Why RVC needs a text to speech step
RVC stands for Retrieval-based Voice Conversion. Feed it a recording and it gives the same recording back in another voice: same words, same timing, same melody, different singer or speaker. That is why RVC models are so popular for covers and voice swaps.
What RVC does not do is read. Give it a sentence of text and there is nothing for it to convert. To make an RVC model talk from typed words you need a second model in front of it: a text to speech voice that turns the text into audio first. RVC then converts that audio into your voice.
So every RVC text to speech setup is a chain of two steps. The quality of the result depends on both: a natural text to speech voice gives RVC clean, well-paced speech to work with, and a good RVC model gives it the target sound.
What you need
First, an RVC voice model. These usually come as a .pth file, often with a matching .index file that helps the model hold on to the voice's character. You can download models people have shared, export your own from a site you used before, or train one from your own recordings.
Second, a text to speech voice to read the line. Pick a base voice close to the target: a male base voice for a male model and a female one for a female model. RVC converts timbre well, but a big jump in pitch range can sound strained, so starting close helps.
Third, a way to pass audio from the first step to the second. This is where most setups get fiddly.
The do-it-yourself route
If you are comfortable with Python, you can wire the chain yourself. There are open source projects that pair a text to speech engine with RVC, WebUIs that include a text to speech tab, and extensions for chat and assistant apps that run RVC as a second pass on whatever their text to speech produces.
The trade-off is setup. You install Python and its dependencies, download the speech models by hand, point the scripts at your .pth and .index files, and deal with version mismatches when something updates. Once it runs it works well, and you learn a lot about how the pieces fit together.
Expect to spend an evening on it the first time, and keep notes, because you will need them again on the next machine.
The one-click way in Xport Studio
Xport Studio puts the whole chain in one window on Mac or Windows. Import your .pth model (and .index if you have it) into the Library, open Text to Speech, type your line, pick the voice and press Generate. The app reads the text with a built-in neural voice, picks a male or female base voice to match your model, converts it with your RVC model and hands you a WAV.
Everything runs on your computer. Your text, the audio and your models are never uploaded, and after the one-time downloads it works offline. On an Apple Silicon Mac the RVC step for a short line takes about a second.
You can also make your own model to read with. Record your voice, train it with voice cloning, and then type lines in your own voice instead of re-recording every fix.
Tips for natural results
Write the way people talk. Short sentences and commas where you would breathe give the text to speech voice better pacing, and RVC keeps that pacing. Spell out numbers and abbreviations the way you want them said.
If the result sounds thin or strained, check the base voice gender and try a different model. Models trained on clean, dry speech usually read text better than models trained only on heavily processed singing.
Once you have the line, treat it like any other vocal. Trim it, EQ it and drop it into your session. If you need a sung version instead, record a guide melody and run it through the same model with the vocal converter.