git clone --depth=1 https://github.com/dusty-nv/jetson-containers
cd jetson-containers
./run.sh --workdir=/opt/llama.cpp/bin $(./autotag llama_cpp) /bin/bash -c  './server --model $(huggingface-downloader TheBloke/openchat_3.5-GGUF/openchat_3.5.Q4_K_S.gguf) --n-gpu-layers 999 --threads $(nproc) -c 2048'

Look here https://docs.nvidia.com/deeplearning/riva/user-guide/docs/quick-start-guide.html

Install Jupyter Notebook

pip install notebook

Run jupyter notebook and open Train_NeMo.ipynb. Uncommenting the install lines in the first cell should install everything needed, but that isn't guaranteed. Run one cell at a time until everything is trained. This should be run on a computer with a decent GPU, not a Jetson.

You will first need to setup NGC. Setup the CLI and also login to nvcr.io in Docker

You can use the models directly from Python.

You can download the Riva files from HERE and skip this step, or generate your own using the commands below. These commands need to be don on the computer that trained the models, not the computer that will be running them (ie. Jetson). Note: All of the filepaths here are for my computer. Just create empty folders to start.

pip3 install whl
pip3 install nemo2riva==2.13.1

nemo2riva --out hifigan.riva hifigan.nemo --key tlt_encode
nemo2riva --out fastpitch.riva fastpitch.nemo --key tlt_encode

Transfer the .riva files from the step above (or you downloaded) to whatever machine will be running the voice. These commands are run on the Riva machine. Note: All of the filepaths here are for my computer. Just create empty folders to start.

docker run --gpus all -it --rm \
    -v /home/davesarmoury/RIVA/artifacts:/servicemaker-dev \
    -v /home/davesarmoury/RIVA/riva_repo:/data \
    --entrypoint="/bin/bash" \
    nvcr.io/nvidia/riva/riva-speech:2.13.1-servicemaker-l4t-aarch64

riva-build speech_synthesis \
    /servicemaker-dev/glados.rmir:tlt_encode \
    /servicemaker-dev/glados_fastpitch.riva:tlt_encode \
    /servicemaker-dev/glados_hifigan.riva:tlt_encode \
    --voice_name=GLaDOS \
    --sample_rate 22050

riva-deploy /servicemaker-dev/glados.rmir:tlt_encode /data/models

exit

These commands will create a bunch of folders inside the "artifacts" directory.

Then, get the Quickstart for Riva. This example is for Jetson (arm64), but will change for different architectures

ngc registry resource download-version nvidia/riva/riva_quickstart_arm64:2.13.1
cd riva_quickstart_arm64_v2.13.1

Edit the config.sh file and change service_enabled_nlp and service_enabled_nmt to false. This isn't necessary, but will speed things up if you aren't using them. Then, download all of the models with the command below

bash riva_init.sh

Take the folders that were created in the previous step and copy them into the riva_quickstart_arm64_v2.13.1/model_repository/models. That should be all you need to setup Riva. Run it with the command below

riva_start.sh

To verify that everything is working properly, use the riva python clients. Follow the install instructions HERE

Once that is setup, run the tts.py file under riva/client. Pass in "--voice GLaDOS" as parameters and whatever string you want her to say. You can choose to save the audio as a file, or play it directly.

Check device number

python3 transcribe_mic.py --input-device=4

Pair bluetooth speaker See https://developer.nvidia.com/embedded/learn/tutorials/connecting-bluetooth-audio

python3 glados_talk.py --voice=GLaDOS --play-audio

roslaunch glados_bringup bringup.launch