Piper Sample Generator

Generates samples using Piper for training a wake word system like openWakeWord.

Install

Create a virtual environment and install the requirements:

git clone https://github.com/rhasspy/piper-sample-generator.git
cd piper-sample-generator/

python3 -m venv .venv
source .venv/bin/activate
python3 -m pip install --upgrade pip
python3 -m pip install -r requirements.txt

Download the LibriTTS generator:

wget -O models/en-us-libritts-high.pt 'https://github.com/rhasspy/piper-sample-generator/releases/download/v1.0.0/en-us-libritts-high.pt'

Run

Generate a small set of samples with the CLI:

python3 generate_samples.py 'okay, piper.' --max-samples 10 --output-dir okay_piper/

Check the okay_piper/ directory for 10 WAV files (named 0.wav to 9.wav).

Generation can be much faster and more efficient if you have a GPU available and PyTorch is configured to use it. In this case, increase the batch size:

python3 generate_samples.py 'okay, piper.' --max-samples 100 --batch-size 10 --output-dir okay_piper/

On an NVidia 2080 Ti with 11GB, a batch size of 100 was possible (generating approximately 100 samples per second).

Setting --max-speakers to a value less than 904 (the number if LibriTTS) is recommended. Because very few samples of later speakers were in the original dataset, using them can cause audio artifacts.

See --help for more options, including adjust the --length-scales (speaking speeds) and --slerp-weights (speaker blending) which are cycled per batch.

Alternatively, you can import the generate function into another Python script:

from generate_samples import generate_samples  # make sure to add this to your Python path as needed

generate_samples(text = ["okay, piper"], max_samples = 100, output_dir = output_dir, batch_size=10)

There are some additional arguments available when importing the function directly, see the docstring of generate_sample for more information.

Augmentation

Once you have samples generating, you can augment them using audiomentation:

python3 augment.py --sample-rate 16000 okay_piper/ okay_piper_augmented/

This will do several things to each sample:

1. Randomly decrease the volume
   • The original samples are normalized, so different volume levels are needed
2. Randomly apply an impulse response using the files in impulses/
   • Change the acoustics of the sample to sound like the speaker was in a room with echo or using a poor quality microphone
3. Resample to 16Khz for training (e.g., openWakeWord)