NNiceVois
Lossless training source

WAV to PTH: train an RVC model from a WAV file

Upload a WAV and start trainingGo straight from a clean WAV recording to cloud RVC training.
Summary

A WAV recording can be uploaded directly and used as the dataset for a cloud RVC training run. The output is a trained .pth model—not a renamed WAV—plus a matching retrieval .index and complete ZIP package.

When you have both an original WAV and a compressed copy of the same recording, the WAV is normally the better training source because it avoids an additional lossy encoding stage.

Voice training

Train an RVC voice from an audio file

Add up to 50 WAV, MP3, FLAC, M4A, or OGG files. NiceVois keeps them separate and prepares them together as one private training dataset.

.pth

This name is used for the .pth, .index, and complete ZIP package.

Standard is selected automatically.
200 epochs

Recommended: add your audio and NiceVois will suggest the right number.

Clean audio produces a better model. Use a dry voice recording with minimal music, echo, or background noise. Prepare your audio first if it needs it.

Persistent cloud storage

Your model library

Your voice models are files you own. Download them once and no platform can take them away.

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Paid model packages stay saved until you delete them.

Your model files and private MP3 listening preview remain available after closing the browser. New trainings also keep the original recordings in a downloadable dataset ZIP. Paid voices and their saved datasets stay until you delete them. Free voices and their saved datasets are kept for 7 days; download them before then. Older voices may only have the MP3 listening copy. Temporary training files are removed after validation.

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What makes a WAV useful for RVC?

The container alone does not guarantee good training data. A clean WAV with one voice, consistent volume, and little echo is useful. A WAV containing a full music mix, clipping, several speakers, or aggressive noise-reduction artifacts is still a weak dataset.

  • Use the original recording or clean isolated vocal whenever possible.
  • Remove silence only when it dominates the file; natural pauses are not inherently harmful.
  • Remove other speakers, backing vocals, and instrumental bleed.
  • Keep natural variation in pitch, words, pace, and expression.

Do you need to resample or split the WAV yourself?

No. The cloud pipeline reads the uploaded source, slices it into workable segments, normalizes and resamples the training material as required, extracts RMVPE pitch and HuBERT features, and then trains the pinned RVC v2 model. Manual splitting is unnecessary for this interface.

Verified WAV workflow

The project’s real end-to-end proof used a 33,189,998-byte WAV. It passed browser upload, private dataset preparation, cloud training, model validation, and named .pth download. A later 100-epoch run produced 53 prepared segments and a 57,590,877-byte inference model that loaded in the native RVC synthesizer.

These numbers verify compatibility and artifact production; they are not a promise that every recording will produce the same perceptual quality or runtime. See the testing methodology.

WAV upload checklist

CheckGood signWarning sign
SpeakerOne consistent target voiceInterviewer, duet, or backing vocals
RoomDry and closeLong echo or distant microphone
LevelClear without clippingCrackling or flattened peaks
BackgroundQuiet or cleanly isolatedMusic, crowd, traffic, or hum
Use your WAV as the training source

The uploader checks the duration, recommends an epoch count, and shows each cloud stage.

Train a PTH model from WAV