Scrappy — a 9M-param CPU voice, distilled in one day
Scrappy is the voice of ScrappyLabs: a full text-to-speech voice
in 9.36M parameters / 37MB, running 10–14× faster than real-time on a plain CPU —
no GPU, no cloud, 0.16s load. It's a fine-tune of
owensong/Inflect-Micro-v2 (Apache-2.0),
trained the same day that model crossed our radar.
Listen: samples/scrappy_intro.wav — Scrappy says hello
(generated on a desktop CPU by this exact checkpoint).
🎮 Try him live:interactive demo on Spaces
— built by the Hugging Face team on free ZeroGPU. Thanks, HF 🤗
Inflect-Micro-v2 ships as an inference-only release — one fixed voice, no trainer, and the
docs say voice replacement is "research use." We wanted to know how true that is, so we
treated it as a one-day exercise: reconstruct the training stack, distill a voice we like
into it, and publish what we learned. This repo is the result plus the missing piece we
had to build — a working fine-tuning setup (see trainer/).
The recipe, end to end:
Teacher renders the corpus. A commercial cloud TTS narrator voice generated ~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. This caught real poison: clips where the teacher API
quietly injected a spoken watermark instead of the requested text. Signal checks
(clipping/silence/duration) run alongside. 98.6% survived.
Warm-start fine-tune. The released model.pth initializes the generator; the
posterior encoder and discriminator start fresh (the release runtime ships the full
training-side model code — only losses, data loading, and the loop needed writing).
Decoder frozen for the first 3k steps, LR 1e-4 → 5e-6 over 50k steps, batch 24, fp32.
50,000 steps ≈ 3 hours on one rented RTX PRO 6000 Blackwell (~$9). A 24GB laptop
GPU does the same run in ~6 hours if you'd rather boil a laptop (we don't recommend it).
Usage
Identical to upstream — this is a drop-in checkpoint for the packaged runtime:
python
1from inference import InflectTTS
23tts = InflectTTS(model_dir=".", device="cpu")4tts.save("Hi, I'm Scrappy. I run on your CPU.","out.wav", seed=7)
python inference.py --model-dir . --device cpu --text "Hello from Scrappy." --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.
Fine-tune your own voice (trainer/)
The trainer/ directory contains the training stack upstream deliberately omits:
prep_filelists.py — phonemizes transcripts with the model's own frontend and validates
every symbol against the release inventory (espeak emits out-of-vocabulary diacritics on
exotic names — unvalidated, they crash training).
train_ft.py — the full loop: VITS losses, torchaudio mel transforms (slaney/slaney —
no librosa dependency), warm-start loading, decoder freeze schedule, drop-in candidate
export every N steps.
eval_candidate.py — renders fixed prompts from any candidate for A/B listening.
You also need the cython monotonic-alignment kernel from the
canonical VITS repo (the release stubs it out) —
build it and drop the package into runtime/. Data contract: mono 24kHz clips, one speaker,
verified transcripts, 1–5+ hours. Gate your corpus with ASR round-trips — it's the only
check that catches audio that says the wrong thing beautifully.
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 — expect a flatter read
than the source voice.
Slight texture softness vs. a large vocoder remains at close listening.
Everything upstream says about biases and English-only applies.
Provenance & takedown
The training audio was synthesized by a commercial cloud TTS narrator voice (a synthetic
persona — 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.
