidden states through a multi-codebook fused embedding. Output codes pass through a multi-codebook fused head, are de-delayed, and decoded back to waveform.

Supported Languages

The model reaches single-digit WER/CER on 102 languages, which split into two tiers.

WER/CER under 5 — polished, production-quality (85)

🇿🇦 Afrikaans · 🇸🇦🇪🇬 Arabic · 🇦🇲 Armenian · 🇮🇳 Assamese · 🇪🇸 Asturian · 🇦🇿 Azerbaijani · 🇷🇺 Bashkir · 🇪🇸 Basque · 🇧🇾 Belarusian · 🇧🇩🇮🇳 Bengali · 🇧🇦 Bosnian · 🇧🇬 Bulgarian · 🇪🇸 Catalan · 🇵🇭 Cebuano · 🇮🇶 Central Kurdish · 🇨🇳 Chinese · 🇭🇷 Croatian · 🇨🇿 Czech · 🇩🇰 Danish · 🇳🇱🇧🇪 Dutch · 🇷🇺 Eastern Mari · 🇺🇸🇬🇧🇦🇺 English · 🌐 Esperanto · 🇪🇪 Estonian · 🇫🇮 Finnish · 🇫🇷🇨🇦 French · 🇪🇸 Galician · 🇬🇪 Georgian · 🇩🇪🇦🇹 German · 🇬🇷 Greek · 🇮🇳 Gujarati · 🇭🇹 Haitian Creole · 🇳🇬 Hausa · 🇮🇱 Hebrew · 🇮🇳 Hindi · 🇭🇺 Hungarian · 🇮🇩 Indonesian · 🇮🇹 Italian · 🇯🇵 Japanese · 🇮🇩 Javanese · 🇮🇳 Kannada · 🇰🇿 Kazakh · 🇰🇷 Korean · 🇷🇼 Kinyarwanda · 🇰🇬 Kyrgyz · 🇱🇻 Latvian · 🇨🇩 Lingala · 🇱🇹 Lithuanian · 🇰🇪 Luo · 🇲🇰 Macedonian · 🇲🇾🇮🇩 Malay · 🇮🇳 Malayalam · 🇲🇹 Maltese · 🇳🇿 Māori · 🇮🇳 Marathi · 🇲🇳 Mongolian · 🇳🇵 Nepali · 🇳🇴 Norwegian · 🇫🇷 Occitan · 🇮🇷🇦🇫 Persian · 🇵🇱 Polish · 🇵🇹🇧🇷 Portuguese · 🇷🇴 Romanian · 🇷🇺 Russian · 🇿🇦 Sepedi · 🇷🇸 Serbian · 🇿🇼 Shona · 🇸🇰 Slovak · 🇸🇮 Slovene · 🇪🇸🇲🇽 Spanish · 🇹🇿🇰🇪 Swahili · 🇸🇪 Swedish · 🇵🇭 Tagalog · 🇹🇯 Tajik · 🇮🇳🇱🇰 Tamil · 🇮🇳 Telugu · 🇹🇭 Thai · 🇹🇷 Turkish · 🇺🇦 Ukrainian · 🇵🇰🇮🇳 Urdu · 🇨🇳 Uyghur · 🇺🇿 Uzbek · 🇻🇳 Vietnamese · 🇿🇦 Xhosa · 🇿🇦 Zulu

WER/CER between 5 and 10 — usable, but less polished (17)

🇦🇱 Albanian · 🇲🇼🇿🇲 Chichewa/Nyanja · 🇮🇳🇵🇰 Eastern Punjabi · 🇺🇬 Ganda · 🇮🇸 Icelandic · 🇮🇪 Irish · 🇩🇿 Kabyle · 🇨🇻 Kabuverdianu · 🇰🇪 Kamba · 🇻🇦 Latin · 🇱🇺 Luxembourgish · 🇪🇹🇰🇪 Oromo · 🇦🇫🇵🇰 Pashto · 🇵🇰🇮🇳 Sindhi · 🇸🇴 Somali · 🇦🇴 Umbundu · 🇬🇧 Welsh

Control Tokens

All tags follow `` syntax and can be inserted mid-utterance.

For how to place these tags when writing the target text (sentence-level vs. inline, sfx formatting, stacking, worked examples), see PROMPTING.md.

• Emotion — elation, amusement, enthusiasm, determination, pride, contentment, affection, relief, contemplation, confusion, surprise, awe, longing, arousal, anger, fear, disgust, bitterness, sadness, shame, helplessness

• Style — singing, shouting, whispering

• Sound effects — cough, laughter, crying, screaming, burping, humming, sigh, sniff, sneeze

• Prosody

  • Speed — speed_very_slow, speed_slow, speed_fast, speed_very_fast
  • Pauses — pause, long_pause
  • Pitch — pitch_low, pitch_high
  • Delivery — expressive_high, expressive_low

Evaluation Benchmarks

Multilingual Voice Clone

We evaluate Higgs TTS 3 on public multilingual TTS suites and our internal 111-language Higgs-Multilingual set, covering both common and lower-resource languages.

WER / CER (↓, ×100) macro-averaged across each benchmark's language set. Lower is better; bold marks the best per row. All numbers are reproducible end-to-end with original metrics and normalization.

Emergent TTS

Win-rate (↑) per category — judge preference vs the BASELINE row; bold marks the highest win-rate per column. For a fair comparison, every model shares the same reference audio per prompt, and we run the benchmark text verbatim — no inline control tags inserted.

Usage

SGLang Usage

Pair the weights in this repo with SGLang-Omni — a production serving stack with continuous batching for multi-codebook decoding and the same inline tag controls. The Higgs TTS cookbook walks you through installation, server launch, request examples, and the full API reference.

See the Higgs TTS cookbook for the full details.

Install and Serve

docker pull lmsysorg/sglang-omni:dev
docker run -it --gpus all --shm-size 32g --ipc host --network host --privileged \
  lmsysorg/sglang-omni:dev /bin/zsh

git clone git@github.com:sgl-project/sglang-omni.git && cd sglang-omni
uv venv .venv -p 3.12 && source .venv/bin/activate
uv pip install