During this tutorial, participants will be introduced to diverse ways how speech-to-text (STT) inferences can be realized on non-cloud, local (i.e. edge-computing) architectures. Participants will acquire knowledge and competence concerning intricacies and nuances of execution of two different types of ASR systems (DeepSpeech and Random Forests) on three different hardware architectures (e.g. RaspberryPiZero (armv6); RaspberryPi 4 (armv7 without CUDA) and NVIDIA Jetson Xavier (armv8 / aarch64 with CUDA). Thus, in 90 minutes of a hands-on tutorial participants will acquire practical know-how about how to transform all three hardware platforms into a low-cost local STT inference engine.

Hope You liked it and let's stay in touch

Daniel & Hyungjoong 

daniel@udk-berlin.de hjk@udk-berlin.de

@DigiEduBerlin

https://github.com/hromi/lesen-mikroserver

please turn off tutorial raspberrys with a voiceshell command "halt"

Let's load some new scorers and see how systems perform.

Please create a list of 10-50 tokens relevant to Your domain of interest and mail them to daniel@udk-berlin.de and hjk@udk-berlin.de , we will create You a microscopic language model (scorer) out of it.

Help extending the "VoiceShell" dataset by doing recordings here: https://fibel.digital/22354 (again, login with l:demo-tutorial p: takarthbr )

Feel free not to do anything or leave the room

the least efficient (1 - 3 Watts...) universal turing machine out there

armv6

1GHz 512 GB RAM

Please connect to Hotspot FibelNet

password: cirrostratus

then try to connect to one two Pi4s we made available for You

ssh demo-tutorial@tutorial0.local

or 

ssh demo-tutorial@tutorial1.local

password: takarthbr

8-core ARM v8.2 64-bit CPU (aarch64) ; 32 GB RAM; 512-core Volta GPU with Tensor Cores

CUDA-support

every now, NVIDIA releases an debian-based package (i.e. Linux4Tegra, L4t) with all packages You need packed in a so-called "Jetpack" suite

full-fledged universal turing machine :: 1.5 GHz 64-bit quad core ARM Cortex-A72 processor, on-board 802.11ac Wi-Fi, Bluetooth 5, full gigabit Ethernet, two USB 2.0 ports, two USB 3.0 ports, 1–8 GB of RAM

5volts only; power-consumption between 3-10 Watt

during this tutorial, You will interact with Raspi4 running Raspbian 10 (buster) 32-bit armv7l

Dramatis personae:

https://github.com/mozilla/DeepSpeech

https://github.com/coqui-ai/STT https://coqui.ai/models

https://gitlab.com/Jaco-Assistant/Scribosermo Quartznet model

Connectionist Temporal Classification (CTC) beam search

Tensorflow & Tensorflow Lite

Random forests (treelite)

Edge computing is a computing paradigm that brings computation and data storage closer to the sources of data. Edge computing concentrates on servers "in proximity to the last mile network".

Web Primer allows wider public to benefit from our growing collection of open educational resources (OERs) without necessity to build a physical Primer. Frontend is a Progressive Web App, backend is a quite sophisticated "knowledge graph". Artificial Intelligence, speech technologies (notably automatic speech recognition ASR) and so-called audio-text play an important role. Current modules:

👄 lesen (ASR)

👂 hören (multi-voice)

👩🏼‍💻 trainieren and 💯 testen (human-machine peer learning)

🎴 memory (single-player) spielen