Rasa Pro 3.7 still supports NLU functionality, so the examples in the documentation (which I’m sure you already know about) are a good starting point.
Thank you, will continue with NLU style for reminders.
Removed post as I solved issue
I’m glad you fixed it!
I generally encourage people to retain problems and solutions in the forums, so that later developers who encounter the same problem can learn from the difficulties we face. But I respect your wish to manage the words you share with the public.
I really like to share all problems and especially in the entire new journey that I am on,it is valauble to share But this time it was issue with GCP firewall hence I considered it is outofscope here. But will consider sharing. Thanks, Geeta
I’m building a multilingual voice bot with CALM using a single model and a language translation service, and it’s slow. Is a multi-model architecture generally better for performance in this scenario or are there any alternatives? Here is brief detail of my flow
- STT module processes the speech.
- Take the output of the STT module (text) and send it to the REST channel of your Rasa server.
- Use Translation service to translate to user language.
- Take the REST response (text) and send it to the TTS module. And time taken for each is as below Conversion took -1(STT) -->: 3.45 seconds
Conversion took -2(Rasa Response) -->: 24.54 seconds response_content → Warm greetings from MitramCares! I’m your friendly helper. How can I make things easier for you today?
Conversion took -3 (Translation)–>: 0.76 seconds
Conversion took -4(TTS) -->: 0.99 seconds
How can I reduce lag in my Rasa API responses? Would an multi model be a suitable solution for overall speed improvement?
But On GCP it takes lesser time but still Rasa server repsonse takes 5 to 6 seconds for simple custom actions