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๐Ÿ’ป ย  Accessing Utils File and Helper Functions

In each notebook on the top menu:

1: ย  Click on "File"

2: ย  Then, click on "Open"

You will be able to see all the notebook files for the lesson, including any helper functions used in the notebook on the left sidebar. See the following image for the steps above.


๐Ÿ’ป ย  Downloading Notebooks

In each notebook on the top menu:

1: ย  Click on "File"

2: ย  Then, click on "Download as"

3: ย  Then, click on "Notebook (.ipynb)"


๐Ÿ’ป ย  Uploading Your Files

After following the steps shown in the previous section ("File" => "Open"), then click on "Upload" button to upload your files.


๐Ÿ“— ย  See Your Progress

Once you enroll in this courseโ€”or any other short course on the DeepLearning.AI platformโ€”and open it, you can click on 'My Learning' at the top right corner of the desktop view. There, you will be able to see all the short courses you have enrolled in and your progress in each one.

Additionally, your progress in each short course is displayed at the bottom-left corner of the learning page for each course (desktop view).


๐Ÿ“ฑ ย  Features to Use

๐ŸŽž ย  Adjust Video Speed: Click on the gear icon (โš™) on the video and then from the Speed option, choose your desired video speed.

๐Ÿ—ฃ ย  Captions (English and Spanish): Click on the gear icon (โš™) on the video and then from the Captions option, choose to see the captions either in English or Spanish.

๐Ÿ”… ย  Video Quality: If you do not have access to high-speed internet, click on the gear icon (โš™) on the video and then from Quality, choose the quality that works the best for your Internet speed.

๐Ÿ–ฅ ย  Picture in Picture (PiP): This feature allows you to continue watching the video when you switch to another browser tab or window. Click on the small rectangle shape on the video to go to PiP mode.

โˆš ย  Hide and Unhide Lesson Navigation Menu: If you do not have a large screen, you may click on the small hamburger icon beside the title of the course to hide the left-side navigation menu. You can then unhide it by clicking on the same icon again.


๐Ÿง‘ ย  Efficient Learning Tips

The following tips can help you have an efficient learning experience with this short course and other courses.

๐Ÿง‘ ย  Create a Dedicated Study Space: Establish a quiet, organized workspace free from distractions. A dedicated learning environment can significantly improve concentration and overall learning efficiency.

๐Ÿ“… ย  Develop a Consistent Learning Schedule: Consistency is key to learning. Set out specific times in your day for study and make it a routine. Consistent study times help build a habit and improve information retention.

Tip: Set a recurring event and reminder in your calendar, with clear action items, to get regular notifications about your study plans and goals.

โ˜• ย  Take Regular Breaks: Include short breaks in your study sessions. The Pomodoro Technique, which involves studying for 25 minutes followed by a 5-minute break, can be particularly effective.

๐Ÿ’ฌ ย  Engage with the Community: Participate in forums, discussions, and group activities. Engaging with peers can provide additional insights, create a sense of community, and make learning more enjoyable.

โœ ย  Practice Active Learning: Don't just read or run notebooks or watch the material. Engage actively by taking notes, summarizing what you learn, teaching the concept to someone else, or applying the knowledge in your practical projects.


๐Ÿ“š ย  Enroll in Other Short Courses

Keep learning by enrolling in other short courses. We add new short courses regularly. Visit DeepLearning.AI Short Courses page to see our latest courses and begin learning new topics. ๐Ÿ‘‡

๐Ÿ‘‰๐Ÿ‘‰ ๐Ÿ”— DeepLearning.AI โ€“ All Short Courses [+]


๐Ÿ™‚ ย  Let Us Know What You Think

Your feedback helps us know what you liked and didn't like about the course. We read all your feedback and use them to improve this course and future courses. Please submit your feedback by clicking on "Course Feedback" option at the bottom of the lessons list menu (desktop view).

Also, you are more than welcome to join our community ๐Ÿ‘‰๐Ÿ‘‰ ๐Ÿ”— DeepLearning.AI Forum


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Welcome to Building AI Voice Agents for Production taught by Russ d'Sa (Sah), Shayne Parmelee and Nedelina Teneva. Russ is the co-founder and CEO and Shane a developer advocate at LiveKit. And Nedelina is head of AI, at RealAvatar and developed a conversational avatar together with the DeepLearning.AI team. RealAvatar is also a portfolio company of AI Fund, which I lead. I'm excited about agents who can converse with users. This is turning out to be an important way for people to interact with AI agents. DeepLearning.AI and RealAvatar's teams, including Nedelina, had a great experience building a conversational avatar using LiveKit. I personally also really enjoyed using LiveKit for various other projects. In this course, we want to share with you some best practices for building voice agents. Let me describe our project, which we'll use as a running example. We started with a conversational agent, similar to many of the projects you may have seen or built in previous short courses. We developed an agentic workflow to get the system to try to choose words to try to say things similar to what to what I would say in different circumstances. We then added on the input side, a speech to text model to convert the user's audio speech to text for the agentic workflow to process. And then on the output side, added a text-to-speech model to take the text output and turned that into speech. That can then be read out to the user using a model from ElevenLabs for the audio generation. The model was trained to sound like me, and I think the audio turned out pretty decent. You hear later and you can decide. We wanted this to scale to a large number of users, and so we moved to a cloud infrastructure that could support many simultaneous users. Finally, users of this service can be anywhere in the world. This introduced real-time networking concerns and audio integration issues. Our solution, use cloud resources to support the front end of the avatar, with an agentic workflow on the back end and we integrate to a LiveKit to provide communication infrastructure. Now Nedelina and Russ will tell you more about this in the course. In the first lesson, you'll learn the components of a voice pipeline, including speech to text and text-to-speech models, as well as voice activity and end of turn detection. You'll also learn how important latency is and some strategies for keeping latency low. Then we'll try out a voice agent. You'll learn how voice agents are really different from other applications. Voice agents have state and to be effective, must have a presence, just as if there was another person on the other end of the conversation. In lesson four and five, you'll build a voice agent that you can use in the course or download to your own machine. You'll learn to measure latency in a voice pipeline to achieve natural conversation. Many people have worked to create this course. I'd like to thank from LiveKit Theo Monnom and from DeepLearning.AI, Geoff Ladwig. Additionally, I'd like to thank Thor Schaeff from ElevenLabs who created the speech-to-text model you'll be using in this course, and to arrange the support for this course. Thank you Andrew. It's great to be a part of this course. I hope you will find conversational AI agents as compelling as we do, and will take the time to not just explore text-to-speech with ElevenLabs, but also our fully fledged conversational AI platform, which allows you to add voice to your agents within minutes. We can't wait to see what you will build. That sounds great. Let's get started with the next lesson an overview of voice agents.
course detail
Next Lesson
Building AI Voice Agents for Production
  • Introduction
    Video
    ใƒป
    3 mins
  • Voice Agent Overview
    Video
    ใƒป
    13 mins
  • End-to-end architecture - Part 1
    Video
    ใƒป
    12 mins
  • End-to-end architecture - Part 2
    Video
    ใƒป
    8 mins
  • Voice Agent Components
    Video with Code Example
    ใƒป
    5 mins
  • Optimizing Latency
    Video with Code Example
    ใƒป
    7 mins
  • Conclusion
    Video
    ใƒป
    1 min
  • Course Feedback
  • Community