Vocence is Bittensor subnet 78 (SN78), a project in the Image/Video/Audio Generation lane. Prompt a voice, score the result
Describe a voice in plain words, a calm middle-aged man reading slowly and warmly, and Vocence asks AI miners to actually produce it, then pays them by how closely they hit the brief.
It is an educational deep dive, not a recommendation to stake. For live price, pool depth, and performance, always open the live subnet page.
What is Vocence (SN78)?
Vocence is a Bittensor subnet built around voice AI. Miners build text-to-speech models that take a line of text plus a written description of how it should sound, and return spoken audio. Validators send the same prompts to every miner and grade how well each one matches the request.
In plain English: It is like commissioning a voice actor by describing them in a sentence, except the actors are AI models competing to match your description most closely.
Centralized reference points: ElevenLabs, OpenAI TTS, Play.ht
How miners and validators work
Like every Bittensor subnet, SN78 is a two-sided market: miners produce work, validators score it, and emissions follow those weights under the network’s dTAO economics.
- Miners train and deploy prompt-driven text-to-speech models that take text plus a voice instruction (tone, emotion, accent, pace, age) and return a spoken audio file.
- Validators send the same evaluation prompts to every miner, then score the returned audio on three things: whether it says the right words, whether it sounds clean, and whether the voice matches the requested traits.
Mechanism and design
The work and the scoring are sourced from the subnet's own repository. The current focus (Q1) is Prompt-based Text-to-Speech, or PromptTTS: a miner receives a prompt such as "a calm middle-aged male voice with a warm tone, speaking slowly and clearly" along with the text to read, and returns a WAV file. Every model runs through a standardized wrapper and exposes a single /speak endpoint, so outputs stay directly comparable across miners. Miners publish their model on Hugging Face and deploy it on Chutes, another Bittensor subnet, for compute, with Hippius used for storage.
Validators pull the list of registered miners, call each one's /speak endpoint with evaluation prompts, and run a shared scoring pipeline measuring content correctness, audio quality, and prompt adherence. Weight setting uses what the repo calls global consensus scoring: each validator reads the most recent evaluation window from every active validator, aggregates those results with stake-weighted scoring, then applies the subnet's winner-take-all and threshold rule before setting weights on chain. The team describes Q1 PromptTTS as the baseline, with the roadmap expanding to speech-to-text, speech-to-speech, and voice cloning.…
Why the project exists
- The problem it solves: Most high-quality voice synthesis sits behind closed APIs, and steering voice traits usually means picking from a fixed set of preset voices. Vocence aims for open models you control with plain-language instructions instead.
- The opportunity: Voice is becoming the interface for AI agents, assistants, audiobooks, and accessibility tools. A steerable, open voice layer that anyone can build on is broadly useful, not tied to one vendor's roadmap.
- The Bittensor advantage: A standing, scored competition keeps every miner's model directly comparable and pushes quality up without a single gatekeeper. Models stay open and reproducible, run through a shared evaluation pipeline.
- Traction signals: Early. The Q1 PromptTTS competition is live, the public repository was committed to through late May 2026, and founder @knakamor recently sat for a recorded interview on decentralized voice AI. On-chain participation is still thin, which is normal for a subnet roughly two months into its life.
What stakers should check before buying alpha
Subnet storytelling is cheap. On-chain and product checks are not. Use the same framework as our subnet metrics guide:
| Check | Why |
|---|---|
| Real product / docs / repo | Separates shipping teams from narrative-only slots |
| Pool depth (TAO) | Your exit door: thin pools amplify slippage |
| Net flows (7d / 30d) | Under flow-based emissions, capital votes with its feet |
| Emission share | Whether the market is funding this subnet right now |
| Ownership concentration | Large holders can move a small alpha market |
| Immunity / deregistration | Post-immunity weak slots can lose the race (see immunity guide) |
Pull the live numbers on Vocence · SN78 rather than trusting any static screenshot in a blog post.
Risk factors
- Execution: Vocence is a young team with a broad roadmap and a narrow live product. Only the PromptTTS task is running today, the deeper voice capabilities (STT, speech-to-speech, cloning) are still ahead, and the public repository shows a single contributor on its graph. Delivering the full stack is unproven.
- Competition: Voice AI is crowded. Well-funded closed providers like ElevenLabs and OpenAI set a high quality bar, and capable open models exist outside Bittensor. Vocence has to be good enough that an open, decentralized market is worth the friction.
- Market: The alpha token is down roughly 24 percent over the past week and 37 percent over the past month, and recent net staking flows have turned negative, so emission share is small and under pressure.
- Concentration: A Gini coefficient of 0.51 across the top stake positions points to moderately concentrated stake distribution. Large positions could meaningfully move pool dynamics.
How this fits a TAO portfolio
Many holders keep a root vs subnet split: root for calmer TAO exposure, subnets like Vocence for thematic upside (and higher variance). If you use TaoScale, you can size SN78 from your wallet without handing over custody. See non-custodial staking.
- Live market: SN78 on TaoScale
- Browse and stake: /invest
- More subnet explainers: /blog/subnets
FAQ
What does Vocence do on Bittensor?
It is like commissioning a voice actor by describing them in a sentence, except the actors are AI models competing to match your description most closely.
What is the SN78 alpha token?
When you stake TAO into subnet 78, you receive that subnet’s alpha. Its price versus TAO moves with the subnet’s pool (see what is dTAO).
How do I research Vocence before staking?
Read the mechanism above, open the official repo/docs when available, then verify pool depth, flows, and activity on SN78. Cross-check with the metrics checklist.
Is this financial advice?
No. TaoScale publishes educational explainers and live tools. Decisions to stake are yours alone.
Not financial advice. Educational overview of Vocence (SN78) compiled from public project materials and ecosystem documentation. Mechanisms and market structure change, so verify live data and official sources before acting.
