Check our open-source repository https://github.com/boson-ai/higgs-audio for more details! > [!TIP] > For **licensing** inquiries (e.g., commercial use) or **collaboration** opportunities, please reach out to us at contact@boson.ai. We introduce a new discretized audio tokenizer that runs at just **25 frames per second** while keeping—or even improving—audio quality compared to tokenizers with twice the bitrate. Our model is the first to train on **24 kHz data** covering speech, music, and sound events in one unified system. It also uses a simple non-diffusion encoder/decoder for fast, batch inference.
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A speech-to-text model combining a Whisper-Large-v3 encoder with a Qwen3-8B decoder (8.91B total parameters), fine-tuned with LoRA on diverse ASR benchmarks.
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Higgs-Llama-3-70B is post-trained from meta-llama/Meta-Llama-3-70B, specially tuned for role-playing while being competitive in general-domain instruction-following and reasoning. We perform supervised fine-tuning with our in-house instruction-following and chat datasets. Afterwards, we construct preference pairs with a semi-automated pipeline that relies on both human-labelers and our private LLMs. We conduct iterative preference optimization to align the model. During alignment, we adopted a special strategy to align the model’s behavior with the system message. Compared with other instruct models, Higgs models follow their roles more closely. All benchmarks lead to eventual overfitting, including those for LLMs. Training on data, particularly beneficial for benchmarks typically does not improve (or even worsen) role-playing performance. We worked to exclude benchmark data, including their training examples, from our fine-tuning data.
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Check our open-source repository https://github.com/boson-ai/higgs-audio for more details! > **Rename note:** Higgs Audio V2 and Higgs Audio V2 Generation have been renamed to Higgs TTS 2. We are open-sourcing Higgs TTS 2, a powerful audio foundation model pretrained on over 10 million hours of audio data and a diverse set of text data. Despite having no post-training or fine-tuning, Higgs TTS 2 excels in expressive audio generation, thanks to its deep language and acoustic understanding.
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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.
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A general-purpose HuBERT-Base checkpoint released by Boson AI, used inside the Higgs Audio Tokenizer as the semantic teacher. - Standard HuBERT-Base architecture (12 transformer layers, hidden size 768, ~95M params) - 16 kHz audio input - Loadable via `AutoModel` with `trust_remote_code=True` - Outputs 768-dim per-layer hidden states (`output_hidden_states=True`) The Higgs Audio Tokenizer distills semantic features from this HuBERT into its semantic branch. From `boson_multimodal/audio_processing/higgs_audio_tokenizer.py` (`semantic_techer="hubert_base_general"`):
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