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## 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_寂寞沙洲冷_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/) |