Like Dirt-Samples, but cleaned up

Overview

Clean-Samples

Like Dirt-Samples, but cleaned up, with clear provenance and license info (generally a permissive creative commons licence but check the metadata for specifics).

The bin/meta.py python script is a reference implementation that can make a '.cleanmeta' metadata file for your own sample pack folder. See below for how to use it and contribute a sample pack of your own.

If you want to use these outside the Tidal/SuperDirt/SuperCollider ecosystem you are very welcome. You're encouraged to join discussion in the github issue tracker so that we can develop a standard way to share and index/signpost these packs.

See /tidalcycles/sounds-repetition for an example sample pack which has two sets of samples in it.

How to contribute a sample pack

Please only contribute samples if you are happy to share them under a permissive license such as CC0 or a similar creative commons license.

If you are unfamiliar with the 'git' software, please create an issue here, with a short description of your samples and a link to them and someone should be along to help shortly.

If you are familiar with git and running python scripts (or happy to learn), please follow the below instructions. This is all new - if anything is unclear please create an issue, thanks!

  1. Get your samples together in .wav format, editing them if necessary (see below for advice).

  2. Create a new repository. This isn't essential, but consider putting 'sounds-' in front of its name, e.g. 'sounds-303bass' for your 303 bass samples.

  3. Add your samples to the repository. For an example of how to organise them, see this sample pack: tidalcycles/sounds-repetition, which has two sets of samples, with a subfolder for each.

  4. Create a '.cleanmeta' metadata file for each subfolder. Again, see tidalcycles/sounds-repetition for examples. There is a python script bin/meta.py which can generate the metadata file for you, run it without parameters for help. Here is an example commandline, that was used to generate repetition.cleanmeta:

    ../Clean-Samples/bin/meta.py --maintainer alex --email [email protected] --copyright "(c) 2021 Alex McLean" --license CC0 --provenance "Various dodgy speech synths" --shortname repetition --sample-subfolder repetition/ --write .
    

    After generating the file, edit it with a text editor to fill in any missing info.

  5. When ready, add te URL of your repository to the https://github.com/tidalcycles/Clean-Samples/blob/main/Clean-Samples.quark for the Clean-Samples quark) in a pull request. You could also add it to the SuperCollider quarks database, or we can do that for you if you prefer, so that we can accept the PR to Clean-Samples once it's accepted as a quark.

Advice for preparing samples

You can use free/open source software like audacity for editing samples.

As a minimum, be sure to trim any silence from beginning/end of the samples, and that the start and end of the sample is at zero to avoid clicks (you might need to fade in / fade out by a tiny amount to achieve this).

Consider adjusting the volume/loudness too, for example normalising to -1.0db - but this is very subjective and will depend on the nature of the samples and the music they're used with. For example distorted gabba samples are intended to be very loud, and a whisper is intended to sound silent. The average non-percussive sample should be around -23dB RMS. Samples shouldn't exceed 0dB true peak. EBU recommends -1dBTP at 4x-oversampling. Samples generally shouldn't have DC offset, although e.g. some kick drum samples naturally have non-zero mean.

For more advice, you could join the discussion here.

Thanks!

Owner
TidalCycles
Live coding environment for making patterns
TidalCycles
Geneva is an artificial intelligence tool that defeats censorship by exploiting bugs in censors

Geneva is an artificial intelligence tool that defeats censorship by exploiting bugs in censors

Kevin Bock 1.5k Jan 06, 2023
Continual reinforcement learning baselines: experiment specifications, implementation of existing methods, and common metrics. Easily extensible to new methods.

Continual Reinforcement Learning This repository provides a simple way to run continual reinforcement learning experiments in PyTorch, including evalu

55 Dec 24, 2022
Local Multi-Head Channel Self-Attention for FER2013

LHC-Net Local Multi-Head Channel Self-Attention This repository is intended to provide a quick implementation of the LHC-Net and to replicate the resu

12 Jan 04, 2023
This project is for a Twitter bot that monitors a bird feeder in my backyard. Any detected birds are identified and posted to Twitter.

