Cartesia AI with Karan Goel — Weaviate Podcast #113!
Weaviate Podcast #113 dives into Cartesia AI with Karan Goel and the latest advances in text-to-speech and long context modeling with State Space Models!
The Genesis of Cartesia AI
Cartesia AI emerged from the halls of Stanford University, where Karan and his colleagues were conducting pioneering research on state space models under the guidance of Chris Re. The company was founded by five co-founders — Karan, Brandon, Albert, Arjun, and Chris — who shared a vision of advancing their academic research into practical applications. In just over a year, they’ve grown to a team of more than 30 people working on cutting-edge AI audio technology.
Breaking Free from Context Constraints
One of the most compelling aspects of Cartesia’s approach is their work on state space models (SSMs), which offer an alternative to traditional transformer architectures. The key challenge they’re addressing is how to efficiently process large amounts of context in AI models. While traditional attention mechanisms in transformers scale quadratically with sequence length, Cartesia’s approach aims to handle virtually unlimited context windows — a crucial capability for building truly intelligent systems.
Sonic: A New Frontier in Text-to-Speech
Six months ago, Cartesia launched Sonic, their flagship text-to-speech model. What makes Sonic unique is that it’s the first production-ready SSM-based model of its kind. The model excels in several key areas:
- Crystal clear, high-quality speech output
- Blazing fast performance with optimized latency
- High controllability for customized audio outputs
- Accurate pronunciation, even for rare words and complex sequences
- Flexible acoustic profiles for different environments and contexts
Real-World Applications
The applications for Cartesia’s technology are vast and growing. Some key use cases include:
1. Voice Agents: AI-driven phone conversations, revolutionizing customer support by enabling instant, high-quality voice interactions.
2. Content Creation: Narration of written content (like scientific papers) into engaging audio formats, with controls for expression and emotion.
3. Education: The potential for personalized, empathetic tutoring systems that combine audio with visual elements for enhanced learning experiences.
4. Edge Computing: Future applications including smart toys and on-device assistants that prioritize privacy and security.
End-to-End Modeling vs. Compound AI Systems
A particularly interesting aspect of the conversation centered on the contrast between end-to-end modeling and compound AI systems. While the industry has seen a trend toward compound AI systems and inference chains (like those used in LangChain or Stanford’s STORM paper for blog writing), Karan presents a compelling case for end-to-end approaches.
The current trend in AI development often involves building cascaded systems that break down tasks into smaller components — for instance, processing a conversation through separate stages of transcription, reasoning, and text-to-speech synthesis. However, Karan argues that this approach can lead to significant loss of crucial information. When audio is converted to text, for example, vital elements like tone, emotion, and pacing are stripped away, limiting the system’s ability to truly understand and respond appropriately.
Instead, Cartesia advocates for end-to-end models that can process multiple modalities simultaneously, preserving all the richness of the original input. This approach allows for more nuanced and context-aware responses. However, Karan also acknowledges the continued importance of planning and search in AI systems, noting that “in machine learning, there’s only two things — there’s learning and there’s search.” The key is finding the right balance between end-to-end processing and strategic planning.
Concluding Overview
The conversation revealed that we’re at an exciting inflection point in AI audio technology. With companies like Cartesia pushing the boundaries of what’s possible, we’re moving toward a future where AI systems can engage with us in increasingly natural and helpful ways. From improving customer service to revolutionizing education and entertainment, the applications of this technology are bound to transform many aspects of how we interact with machines.
