Here Are The NVIDIA Riva Demo Applications
And What’s Available In Jupyter Notebooks
Introduction
This story is a brief overview of what is available in the NVIDIA Riva demo applications and what can be learned from them.
NVIDIA recently released Riva, which is a is described as an application framework for Multimodal Conversational AI.
The focus is on low latency, less than 300 milliseconds, and high performance demands.
The multimodal aspect of Riva is best understood in the context of where NVIDIA wants to take Riva in terms of functionality.
These include:
- ASR (Automatic Speech Recognition) / STT (Speech To Text)
- NLU (Natural Language Understanding)
- Gesture Recognition
- Lip Activity Detection
- Object Detection
- Gaze Detection
- Sentiment Detection
The exciting part of this collection of functionality, is that Riva is poised to become a true Conversational Agent. We communicate as humans not only in voice, but by detecting the gaze of the speaker, lip activity etc.
Another key focus are of Riva is transfer learning. There is significant cost saving when it comes to taking the advanced base models of Riva and repurposing them for specific uses.
The functionality which is currently available in Riva 1.0 Beta includes ASR, STT and NLU.
Virtual Voice Assistant
This Virtual Assistant sample application demonstrates how to use Riva AI Services, specifically ASR, NLP, and TTS, to build a simple but complete conversational AI application.
It demonstrates receiving input via speech from the user, interpreting the query via intent recognition and slot filling approach, compiling a response, and speaking this back to the user in a natural voice.
Read more about the installation process here.
To install and run the Riva Voicebot demo, start your Riva services:
cd riva_quickstart_v1.4.0-beta
bash riva_init.shDownload the samples image from NGC.
docker pull nvcr.io/nvidia/riva/riva-speech-client:1.4.0-beta-samplesRun the service within a Docker container.
docker run -it --rm -p 8009:8009 nvcr.io/nvidia/riva/riva-speech-client:1.4.0-beta-samples /bin/bashWithin this directory…
cd samples/virtual-assistantEdit config.py with the right Riva IP, hosting port and your weatherstack API access key (from https://weatherstack.com/). Then, start the server.
Getting your weatherstack API, on the free tier…
Getting your API Access Key…
Start the service…
python3 main.pyBelow you can see the NVIDIA Riva weather bot accessible on the url https://127.0.0.1:8009/rivaWeather.
Again, you will have to setup to SSH tunneling from your virtual machine. Read about that here.
To take a closer look at example code for ASR, TTS and NLU take a look at the Jupyter Notebook examples…
Jupyter Notebook Examples
The functionality on display via Jupyter notebook:
- Offline ASR Example
- Core NLP Service Examples
- TTS Service Example
- Riva NLP Service Examples
In short, here are a few extracts from the example applications.
The command to run to start the Riva service Jupyter Notebook.
bash riva_start_client.shjupyter notebook --ip=0.0.0.0 --allow-root --notebook-dir=/work/notebooks
Add Punctuation To Text
Adding punctuation is a very useful feature, especially in the following use cases:
- When user speech is generated when interacting with a voicebot and presented on a display.
- Archiving user input speech in text formant.
- When text is generated, this function can also be helpful.
Example Input:
add punctuation to this sentence here please ok
do you have any red nvidia shirts
i need one cpu four gpus and lots of memory
for my new computer it's going to be very coolExample Output:
Add punctuation to this sentence here, please, Ok?
Do you have any red Nvidia shirts?
I need one cpu, four gpus and lots of memory for my new computer. It's going to be very cool.Named Entitles
In NLP, a named entity is a real-world object, such as people, places, companies, products etc.
These named entities can be abstract or have a physical existence. Below are examples of named entities being detected by Riva NLU.
Example Input:
Jensen Huang is the CEO of NVIDIA Corporation, located in Santa Clara, California.Example Output:
Named Entities:
jensen huang (PER)
nvidia corporation (ORG)
santa clara (LOC)
california (LOC)Text Classification
Riva NLP has a default text classification feature.
Intents & Entities With Input Domain
This is a good example of how a NLU API can be implemented to extract intents and entities; or as Riva revers to it, slots. The input domain is defined upfront.
Intents & Entities
Here the domain is not provided, the intent and slot is shown with the score.
Example Input:
Is it going to rain tomorrow?Example Output:
intent {
class_name: "weather.rainfall"
score: 0.9661880135536194
}
slots {
token: "tomorrow"
label {
class_name: "weatherforecastdaily"
score: 0.5325539708137512
}
}
slots {
token: "?"
label {
class_name: "weatherplace"
score: 0.6895459890365601
}
}
domain_str: "weather"
domain {
class_name: "weather"
score: 0.9975590109825134
}Weather Intent Batch Queries
What I particularly like about this function is the intent of weather in this case, and the sub-intents of cloudy, rainfall or humidity.
This can also be useful for real-time disambiguation in conversations.
Example Input:
"Is it currently cloudy in Tokyo?",
"What is the annual rainfall in Pune?",
"What is the humidity going to be tomorrow?Example Output:
[weather.cloudy] Is it currently cloudy in Tokyo?
[weather.rainfall] What is the annual rainfall in Pune?
[weather.humidity] What is the humidity going to be tomorrow?A Few Other Random Examples
Conclusion
The NVIDIA Riva team made sure the documentation is thorough and comprehensive. The demo and quick start applications is of great help to get started. Especially in an environment which is complex and can be very tricky to get started and prototype.
The services available now via Riva are:
- Speech recognition trained on thousands of hours of speech data with stream or batch mode.
- Speech synthesis available in batch and streaming mode.
- NLU API’s with a host of services.
The advent of Riva will surely be a jolt to the current marketplace, especially with imbedded conversational AI solutions. The freedom on installation of open architecture will stand NVIDIA in good stead. Deployment and production architecture will demand careful consideration.
