Documents live on bigfoot and show up in the iPhone app's library under “On bigfoot”. PDFs keep their first 500 pages.
People
A render is hours of the one GPU, so anyone who is not an admin has their
submissions held here until you say yes to that particular job.
Each person gets a token. The app and this page send it with every
request; anyone without one is public and sees only what
has been shared with everyone. While there are no accounts at all,
every request is treated as an admin — creating the first one is
what turns the lock on, so make it your own and sign in with it before
handing anyone else a token.
Storage
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Re-encodes every archived sentence from wav to FLAC — lossless, and
verified sample-for-sample before the wav is deleted. Gives back about
58%. The archive keeps working exactly as before: playback still hits it,
audiobooks still assemble from it, and a cast change still re-renders only
the lines that changed. A book being rendered right now is skipped.
One-off Synthesize outputs and finished reader jobs, older than the given
age. Render archives are never touched by this.
days
Reader
Default voice: Pemberton. Paste long text and the reader will queue chunks for continuous playback.
Loads a document from the library above straight into the text box.
On iPhone, tap this to take a picture of a printed page and have it read aloud.
Create a voice — record
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Letters, digits, _, -. No spaces. Must start with a letter.
Create a voice — upload
Letters, digits, _, -. No spaces.
Create a voice — design
Describe a voice and the model invents one — no reference recording, no
licence to check. Each take is a different person, so roll until you
hear one you want, then keep it. Keeping freezes that take as the reference
clip, after which it behaves like any other enrolled voice.
LoRA fine-tuning teaches the model a specific voice more deeply than zero-shot cloning. Use when zero-shot output doesn't capture cadence, accent, or character well enough.
How it works:
Upload 5–30 min of clean, single-speaker audio.
Provide a transcript. Either attach one you already have (.txt, .srt, .vtt, .json) or click Transcribe with Whisper. Whisper is only started if no transcript is supplied.
Review the segments; edit any misheard text in place.
Name the new voice (e.g. Bradley_LoRA1) and submit. Training runs in the background on pinkie's 3060 (~15–60 min).
When done, the new voice appears in the list with a [lora] marker. Select it and synthesize as usual — the adapter hot-swaps automatically.
Faster path: to train on the RTX 3090 and keep synthesis online, use the Enrollment Wizard above (Step B → per-speaker LoRA mode) — it trains on bigfoot with audio-aligned Parakeet transcripts. This upload flow is the manual, single-file alternative.
Note: this upload flow trains on pinkie's 3060 and pauses synthesis until done (VoxCPM auto-reloads on your next Generate). Transcription (step 2) now runs on bigfoot's 3090, so only the training step is local.
Accepted: plain .txt (aligned via silence detection), .srt/.vtt with timestamps,
or .json in the form {"segments":[{"start":0,"end":5,"text":"..."}]}.
Leave empty to transcribe with Whisper in the next step.
No transcript was attached. Click below to run Whisper on bigfoot's 3090 — synthesis stays online while it runs. If the 3090 is offline it falls back to pinkie's 3060, which briefly pauses synthesis. The active GPU is shown in the status below.
Letters, digits, _, -. No spaces. Must start with a letter.
After training, a reference clip is also saved for the voice. You can point it at an existing voice or let the service carve one from the training audio.
Default 1000. Higher = stronger adaptation, longer training. Halve this to iterate faster.