This repository contains text-to-speech (TTS) models and utilities designed produce synthetic training datasets for other speech-related models (e.g., openWakeWord).

It includes two specific open-source TTS models that I have found to be useful when generating synthetic speech. Specifically:

• Nvidia Waveglow
• VITS

Note that the code in this repository varies greatly in quality and structure as it was derived from multiple sources. It is primarily meant for research and experimentation, and you are encouraged to makes changes and updates before relying on this code for production purposes. Also, these models are only trained on English TTS datasets (VCTK and LibriTTS), and will not produce accurate speech for other languages.

First clone this repository:

git clone https://github.com/dscripka/synthetic_speech_dataset_generation

Then install the requirements into your virtual environment of choice:

pip install -r requirements.txt

If installing in an environment with GPUs available, you will need to update requirements.txt to include versions of Torch compatible with your GPU configuration. Note that while it is possible to generate data on CPUs only, the WAVEGLOW model will be very slow (e.g., 5-10 seconds per generation). The VITS model is somewhat faster on CPU (~1-3 seconds per generation), but for the large amounts of data generation that is often needed to train robust models, a GPU is strongly recommended.

The TTS models themselves are not stored in this repository and need to be downloaded separately. There is an included script that will download the files and place them in the appropriate location within the repository.

python download_tts_models.py

To test that everything is working correctly after these steps, use this command and listen to the output in the generated_clips directory that is created:

python generate_clips.py --model VITS --text "This is some test speech" --N 1 --output_dir generated_clips

The primary way to generate synthetic speech is via the CLI in generate_clips.py. To see all of the possible arguments, use python generate_clips.py --help.

As a quick example of usage, the following command will generate 5000 clips of the phrase "turn on the office lights" using the Nvidia Waveglow model (on a GPU) trained on the LibriTTS dataset. Additionally, the --max_per_speaker argument will limit the number of generations for each of the ~2300 LibriTTS training voices to 1, and after that limit is reached a random voice will be created by spherical interpolation of random speaker embeddings.

python generate_clips.py \
    --model WAVEGLOW \
    --enable_gpu \
    --text "turn on the office lights" \
    --N 5000 \
    --max_per_speaker 1 \
    --output_dir /path/to/output/directory

The generate_clips.py code in this repository is licensed under Apache 2.0. The included TTS models (and the associated code from the source repos) have their own licenses, and you are strongly encouraged to review the original repositories to determine if the license is appropriate for a given use-case.

• Nvidia Waveglow
• VITS