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claymorecrystal/higgs-tts-3-4b
higgs-tts-3-4b is a text-to-speech model from claymorecrystal. Use it when you need text read aloud. It is set up for transformers. The card lists the license as other.
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From the Hugging Face model README
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, or revenue-generating use requires a separate commercial license. Prohibited: voice cloning without consent, impersonation, fraud, election deception, biometric surveillance, or any unlawful use.

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.
| Component | Spec |
|---|---|
| Backbone | ~4B autoregressive decoder (36 L, hidden=2560, GQA 32/8) |
| Multi-codebook embedding / head | Fused single-tensor, tied with text embedding |
| Context length | 8,192 tokens (training sequence length) |
| Audio tokens | 8 codebooks × 1026 vocab, delay pattern |
| Sample rate | 24 kHz |
| Frame rate | 25 fps (40 ms / frame) |
The model reaches single-digit WER/CER on 102 languages, which split into two tiers.
🇿🇦 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
🇦🇱 Albanian · 🇲🇼🇿🇲 Chichewa/Nyanja · 🇮🇳🇵🇰 Eastern Punjabi · 🇺🇬 Ganda · 🇮🇸 Icelandic · 🇮🇪 Irish · 🇩🇿 Kabyle · 🇨🇻 Kabuverdianu · 🇰🇪 Kamba · 🇻🇦 Latin · 🇱🇺 Luxembourgish · 🇪🇹🇰🇪 Oromo · 🇦🇫🇵🇰 Pashto · 🇵🇰🇮🇳 Sindhi · 🇸🇴 Somali · 🇦🇴 Umbundu · 🇬🇧 Welsh
All tags follow <|category:value|> syntax and can be inserted mid-utterance.
For how to place these tags when writing the target text (sentence-level vs. inline,
sfxformatting, stacking, worked examples), see PROMPTING.md.
elation, amusement, enthusiasm, determination, pride, contentment, affection, relief, contemplation, confusion, surprise, awe, longing, arousal, anger, fear, disgust, bitterness, sadness, shame, helplessnesssinging, shouting, whisperingcough, laughter, crying, screaming, burping, humming, sigh, sniff, sneezespeed_very_slow, speed_slow, speed_fast, speed_very_fastpause, long_pausepitch_low, pitch_highexpressive_high, expressive_lowWe 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.
<div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;width:100%;padding:16px 0;overflow-x:auto;-webkit-overflow-scrolling:touch"> <table style="width:100%;border-collapse:collapse;font-size:clamp(11px,1.4vw,13px);text-align:center"> <thead><tr> <th style="padding:18px 24px;text-align:center;font-weight:600;border-bottom:2px solid #16a34a;color:#16a34a">Benchmark</th> <th style="padding:18px 24px;text-align:center;font-weight:500;white-space:nowrap;border-bottom:2px solid #16a34a;color:#16a34a;font-size:14px">Higgs TTS v2</th> <th style="padding:18px 24px;text-align:center;font-weight:500;white-space:nowrap;border-bottom:2px solid #16a34a;color:#16a34a;font-size:14px">Higgs TTS 3</th> <th style="padding:18px 24px;text-align:center;font-weight:500;white-space:nowrap;border-bottom:2px solid #16a34a;color:#16a34a;font-size:14px">Fish Audio S2 Pro</th> <th style="padding:18px 24px;text-align:center;font-weight:500;white-space:nowrap;border-bottom:2px solid #16a34a;color:#16a34a;font-size:14px">Qwen3-TTS-1.7B</th> <th style="padding:18px 24px;text-align:center;font-weight:500;white-space:nowrap;border-bottom:2px solid #16a34a;color:#16a34a;font-size:14px">VibeVoice-7B</th> <th style="padding:18px 24px;text-align:center;font-weight:500;white-space:nowrap;border-bottom:2px solid #16a34a;color:#16a34a;font-size:14px">IndexTTS-2</th> <th style="padding:18px 24px;text-align:center;font-weight:500;white-space:nowrap;border-bottom:2px solid #16a34a;color:#16a34a;font-size:14px">MiMo-Audio-7B-Instruct</th> <th style="padding:18px 24px;text-align:center;font-weight:500;white-space:nowrap;border-bottom:2px solid #16a34a;color:#16a34a;font-size:14px">MOSS-TTS-v1.5</th> <th style="padding:18px 24px;text-align:center;font-weight:500;white-space:nowrap;border-bottom:2px solid #16a34a;color:#16a34a;font-size:14px">OmniVoice</th> <th style="padding:18px 24px;text-align:center;font-weight:500;white-space:nowrap;border-bottom:2px solid #16a34a;color:#16a34a;font-size:14px">ChatterBox</th> <th style="padding:18px 24px;text-align:center;font-weight:500;white-space:nowrap;border-bottom:2px solid #16a34a;color:#16a34a;font-size:14px">FireRedTTS-2</th> </tr></thead> <tbody> <tr> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">SeedTTS</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">2.10</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);font-weight:600">1.11</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">1.31</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">1.30</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">3.59</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">1.63</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">3.70</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">1.73</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">1.21</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">17.00</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">1.72</td> </tr> <tr> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">CV3</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">21.19</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);font-weight:600">4.41</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">4.60</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">7.73</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">11.66</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">129.26</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">71.55</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">6.11</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">4.92</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">32.62</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">19.20</td> </tr> <tr> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">MiniMax-Multilingual</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">49.86</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);font-weight:600">2.74</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">5.15</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">