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Educator Developer Blog
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Build a Virtual Assistant with Azure Open AI and Azure Speech Service

cyruswong's avatar
cyruswong
Iron Contributor
Feb 09, 2023

You can now sign up for Azure OpenAI Service!

 

The current releases of this demo uses OpenAI. The OpenAI API is designed for developers with API call or you can use Azure OpenAI studio. The goal of this demos is to make a fun and easy to use fine-tuning model which will answer all student course related questions anytime.

The goals of this AI is to save educators’s time. For IVE in Hong Kong, we are speaking Cantonese and students always ask question in traditional Chinese in general.

Therefore, we decide to make a virtual assistant that make use of Azure Open AI and Azure speech services to solve the problem.

Students can talk to her with all languages supported by Microsoft Cognitive Services!


Demo In English



Demo In Cantonese


Please note the current version changes to upload a json config file instead of input 4 text fields.

Try it out

Prerequisite

  1. You need to have an Azure Subscription with Azure Open AI and Microsoft Cognitive Services Text to Speech services.
  2. Create OpenAI model deployment, then note down the endpoint URL and key, and you can follow the “Quickstart: Get started generating text using Azure OpenAI”. For chatbot use case, you can pick “text-davinci-003”.
  3. Create “Text to speech” resources, and you can follow “How to get started with neural text to speech in Azure | Azure Tips and Tricks”. Note down the region and key.


Try the Live2D Azure OpenAI chatbot

  1. Go to https://wongcyrus.github.io/AzureOpenAILive2DChatbotDemo/index.html
  2. Click on “Sample” link and download the json config file.
  3. Edit the config file with your own endpoint and API key.
  4. Upload your config file.
  5. For text input, type in the “Prompt” text box, click on the avatar, and it will response to your prompt!
    For voice input, select your language, click on the red dot, and speak with your Mic. Click on the red dot again to indicate your speech is completed, and it will response to your speech.


How does it work?

This application is just a simple client-side static web with HTML5, CSS3, and JavaScript. We fork and modify Cubism Web Samples from Live2D which is a software technology that allows you to create dynamic expressions that breathe life into an original 2D illustration.


High level overview

Live2D Azure OpenAI Chatbot Overview


Behind the scene,  it is a TypeScript application and we hack the sample to add ajax call when event happens.

For voice input:

  1. When user click on the red dot, it starts capturing mic input with MediaRecorder.
  2. When user click on the red dot again, it stops capturing mic input, and call startVoiceConversation method with language and a Blob object in webm format.
  3. The startVoiceConversation chains down to different Live2D objects from main.ts, LAppDelegate, to LAppLive2DManager which makes a series of Ajax call to Azure Services through AzureAi class. They are getTextFromSpeech, getOpenAiAnswer, and getSpeechUrl.
    Since Azure Text-to-Speech for short audio does not support webm format and getTextFromSpeech converts webm to wav format with webm-to-wav-converter.
  4. With the wav data from Azure Text-to-Speech, it calls wavFileHandler loadWavFile method which sample the voice and get the voice level.
  5. Call the startRandomMotion method of the model object, and it adds lipsync actions according to the voice level. Play the audio right before the parent model update call.

For text input, it is very similar, but the trigger event is model on tap and skips step 1 and 2.


There is no Azure Open AI JavaScript SDK at this moment. For speech service, we was tried microsoft-cognitiveservices-speech-sdk but we hits a Webpack problem, then we decide to use REST API for all Azure API call instead.


AzureAi Class

 

 

 

import { LAppPal } from "./lapppal";
import { getWaveBlob } from "webm-to-wav-converter";
import { LANGUAGE_TO_VOICE_MAPPING_LIST } from "./languagetovoicemapping";


export class AzureAi {
  private _openaiurl: string;
  private _openaipikey: string;
  private _ttsapikey: string;
  private _ttsregion: string;

  private _inProgress: boolean;

  constructor() {
    const config = (document.getElementById("config") as any).value;

    if (config !== "") {
      const json = JSON.parse(config);
      this._openaiurl = json.openaiurl;
      this._openaipikey = json.openaipikey;
      this._ttsregion = json.ttsregion;
      this._ttsapikey = json.ttsapikey;
    }

