Hume AI

Today we're releasing our first open source TTS model, TADA! TADA (Text Audio Dual Alignment) is a speech-language model that generates text and audio in one synchronized stream to reduce token-level hallucinations and improve latency. This means: → Zero content hallucinations across 1,000+ test samples → 5x faster than similar-grade LLM-based TTS → Fits much longer audio: 2,048 tokens cover ~700 seconds with TADA vs. ~70 seconds in conventional systems → Free transcript alongside audio with no added latency

@derek1159 We are too!

@hume_ai 743 hours of dyadic data? That’s a serious dataset. Looking forward to the results

We’re excited to launch the 2026 ACII Dyadic Contest (DaiKon) Workshop & Challenge—a new benchmark for modeling emotional influence in dyadic dialogue. Explore a sample of our conversational audio dataset: 945 sessions, 743 hours, across 5 languages. Submissions due May 25. We look forward to your participation!

This dataset at scale is also available in its original dual-channel format. Explore more of our training datasets at hume.ai/training-data

Register and find out more at competitions.hume.ai/daikon2026

Find more here 👇 huggingface.co/HumeAI/mlx-tad… huggingface.co/HumeAI/mlx-tad…

Today, we're shipping MLX support for TADA, our open-source text-to-speech model, which means the entire pipeline (LLM, flow-matching, and decoder) can now run locally on any Apple Silicon device. We're seeing a 45% reduction in memory usage and a 10x speed-up when using it quantized. With these improvements, you can use TADA on-device for OpenClaw or any personal chatbot. If you own a MacBook, Mac Mini, or Mac Studio, record a 10-second clip of any voice, type any text, and get high-quality, natural and expressive speech in real-time. Completely offline, completely free.

@fffiloni @huggingface @Gradio Great work!

I made a @huggingface Space @Gradio demo for TADA to make the paper’s workflow easier to explore. The original demo was a bit confusing, so this one is more guided and helps you understand what’s really going on — and in what order the pipeline is supposed to work.

@wuzhu_ @huggingface @grok It is not fine-tuned for languages outside of English, though it has some multilingual capabilities!

@hume_ai @huggingface @grok 这个项目支持中文？

Today we're releasing our first open source TTS model, TADA! TADA (Text Audio Dual Alignment) is a speech-language model that generates text and audio in one synchronized stream to reduce token-level hallucinations and improve latency. This means: → Zero content hallucinations across 1,000+ test samples → 5x faster than similar-grade LLM-based TTS → Fits much longer audio: 2,048 tokens cover ~700 seconds with TADA vs. ~70 seconds in conventional systems → Free transcript alongside audio with no added latency

@CastrikoEdit It is not fine-tuned for languages outside of English, though it has some multilingual capabilities!

@hume_ai I installed it locally on my PC to test it with my 4090. It uses almost all of my 24GB of RAM, and it takes nearly 300 seconds to generate this text with a voice that speaks quickly, skipping some words. In Spanish, the result is even worse, as it sounds robotic.

@overbeight Please try again and let us know if you're still seeing this!

@hume_ai First try - Error Second try - you have exceeded your quota. See you in 24hr!👏

@_NeutralFan_ It is not fine-tuned for languages outside of English, though it has some multilingual capabilities!

@hume_ai This is really good, any plans to support multiple languages or only English?

@rajbreno It is not fine-tuned for languages outside of English, though it has some capabilities in other languages.

@hume_ai Is it multilingual?

@brubbleR Try again and let us know if you're running into issues!

@hume_ai its not working in the hf space.

@0xecall Please try again and let us know if you're still running into this issue!

@hume_ai I would love to try this but unfortunately your space isnt't spacing

Read our blog: hume.ai/blog/opensourc…

Try the model: huggingface.co/collections/Hu…

Today, we're announcing significant new traction in our voice AI research and development partnerships. Over the past few months, we’ve quietly been expanding our focus on providing frontier voice AI training data to research labs looking to imbue emotion understanding and cutting edge expressivity into their foundation models. We believe some of the most exciting possibilities for deeper speech and emotion understanding will come to fruition this year. By training today’s frontier models to understand the nuances voice interaction—rife with subtle tones of frustration or satisfaction, “aha” moments, chuckles, sighs, backchannels, and interruptions—we believe that labs will unlock new possibilities for voice AI to become a primary interface within many applications. In our experience working with leading research labs and AI-first enterprises, a consistent pattern we’ve seen is a greater need for high-quality datasets and evaluation pipelines than for new algorithms or architectures. Researchers spend up to 80% of their time curating the data they need to diagnose model issues, fix failure modes, and improve model behavior. That’s why Hume is now focused on building the data and evaluation infrastructure needed to train next-generation voice models across industry.  With the right training, we hope that deeper voice understanding and empathy can be translated not just into more efficient interfaces but into better alignment of AI with human well-being. Read more below hume.ai/blog/data-blog…

Can you convince Santa to take a villain off the naughty list? Visit 12DaysofAI.app for a whimsical voice AI puzzle every day leading up to Christmas 🎅 All made using the EVI API, the most expressive conversational agent platform in the industry. How about build your own challenge, jolly or otherwise, at app.hume.ai!