Simple TTS

A simple machine learning text-to-speech program powered by kokoro.

Warning

This is currently only tested working on Ubuntu-based distros due to the required packages.

Setup

Clone repo and go into the directory

$ git clone https://git.ayo.run/ayo/simple-tts
$ cd simple-tts

Create new Python virtual environment. Here I use conda, but venv is also good.

$ conda create -n tts

### (optional) for Intel XPU specific device usage:
$ conda create -n tts --clone llm-pt26

Note

Optional for using Intel XPUs, you need to set up ipex-llm environment with pytorch 2.6. Also, see Intel XPU environmental variables" section below.

Activate the environment and install the dependencies

$ conda activate tts
$ python -m pip install -r requirements.txt

Required packages

The following are required packages aside from the python dependencies. espeak-ng is used by kokoro under the hood for english languages, and libvlc is used as the default audio player for the generated audio.

$ sudo apt update
$ sudo apt install vlc espeak-ng

Note

Installing vlc via flatpak or snap will not work, as the code need access to libvlc.

Intel XPU environmental variables (Optional)

For XPUs, we need to set some environmental variables. I have added a env.sh script which will activate the conda environment tts and set the environmental variables.

$ . env.sh

Usage

Go into the directory and activate the environment:

$ cd simple-tts
$ conda activate tts

If using Intel XPUs, set the env variables

$ . env.sh

Running the program without arguments will use the demo text tongue-twister.txt with the default voice.

$ python tts.py # will use default arguments

Providing text inputs

You can pass a string as first argument:

$ python tts.py "Hello world!" # will be read by the default voice

To run the program with an input file, use flag --input_file.

$ python tts.py --input_file demo/tongue-twister.txt

# or shorter...
$ python tts.py -i demo/tongue-twister.txt

You can also use the text stored in your clipboard (i.e., copied text). Select a text from anywhere (e.g., your web browser), copy it with <ctrl>+C or the context menu, then use the flag --clipboard:

$ python tts.py --clipboard

# or shorter...
$ python tts.py -c

Labeling your outputs

You can indicate a title to be used as label (i.e., file name prefix and directory name) to the generated outputs using --title

# This will put the generated files in ./outputs/siple-greeting/
$ python tts.py "Hello there!" --title "simple-greeting"

# or shorter
$ python tts.py "Hello there!" -t "simple-greeting"

Voices

Optionally, you can indicate a voice you want to use with the --voice flag. See all voices available.

$ python tts.py --voice am_michael

# or shorter...
$ python tts.py -v am_michael

There are four shortcuts available to the best voices: pro, hot, asmr, brit (i.e., best trained voices), and pro is the default if no value is given

$ python tts.py "Hello there!" --voice pro # af_heart

$ python tts.py "Hello there!" --voice hot # af_bella

$ python tts.py "Hello there!" --voice asmr # af_nicole

$ python tts.py "Hello there!" --voice brit # bf_emma

Disable audio player

You can disable the built-in audio player with --skip_play if you choose to play the audio files generated with your preferred player.

$ python tts.py "Hello there!" --voice asmr --skip_play

# or shorter...
$ python tts.py "Hello there!" --voice asmr -s

Advanced usages

The --verbose flag can be used to show more informative messages.

$ python tts.py --verbose

The --device or -d flag can be used to set the desired device (i.e., processor) to use:

$ python tts.py --device cpu # will use the cpu
$ python tts.py --device cuda # will use the NVIDIA GPU
$ python tts.py --device xpu # will use the Intel GPU