Product
Announcing our Custom Model API
Hume's Custom Model API integrates dynamic patterns of facial expression, vocal expression, and/or language into a custom multimodal model, leveraging insights gained by pretraining on millions of videos and audio files to predict your labels as accurately as possible, even after seeing just a few dozen new examples.

Meet our Custom Model API — a cutting edge AI tool that integrates language, voice, and/or facial movement to predict human preferences and needs more accurately than any LLM
You can now use our Custom Model API to predict well-being, satisfaction, mental health, and more. Using a few labeled examples, our API integrates dynamic patterns of language, vocal expression, and/or facial expression into a custom multimodal model.
Leveraging Hume’s AI models pretrained on millions of videos and audio files, our API can usually predict your labels accurately after seeing just a few dozen examples. That means that with just a few labeled examples and a few clicks, you can deploy powerful AI models that predict the outcomes your users care about most. Of course, the models you train using our API are yours alone to deploy and share.
Visit dev.hume.ai/docs/custom-models or login to beta.hume.ai to get started!
Our new API translates nuanced multimodal measures into personalized insights more accurately than any LLM

For instance, we partnered with Lawyer.com to predict the quality of customer support calls. Using just 73 calls, we were able to train a model that predicted expert ratings of whether a call went well or poorly with 97.3% accuracy. By contrast, using language models alone—including one of the world’s most capable language models along with our in-house language emotion model—resulted in a 3x higher error rate.
Leveraging dynamic patterns of language, vocal expression, and/or facial movement
Our Custom Model API works by integrating complex patterns of language, vocal expression, and/or facial movement captured using Hume’s expression AI models.

To combine these signals with language, we inserted the expression measures extracted by each of our expression models along with transcribed language into a novel empathic large language model (eLLM). We then pretrained our eLLM on millions of human interactions.

When you train a custom model using our Custom Model API, you are leveraging the joint language-expression embeddings extracted by our eLLM to predict your own labels.
Pricing
There are two steps to using our Custom Model API, (1) Training and (2) Inference:
1. Training: During our beta release, the model training process is completely free. This includes uploading data, training, evaluating results, and retraining.
2. Inference: When deploying your custom model in your application, a fee is charged for each file processed by your model. Detailed pricing can be found on our pricing page.
Hope you enjoy using our Custom Model API! If you have any questions, you can post them on our Discord channel. We look forward to seeing what you build.
Keep reading

Introducing Expression APIs: Give your AI more of what people mean
Human communication carries meaning beyond words. A change in tone, a hesitant delivery, or a surprised expression can change how we understand what someone says, and how we respond. Consider “I can’t believe it.” After good news, those words might convey excitement. After a setback, they might express frustration. During a playful exchange, they might communicate amusement. When AI applications hear these words in any of these contexts, the words they transcribe stay exactly the same.
Oct 6, 2026

Building Voice Models Is No Longer a Modeling Problem
What’s changed isn’t just where voice is used, but what it represents. Voice is no longer a feature layered on top of an intelligent system. It’s becoming a foundational modality through which models reason, interact, and are judged by users.
Jeremy Baum / Jan 21, 2026