Kokoro 82M
Punching far above its size; the default local voice when you do not have a GPU.
Plate list
Approach, license, cloning, streaming, language. If a well-known system has no redistributable clip, it still belongs on the list — with a quiet empty well and a link to the official demo.
Punching far above its size; the default local voice when you do not have a GPU.
Edge and offline deployment; hundreds of community voices.
Tiny footprint, many languages, the classic machine accent.
The system a generation of papers compared against.
TTS that fit in feature-phone memory.
Voice library, cloning, and inline performance tags like [whispers].
Steerability through instructions; one-vendor convenience.
End-to-end spoken dialogue, not a vocoder bolted onto a chatbot.
The Cloud TTS line that finally sounds like a conversation.
Workhorse Cloud TTS after WaveNet, before Chirp.
The moment neural vocoders entered production.
Cheap, intelligible, obviously synthetic.
Audiobook-style Cloud TTS.
Leaderboard-era Gemini speech with SynthID watermarking.
Being the TTS you can turn on with an IAM role.
AWS’s answer to GPT Realtime and Gemini Live.
Enterprise custom neural voice and SSML depth.
The lowest published time-to-first-audio among flagship APIs.
PlayDialog and ultra-low-latency streaming tiers.
Emotion as a first-class modelling target, not a slider.
Price-to-expressiveness on multilingual leaderboards.
Realtime agent voices at a cost that can stay always-on.
The other half of a Deepgram voice stack.
Top open-weight arena rank with a non-permissive license asterisk.
The ‘this doesn’t sound like TTS’ conversational demo.
Not sounding like an audiobook when it reads an account number.
Voiceover tools for people who do not want an SDK.
Training-video and e-learning narration.
Consumer reading, then a competitive API.
Cloning as a product, then open-sourcing a strong local model.
Making voice cloning a text cursor.
Marketplace voices for ads and explainers.
On-prem streaming TTS next to the GPU that ran the LLM.
Human-level MOS on a single-speaker corpus, and the recipe Kokoro slimmed down.
Making two-stage TTS look overbuilt.
The flow model VITS grew out of.
Ending the era of hand-built TTS frontends.
The canonical two-stage neural TTS recipe.
Making neural TTS fast enough to feel like a vocoder problem again.
Controllable parallel TTS without a Tacotron teacher.
The pre-neural baseline every 2017–2019 paper had to beat.
Claiming human-level MOS, then scaling to zero-shot.
Simple recipe, strong zero-shot, a lot of local fine-tunes.
The short paper you read before you train a flow TTS.
Zero-shot cloning as in-context learning.
[laughs] as a first-class output, not a failure mode.
The cloning model everyone actually ran in 2024.
Multilingual zero-shot cloning before XTTS was a product name.
Granular style control on top of a cloned timbre.
Open streaming clone quality that is actually usable live.
Another open clone on the same fair sentence as IndexTTS’s neighbours.
Making cloned speech fit a picture cut.
Open cloning with a license you can actually ship.
Open codec-LM TTS without a 7B backbone.
Prosody that sounds like a person on a call.
LLM-backbone TTS with a permissive license.
Two people in one pass, with [laughs] still intact.
The open all-rounder a lot of 2026 local agents actually use.
Coverage as the feature: languages nobody else’s catalog has.
Overlapping conversation, not turn-taking TTS.
Open voice cloning that actually fits in a laptop CPU budget.
Context length as the product.
A frontier lab’s speech model that is not a TTS startup.
Quality by burning latency on purpose.
64 engines in view · 46 with playable audio on the bench