Backyard Birdbot Introduction This is a silly hobby project to use existing ML models to: Detect any birds sighted by a webcam Identify whic

Chi Young Moon 71 Dec 25, 2022
Official PyTorch Implementation of Learning Architectures for Binary Networks

Learning Architectures for Binary Networks An Pytorch Implementation of the paper Learning Architectures for Binary Networks (BNAS) (ECCV 2020) If you

Computer Vision Lab. @ GIST 25 Jun 09, 2022
PyTorch implementation of ECCV 2020 paper "Foley Music: Learning to Generate Music from Videos "

Foley Music: Learning to Generate Music from Videos This repo holds the code for the framework presented on ECCV 2020. Foley Music: Learning to Genera

Chuang Gan 30 Nov 03, 2022
Model-based Reinforcement Learning Improves Autonomous Racing Performance

Racing Dreamer: Model-based versus Model-free Deep Reinforcement Learning for Autonomous Racing Cars In this work, we propose to learn a racing contro

Cyber Physical Systems - TU Wien 38 Dec 06, 2022
Ultra-Data-Efficient GAN Training: Drawing A Lottery Ticket First, Then Training It Toughly

Ultra-Data-Efficient GAN Training: Drawing A Lottery Ticket First, Then Training It Toughly Code for this paper Ultra-Data-Efficient GAN Tra

VITA 77 Oct 05, 2022
automatic color-grading

color-matcher Description color-matcher enables color transfer across images which comes in handy for automatic color-grading of photographs, painting

hahnec 168 Jan 05, 2023
JDet is Object Detection Framework based on Jittor.

JDet is Object Detection Framework based on Jittor.

135 Dec 14, 2022
Code for: Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. Nicholas Monath, Manzil Zaheer, Daniel Silva, Andrew McCallum, Amr Ahmed. KDD 2019.

gHHC Code for: Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. Nicholas Monath, Manzil Zaheer, D

Nicholas Monath 35 Nov 16, 2022
A set of tools for converting a darknet dataset to COCO format working with YOLOX

darknet格式数据→COCO darknet训练数据目录结构(详情参见dataset/darknet): darknet ├── class.names ├── gen_config.data ├── gen_train.txt ├── gen_valid.txt └── images

RapidAI-NG 148 Jan 03, 2023
AdaDM: Enabling Normalization for Image Super-Resolution

AdaDM AdaDM: Enabling Normalization for Image Super-Resolution. You can apply BN, LN or GN in SR networks with our AdaDM. Pretrained models (EDSR*/RDN

58 Jan 08, 2023
This repo contains the implementation of the algorithm proposed in Off-Belief Learning, ICML 2021.

Off-Belief Learning Introduction This repo contains the implementation of the algorithm proposed in Off-Belief Learning, ICML 2021. Environment Setup

Facebook Research 32 Jan 05, 2023
Voice assistant - Voice assistant with python

🌐 Python Voice Assistant 🌵 - User's greeting 🌵 - Writing tasks to todo-list ?

PythonToday 10 Dec 26, 2022
Face2webtoon - Despite its importance, there are few previous works applying I2I translation to webtoon.

Despite its importance, there are few previous works applying I2I translation to webtoon. I collected dataset from naver webtoon 연애혁명 and tried to transfer human faces to webtoon domain.

이상윤 64 Oct 19, 2022
A PyTorch Implementation of Single Shot Scale-invariant Face Detector.

S³FD: Single Shot Scale-invariant Face Detector A PyTorch Implementation of Single Shot Scale-invariant Face Detector. Eval python wider_eval_pytorch.

carwin 235 Jan 07, 2023
Measuring and Improving Consistency in Pretrained Language Models

ParaRel 🤘 This repository contains the code and data for the paper: Measuring and Improving Consistency in Pretrained Language Models as well as the

Yanai Elazar 26 Dec 02, 2022
Pose Transformers: Human Motion Prediction with Non-Autoregressive Transformers

Pose Transformers: Human Motion Prediction with Non-Autoregressive Transformers This is the repo used for human motion prediction with non-autoregress

Idiap Research Institute 26 Dec 14, 2022
A Planar RGB-D SLAM which utilizes Manhattan World structure to provide optimal camera pose trajectory while also providing a sparse reconstruction containing points, lines and planes, and a dense surfel-based reconstruction.

ManhattanSLAM Authors: Raza Yunus, Yanyan Li and Federico Tombari ManhattanSLAM is a real-time SLAM library for RGB-D cameras that computes the camera

117 Dec 28, 2022