    this._inProgress = false;
  }

  async getOpenAiAnswer(prompt: string) {

    if (this._openaiurl === undefined || this._inProgress || prompt === "") return "";

    this._inProgress = true;

    const conversations = (document.getElementById("conversations") as any).value;
    LAppPal.printMessage(prompt);

    const conversation = conversations + "\n\n## " + prompt
    const m = {
      "prompt": `##${conversation}\n\n`,
      "max_tokens": 300,
      "temperature": 0,
      "frequency_penalty": 0,
      "presence_penalty": 0,
      "top_p": 1,
      "stop": ["#", ";"]
    }

    const repsonse = await fetch(this._openaiurl, {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'api-key': this._openaipikey,
      },
      body: JSON.stringify(m)
    });
    const json = await repsonse.json();
    const answer: string = json.choices[0].text
    LAppPal.printMessage(answer);
    (document.getElementById("reply") as any).value = answer;
    (document.getElementById("conversations") as any).value = conversations + "\n\n" + answer;

    return answer;
  }

  async getSpeechUrl(language: string, text: string) {

    if (this._ttsregion === undefined) return;

    const requestHeaders: HeadersInit = new Headers();
    requestHeaders.set('Content-Type', 'application/ssml+xml');
    requestHeaders.set('X-Microsoft-OutputFormat', 'riff-8khz-16bit-mono-pcm');
    requestHeaders.set('Ocp-Apim-Subscription-Key', this._ttsapikey);

    const voice = LANGUAGE_TO_VOICE_MAPPING_LIST.find(c => c.voice.startsWith(language) && c.IsMale === false).voice;

    const ssml = `
<speak version=\'1.0\' xml:lang=\'${language}\'>
  <voice xml:lang=\'${language}\' xml:gender=\'Female\' name=\'${voice}\'>
    ${text}
  </voice>
</speak>`;

    const response = await fetch(`https://${this._ttsregion}.tts.speech.microsoft.com/cognitiveservices/v1`, {
      method: 'POST',
      headers: requestHeaders,
      body: ssml
    });

    const blob = await response.blob();

    var url = window.URL.createObjectURL(blob)
    const audio: any = document.getElementById('voice');
    audio.src=url;
    LAppPal.printMessage(`Load Text to Speech url`);
    this._inProgress = false;
    return url;
  }

  async getTextFromSpeech(language: string, data: Blob) {
    if (this._ttsregion === undefined) return "";

    LAppPal.printMessage(language);
    const requestHeaders: HeadersInit = new Headers();
    requestHeaders.set('Accept', 'application/json;text/xml');
    requestHeaders.set('Content-Type', 'audio/wav; codecs=audio/pcm; samplerate=16000');
    requestHeaders.set('Ocp-Apim-Subscription-Key', this._ttsapikey);

    const wav = await getWaveBlob(data, false);

    const response = await fetch(`https://${this._ttsregion}.stt.speech.microsoft.com/speech/recognition/conversation/cognitiveservices/v1?language=${language}`, {
      method: 'POST',
      headers: requestHeaders,
      body: wav
    });
    const json = await response.json();
    return json.DisplayText;
  }
}

 

 

 

 

LAppLive2DManager startVoiceConversation method

 

 

 

  public startVoiceConversation(language: string, data: Blob) {
    for (let i = 0; i < this._models.getSize(); i++) {
      if (LAppDefine.DebugLogEnable) {
        LAppPal.printMessage(
          `startConversation`
        );
        const azureAi = new AzureAi();

        azureAi.getTextFromSpeech(language, data)
          .then(text => {
            (document.getElementById("prompt") as any).value = text;
            return azureAi.getOpenAiAnswer(text);
          }).then(ans => azureAi.getSpeechUrl(language, ans))
          .then(url => {
            this._models.at(i)._wavFileHandler.loadWavFile(url);
            this._models
              .at(i)
              .startRandomMotion(
                LAppDefine.MotionGroupTapBody,
                LAppDefine.PriorityNormal,
                this._finishedMotion
              );
          });
      }
    }
  }

 

 

 

 

Simulate ChatGPT in Azure OpenAI

It is very simple! You just need to keep your conversation and send the whole conversation to Azure OpenAI completion API.


