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ekto voicetranslate.app

Can AI replace ekto?

The pipeline is no longer exotic: capture mic audio, segment it with a voice activity detector, transcribe with Whisper, translate, speak it back with a local TTS voice. An agent can wire that into a working local app in a weekend and it will genuinely translate a conversation. What it will not do out of the gate is stay graceful for an hour: chunk boundaries clip words, speaker turns bleed together, latency creeps as the buffer grows, and the sentence by sentence pacing that makes these apps usable in real conversation is a tuning problem, not a coding problem. You also get no phone app, which is where voice translation actually happens. Fine for a desk setup and travel prep, unconvincing when you are holding it out to a stranger in a market.

Verdict: Half-bot · AI gets you partway; the hard part stays hardBuild time: a weekend
Half-bot

01What it costs

$29.99/moMonthly Unlimited PRO, monthly subscription
$359.88per year at that price

Checked Aug 18, 2026 · source: voicetranslate.app.

02Could AI build it for you?

The core job: Streams mic audio over a WebSocket to a local Whisper plus translation plus TTS chain and plays the translated speech back with running transcript.

What a working version needs:

  • Python 3.11 and a machine with at least 8GB RAM, GPU strongly preferred
  • Local model downloads: faster-whisper and a Piper voice per target language
  • A browser with mic permission, or an API key if you swap in a hosted translation model
  • Headphones, otherwise the TTS output feeds back into the mic

Very buildable as a desk toy, much harder as something you would trust in a taxi.

03What you'd give up

  • Long session reliability: memory growth, drifting segmentation and dropped turns after the first 20 minutes
  • Clean sentence by sentence pacing and turn detection, which is most of the perceived quality
  • A mobile app, so no translating anything while standing up
  • Offline or low-bandwidth behavior tuned for actual travel
  • Latency budgets someone else already fought for: streaming partial results instead of waiting for a full segment

Because voice translation is judged entirely on the seconds between someone finishing a sentence and you hearing it, and on whether it still works on minute 40. A local build nails the demo and then frays: barge-in, background noise, two people talking over each other, the phone locking. Paying gets you a phone in your pocket that handles those cases without you adding VAD thresholds mid-conversation.

05The build prompt

Paste this into an AI coding tool (such as Claude, ChatGPT, Lovable or Replit) to build your own version.

prompt.txt
Build a local real-time voice translation app. No accounts, no cloud services, no telemetry.

Stack, non-negotiable:
- Python 3.11 + FastAPI, served with uvicorn on port 8000.
- One HTML page with vanilla JS, no framework, no build step.
- Audio in: browser getUserMedia, 16kHz mono, streamed to the server over a WebSocket in 250ms PCM chunks.
- Speech to text: faster-whisper (small model default, configurable via .env).
- Segmentation: silero-vad or webrtcvad to detect end of utterance. Do not translate on fixed timers, translate on detected utterance boundaries.
- Translation: argostranslate with locally installed language pairs.
- Text to speech: piper, one voice per target language, downloaded on first run into ./models.

Behavior:
- User picks source and target language in a dropdown before starting.
- Press Start, speak, and on each detected utterance the server returns: original text, translated text, and a WAV of the translated speech. The page appends both lines to a running transcript and plays the audio.
- Show live latency per utterance in ms in the corner. Be honest, measure end of speech to audio ready.
- Handle overlap: if a new utterance arrives while audio is playing, queue it, never drop it.
- Long session hygiene: cap the in-memory transcript at 500 lines, reset the whisper buffer after every utterance, log RSS every 60 seconds.

Out of scope, do not build: mobile app, user accounts, cloud sync, speaker diarization, a two-phone conversation mode.

Deliverables: main.py, static/index.html, static/app.js, requirements.txt, .env.example (WHISPER_MODEL, DEVICE, COMPUTE_TYPE), scripts/download_models.py, and a README with exact run steps plus one paragraph on where this degrades in sessions over 20 minutes.

Run it, speak a test sentence in English with Spanish as target, and paste the measured latency into the README.
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App prices, verdicts, alternatives and build prompts are adapted from Can I Vibecode It? (MIT License, © 2026 Rob Hallam). Each price shows the date it was checked and its source. Prices change; confirm on the vendor's site before you decide.