https://github.com/dynamic-calm/screw
Slowed + Reverb
https://github.com/dynamic-calm/screw
Last synced: 8 months ago
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Slowed + Reverb
- Host: GitHub
- URL: https://github.com/dynamic-calm/screw
- Owner: dynamic-calm
- Created: 2024-12-27T20:56:03.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-01-24T19:17:06.000Z (10 months ago)
- Last Synced: 2025-02-16T11:27:42.233Z (9 months ago)
- Language: Go
- Homepage:
- Size: 1.04 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Screw
A little project with [`Go`](https://go.dev/), [`Next.js`](https://nextjs.org/), [`0Auth2.0`](https://datatracker.ietf.org/doc/html/rfc6749), [`Nginx`](https://nginx.org/), [`SQLite`](https://sqlite.org/), [`WebSocket`](https://developer.mozilla.org/en-US/docs/Web/API/WebSocket), [`FFmpeg`](https://www.ffmpeg.org/), [`Docker`](https://www.docker.com/),[`Grafana`](https://grafana.com/) and [`Prometheus`](https://prometheus.io/).
The audio streams to a `Go` `API` via `WebSocket` connections, where `FFmpeg` processes each stream in real-time. The processed audio is returned through the same connections and buffered client-side, where it's rendered as a waveform.
[About `screw` (Slowed + Reverb)](https://en.wikipedia.org/wiki/Chopped_and_screwed#Slowed_and_reverb)
> [!NOTE]
> This is a learning project. Not a production service.
## Features
- Concurrent processing of audio files.
- Real-time slowed + reverb audio processing using `FFmpeg`.
- In memory rate limiting.
- Middleware.
- Google `OAuth2.0` authentication with `PKCE`
- Database sessions.
- Performance monitoring via `Grafana`.
## Overview
## Demo
### Client
https://github.com/user-attachments/assets/d834d963-7287-4f32-8dd0-a3527dfc27bc
### Audio example
Beat made by me in `2020`.
#### Before
https://github.com/user-attachments/assets/b48dc2ef-3bf1-49f7-93ee-2407e95f2413
#### After
https://github.com/user-attachments/assets/6007d385-64de-4623-8125-c2dbb9e80ec0
## How it works
### High level
From the client you can select audio files. For each selected file a new `WebSocket` connection gets created. The `Go` `API` handles them concurrently. And spawns a new `FFmpeg` process for each. The data flows from the client, through the `API`, through `FFmpeg` `stdin`, and gets sent back from `stdout` to the `WebSocket`. In the client the data gets buffered in a `Blob`. Then a new `URL` gets created for it. The waveform and playback are enabled by [`wavesurfer.js`](https://wavesurfer.xyz/).
### Audio Processing
Check [ffmpeg.go](api/ffmpeg/ffmpeg.go).
- Adds reverb using [ir.wav](api/audio/ir.wav).
- Filters frequencies to 40Hz-2.3kHz range.
- Slows speed to 90% and lowers pitch.
### OAuth2.0
The authentication is implemented using Google's `OAuth2.0` with `PKCE` flow, based on [Pilcrow's](https://github.com/pilcrowonpaper) [excellent](https://pilcrowonpaper.com/blog/oauth-guide/) [blog](https://pilcrowonpaper.com/blog/how-i-would-do-auth/) [posts](https://lucia-auth.com/). This project adapts and expands his [`Next.js` example](https://github.com/lucia-auth/example-nextjs-google-oauth) into `Go`.
While there's an [official `Go` `OAuth2.0` package](https://pkg.go.dev/golang.org/x/oauth2) available, I decided not to use it for this project.
Key components:
- Custom `PKCE` code challenge generation.
- `State` parameter validation.
- Session management using `SQLite`.
- Token refresh handling.
The auth flow is in [api/auth/google.go](api/auth/google.go).
## How to run
> [!NOTE]
> At one point this project was deployed to `AWS` with a `GitHub Actions` pipeline. That's why you'll find `HCL` code in [`/terraform`](/terraform) and `CI/CD` workflow in [`.github/workflows`](.github/workflows/main.yaml.disabled). I'm no longer running it to avoid costs. If you want to deploy this, you'll need to adapt the configuration for your use case. To try it out locally, follow the instructions below.
### Prerequisites
- `Docker` and `Docker Compose`.
- Google `OAuth2.0` credentials (optional).
### Setup
1. Clone the repository.
2. Copy the example env file:
```bash
cp .env.example .env
```
3. Configure your `OAuth2.0` credentials in `.env` (optional).
4. Start the application:
```bash
make dev
```
5. Wait until the `api` container starts:
```bash
api-1 | 2025/01/20 10:29:55 INFO Server is listening port=3000
```
6. Navigate to `localhost:8080`.
## Grafana
To setup the dashboard:
1. Go to `localhost:8080/grafana`.
2. Log in with default credentials:
- Username: `admin`
- Password: `admin`
3. Create a new `password`.
4. Click on the burger menu on the left. Click `Connections` > `Data Sources` > `+ Add new data sources`.
5. Click `Prometheus` from the list.
6. Set `http://prometheus:9090` in the `Connection` input.
7. Click `Save & test`.
8. Click on the burger menu again > `Dashboards` > `New` > `Import`.
9. Copy the contents of the file at the root of the repo named: `go-process-grafana-dashboard.json`. Click `Load`. Paste.
10. Select `prometheus` as the data source.
You should see this:
### Dashboard credits
You can check the original dashboard [here](https://grafana.com/grafana/dashboards/6671-go-processes/).