There is a node package called chatgpt which is a hack to call chatgpt from node.js. It introduces conversationId and parentMessageId.

For our case, we just keep sending the whole conversation to Azure Open AI model.

 

GitHub Repo

Project Source

https://github.com/wongcyrus/AzureOpenAILive2DChatbot

Remember follow Live2D instruction! You need to download and copy Core & SDK files.

I suggest you to use CodeSpace for your development.

Demo Site

https://github.com/wongcyrus/AzureOpenAILive2DChatbotDemo

I just code some files after the production build.


Conclusion

This project is a Single Page Application and 100% server less. It is possible to wrap the Azure OpenAI API with API management, deploy to Azure Static Web Apps and use Azure Function to have a fine grant control of the usage.


For education, we are thinking about using fine-tuned/customize model to build a virtual tutor and accurately answer our course related questions.


For fun, it is just like a virtual girl friend and you can chat with her for anything!


Project collaborators include Lo Hau Yin Samuel, and Shing Seto plus 3 Microsoft Student Ambassador Andy LumPeter Liu, and Jerry Lee, from the IT114115 Higher Diploma in Cloud and Data Centre Administration


About the Author

Cyrus Wong is the senior lecturer of Department of Information Technology (IT) of the Hong Kong Institute of Vocational Education (Lee Wa... and he focuses on teaching public Cloud technologies. He is one of the Microsoft Learn for Educators Ambassador and Microsoft Azure MVP from Hong Kong.


 

 

Updated Feb 17, 2023
Version 8.0

5 Comments

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\n

This post shows you how to create an extremely powerful virtual assistant with Azure OpenAI and Azure Speech Services for all languages. It is just a static web application without running any server and everything done with client side JavaScript. Azure OpenAI Service provides developers with API calls to make a virtual assistant that uses Azure AI and speech services. Students can use it to get course-related answers. You can try the Live2D Azure OpenAI chatbot by creating an Azure subscription and configuring it.

","body":"

You can now sign up for Azure OpenAI Service!

\n

 

\n

The current releases of this demo uses OpenAI. The OpenAI API is designed for developers with API call or you can use Azure OpenAI studio. The goal of this demos is to make a fun and easy to use fine-tuning model which will answer all student course related questions anytime.

The goals of this AI is to save educators’s time. For IVE in Hong Kong, we are speaking Cantonese and students always ask question in traditional Chinese in general.

\n

Therefore, we decide to make a virtual assistant that make use of Azure Open AI and Azure speech services to solve the problem.

\n

Students can talk to her with all languages supported by Microsoft Cognitive Services!

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Demo In English

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Demo In Cantonese

\n

\n


Please note the current version changes to upload a json config file instead of input 4 text fields.

\n

Try it out

\n

Prerequisite

\n
    \n
  1. You need to have an Azure Subscription with Azure Open AI and Microsoft Cognitive Services Text to Speech services.
  2. \n
  3. Create OpenAI model deployment, then note down the endpoint URL and key, and you can follow the “Quickstart: Get started generating text using Azure OpenAI”. For chatbot use case, you can pick “text-davinci-003”.
  4. \n
  5. Create “Text to speech” resources, and you can follow “How to get started with neural text to speech in Azure | Azure Tips and Tricks”. Note down the region and key.
  6. \n
\n


Try the Live2D Azure OpenAI chatbot

\n
    \n
  1. Go to https://wongcyrus.github.io/AzureOpenAILive2DChatbotDemo/index.html
  2. \n
  3. Click on “Sample” link and download the json config file.
  4. \n
  5. Edit the config file with your own endpoint and API key.
  6. \n
  7. Upload your config file.
  8. \n
  9. For text input, type in the “Prompt” text box, click on the avatar, and it will response to your prompt!
    For voice input, select your language, click on the red dot, and speak with your Mic. Click on the red dot again to indicate your speech is completed, and it will response to your speech.
  10. \n
\n


How does it work?

\n

This application is just a simple client-side static web with HTML5, CSS3, and JavaScript. We fork and modify Cubism Web Samples from Live2D which is a software technology that allows you to create dynamic expressions that breathe life into an original 2D illustration.

\n


High level overview

\"Live2D

\n


Behind the scene,  it is a TypeScript application and we hack the sample to add ajax call when event happens.

