Vosk Api Gpu Save

Vosk API - Docker/GPU

Vosk docker images with GPU for Jetson Nano / Xavier NX boards and PCs with NVIDIA cards.

Usage

Pull an existing image with a required tag.

docker pull sskorol/vosk-api:$TAG

Use it as a baseline in your app's Dockerfile:

FROM sskorol/vosk-api:$TAG

Build prerequisites

• You have to enable nvidia runtime before building the images.
• In the case of Jetson boards, your JetPack should match at least 32.5 version (0.3.32 images were built against 32.6.1).
• For PCs make sure you met the following prerequisites.

Building

Clone sources and check a build file help:

git clone https://github.com/sskorol/vosk-api-gpu.git
cd vosk-api-gpu

Jetson boards

Run a build script with the required args depending on your platform, e.g.:

cd jetson && ./build.sh -m nano -i ml -t 0.3.37

You can check the available NVIDIA base image tags here and here.

PCs

To build images for PC, use the following script:

cd pc && ./build.sh -c 11.3.1-devel-ubuntu20.04 -t 0.3.37

Here, you have to provide a base cuda image tag and the output container's tag. You can read more by running the script with -h flag.

This script will build 2 images: base and a sample Vosk server.

Windows 11 with WSL2

• Follow the official instructions to install Docker Desktop.
• Make sure you fully accomplished the GPU part of the above guide.
• Either use an existing image or build a new one following PCs part of this README.
• Follow the Running part of this README to test your recording.

Apple M1

To build images (w/o GPU) for Apple M1, use the following script:

cd m1 && ./build.sh -t 0.3.37

To build Kaldi and Vosk API locally (w/o Docker), use the following script (thanks to @aivoicesystems):

cd m1 && ./build-local.sh

Note that there's a required software check when you start this script. If you see missing requirements, chances are you'll need to install the following packages:

brew install autoconf cmake automake libtool

Also note that this script installs Vosk API globally. If you want to use it for a specific project, just activate virtual env before running the script.

GCP

To test images on GCP with NVIDIA Tesla T4, use the following steps:

• Install terraform
• Create a new project on GCP
• Install and init gcloud-cli
• Deploy a new Compute Engine instance with the following commands:

cd gcp && terraform init && terraform apply

Note that you'll be prompted to type your GCP project name and zone. When a new instance is deployed, you can now ssh to it:

gcloud compute ssh --project $PROJECT_NAME --zone $ZONE gpu

Clone this repo and build Vosk images on a relatively powerful machine:

git clone https://github.com/sskorol/vosk-api-gpu && cd vosk-api-gpu/gcp
./build.sh -c 11.3.1-devel-ubuntu20.04 -t 0.3.37 -m vosk-model-en-us-0.22

See build script's help for more details regarding input arguments.

Running

The following script will start docker-compose, install requirements and run a simple test:

./test.sh $TAG $WAV_FILE

• Pass a newly built image tag as an argument.
• You have to download and extract a required model into ./model folder first, unless you use a GCP instance.
• Pass your own recording as a second argument. en.wav is used by default.

Important notes

• Jetson Nano won't work with the latest large model due to high memory requirements (at least 8Gb RAM).
• Jetson Xavier will work with the latest large model if you remove rnnlm folder from model.
• Make sure you have at least Docker (20.10.6) and Compose (1.29.1) versions.
• Your host's CUDA version must match the container's as they share the same runtime. Jetson images were built with CUDA 10.1. As per the desktop version: CUDA 11.3.1 was used.
• If you plan to use rnnlm, make sure you allocated at least 12Gb of RAM to your Docker instance (16Gb is optimal).
• In case of GCP usage, there's a know issue with K80 instance. Seems like it has an outdated architecture. So it's recommended to take at least NVIDIA T4.
• Not all the models are adopted for GPU-usage, e.g. in RU model, you have to manually patch configs to make it work (it's done automatically for GCP instance):
  • remove min-active flag from model/conf/model.conf
  • copy/paste ivector.conf from big EN model