27.41</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">8.21</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">112.91</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">85.67</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">3.78</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">2.98</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">49.30</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">12.52</td> </tr> <tr> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">Higgs-Multilingual</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">52.24</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);font-weight:600">3.61</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">8.68</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">97.09</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">13.74</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">57.71</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">59.61</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">21.28</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">3.63</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">57.52</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">33.69</td> </tr> </tbody> </table> </div>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.
<div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;width:100%;padding:16px 0;overflow-x:auto;-webkit-overflow-scrolling:touch"> <table style="width:100%;border-collapse:collapse;font-size:clamp(11px,1.4vw,13px);text-align:center"> <thead><tr> <th style="padding:18px 24px;text-align:center;font-weight:600;border-bottom:2px solid #16a34a;color:#16a34a">Model</th> <th style="padding:18px 24px;text-align:center;font-weight:500;white-space:nowrap;border-bottom:2px solid #16a34a;color:#16a34a;font-size:14px">Overall ↑</th> <th style="padding:18px 24px;text-align:center;font-weight:500;white-space:nowrap;border-bottom:2px solid #16a34a;color:#16a34a;font-size:14px">Emotions ↑</th> <th style="padding:18px 24px;text-align:center;font-weight:500;white-space:nowrap;border-bottom:2px solid #16a34a;color:#16a34a;font-size:14px">Foreign Words ↑</th> <th style="padding:18px 24px;text-align:center;font-weight:500;white-space:nowrap;border-bottom:2px solid #16a34a;color:#16a34a;font-size:14px">Paralinguistics ↑</th> <th style="padding:18px 24px;text-align:center;font-weight:500;white-space:nowrap;border-bottom:2px solid #16a34a;color:#16a34a;font-size:14px">Complex Pronunciation ↑</th> <th style="padding:18px 24px;text-align:center;font-weight:500;white-space:nowrap;border-bottom:2px solid #16a34a;color:#16a34a;font-size:14px">Questions ↑</th> <th style="padding:18px 24px;text-align:center;font-weight:500;white-space:nowrap;border-bottom:2px solid #16a34a;color:#16a34a;font-size:14px">Syntactic Complexity ↑</th> </tr></thead> <tbody> <tr> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">Higgs TTS 3</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);font-weight:600">53.65%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">53.75%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);font-weight:600">48.75%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);font-weight:600">68.57%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">25.10%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);font-weight:600">61.43%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);font-weight:600">60.71%</td> </tr> <tr> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">Fish Audio S2 Pro</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">43.80%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">53.04%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">33.93%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">53.75%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">18.16%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">55.00%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">45.71%</td> </tr> <tr> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">Qwen3-TTS-1.7B</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">38.84%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">45.54%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">24.64%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">44.29%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);font-weight:600">30.00%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">53.39%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">34.11%</td> </tr> <tr> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">IndexTTS-2</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">31.12%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">39.29%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">5.36%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">42.50%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">12.45%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">45.89%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">38.93%</td> </tr> <tr> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">MOSS-TTS-v1.5</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">43.89%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">60.54%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">35.18%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">51.43%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">11.63%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">53.21%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">47.32%</td> </tr> <tr> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">OmniVoice</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">40.82%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15);font-weight:600">61.07%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">28.75%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">52.68%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">13.67%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">45.00%</td> <td style="padding:16px 24px;text-align:center;border-bottom:1px solid rgba(128,128,128,0.15)">40.36%</td> </tr> </tbody> </table> </div>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.