\n

For voice input:

\n
    \n
  1. When user click on the red dot, it starts capturing mic input with MediaRecorder.
  2. \n
  3. When user click on the red dot again, it stops capturing mic input, and call startVoiceConversation method with language and a Blob object in webm format.
  4. \n
  5. The startVoiceConversation chains down to different Live2D objects from main.ts, LAppDelegate, to LAppLive2DManager which makes a series of Ajax call to Azure Services through AzureAi class. They are getTextFromSpeech, getOpenAiAnswer, and getSpeechUrl.
    Since Azure Text-to-Speech for short audio does not support webm format and getTextFromSpeech converts webm to wav format with webm-to-wav-converter.
  6. \n
  7. With the wav data from Azure Text-to-Speech, it calls wavFileHandler loadWavFile method which sample the voice and get the voice level.
  8. \n
  9. Call the startRandomMotion method of the model object, and it adds lipsync actions according to the voice level. Play the audio right before the parent model update call.
  10. \n
\n

For text input, it is very similar, but the trigger event is model on tap and skips step 1 and 2.

\n


There is no Azure Open AI JavaScript SDK at this moment. For speech service, we was tried microsoft-cognitiveservices-speech-sdk but we hits a Webpack problem, then we decide to use REST API for all Azure API call instead.

\n


AzureAi Class

\n

 

\n

 

\n

 

\n
import { LAppPal } from \"./lapppal\";\nimport { getWaveBlob } from \"webm-to-wav-converter\";\nimport { LANGUAGE_TO_VOICE_MAPPING_LIST } from \"./languagetovoicemapping\";\n\n\nexport class AzureAi {\n  private _openaiurl: string;\n  private _openaipikey: string;\n  private _ttsapikey: string;\n  private _ttsregion: string;\n\n  private _inProgress: boolean;\n\n  constructor() {\n    const config = (document.getElementById(\"config\") as any).value;\n\n    if (config !== \"\") {\n      const json = JSON.parse(config);\n      this._openaiurl = json.openaiurl;\n      this._openaipikey = json.openaipikey;\n      this._ttsregion = json.ttsregion;\n      this._ttsapikey = json.ttsapikey;\n    }\n\n    this._inProgress = false;\n  }\n\n  async getOpenAiAnswer(prompt: string) {\n\n    if (this._openaiurl === undefined || this._inProgress || prompt === \"\") return \"\";\n\n    this._inProgress = true;\n\n    const conversations = (document.getElementById(\"conversations\") as any).value;\n    LAppPal.printMessage(prompt);\n\n    const conversation = conversations + \"\\n\\n## \" + prompt\n    const m = {\n      \"prompt\": `##${conversation}\\n\\n`,\n      \"max_tokens\": 300,\n      \"temperature\": 0,\n      \"frequency_penalty\": 0,\n      \"presence_penalty\": 0,\n      \"top_p\": 1,\n      \"stop\": [\"#\", \";\"]\n    }\n\n    const repsonse = await fetch(this._openaiurl, {\n      method: 'POST',\n      headers: {\n        'Content-Type': 'application/json',\n        'api-key': this._openaipikey,\n      },\n      body: JSON.stringify(m)\n    });\n    const json = await repsonse.json();\n    const answer: string = json.choices[0].text\n    LAppPal.printMessage(answer);\n    (document.getElementById(\"reply\") as any).value = answer;\n    (document.getElementById(\"conversations\") as any).value = conversations + \"\\n\\n\" + answer;\n\n    return answer;\n  }\n\n  async getSpeechUrl(language: string, text: string) {\n\n    if (this._ttsregion === undefined) return;\n\n    const requestHeaders: HeadersInit = new Headers();\n    requestHeaders.set('Content-Type', 'application/ssml+xml');\n    requestHeaders.set('X-Microsoft-OutputFormat', 'riff-8khz-16bit-mono-pcm');\n    requestHeaders.set('Ocp-Apim-Subscription-Key', this._ttsapikey);\n\n    const voice = LANGUAGE_TO_VOICE_MAPPING_LIST.find(c => c.voice.startsWith(language) && c.IsMale === false).voice;\n\n    const ssml = `\n<speak version=\\'1.0\\' xml:lang=\\'${language}\\'>\n  <voice xml:lang=\\'${language}\\' xml:gender=\\'Female\\' name=\\'${voice}\\'>\n    ${text}\n  </voice>\n</speak>`;\n\n    const response = await fetch(`https://${this._ttsregion}.tts.speech.microsoft.com/cognitiveservices/v1`, {\n      method: 'POST',\n      headers: requestHeaders,\n      body: ssml\n    });\n\n    const blob = await response.blob();\n\n    var url = window.URL.createObjectURL(blob)\n    const audio: any = document.getElementById('voice');\n    audio.src=url;\n    LAppPal.printMessage(`Load Text to Speech url`);\n    this._inProgress = false;\n    return url;\n  }\n\n  async getTextFromSpeech(language: string, data: Blob) {\n    if (this._ttsregion === undefined) return \"\";\n\n    LAppPal.printMessage(language);\n    const requestHeaders: HeadersInit = new Headers();\n    requestHeaders.set('Accept', 'application/json;text/xml');\n    requestHeaders.set('Content-Type', 'audio/wav; codecs=audio/pcm; samplerate=16000');\n    requestHeaders.set('Ocp-Apim-Subscription-Key', this._ttsapikey);\n\n    const wav = await getWaveBlob(data, false);\n\n    const response = await fetch(`https://${this._ttsregion}.stt.speech.microsoft.com/speech/recognition/conversation/cognitiveservices/v1?language=${language}`, {\n      method: 'POST',\n      headers: requestHeaders,\n      body: wav\n    });\n    const json = await response.json();\n    return json.DisplayText;\n  }\n}\n
\n

