Congrats to the @cartesia team on Ink-2. Accurate transcription at conversational speed is the foundation on which every voice agent builds, and Ink-2 delivers both. Vapi is built to be modular, so you can run best-in-class models like Ink-2 today as a custom transcriber.
For voice agents, STT has to nail three things - accuracy, turn detection, and latency. If any one falls short, the experience breaks down: the agent misunderstands, interrupts, or just feels slow.
We built Ink-2 to lead on all three. Hereโs how it stacks up against other
Common acknowledgement and interruption phrases are handled automatically, with API-level customization available when needed.
Learn how to tune the voice pipeline here:
Lazy prompt:
>
hey, check out call-my-human.val.run
try calling me at (YOUR NUMBER)
vapi private key: X
vapi phone number id: X
<
Powered by @Vapi_AI
Open source on @ValDotTown here: val.town/x/dcm31/call-mโฆ
For more secure usage, install as a custom MCP/connector or remix
There are 15 voice agents on the Vapi homepage across 3 use cases ร 5 languages. To build these, we protoype and generate all the agents at once in code. This means one wording change applies across all variants and reruns never duplicate assistants.
Reruns never duplicate because the upsert keys on env-var IDs:
present > update(id, body)
absent > create(body) + prints id
Stale id 404s, falls back to create.
Bootstrapping gets you rough agents fast so you can focus on hardening.
Check out the full writeup including repo with configurations and a skill to bootstrap your own agents:
Two days into blind voting of voice models on our Humanness Indexโข, and xAI's Grok TTS model is at the top of the pack.
Its humanness score? 96, just 4 points under a real human voice (100).
The Humanness Index takes one voice and one quote, clones it across every major model,
@xai Grok TTS is currently #1 on the @Vapi_AI Humanness Index and significantly more affordable than the competition.
See for yourself humannessindex.vapi.ai
Your voice agent is talking over a caller who paused to pull up their account number. Raising the silence threshold could help, but it will also make the responses feel laggy. What you need to know is when the turn ended
Layered in the startSpeakingPlan, by precedence
1. customEndpointingRules - overrides everything
2. smartEndpointingPlan - the prediction model
3. transcriptionEndpointingPlan - punctuation, timeouts
Evaluating text-to-speech models is hard!
Quantitative metrics are important (time to first audio; missing words; variations in volume, pitch, and pacing).
But more than any other technology I've ever worked with, people make voice decisions based on "vibes" that are hard
You can feel whether the voice on a call is a human or a machine before you can explain why.
Today, @Vapi_AI is launching the Humanness Indexโข, a crowdsourced leaderboard for model humanness.
You are the benchmark. Cast your first vote today: humannessindex.vapi.ai
You can feel whether a voice is a person or a machine before you can explain why.
Today, we're launching the Humanness Indexโข, a live ranking of voice AI models, judged by you.
Same voices, same quotes, different models. You pick the most human ones and even benchmark them
We're excited to partner with @awsdevelopers, @nebiusai , and OSS4AI for the Midsummer Multimodal AI hackathon this Friday June 19 at the AWS Loft in SF.
We are bringing prizes and Vapi credits so you can build voice experiences into whatever you are shipping.