![]() ![]() ![]() A new Weatherstack API key can be obtained from here. In index.js, at line 4, update the weatherstack_APIkey with your Weatherstack API key. Open the Fulfillment section and enable the Inline Editor in the Dialogflow console.Ĭopy and paste the contents of the sample-apps/virtual-assistant-dialogflow/dialogflow-weatherbot/fulfillment/index.js into index.js under the Inline Editor.Ĭopy and paste the contents of the sample-apps/virtual-assistant-dialogflow/dialogflow-weatherbot/fulfillment/package.json into package.json under the Inline Editor. Under the Export and Import tab, choose Restore From ZIP and upload the zipped folder from your host at sample-apps/virtual-assistant-dialogflow/dialogflow-weatherbot/dialogflow-weatherbot.zip. Initialize and start the Dialogflow Weatherbot.įollow the steps here to create an agent.Ĭlick the Setting button next to the agent name in the Dialogflow console. ![]() # uncomment and populate the section below riva_config = This file contains the Chatbot class which is responsible for pipelining all the ASR, TTS and DialogflowĬreates one instance of the Chatbot class per conversation. This can be set by changing the import statements in lines 12 and 13 in the virtual-assistant-dialogflow/dialogflow-riva-weatherbot-webapp/riva/chatbot/chatbot.py script. TTS can be used in either Batch or Streaming mode, depending on whether tts.py or tts_stream.py is used. Libraries, with a text snippet, and returns the corresponding audio speech. These files contain the functionality to make the gRPC call to Riva TTS, using the Riva Python Client This file contains the functionality to make an API call to Dialogflow, with the user input and sender IDĪnd returns a text response obtained by Dialogflow. Libraries with the audio snippet and returns the text transcript. This file contains the functionality to make the gRPC call to Riva ASR, using the Riva Python Client This section shows the high-level code structure of the Weatherbot Client application (in Pipelines ASR, NLP, TTS, and dialog manager functionalities.Ĭontains the Weatherbot Client application (web UI and web service). Responsible for fulfillment, when needed.Ĭommunicates with Riva AI Services and Dialogflow Weatherbot over gRPC and REST API endpoints respectively. Takes user text as input and returns a response. Here is the implementation at a high-level:Įxposes Speech Services (ASR/NLP/TTS) over gRPC endpoints.Įxposes API endpoints to communicate with the chatbot. There are three primary parts to this solution Riva AI Services, Dialogflow Weatherbot, and the Weatherbot Client The Weatherbot Client coordinates the workflow with Riva Services and Dialogflow, then interacts with theĮnd-user via a web UI. Virtual Assistant (with Google Dialogflow)Īt a high-level, the integration takes advantage of the native API support of Google Dialogflow and gRPC support in How to deploy a NeMo-finetuned NMT model on Riva Speech Skills server?īasics of Speech Recognition and Customization of Riva ASR How to fine-tune a Riva NMT Bilingual model with Nvidia NeMo How do I perform Language Translation using Riva NMT APIs with out-of-the-box models? How do I use Riva TTS APIs with out-of-the-box models?Ĭalculate and Plot the Distribution of Phonemes in a TTS Dataset Speech Recognition - New Language Adaptation How To Train, Evaluate, and Fine-Tune an n-gram Language Model How to Deploy a Conformer-CTC Acoustic Model with WFST Decoders Support for Class Based n-gram Language Models in Riva (WFST Decoder) How do I boost specific words at runtime with word boosting? How to Improve the Accuracy on Noisy Speech by Fine-Tuning the Acoustic Model (Conformer-CTC) in the Riva ASR Pipeline How to Improve Recognition of Specific Words ![]() How to Fine-Tune a Riva ASR Acoustic Model with NVIDIA NeMo How to Customize a Riva ASR Acoustic Model (Conformer-CTC) with Adapters How to Deploy a Custom Acoustic Model (Conformer-CTC) Trained with NeMo on Riva How to Deploy a Custom Acoustic Model (Citrinet) Trained with NeMo on Riva How to Deploy a Custom Language Model (n-gram) Trained with NeMo on Riva How to Customize Riva ASR Vocabulary and Pronunciation with Lexicon Mapping How do I use Riva ASR APIs with out-of-the-box models? How to Deploy Riva at Scale on AWS with EKS ![]()
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