 

\n

 

\n

 

\n

 

\n

LAppLive2DManager startVoiceConversation method

\n

 

\n

 

\n

 

\n
  public startVoiceConversation(language: string, data: Blob) {\n    for (let i = 0; i < this._models.getSize(); i++) {\n      if (LAppDefine.DebugLogEnable) {\n        LAppPal.printMessage(\n          `startConversation`\n        );\n        const azureAi = new AzureAi();\n\n        azureAi.getTextFromSpeech(language, data)\n          .then(text => {\n            (document.getElementById(\"prompt\") as any).value = text;\n            return azureAi.getOpenAiAnswer(text);\n          }).then(ans => azureAi.getSpeechUrl(language, ans))\n          .then(url => {\n            this._models.at(i)._wavFileHandler.loadWavFile(url);\n            this._models\n              .at(i)\n              .startRandomMotion(\n                LAppDefine.MotionGroupTapBody,\n                LAppDefine.PriorityNormal,\n                this._finishedMotion\n              );\n          });\n      }\n    }\n  }
\n

 

\n

 

\n

 

\n

 

\n

Simulate ChatGPT in Azure OpenAI

\n

It is very simple! You just need to keep your conversation and send the whole conversation to Azure OpenAI completion API.

\n


There is a node package called chatgpt which is a hack to call chatgpt from node.js. It introduces conversationId and parentMessageId.

\n

For our case, we just keep sending the whole conversation to Azure Open AI model.

\n

 

\n

GitHub Repo

\n

Project Source

\n

https://github.com/wongcyrus/AzureOpenAILive2DChatbot

\n

Remember follow Live2D instruction! You need to download and copy Core & SDK files.

\n

I suggest you to use CodeSpace for your development.

\n

Demo Site

\n

https://github.com/wongcyrus/AzureOpenAILive2DChatbotDemo

\n

I just code some files after the production build.

\n


Conclusion

\n

This project is a Single Page Application and 100% server less. It is possible to wrap the Azure OpenAI API with API management, deploy to Azure Static Web Apps and use Azure Function to have a fine grant control of the usage.

\n


For education, we are thinking about using fine-tuned/customize model to build a virtual tutor and accurately answer our course related questions.

\n


For fun, it is just like a virtual girl friend and you can chat with her for anything!

\n


Project collaborators include Lo Hau Yin Samuel, and Shing Seto plus 3 Microsoft Student Ambassador Andy LumPeter Liu, and Jerry Lee, from the IT114115 Higher Diploma in Cloud and Data Centre Administration

\n


About the Author

\n

Cyrus Wong is the senior lecturer of Department of Information Technology (IT) of the Hong Kong Institute of Vocational Education (Lee Wa... and he focuses on teaching public Cloud technologies. He is one of the Microsoft Learn for Educators Ambassador and Microsoft Azure MVP from Hong Kong.

