Merge branch 'master' of https://github.com/simonrouard/CRASH

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README.md

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+# CRASH: Raw Audio Score-based Generative Modeling for Controllable High-resolution Drum Sound Synthesis
+This repo contains a PyTorch implementation for the paper [CRASH: Raw Audio Score-based Generative Modeling for Controllable High-resolution Drum Sound Synthesis](https://arxiv.org/abs/2106.07431) 
+by [Simon Rouard](https://github.com/simonrouard) and [Gaëtan Hadjeres](https://github.com/Ghadjeres) accepted at [ISMIR 2021](https://ismir2021.ismir.net). 
+You can hear some material on [this link](https://crash-diffusion.github.io/crash/).
+--------------------
+
+![snare_generation](assets/gif_snare.gif) ![kick_generation](assets/kick.gif)
+
+
+
+We propose to use the [continuous framework of diffusion models](https://arxiv.org/abs/2011.13456) to the task of unconditional audio generation on drum sounds. 
+
+Moreover, the flexibility of diffusion models lets us perform sound design on drums such as : regeneration of variations of a sound, class-conditional/class mixing 
+generation, interpolations between sounds or inpainting. By using the latent representation given by the forward Ordinary Differential Equation, you can also load 
+any 44.1kHz drum sound and manipulate it. It has to be of length 21.000 if you use the pretrained checkpoints provided.  
+
+
+## Requirements
+Run the following line in your terminal in order to install all the requirements
+```sh
+pip install -r requirements.txt
+```
+
+## What is in the repo?
+* All the python files excepts `inference.py`, `model_classifier.py` and `inference_notebook.ipynb` are dedicated to the training of the model on a mono sound dataset. 
+To train a model, you need to adapt the `params.py` file to your configuration. Then, you just have to run:
+```sh
+python3 __main__.py
+```
+You can monitor the model during training by running:
+`tensorboard --logdir weights` (if you chose 'weights' as 'model_dir' in the `params.py` file)
+* The file `model_classifier.py` contains the architecture of the noise conditioned classifier necessary to the class-conditional generations. It has only been trained on VP SDEs.
+* The file `inference.py` contains all the types of sampling. See the Jupyter Notebook `inference_notebook.ipynb` to understand all the possibilities that the model offers. 
+
+## Checkpoints of the model
+
+You can download the folder with the saved weights on this [link](https://drive.google.com/drive/folders/1UFVVnTFDmPSdzwuV_1GIVBW4BoJWKaFm?usp=sharing).
+Then, put the folder `saved_weights` in your repository so that the notebook works well. 
+
+## How to extend the code?
+* New SDEs: you can train the model with a new SDE by creating a new class in the `sde.py` file. It must contain the functions sigma(t), mean(t), beta(t) and g(t) which are linked in the Appendix D of the paper, formula (33).