Project Structure

This page provides structure overview of Meetra AI code.

/data

• /raw - Original, immutable data dump.

• /processed - Final, canonical data sets for modeling.

• /external - Data from third-party sources.

• /interim - Intermediate transformed data.

• /profanity_data - Data for profanity detection model.

• /test - Data for unit tests.

/db

• db_init.sql - Script for initializing the database.

• db_stream_init.sql - Script for initializing streaming database.

/RabbitMQ

• advanced_config - Configuration file for RabbitMQ service.

• rabbitmq.dockerfile - Dockerfile for RabbitMQ container.

/gpu_utils

• daemon.json - Configuration file to force GPU usage (not used for CPU-only devices).

/mysql

• Deprecated MySQL database

/tqai

• tqai.dockerfile - Dockerfile for app container.

• setup_docker.sh - Bash script for installing Docker & MySQL dependencies.

/docs

• Directory for storing documentation configuration.

  • /_templates

    • layout.html - Custom template for docs.

  • conf.py - Documentation configuration file.

/models

Audio Quality Classification (binary saved models):

• /audio_lag_detector - Lagging audio classification.

• /audio_reverb_detector - Reverb audio classification.

• /music_detector - Music vs. speech classification.

Speech Emotion Classification (binary saved models):

• /speech_neg_emotion - Negative emotions.

• /speech_neutral - Neutral vs. non-neutral (sadness, anger, fear, disgust).

• /speech_pos_emotion - Positive emotions (calm, happiness, surprised).

• /speech_posneg_emotion - Positive vs. negative.

/ntlk_data

• Locally saved data for NLTK model.

/notebooks

• Directory for storing research notebooks.

/reports

• Directory for storing meeting reports.

/src

• config.py - Script for loading settings from config.yaml.

/app

• Flask app structure:

  • /static - Static files for app.

  • /templates - Templates for app.

  • app.py - Flask app (API, report generation, etc.).

  • default_settings.pkl - Default settings for endpoint /meeting/<meeting_id>/ (custom report generation).

/data

• Data Processing Scripts:

  • audio_splitter.py - Splits audio files into shorter fragments.

  • audiodataframe.py - Class for processing audio files.

  • basedataframe.py - Abstract base class for audio and text dataframes.

  • textdataframe.py - Class for processing text data.

/database

• DB Models & Scripts:

  • create_database.py - Script for creating database.

  • /models/init.py - DB models and methods.

/features

• fourier_t.py - Generates Fourier transform of audio file.

• rolling_emotions.py - Calculates time-weighted moving average scores for emotions.

/models

Audio-Based Models:

• basemodel.py - Base class for some audio models.

• audio_lag_detector.py - Detects audio lag.

• audio_reverb_detector.py - Detects audio reverb.

• speech_emotion_recognition_model.py - Classifies emotions in audio (hybrid architecture).

• music_detector.py - Detects music vs. speech.

• diarizationmodel.py - Speaker diarization.

• speaker_detection_model.py - Compares speaker recordings.

Text-Based Models:

• speech_to_text.py - Speech-to-text processing.

• key_point_finder.py - Finds key parts of speech.

• rpunct.py - Local source code for rpunct.

• text_emotion_recognition_model.py - Classifies emotions from text (hybrid architecture).

• text_summarizer.py - Generates text summary.

• toxicity_detection_model.py - Detects toxicity in text fragments.

• profanitydetectionmodel.py - Detects profanity in text fragments.

• name_recognition_model.py - Detects introductions (e.g., "Hello I’m George").

• offensive_lang_detection_model.py - Detects offensive language in text.

• restore_punctuation_model.py - Corrects punctuation in speech-to-text output.

Environmental Recommendations:

• /sensorsmodels.py - Environmental recommendations.

/services

Streaming & Report Services:

• audio_report_service.py - Service for audio meeting report generation.

• services_bridge.py - Connects streaming service and audio report service.

• meetings_api.py - Class for API endpoints.

/streaming_service

• Service for streaming audio from Timeqube Hardware Prototype to server.

/tests

• Unit tests directory.

/tools

• GUI tool for labeling audio files.

/utils

• Auxiliary Scripts:

  • audio/ - Audio processing utilities (e.g., audio.py, audio_data.py).

  • check_upsampling.py - Checks audio upsampling from low frequency.

  • format_converter.py - Converts various audio/video formats.

  • docsutils/ - Utilities for documentation (e.g., dictutils.py).

  • logging_utils.py - Error logger for services.

  • nlp_utils.py - Extracts topics from text.

  • reportutils.py - Sends report/message via email.

  • rttm_utils.py - Merges short RTTM files.

  • speaker_statistics_model.py - Calculates speaker statistics.

  • textutils.py - Processes text (e.g., splits text into statements).

/visualization

• Report generation and plot creation

  • /images - JPG files to include in reports.

  • /templates - HTML templates for rendering reports.

  • audio_feature_plot.py - Plots audio features (e.g., from AudioDataframe) for better understanding.

  • generate_business_report.py - Generates HTML report for meetings.

  • meeting_visualizer.py - Class for generating various plots based on meeting data.

Other Files in Root

Database Management

• add_user_to_db.py - Script for adding a new user to the database.

• service_cleanup.py - Cleans the database and service files (e.g., reports, WAV fragment files) and creates default accounts.

Docker & Project Setup

• docker-compose.yml - Docker Compose settings.

• requirements.txt - External requirements for the project.

• config.yaml - Project configuration parameters.

Pre-commit Hooks

• install_precommit_hooks.bash - Script for installing pre-commit hooks requirements.

• precommitutils.py - Configuration for hooks.

• pyproject.toml - Configuration for the interrogate package.

Documentation

• interrogate_badge.svg - Infographic of docstring coverage percentage.

• show_docs.py - Creates and displays project documentation.