\n


\n

 

\n

 

","body@stringLength":"18156","rawBody":"

You can now sign up for Azure OpenAI Service!

\n

 

\n

The current releases of this demo uses OpenAI. The OpenAI API is designed for developers with API call or you can use Azure OpenAI studio. The goal of this demos is to make a fun and easy to use fine-tuning model which will answer all student course related questions anytime.

The goals of this AI is to save educators’s time. For IVE in Hong Kong, we are speaking Cantonese and students always ask question in traditional Chinese in general.

\n

Therefore, we decide to make a virtual assistant that make use of Azure Open AI and Azure speech services to solve the problem.

\n

Students can talk to her with all languages supported by Microsoft Cognitive Services!

\n


Demo In English

\n



Demo In Cantonese

\n

\n


Please note the current version changes to upload a json config file instead of input 4 text fields.

\n

Try it out

\n

Prerequisite

\n
    \n
  1. You need to have an Azure Subscription with Azure Open AI and Microsoft Cognitive Services Text to Speech services.
  2. \n
  3. Create OpenAI model deployment, then note down the endpoint URL and key, and you can follow the “Quickstart: Get started generating text using Azure OpenAI”. For chatbot use case, you can pick “text-davinci-003”.
  4. \n
  5. Create “Text to speech” resources, and you can follow “How to get started with neural text to speech in Azure | Azure Tips and Tricks”. Note down the region and key.
  6. \n
\n


Try the Live2D Azure OpenAI chatbot

\n
    \n
  1. Go to https://wongcyrus.github.io/AzureOpenAILive2DChatbotDemo/index.html
  2. \n
  3. Click on “Sample” link and download the json config file.
  4. \n
  5. Edit the config file with your own endpoint and API key.
  6. \n
  7. Upload your config file.
  8. \n
  9. For text input, type in the “Prompt” text box, click on the avatar, and it will response to your prompt!
    For voice input, select your language, click on the red dot, and speak with your Mic. Click on the red dot again to indicate your speech is completed, and it will response to your speech.
  10. \n
\n


How does it work?

\n

This application is just a simple client-side static web with HTML5, CSS3, and JavaScript. We fork and modify Cubism Web Samples from Live2D which is a software technology that allows you to create dynamic expressions that breathe life into an original 2D illustration.

\n


High level overview

\"Live2D

\n


Behind the scene,  it is a TypeScript application and we hack the sample to add ajax call when event happens.

\n

For voice input:

\n
    \n
  1. When user click on the red dot, it starts capturing mic input with MediaRecorder.
  2. \n
  3. When user click on the red dot again, it stops capturing mic input, and call startVoiceConversation method with language and a Blob object in webm format.
  4. \n
  5. The startVoiceConversation chains down to different Live2D objects from main.ts, LAppDelegate, to LAppLive2DManager which makes a series of Ajax call to Azure Services through AzureAi class. They are getTextFromSpeech, getOpenAiAnswer, and getSpeechUrl.
    Since Azure Text-to-Speech for short audio does not support webm format and getTextFromSpeech converts webm to wav format with webm-to-wav-converter.
  6. \n
  7. With the wav data from Azure Text-to-Speech, it calls wavFileHandler loadWavFile method which sample the voice and get the voice level.
  8. \n
  9. Call the startRandomMotion method of the model object, and it adds lipsync actions according to the voice level. Play the audio right before the parent model update call.
  10. \n
\n

For text input, it is very similar, but the trigger event is model on tap and skips step 1 and 2.

\n


There is no Azure Open AI JavaScript SDK at this moment. For speech service, we was tried microsoft-cognitiveservices-speech-sdk but we hits a Webpack problem, then we decide to use REST API for all Azure API call instead.

