ftreml

partially agree. In another repo, the same vulnerability was only fixed after years ...

https://github.com/watson-developer-cloud/assistant-simple/c...

I wrote about security threats for chatbots

https://floriantreml.medium.com/security-threats-and-securit...

https://dev.to/codeforequityat/we-are-using-javascript-for-o...

awesome, many thanks!

We built a software (Botium Box - https://botium.ai) mainly for On-Premise use, and we delivered as Kubernetes, Openshift, Docker. We added a hosted plan later and thought it would be a good idea to just use managed Kubernetes for this offer as it didn't require much coding changes.

We have to support multiple clouds (Azure and AWS), but with Rancher, it is really easy in usage - setting up new clusters, deploying new services, restarting, logging etc. But now that we built up container technology know-how we are transitioning every service where we don't need the scaling capabilities of Kubernetes to plain old docker-compose on baremetal.

Thanks for the interesting article, didn't know about Nomad and will try it for sure.

sure, that's a good point. and scaling a one-node-cluster to a multi-node-cluster is also nearly no effort on AWS. but for the topics where we can predict the computing power for the next months, we migrated everything to baremetal.

fully agree - that's how we started - getting some servers up and running is quick and easy, but as soon as you can foresee what computer power you will need in the next months and years, baremetal is surely the better choice. If you can deal with the technical stuff, of course.

The only people who are optimizing for three-figure costs every month are either:

In our case, we are doing the same for around 50 clients, so it sums up :-)

Who would do this and why? Why would you have a load balancer in front of a single server?

afaik, for publishing something to the outside world from EKS, that's the only way - even for single node clusters

thanks for this valuable input

nobody asks how to make money if you already have a hell lot of money and a growth rate like clubhouse. there is plenty of venture money available, more that can be spent.

After several months of beta test we now published Botium Speech Processing version 1.0.0. It was proven to work in several client-specific speech processing tasks, mainly related to automated testing of voice interfaces.

gsuite, hubspot, github, jira, bitbucket, confluence, codeship, vs code, slack, discord

partially our product botium is open source, thats why we have github and bitbucket.

same here

Personally, when using node.js i am a little bit envious on python, for pandas, sklearn, keras.

hi, from your point of view, how does this compare to amazon polly snd google cloud speech ?

here is the link with the changelog: https://github.com/codeforequity-at/botium-speech-processing

Recently we had to evaluate if chatbot we built for an Austrian telecommunication provider would perform better on other NLP engines than the one we had in use (a cloud-based one). We took the training data and calculated common performance metrics, confusion matrices and accuracy scores for a bunch of the blockbuster providers (IBM Watson, Google Dialogflow, Amazon Lex, Microsoft LUIS, Rasa and some more).

We published the scripts in a Github repository and a blog article with instructions: https://medium.com/@floriantreml/tutorial-benchmark-your-cha...

the german model is from the kaldi tuda recipe with WER of 15%.

the english is from the tedlium recipe with WER of 7%.

room for improvement, but for our original purpose it was sufficient.

Few years ago, when building a chatbot for an Austrian telecommunication provider, we noticed that none of the available test automation frameworks was really helping us in testing and training. So we started to build something from scratch, published it on Github, gave it the name "Botium" (Selenium for Websites, Appium for Apps, Botium for Bots ...), and 50k awesome developers downloaded it.

We gave it a pluggable architecture to work with all relevant Conversational AI and NLP/NLU providers out there, made it DevOps- and TestOps-friendly with a CLI and bindings to most loved test runners out there (Mocha, Jest, Jasmine, ...), and still the whole stack is Open Source on Github - thanks to our awesome community and cooperations with ISVs.

Curious to hear your thoughts on the topic - clearly, testing a Conversational AI holds some special challenges for you in regards of test coverage and test levels (API vs E2E).

Here is the link to the Github repository: https://github.com/codeforequity-at/botium-core

Few years ago, when building a chatbot for an Austrian telecommunication provider, we noticed that none of the available test automation frameworks was really helping us in testing and training. So we started to build something from scratch, published it on Github, gave it the name "Botium" (Selenium for Websites, Appium for Apps, Botium for Bots ...), and 50k awesome developers downloaded it.

We gave it a pluggable architecture to work with all relevant Conversational AI and NLP/NLU providers out there, made it DevOps- and TestOps-friendly with a CLI and bindings to most loved test runners out there (Mocha, Jest, Jasmine, ...), and still the whole stack is Open Source on Github - thanks to our awesome community and cooperations with ISVs.

Curious to hear your thoughts on the topic - clearly, testing a Conversational AI holds some special challenges for you in regards of test coverage and test levels (API vs E2E).

so this is a real cool project. as soon as you finished your training I will be happy to add it as option (or maybe default setup) in the botium speech processing setup (if you want that).

Do you have any experience with online decoding in wav2letter ? Is there something like a Websocket API available somewhere ?

are there any wav2letter models ready for download ?

picotts is a command line tool. marytts is a client/server tool. (both included in botium speech processing and callable with curl).

high quality with google cloud speech and amazon polly.

this seems like a joke ...

wonder if this works in real life without additional training phrases for an FAQ ?

I point you to this article: https://medium.com/@klintcho/creating-an-open-speech-recogni...

It basically describes the thing you mentioned - matching freely available audio books with the source text and using some tools to preprocess the data suitable for ASR training (alignment, splitting).

are you ready for a pull request ?

It's an API, of course it is hard to use without any real user interface ... but as an STT/TTS API, it won't get more easy than that ...

Of course it is not a competitor to Google in any sense.

see here: https://speech.botiumbox.com