SHADOWWW · Voice Models

Four takes. Four voices. Free to create with.

RVC v2 · 48 kHz · 200 epochs · CC BY-NC 4.0

Each model here is a single session, captured as recorded. Shadowww cut four vocal takes, and each take became its own model: its own tone, its own register, its own amount of tuning. Nothing was pitched, tuned or polished after the fact. Only the silence was taken out.

Pick the one that fits your track, load it into any RVC v2 tool, and sing through it.

The voices

	Voice	Register	Tuning in the take	Best for	Download
T02	Raw and low	C♯3 · ~141 Hz	None, fully natural	Low, unprocessed, intimate vocals	zip
T03	Bright and lifted	G3 · ~190 Hz	Moderate	Higher melodies, hooks	zip
T04	Natural mid	F♯3 · ~181 Hz	None to light	Mid-range, conversational lines	zip
T05	Polished mid	E3 · ~166 Hz	Strongest	A tighter, auto-tuned sound	zip

_{"Tuning in the take" is measured, not a setting: the share of sung notes landing within 15 cents
of a semitone. Natural singing sits around 30%: T02 22% · T03 45% · T04 28% · T05 51%.}

Not sure which one? Start with T02 for a natural sound or T05 for a tuned one. T04 was trained on the least audio (3.2 min); it is great for short phrases and can waver on long held notes.

Alternates. T02 and T03 each include an earlier checkpoint (-e120, -e140) from the point where training loss was lowest. If the final model sounds over-processed or metallic on your material, try the alternate.

Quick start

Download a voice's .zip from the table above. It holds the model (.pth) and its index (.index).
Load it in your RVC v2 tool:
- Applio: open the Download tab, paste the zip link from the table, and download; the voice then appears in Inference.
- Replay (Mac): unzip, then put the .pth and .index in a new folder inside ~/Library/Application Support/Replay/com.replay.Replay/models/ and restart Replay.
- RVC WebUI: put the .pth in assets/weights and the .index in logs/, then refresh.
Feed it a clean vocal: dry, solo, no reverb, no backing track. Separate the vocal first if you are starting from a full song.
Start from these settings and adjust by ear:

Setting	Start at	What it does
Pitch extractor	`rmvpe`	Most stable pitch tracking for singing
Transpose	`0` from a male voice, `-12` from a female voice	Moves the input into the model's range
Index rate	`0.6` (range 0.5–0.75)	Higher = more Shadowww timbre, lower = clearer words
Protect	`0.33`	Keeps breaths and consonants natural
Filter radius	`3`	Smooths pitch jitter

If it sounds off: robotic or warbly means lower the index rate or try the alternate checkpoint. Cracking on high notes means transpose down, or try T03. Muddy words mean lower the index rate to about 0.5 and make sure the input is dry.

What's in each folder

t02/  shadowww-t02.pth · added_shadowww-t02.index · shadowww-t02.zip
      shadowww-t02-e120.pth · shadowww-t02-e120.zip · dataset.json
t03/  same layout, alternate = e140
t04/  shadowww-t04.pth · added_shadowww-t04.index · shadowww-t04.zip · dataset.json
t05/  same as t04
SHA256SUMS   checksum for every file

dataset.json records how each take was prepared. No audio is published.

How they were made


Framework	Applio 3.6.5 · RVC v2
Sample rate	48 kHz
Pitch extractor	RMVPE
Embedder	ContentVec
Pretrained base	KLM 4.1 48k (generator and discriminator)
Training	200 epochs · batch 8 · 2 × NVIDIA T4
Training audio	T02 5.9 min · T03 8.2 min · T04 3.2 min · T05 4.8 min

Data preparation. Each take is a raw vocal recording by Shadowww. The only processing was silence trimming: every pause of 0.4 s or more was removed, using a quiet threshold set per take at the split between room noise and voice, with 0.15 s kept at each edge. Audio was then peak normalised to −1 dBFS. No pitch shifting, tuning, effects or vocal separation were added; any tuning you hear was part of the original performance.

Licence and fair use

Released under CC BY-NC 4.0.

✅ Free to use, remix and share, with credit: "Voice model: Shadowww"
✅ Covers, demos, experiments and personal projects
❌ No commercial use without permission from Shadowww
❌ No impersonation: don't present AI output as a genuine Shadowww recording, and don't use the voice to deceive, harass or defraud anyone
🏷️ Label AI-generated vocals as AI

The voice belongs to Shadowww, who recorded, trained and published these models.

Credits

Voice and recordings: Shadowww
Pretrained base: KLM 4.1 by SeungHyun Han (Seoul Streaming Station), SeoulStreamingStation/KLM4.1, used under its licence, which requires this acknowledgement
Training framework: Applio

SHADOWWW · three w's, one voice, four takes

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