\n


AzureAi Class

\n

 

\n

 

\n

 

\nimport { LAppPal } from \"./lapppal\";\nimport { getWaveBlob } from \"webm-to-wav-converter\";\nimport { LANGUAGE_TO_VOICE_MAPPING_LIST } from \"./languagetovoicemapping\";\n\n\nexport class AzureAi {\n private _openaiurl: string;\n private _openaipikey: string;\n private _ttsapikey: string;\n private _ttsregion: string;\n\n private _inProgress: boolean;\n\n constructor() {\n const config = (document.getElementById(\"config\") as any).value;\n\n if (config !== \"\") {\n const json = JSON.parse(config);\n this._openaiurl = json.openaiurl;\n this._openaipikey = json.openaipikey;\n this._ttsregion = json.ttsregion;\n this._ttsapikey = json.ttsapikey;\n }\n\n this._inProgress = false;\n }\n\n async getOpenAiAnswer(prompt: string) {\n\n if (this._openaiurl === undefined || this._inProgress || prompt === \"\") return \"\";\n\n this._inProgress = true;\n\n const conversations = (document.getElementById(\"conversations\") as any).value;\n LAppPal.printMessage(prompt);\n\n const conversation = conversations + \"\\n\\n## \" + prompt\n const m = {\n \"prompt\": `##${conversation}\\n\\n`,\n \"max_tokens\": 300,\n \"temperature\": 0,\n \"frequency_penalty\": 0,\n \"presence_penalty\": 0,\n \"top_p\": 1,\n \"stop\": [\"#\", \";\"]\n }\n\n const repsonse = await fetch(this._openaiurl, {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n 'api-key': this._openaipikey,\n },\n body: JSON.stringify(m)\n });\n const json = await repsonse.json();\n const answer: string = json.choices[0].text\n LAppPal.printMessage(answer);\n (document.getElementById(\"reply\") as any).value = answer;\n (document.getElementById(\"conversations\") as any).value = conversations + \"\\n\\n\" + answer;\n\n return answer;\n }\n\n async getSpeechUrl(language: string, text: string) {\n\n if (this._ttsregion === undefined) return;\n\n const requestHeaders: HeadersInit = new Headers();\n requestHeaders.set('Content-Type', 'application/ssml+xml');\n requestHeaders.set('X-Microsoft-OutputFormat', 'riff-8khz-16bit-mono-pcm');\n requestHeaders.set('Ocp-Apim-Subscription-Key', this._ttsapikey);\n\n const voice = LANGUAGE_TO_VOICE_MAPPING_LIST.find(c => c.voice.startsWith(language) && c.IsMale === false).voice;\n\n const ssml = `\n<speak version=\\'1.0\\' xml:lang=\\'${language}\\'>\n <voice xml:lang=\\'${language}\\' xml:gender=\\'Female\\' name=\\'${voice}\\'>\n ${text}\n </voice>\n</speak>`;\n\n const response = await fetch(`https://${this._ttsregion}.tts.speech.microsoft.com/cognitiveservices/v1`, {\n method: 'POST',\n headers: requestHeaders,\n body: ssml\n });\n\n const blob = await response.blob();\n\n var url = window.URL.createObjectURL(blob)\n const audio: any = document.getElementById('voice');\n audio.src=url;\n LAppPal.printMessage(`Load Text to Speech url`);\n this._inProgress = false;\n return url;\n }\n\n async getTextFromSpeech(language: string, data: Blob) {\n if (this._ttsregion === undefined) return \"\";\n\n LAppPal.printMessage(language);\n const requestHeaders: HeadersInit = new Headers();\n requestHeaders.set('Accept', 'application/json;text/xml');\n requestHeaders.set('Content-Type', 'audio/wav; codecs=audio/pcm; samplerate=16000');\n requestHeaders.set('Ocp-Apim-Subscription-Key', this._ttsapikey);\n\n const wav = await getWaveBlob(data, false);\n\n const response = await fetch(`https://${this._ttsregion}.stt.speech.microsoft.com/speech/recognition/conversation/cognitiveservices/v1?language=${language}`, {\n method: 'POST',\n headers: requestHeaders,\n body: wav\n });\n const json = await response.json();\n return json.DisplayText;\n }\n}\n\n

 

\n

 

\n

 

\n

 

\n

LAppLive2DManager startVoiceConversation method

\n

 

\n

 

\n

 

