Ultravox.ai

ElevenLabs

Ultra-realistic AI voices and voice cloning

A leading AI voice platform for generating ultra-realistic speech, voice cloning, and multilingual audio content.

Suno

Generate full songs with lyrics and vocals

An AI music creation tool that generates complete songs (vocals and instrumentation) from simple text prompts in various genres.

Udio

The most musical AI track generator

An AI music creation platform known for its exceptional audio fidelity and sophisticated musical arrangements across every imaginable genre.

AssemblyAI

The API for speech AI

A developer-first platform for transcription, speaker diarization, and sentiment analysis.

Cartesia

Ultra-low-latency real-time voice generation for AI agents

Cartesia is an AI audio and voice intelligence company that powers conversational AI agents and interactive applications with ultra-low-latency, hyper-realistic voice synthesis. Its flagship State Space Model (SSM) architecture, Sonic, delivers human-like voice generation with sub-90ms time-to-first-audio latency. While traditional transformer-based text-to-speech models struggle with high latency and compute overhead, Cartesia's lightweight SSM architecture allows developers to build fluid, conversational voice bots that feel instantaneous and natural, without awkward pauses. Cartesia is used by developers, contact centers, game studios, and AI agent builders across the world to power real-time phone assistants, gaming NPCs, live transcription translation, and voice-enabled enterprise interfaces.

Moshi by Kyutai

Real-time full-duplex conversational voice AI model with sub-200ms latency

Moshi is an open-source real-time conversational voice AI foundation model developed by Kyutai, the non-profit AI research lab based in Paris. Engineered to revolutionize human-AI verbal communication, Moshi operates on a full-duplex architecture capable of listening, thinking, and speaking simultaneously with sub-200ms end-to-end latency. Unlike traditional voice assistants that chain separate Speech-to-Text (STT), Large Language Model (LLM), and Text-to-Speech (TTS) pipelines together, Moshi processes raw multi-stream audio natively as continuous speech tokens. This allows Moshi to understand emotional nuances, interrupt and be interrupted naturally, chuckle, whisper, and express genuine conversational timing. Moshi is fully open-source with openly accessible weights, training recipes, and inference code, serving as a foundational milestone for research in real-time spoken language modeling and multi-modal conversational systems.