# Project Structure

### /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.