\n public startVoiceConversation(language: string, data: Blob) {\n for (let i = 0; i < this._models.getSize(); i++) {\n if (LAppDefine.DebugLogEnable) {\n LAppPal.printMessage(\n `startConversation`\n );\n const azureAi = new AzureAi();\n\n azureAi.getTextFromSpeech(language, data)\n .then(text => {\n (document.getElementById(\"prompt\") as any).value = text;\n return azureAi.getOpenAiAnswer(text);\n }).then(ans => azureAi.getSpeechUrl(language, ans))\n .then(url => {\n this._models.at(i)._wavFileHandler.loadWavFile(url);\n this._models\n .at(i)\n .startRandomMotion(\n LAppDefine.MotionGroupTapBody,\n LAppDefine.PriorityNormal,\n this._finishedMotion\n );\n });\n }\n }\n }\n

 

\n

 

\n

 

\n

 

\n

Simulate ChatGPT in Azure OpenAI

\n

It is very simple! You just need to keep your conversation and send the whole conversation to Azure OpenAI completion API.

\n


There is a node package called chatgpt which is a hack to call chatgpt from node.js. It introduces conversationId and parentMessageId.

\n

For our case, we just keep sending the whole conversation to Azure Open AI model.

\n

 

\n

GitHub Repo

\n

Project Source

\n

https://github.com/wongcyrus/AzureOpenAILive2DChatbot

\n

Remember follow Live2D instruction! You need to download and copy Core & SDK files.

\n

I suggest you to use CodeSpace for your development.

\n

Demo Site

\n

https://github.com/wongcyrus/AzureOpenAILive2DChatbotDemo

\n

I just code some files after the production build.

\n


Conclusion

\n

This project is a Single Page Application and 100% server less. It is possible to wrap the Azure OpenAI API with API management, deploy to Azure Static Web Apps and use Azure Function to have a fine grant control of the usage.

\n


For education, we are thinking about using fine-tuned/customize model to build a virtual tutor and accurately answer our course related questions.

\n


For fun, it is just like a virtual girl friend and you can chat with her for anything!

\n


Project collaborators include Lo Hau Yin Samuel, and Shing Seto plus 3 Microsoft Student Ambassador Andy LumPeter Liu, and Jerry Lee, from the IT114115 Higher Diploma in Cloud and Data Centre Administration

\n


About the Author

\n

Cyrus Wong is the senior lecturer of Department of Information Technology (IT) of the Hong Kong Institute of Vocational Education (Lee Wa... and he focuses on teaching public Cloud technologies. He is one of the Microsoft Learn for Educators Ambassador and Microsoft Azure MVP from Hong Kong.

\n


\n

 

\n

 

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\n

This post shows you how to create an extremely powerful virtual assistant with Azure OpenAI and Azure Speech Services for all languages. It is just a static web application without running any server and everything done with client side JavaScript. Azure OpenAI Service provides developers with API calls to make a virtual assistant that uses Azure AI and speech services. Students can use it to get course-related answers. You can try the Live2D Azure OpenAI chatbot by creating an Azure subscription and configuring it.

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Build a Virtual Assistant with Azure Open AI and Azure Speech Service","moderationData":{"__ref":"ModerationData:moderation_data:3777795"},"body":"

This is now a previous version of Azure OpenAI Services Virtual Assistant which was limited to being a client-side application.

The application required users to share two API keys, making it less secure and not suitable for production. Additionally, monitoring usage and preparing for course assessments on using OpenAI Chatgpt was challenging.

Finally, cost-saving was also a concern. 
To address these issues, we have developed a serverless solution using Azure Static Web Apps. Read the latest blog at Maximizing Your Academic Potential with Azure Open AI ChatGPT Virtual Assistant (microsoft.com)

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hello vpax22 
Thank you for this, i have already built my embedding model in python and it is ready to be connected to a website, so specificly looking for how to do that.
Thank you!

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@Gyorgi2255 : 
Note: 

\n

\n

Hope this link helps you out :  in your own Azure Tenant : Chat playground (Preview) | Azure OpenAI Studio

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Hello 🙂
Do you have perhaps just the chat part ? Building an embedding model from azure openai than use text-davinci on it and connecting it to a website to have the interaction possibility?

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Excellent work here 👏 👍 🙌 👌 😀. Looking forward to networking with you. 

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