|
## SingVisio Webpage |
|
|
|
This is the source code for the SingVisio Webpage. This README file will introduce the project and provide an installation guide. For introduction to SingVisio, please check this [README.md](../../../egs/visualization/SingVisio/README.md) file. |
|
|
|
### Tech Stack |
|
|
|
- [Tailwind CSS](https://tailwindcss.com/) |
|
- [Flowbite](https://flowbite.com/) |
|
- [D3.js](https://d3js.org/) |
|
- [Driver.js](https://driverjs.com/) |
|
|
|
### Structure |
|
|
|
- `index.html`: The entry point file. |
|
- `config`: Contains JSON configuration files loaded by `index.html`. |
|
- `img`: Image files. |
|
- `resources`: Contains CSS styles and JavaScript files. |
|
- `init.js`: Loads the configuration and initializes variables. |
|
- `function.js`: Houses the functions used in this project. |
|
- `event.js`: Binds webpage mouse and keyboard events to functions. |
|
- `Dockerfile`: For building a Docker image if deployment is needed. |
|
|
|
### Configuration |
|
|
|
Before installation, you need to configure the data path in the `config/default.json` file. |
|
|
|
To better understand our project, please note that this configuration pertains to our pre-processed data. If you want to visualize your own data, you can follow the guide below to properly set up the system. |
|
|
|
1. **Update the Data Configuration** in the `config/default.json` file. |
|
|
|
SingVisio will read the configuration from this JSON file and render the webpage. Be aware that any errors in the JSON file may cause the system to shut down. |
|
|
|
```json |
|
{ |
|
"pathData": { |
|
"<mode_name>": { // supports multiple modes |
|
"users": ["basic", "advanced"], // mode choice: "basic" or "advanced" |
|
"multi": ["<id>"], // song_id, sourcesinger_id, or target_id. Set to false to disable. Enables multiple choices for the configured checkbox. |
|
"curve": true, // set to true if the metric curve is needed |
|
"referenceMap": { // configures reference paths when multiple choices are enabled. |
|
"<sourcesinger_id>": [ // e.g., m4singer_Tenor-6 |
|
"<path_to_wav>", // e.g., Tenor-6_ε―ε―ζ²ζ΄²ε·_0002 |
|
] |
|
}, |
|
"data": [ |
|
{ // supports multiple datasets |
|
"dataset": "<dataset_name>", |
|
"basePath": "<path_to_the_processed_data>", |
|
"pathMap": { |
|
"<sourcesinger_id>": { |
|
"songs": [ |
|
"<song_id>" // set song ID; supports multiple IDs |
|
], |
|
"targets": [ |
|
"<target_id>" // set target singer ID; supports multiple IDs |
|
] |
|
} |
|
} |
|
} |
|
] |
|
} |
|
}, |
|
"mapToName": { |
|
"<map_from>": "<map_to>" |
|
}, |
|
"mapToSong": { |
|
"<map_from>": "<map_to>" |
|
}, |
|
"mapToSpace": { |
|
"<map_from>": "<map_to>" |
|
}, |
|
"picTypes": [ |
|
"<pic_type>" // supports multiple types |
|
], |
|
"evaluation_data": [ |
|
{ // supports multiple data sets |
|
"target": "<target_id>", |
|
"sourcesinger": "<sourcesinger_id>", |
|
"song": "<song_id>", |
|
"best": [ |
|
"<best_metric>" // activated when clicking the respective metric |
|
] |
|
}, |
|
], |
|
"colorList": [ |
|
"<color_hex_code>" // supports multiple colors |
|
], |
|
"histogramData": [ |
|
{ // displayed in the top left graph |
|
"type": "high", // "high" or "low"; "high" means the higher, the better |
|
"name": "<metric_name>", |
|
"value": <metric_value> |
|
} |
|
] |
|
} |
|
``` |
|
|
|
2. **Change the Data Source Path** |
|
|
|
The total size of our pre-processed data is approximately 60-70 GB. We provide an online host server, and the server path (`baseLink`) can be modified in the `index.html` file on line 15. |
|
|
|
If you prefer to host the data on your local computer, you can set the `baseLink` value to an empty string as shown below. This will direct the server to read data from your local `data` folder. |
|
|
|
```html |
|
<script> |
|
const baseLink = ''; // do not end with '/' |
|
</script> |
|
``` |
|
|
|
### Installation |
|
|
|
This project does not require a build process. There are multiple ways to run it, but here we introduce the simplest method: |
|
|
|
1. Install Python 3.10 and required packages. |
|
```bash |
|
pip install numpy scikit-learn flask flask_cors gunicorn |
|
``` |
|
|
|
2. Run the following command to start the HTTP server: |
|
|
|
```bash |
|
cd webpage |
|
gunicorn -w 8 -b 0.0.0.0:8080 server:app |
|
``` |
|
|
|
3. After starting the HTTP web server, open the following link in your browser: [http://localhost:8080/](http://localhost:8080/) |