Skip to content
Atlas by Comet Lab
Capability layer

Voice and transcription tools, compared

Voice tools are the audio edges of an AI system: speech to text on the way in, text to speech on the way out. Two questions decide most choices: may the audio leave your environment, and does it need to happen in real time?

Learn the idea Multimodal AI: voice and audio on Horizon

Open this layer in the explorer

At a glance

Every tool in this layer, with where it fits and the facts that usually decide it.

Tool Where it fits Licence Self-hostable Hosted API Maturity
Whisper Speech-to-text model The default when you need solid, multilingual transcription you can run yourself, with no audio leaving your environment. Open source Yes No Established
AssemblyAI Speech-to-text API A fit when a product needs transcription plus higher-level audio understanding from one managed API. Proprietary No Yes Established
Cartesia Text-to-speech API A fit when a live voice agent needs natural speech with the lowest possible time-to-first-sound. Proprietary No Yes Growing
Deepgram Speech-to-text API Best when transcription is on the critical path (real-time or high-volume) and a managed, low-latency API is worth paying for. Proprietary No Yes Established
ElevenLabs Text-to-speech API The pick when voice quality is part of the product experience (assistants, narration, conversational agents) and hosted is acceptable. Proprietary No Yes Established
faster-whisper Speech-to-text runtime The default when you want Whisper-quality transcription self-hosted, but the reference implementation is too slow or memory-hungry. Open source Yes No Established
Kokoro Text-to-speech model A fit when you need good-quality, self-hosted text-to-speech on limited hardware, with a permissive licence. Open weight Yes No Growing
Piper Text-to-speech engine A fit for offline or embedded voice (devices, kiosks, local assistants) where speed and footprint outrank polish. Open source Yes No Established
Vapi Voice agent platform A fit when you need a production phone voice agent and do not want to assemble the real-time speech pipeline yourself. Proprietary No Yes Growing
WhisperX Transcription pipeline A fit when a transcript needs precise timing or speaker labels: captions, multi-speaker meeting notes, audio search. Open source Yes No Established

How to choose

Start from your situation. Where a line says “our”, it comes from Comet Lab’s own builds.

  1. Transcription that stays in-house

    Whisper is the model; faster-whisper is what we run in production, with the same transcript at a fraction of the cost.

  2. Transcripts need timestamps or speaker labels

    Word-level timestamps and speaker diarisation on top of Whisper, for captions and multi-speaker meetings.

  3. Transcription is on the critical path, and a hosted API is allowed

    Deepgram for real-time and high-volume work; AssemblyAI pairs transcription with audio-understanding models.

  4. Voice quality is part of the product

    ElevenLabs leads on natural, expressive speech; Cartesia on the shortest time to first sound for live agents.

  5. Speech has to be generated offline

    Kokoro gives natural voices on modest hardware; Piper is fast and small enough for devices and kiosks.

  6. A voice agent on the phone

    The real-time speech pipeline, assembled for you.