xtts-api-server 0.3.0

A simple FastAPI server to host XTTSv2

A simple FastAPI Server to run XTTSv2

There's a google collab version you can use it if your computer is weak. You can check out the guide

This project is inspired by silero-api-server and utilizes XTTSv2.

I created a Pull Request that has been merged into the dev branch of SillyTavern: here.

The TTS module or server can be used in any way you prefer.

Installation

To begin, install the xtts-api-server package using pip:

pip install xtts-api-server

I strongly recommend installing PyTorch with CUDA support to leverage the processing power of your video card, which will enhance the speed of the entire process:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Starting Server

python -m xtts_api_server will run on default ip and port (localhost:8020)

usage: xtts_api_server [-h] [-hs HOST] [-p PORT] [-sf SPEAKER_FOLDER] [-o OUTPUT]

Run XTTSv2 within a FastAPI application

options:
  -h, --help show this help message and exit
  -hs HOST, --host HOST
  -p PORT, --port PORT
  -sf SPEAKER_FOLDER, --speaker_folder The folder where you get the samples for tts
  -o OUTPUT, --output Output folder

The first time you run or generate, you may need to confirm that you agree to use XTTS.

API Docs

API Docs can be accessed from http://localhost:8020/docs

Voice Samples

You can find the sample in this repository, also by default samples will be saved to /output/output.wav or you can change this, more details in the API documentation

Selecting Folder

You can change the folders for speakers and the folder for output via the API.

Get Speakers

Once you have at least one file in your speakers folder, you can get its name via API and then you only need to specify the file name.

Note on creating samples for quality voice cloning

The following post is a quote by user Material1276 from reddit

Some suggestions on making good samples

• Keep them about 7-9 seconds long. Longer isn't necessarily better.

• Make sure the audio is down sampled to a Mono, 24000Hz, 16 Bit wav file. You will slow down processing by a large % and it seems cause poor quality results otherwise (based on a few tests). 24000Hz is the quality it outputs at anyway!

Using the latest version of Audacity, select your clip and Tracks > Resample to 24000Hz (type in 24000 in the box), then Tracks > Mix > Stereo to Mono. and then File > Export Audio, saving it as a WAV of 24000Hz

-If you need to do any audio cleaning, do it before you compress it down to the above settings (Mono, 24000Hz, 16 Bit).

• Ensure the clip you use doesn't have background noises or music on e.g. lots of movies have quiet music when many of the actors are talking. Bad quality audio will have hiss that needs clearing up. The AI will pick this up, even if we don't, and to some degree, use > it in the simulated voice to some extent, so clean audio is key!

• Try make your clip one of nice flowing speech, like the included example files. No big pauses, gaps or other sounds. Preferably one that the person you are trying to copy will show a little vocal range. Example files are in \text-generation- >webui\extensions\coqui_tts\voices

• Make sure the clip doesn't start or end with breathy sounds (breathing in/out etc).

Using AI generated audio clips may introduce unwanted sounds as its already a copy/simulation of a voice, though, this would need testing.