Pip is the fast, upbeat voice in the ScrappyLabs voice family:
a young American female read with podcast-host energy and crisp diction, in
9.36M parameters / 37MB (fp32) at 24kHz, running 10–14× faster than real-time on a
plain desktop CPU — no GPU, no cloud, no network. It's a fine-tune of
owensong/Inflect-Micro-v2 (Apache-2.0).
Listen: samples/pip_intro.wav — Pip introduces herself
(generated on a desktop CPU by this exact checkpoint).
Where she sits in the family:
scrappy (warm expressive narrator) ·
clara (clear professional broadcast) ·
silas (deep, slow, trailer gravitas) ·
pip (this one) ·
the family model — all four
voices in one 41MB checkpoint, selectable by name, with blending between them.
🎮 Try all four voices — and blend between them:interactive demo on Spaces — the Space was built and gifted to us by the Hugging Face team. Thanks, HF 🤗
What it is
One voice, one checkpoint, drop-in compatible with the upstream Inflect-Micro-v2 runtime.
If you want a single always-on voice with nothing to configure, take this. If you want all
four voices (and morphs between them) out of one file, take the family model instead — it's
the same size and the same speed.
How it was made
A teacher renders the corpus. Pip did not exist before this — she was described,
not recorded. A voice-design model running on our own hardware turned one sentence,
"A bright, energetic young American female voice, quick and playful, podcast-host energy, crisp diction.",
into a speaking voice, and that voice then read ~4,400 short clips (~5.5h @ 24kHz) from a
text corpus we control — so every transcript is known by construction.
An ASR gate cleans it. Every clip is round-tripped through speech recognition and
scored against its transcript (≥0.85 word overlap), with signal checks (clipping,
silence, duration) alongside. Pip's corpus passed at 98.5% — the strictest cut of the
four, which is what you'd expect from the fastest, most clipped delivery.
Warm-start fine-tune. Warm-started rather than trained from scratch; decoder frozen
for the first 3k steps, LR 1e-4 → 5e-6, batch 24, fp32, 50,000 steps on one RTX PRO
6000 Blackwell. Final mel loss 21.0.
Honest number, freely given: 21.0 is the highest final mel loss of the three new solo
voices (clara 19.4, silas 18.1). Fast, bright, high-variance delivery is simply harder to
fit than a slow one. The four-voice family model reached 18.4–18.9 training on all the
voices pooled — level with the best solo run rather than worse for the sharing —
and Pip scored a perfect 1.00 ASR word-overlap on held-out intro material there. If you
care most about intelligibility, the family checkpoint is the stronger Pip.
Usage
Identical to upstream — a drop-in checkpoint for the packaged runtime:
python
1from inference import InflectTTS
23tts = InflectTTS(model_dir=".", device="cpu")4tts.save("Hey there, I'm Pip. Quick, bright, and ready to go.","out.wav", seed=7)
python inference.py --model-dir . --device cpu --text "Hello from Pip." --output out.wav
Notes carried over from upstream: English only, single voice, deterministic seeds,
punctuation-aware long-form chunking, speed 0.5–2.0, variation 0.0–1.0. Write numbers
out as words for best results.
Honest limitations
Prosody is where distillation loses the most. Timbre and identity transfer well; the
teacher's long-range timing instincts (dramatic pauses, phrase-level planning) get
averaged. The duration predictor is the smallest organ in a VITS. For Pip specifically,
the energy survives better than the timing of the energy — expect a slightly more even
read than the source.
Slight texture softness vs. a large vocoder remains at close listening.
English only. The frontend is espeak-ng-based; it also mispronounces uncommon proper
nouns. If a name comes out wrong, respell it phonetically in the synthesis input.
9.36M parameters is genuinely small. This is a good voice for narration, UI speech, and
embedded/offline work — not a singing model, not an emotion-control model.
Do not use this stack to clone a real person's voice without their explicit consent.
Provenance & takedown
The training audio was synthesized by a voice-design model from a written description (a
synthetic persona that never existed until we described it — no real person's voice was cloned). If you're a rights holder with a concern,
open a discussion on this repo and we'll respond promptly.
