This topic surveys the current landscape of AI audio and voice models for developers, covering text-to-speech (TTS), speech-to-text, voice cloning, real-time voice agents, and conversation-intelligence tooling. In 2026 these capabilities are increasingly production-ready: low-latency, expressive TTS and high-fidelity cloning are used in customer agents and media workflows, while lightweight browser and on-device transcription support privacy-sensitive applications. Key categories and representative tools: Voice Synthesis and Transcription (ElevenLabs for ultra-realistic TTS, cloning, and transcription; Transcribe Audio for quick in-browser STT); Text-to-Speech Tools (Murf AI and Smallest.ai for multilingual, studio-grade TTS, dubbing, and emotion control); Real-time/Agent Frameworks (Voila as an open-source, low-latency family of voice-language models for persona-aware conversations); and Conversation Intelligence / Audio Quality (Krisp for noise cancellation, meeting transcription, and audio enhancement). Also relevant are audio asset marketplaces that surface licensed voices and sound assets for reuse and localization. Why it matters now: developers are balancing fidelity, latency, cost, and legal/ethical constraints—voice consent, licensing, and on-device inference are major design drivers. Platform incumbents (OpenAI, Apple, and specialist providers) influence API ergonomics and privacy defaults; specialist vendors focus on production-grade pipelines, multilingual dubbing, or ultra-low-latency interaction. Choosing the right stack depends on use case: media dubbing and voiceovers prioritize fidelity and licensing, voice agents need low latency and conversational state, and enterprise meetings require robust noise reduction and transcription. This comparison helps developers map requirements to the trade-offs and vendor capabilities available in early 2026.