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
Official code of our work, Unified Pre-training for Program Understanding and Generation [NAACL 2021].

PLBART Code pre-release of our work, Unified Pre-training for Program Understanding and Generation accepted at NAACL 2021. Note. A detailed documentat

Wasi Ahmad 138 Dec 30, 2022
MINOS: Multimodal Indoor Simulator

MINOS Simulator MINOS is a simulator designed to support the development of multisensory models for goal-directed navigation in complex indoor environ

194 Dec 27, 2022
A general framework for deep learning experiments under PyTorch based on pytorch-lightning

torchx Torchx is a general framework for deep learning experiments under PyTorch based on pytorch-lightning. TODO list gan-like training wrapper text

Yingtian Liu 6 Mar 17, 2022
Minecraft Hack Detection With Python

Minecraft Hack Detection An attempt to try and use crowd sourced replays to find

Kuleen Sasse 3 Mar 26, 2022
Official implementations of PSENet, PAN and PAN++.

News (2021/11/03) Paddle implementation of PAN, see Paddle-PANet. Thanks @simplify23. (2021/04/08) PSENet and PAN are included in MMOCR. Introduction

395 Dec 14, 2022
CBKH: The Cornell Biomedical Knowledge Hub

Cornell Biomedical Knowledge Hub (CBKH) CBKG integrates data from 18 publicly available biomedical databases. The current version of CBKG contains a t

44 Dec 21, 2022
Code for the paper Open Sesame: Getting Inside BERT's Linguistic Knowledge.

Open Sesame This repository contains the code for the paper Open Sesame: Getting Inside BERT's Linguistic Knowledge. Credits We built the project on t

9 Jul 24, 2022
GT China coal model

GT China coal model The full version of a China coal transport model with a very high spatial reslution. What it does The code works in a few steps: T

0 Dec 13, 2021
Simple data balancing baselines for worst-group-accuracy benchmarks.

BalancingGroups Code to replicate the experimental results from Simple data balancing baselines achieve competitive worst-group-accuracy. Replicating

Meta Research 29 Dec 02, 2022
A PyTorch implementation of SIN: Superpixel Interpolation Network

SIN: Superpixel Interpolation Network This is is a PyTorch implementation of the superpixel segmentation network introduced in our PRICAI-2021 paper:

6 Sep 28, 2022
This package proposes simplified exporting pytorch models to ONNX and TensorRT, and also gives some base interface for model inference.

PyTorch Infer Utils This package proposes simplified exporting pytorch models to ONNX and TensorRT, and also gives some base interface for model infer

Alex Gorodnitskiy 11 Mar 20, 2022
A Repository of Community-Driven Natural Instructions

A Repository of Community-Driven Natural Instructions TLDR; this repository maintains a community effort to create a large collection of tasks and the

AI2 244 Jan 04, 2023
[ICCV2021] Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving

Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving

Xuanchi Ren 44 Dec 03, 2022
A PyTorch implementation of DenseNet.

A PyTorch Implementation of DenseNet This is a PyTorch implementation of the DenseNet-BC architecture as described in the paper Densely Connected Conv

Brandon Amos 771 Dec 15, 2022
InterfaceGAN++: Exploring the limits of InterfaceGAN

InterfaceGAN++: Exploring the limits of InterfaceGAN Authors: Apavou Clément & Belkada Younes From left to right - Images generated using styleGAN and

Younes Belkada 42 Dec 23, 2022
It is a simple library to speed up CLIP inference up to 3x (K80 GPU)

CLIP-ONNX It is a simple library to speed up CLIP inference up to 3x (K80 GPU) Usage Install clip-onnx module and requirements first. Use this trick !

Gerasimov Maxim 93 Dec 20, 2022
MOOSE (Multi-organ objective segmentation) a data-centric AI solution that generates multilabel organ segmentations to facilitate systemic TB whole-person research

MOOSE (Multi-organ objective segmentation) a data-centric AI solution that generates multilabel organ segmentations to facilitate systemic TB whole-person research.The pipeline is based on nn-UNet an

QIMP team 30 Jan 01, 2023
A Pytorch Implementation for Compact Bilinear Pooling.

CompactBilinearPooling-Pytorch A Pytorch Implementation for Compact Bilinear Pooling. Adapted from tensorflow_compact_bilinear_pooling Prerequisites I

169 Dec 23, 2022
SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data (AAAI 2021)

SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data (AAAI 2021) PyTorch implementation of SnapMix | paper Method Overview Cite

DavidHuang 126 Dec 30, 2022