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
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.

The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate. Website • Key Features • How To Use • Docs •

Pytorch Lightning 21.1k Jan 01, 2023
PyTorch implementation of "Dataset Knowledge Transfer for Class-Incremental Learning Without Memory" (WACV2022)

Dataset Knowledge Transfer for Class-Incremental Learning Without Memory [Paper] [Slides] Summary Introduction Installation Reproducing results Citati

Habib Slim 5 Dec 05, 2022
A new play-and-plug method of controlling an existing generative model with conditioning attributes and their compositions.

Viz-It Data Visualizer Web-Application If I ask you where most of the data wrangler looses their time ? It is Data Overview and EDA. Presenting "Viz-I

NVIDIA Research Projects 66 Jan 01, 2023
PyTorch implementation of the YOLO (You Only Look Once) v2

PyTorch implementation of the YOLO (You Only Look Once) v2 The YOLOv2 is one of the most popular one-stage object detector. This project adopts PyTorc

申瑞珉 (Ruimin Shen) 433 Nov 24, 2022
Implementation of light baking system for ray tracing based on Activision's UberBake

Vulkan Light Bakary MSU Graphics Group Student's Diploma Project Treefonov Andrey [GitHub] [LinkedIn] Project Goal The goal of the project is to imple

Andrey Treefonov 7 Dec 27, 2022
Official PyTorch Implementation for InfoSwap: Information Bottleneck Disentanglement for Identity Swapping

InfoSwap: Information Bottleneck Disentanglement for Identity Swapping Code usage Please check out the user manual page. Paper Gege Gao, Huaibo Huang,

Grace Hešeri 56 Dec 20, 2022
Sharpness-Aware Minimization for Efficiently Improving Generalization

Sharpness-Aware-Minimization-TensorFlow This repository provides a minimal implementation of sharpness-aware minimization (SAM) (Sharpness-Aware Minim

Sayak Paul 54 Dec 08, 2022
HTSeq is a Python library to facilitate processing and analysis of data from high-throughput sequencing (HTS) experiments.

HTSeq DEVS: https://github.com/htseq/htseq DOCS: https://htseq.readthedocs.io A Python library to facilitate programmatic analysis of data from high-t

HTSeq 57 Dec 20, 2022
Perception-aware multi-sensor fusion for 3D LiDAR semantic segmentation (ICCV 2021)

Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation (ICCV 2021) [中文|EN] 概述 本工作主要探索一种高效的多传感器(激光雷达和摄像头)融合点云语义分割方法。现有的多传感器融合方法主要将点云投影

ICE 126 Dec 30, 2022
Examples of using f2py to get high-speed Fortran integrated with Python easily

f2py Examples Simple examples of using f2py to get high-speed Fortran integrated with Python easily. These examples are also useful to troubleshoot pr

Michael 35 Aug 21, 2022
PyTorch implementations for our SIGGRAPH 2021 paper: Editable Free-viewpoint Video Using a Layered Neural Representation.

st-nerf We provide PyTorch implementations for our paper: Editable Free-viewpoint Video Using a Layered Neural Representation SIGGRAPH 2021 Jiakai Zha

Diplodocus 258 Jan 02, 2023
This repository contains the source code for the paper First Order Motion Model for Image Animation

!!! Check out our new paper and framework improved for articulated objects First Order Motion Model for Image Animation This repository contains the s

13k Jan 09, 2023
Implementation of momentum^2 teacher

Momentum^2 Teacher: Momentum Teacher with Momentum Statistics for Self-Supervised Learning Requirements All experiments are done with python3.6, torch

jemmy li 121 Sep 26, 2022
Algorithm to texture 3D reconstructions from multi-view stereo images

MVS-Texturing Welcome to our project that textures 3D reconstructions from images. This project focuses on 3D reconstructions generated using structur

Nils Moehrle 766 Jan 04, 2023
Omnidirectional Scene Text Detection with Sequential-free Box Discretization (IJCAI 2019). Including competition model, online demo, etc.

Box_Discretization_Network This repository is built on the pytorch [maskrcnn_benchmark]. The method is the foundation of our ReCTs-competition method

Yuliang Liu 266 Nov 24, 2022
This repository provides a PyTorch implementation and model weights for HCSC (Hierarchical Contrastive Selective Coding)

HCSC: Hierarchical Contrastive Selective Coding This repository provides a PyTorch implementation and model weights for HCSC (Hierarchical Contrastive

YUANFAN GUO 111 Dec 20, 2022
MQBench Quantization Aware Training with PyTorch

MQBench Quantization Aware Training with PyTorch I am using MQBench(Model Quantization Benchmark)(http://mqbench.tech/) to quantize the model for depl

Ling Zhang 29 Nov 18, 2022
A repo for Causal Imitation Learning under Temporally Correlated Noise

CausIL A repo for Causal Imitation Learning under Temporally Correlated Noise. Running Experiments To re-train an expert, run: python experts/train_ex

Gokul Swamy 5 Nov 01, 2022
A Strong Baseline for Image Semantic Segmentation

A Strong Baseline for Image Semantic Segmentation Introduction This project is an open source semantic segmentation toolbox based on PyTorch. It is ba

Clark He 49 Sep 20, 2022
Implementation of paper "DCS-Net: Deep Complex Subtractive Neural Network for Monaural Speech Enhancement"

DCS-Net This is the implementation of "DCS-Net: Deep Complex Subtractive Neural Network for Monaural Speech Enhancement" Steps to run the model Edit V

Jack Walters 10 Apr 04, 2022