TravelTales 🚗💬

Welcome to the documentation for TravelTales, your go-to in-car conversation recommendation system! 🌟

Explore the innovative TravelTales system, a solution balancing in-car entertainment with driver safety. Leveraging AI and ML, TravelTales provides personalized conversation prompts, enhancing the overall driving experience. 🚀

State of the Art

Delve into conversation engagement monitoring, drawing inspiration from Morreale et al.'s multimodal approach. Our project adopts a similar strategy, integrating audio sentiment, speech rate, and video sentiment for dynamic engagement estimation.

Architecture

Discover the client-server structure of TravelTales. The client, implemented in Python, runs on a Raspberry Pi, while the server, built with Python Flask, handles requests and responses. The indexer, created in Google Colab, uses GPUs for efficient processing.

Indexer

Explore the comprehensive Indexer module, where the collection of news is created, including the TTS model, Summarization model, and News Embedding model.

TTS Model

Utilizing Coqui TTS, TravelTales generates natural-sounding audio news tracks. The TTS model creates audio from news article summaries, enhancing the in-car experience.

Summarization Model

The summarization model uses Latent Semantic Analysis and a BART Transformer for concise news summaries.

News Embedding Model

Learn about the news embedding model's real-valued vector generation from textual input. Based on a DistilBERT model, it maps news articles into a 14-dimensional space for effective similarity calculations. Operations include summarization, embedding creation, and audio generation from news articles.

Server

Implemented in Flask, the server handles user registration, audio processing, news suggestions, and feedback. Explore endpoints for user interactions and data retrieval.

Server deployment

Run the server.py script from the /Server directory.

You have to install ffmpeg codec at this link: https://www.gyan.dev/ffmpeg/builds/

For windows installation you can type in the powershell: winget install "FFmpeg (Essentials Build)"

Deployment on Docker using docker compose:

Upload the directory "Server" on the server running docker by using a command like:

scp -r /PATH/TO/THIS/REPO/Server remote_username@server_address:~/travel_tales_server

The Docker server needs docker-compose:

apk update
apk add docker-cli-compose

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Then you have to build the docker pod:

run this command from the docker server in the directory where there is the docker-compose.yaml. (i.e. ~/travel_tales_server):

docker compose build
docker compose up

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The name of the generated docker container corresponds to the folder name where the docker-compose.yaml is located.

Client

For the registration of a new user:

• open a web browser and visit: https://server_ip_addr:5000

Alternative:

• set the environment variable DEFAULT_WEB_BROWSER inside Client/.env and specify the path of the preferred browser to use:

# MacOS
DEFAULT_WEB_BROWSER = 'open -a /Applications/Google\ Chrome.app %s'

# Windows
DEFAULT_WEB_BROWSER = 'C:/Program Files (x86)/Google/Chrome/Application/chrome.exe %s'

# Linux
DEFAULT_WEB_BROWSER = '/usr/bin/google-chrome %s'

then start the client GUI and click on Register button. It will prompt to the registration page on the desired browser.

Engagement Level Regressor

Learn about the heuristic-based engagement level estimation, considering speech rate, sentiment, and audio duration.

Speaker Recognition Module

Discover the real-time speaker recognition module using Picovoice Eagle API, capturing and storing speaker-specific speech segments.

Audio Sentiment Classification

Explore the module predicting sentiment from audio files, leveraging a pre-trained CNN for sentiment analysis.

Data Visualization Module

This module provides a GUI for data visualization, showcasing PCA and radar chart plots to represent user interests and news categories.

News Player Interface

Feedback gathering

Further Improvements

While TravelTales presents a significant advancement, future enhancements could involve iterative updates based on passenger feedback. This approach ensures continual adaptability, refining the accuracy and relevance of personalized conversation prompts. The challenges of obtaining a suitable dataset for training an updating model and tuning the update process parameters must be addressed for successful implementation. Feel free to embark on a journey with TravelTales! 🌐🗣️

Demo Videos

User registration and hardware architecture (click to play)

News player interface (click to play)

Data visualization interface (click to play)

Feedback gathering demo with face recognition and explicit user feedback store system (click to play)