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 [email protected]:sgl-project/sglang-omni.git && cd sglang-omni
uv venv .venv -p 3.12 && source .venv/bin/activate
uv pip install -v -e .
export HF_TOKEN=hf_xxxxxxxxxxxxxxxx
hf download bosonai/higgs-tts-3-4b
sgl-omni serve \
--model-path bosonai/higgs-tts-3-4b \
--port 8000
curl -X POST http://localhost:8000/v1/audio/speech \
-H "Content-Type: application/json" \
-d '{"input": "Hello, how are you?"}' \
--output output.wav
Supplying the reference transcript (text) materially improves cloning fidelity.
import requests
resp = requests.post(
"http://localhost:8000/v1/audio/speech",
json={
"input": "Have a nice day and enjoy south california sunshine.",
"references": [{
"audio_path": "ref.wav",
"text": "Hey, Adam here. Let's create something that feels real, sounds human, and connects every time.",
}],
"temperature": 0.8, "top_k": 50, "max_new_tokens": 1024,
},
)
with open("output.wav", "wb") as f:
f.write(resp.content)
Set "stream": true to receive base64-encoded WAV chunks as the vocoder emits them — sub-second time-to-first-audio. Each event carries audio.data (base64 WAV bytes); the terminal event has finish_reason: "stop" plus usage metadata.
import requests, base64, json
with requests.post(
"http://localhost:8000/v1/audio/speech",
json={"input": "Get the trust fund to the bank early.", "stream": True},
stream=True,
) as resp, open("output.wav", "wb") as f:
for line in resp.iter_lines():
if not line or not line.startswith(b"data: ") or line == b"data: [DONE]":
continue
event = json.loads(line[6:])
if event.get("finish_reason") == "stop":
break
audio = event.get("audio") or {}
if audio.get("data"):
f.write(base64.b64decode(audio["data"]))
Embed <|emotion:…|>, <|style:…|>, <|prosody:…|>, and <|sfx:…|> tokens directly in input. Two rules:
input. Positional tokens (<|prosody:pause|>, <|prosody:long_pause|>, <|sfx:…|>) go inline exactly where they fire.<|sfx:…|> with its onomatopoeia. E.g. <|sfx:laughter|>Haha, <|sfx:sigh|>Uh, <|sfx:sneeze|>Achoo. The written sound gives the model the acoustic cue to realize the effect.Example — amusement + laughter:
curl -X POST http://localhost:8000/v1/audio/speech \
-H "Content-Type: application/json" \
-d '{"input": "<|emotion:amusement|><|prosody:expressive_high|>Wait, wait, that was kind of hilarious. <|sfx:laughter|>Hehe, no, seriously, I was not ready for that."}' \
--output output.wav
Throughput on Seed-TTS EN (full set, N=1088 per run). Client --max-concurrency sweep against a Higgs server (max_running_requests=16, bf16, CUDA Graph on). Each row is the mean of 3 runs. Hardware: 1× H100.
--max-concurrency).To reproduce the results, follow the instructions in this script.
You can also serve these weights with vLLM-Omni, which exposes the same OpenAI-compatible /v1/audio/speech API with zero-shot voice cloning.
hf download bosonai/higgs-tts-3-4b
vllm-omni serve bosonai/higgs-tts-3-4b \
--host 0.0.0.0 --port 8095 \
--trust-remote-code --omni
curl -X POST http://localhost:8095/v1/audio/speech \
-H "Content-Type: application/json" \
-d '{"model": "bosonai/higgs-tts-3-4b", "input": "Hello, how are you?"}' \
--output output.wav
Plain-text TTS, voice-clone, and benchmark recipes are in the vLLM-Omni Higgs TTS 3 recipe.
For zero-ops deployment, use the Boson AI API.
@misc{bosonai_higgs_audio_tts_v3_2026,
title = {Higgs TTS 3: Conversational Speech for Voice AI from Boson AI},
author = {Boson AI},
year = {2026},
howpublished = {https://huggingface.co/bosonai/higgs-tts-3-4b},
}
Boson Higgs TTS 3 Research and Non-Commercial License — see LICENSE.