flow_synthesizer - GitLite

KO | EN

gitlite — search

#react #ai #typescript #llm #golang #android #deep-learning #java #awesome #kubernetes #security #ios #mcp #open-source

flow_synthesizer

(★ 164)

Universal audio synthesizer control learning with normalizing flows

파일 탐색기

최종 버전 다운로드 (.zip)

- - evaluate.cpython-37.pyc
  - osc_server.cpython-37.pyc
- - reference_split_64par_mel.th
- - .DS_Store
  - __init__.py
  - basic.py
  - disentangling.py
  - layers.py
  - loss.py
  - regression.backup.py
  - regression.py
- - .DS_Store
  - __init__.py
  - diva_params.txt
  - librenderman.so
  - osc_reset.fxb
  - param_default_32.json
  - param_modes.json
  - param_nomod.json
  - synthesize.py
- - __init__.py
  - data.py
  - gen_transforms.py
  - generate.py
  - plot.py
  - transforms.py
- .DS_Store
- __init__.py
- dataset.json
- dataset.txt
- evaluate.py
- latent_neighbors.py
- osc_launch.py
- osc_server.py
- osc_utils.py
- semantic.py
- synthesize_model.py
- train.py
- - .DS_Store
  - index.md
- - index.md
  - interpolation.png
  - logo.png
  - meta_parameters.png
  - meta_parameters_semantic.png
  - results_models.png
- .DS_Store
- _config.yml
- index.md
- - .DS_Store
- acids.button.graphics.maxpat
- acids.button.maxpat
- acids.leap.hand.maxpat
- acids.leap.maxpat
- acids.midi.autoplay.maxpat
- acids.multibpf.maxpat
- acids.osc.live.maxpat
- acids.presets.maxpat
- acids.scatter.maxpat
- acids.vst.maxpat
- acids.wave.live.maxpat
- acids.wave.maxpat
- acids.xy.maxpat
- ACIDS_logo_ctrl.png
- diva_reset.fxb
- flow_model.maxpat
- flow_recorder.amxd
- flow_recorder.maxpat
- flow_synth.amxd
- flow_synth.maxpat
- flow_synth.model.maxpat
- init.fxb
- leapmotion.maxhelp
.gitignore
_config.yml
LICENSE
README.md
requirements.txt

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/acids-ircam/flow_synthesizer

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd flow_synthesizer

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

$ pip install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Universal audio synthesizer control with normalizing flows

This repository hosts code and additional results for the paper [Universal audio synthesizer control with normalizing flows](https://arxiv.org/abs/1907.00971). You can check out the video demonstration of the FlowSynth on Youtube

[![](https://img.youtube.com/vi/UufQwUitBIw/0.jpg)](https://www.youtube.com/watch?v=UufQwUitBIw)

[https://www.youtube.com/watch?v=UufQwUitBIw](https://www.youtube.com/watch?v=UufQwUitBIw)

## Installing the flow synthesizer plugin

In order to try out the _Flow synthesizer_ plugin, you must 
1. Have an installed version of the [Diva VST](https://u-he.com/products/diva/) (The system works with the free tryout version but will produce noise every now and then). For simplicity, please ensure that it is located here
```
/Library/Audio/Plug-Ins/VST/u-he/Diva.vst
```
2. Install the latest (bleeding-edge) version of both the [Bach and Dada library](https://www.bachproject.net/dl/) for MaxMsp
3. Install the [Mubu library](https://forum.ircam.fr/projects/detail/mubu/) for MaxMsp
4. Have an updated version of `Python 3.7`
5. Install the Python dependencies by running the following line at the root of this folder
```bash
$ pip install -r requirements.txt
```
6. Put the `plugin/flow_synth.amxd` device inside a MIDI track in `Ableton Live`
7. Optionally, if you happen to have a [LeapMotion sensor](https://www.leapmotion.com/), you can install the Leap framework to enjoy it with the synth.
7. ???
7. Profit

NB: If the device seems non-responding, you can try to run the server manually
```bash
$ cd code && python osc_launch.py
```

Note that the plugin has only been tested on MacOS X High Sierra (10.13.6)

## Supporting webpage

For a better viewing experience, please **visit the corresponding [supporting website](https://acids-ircam.github.io/flow_synthesizer/ "Flow synthesizer")**.

It embeds the following:
  * Supplementary figures
  * Audio examples
	* Reconstruction
	* Macro-control learning
	* Neighborhood exploration
	* Interpolation
	* Vocal sketching
  * Real-time implementation in Ableton Live
  
You can also directly parse through the different sub-directories of the main [`docs`](docs) directory.

## Dataset

The dataset can be downloaded here: [https://nubo.ircam.fr/index.php/s/nL3NQomqxced6eJ](https://nubo.ircam.fr/index.php/s/nL3NQomqxced6eJ)

## Code

### Dependencies

#### Python

Code has been developed with `Python 3.7`. It should work with other versions of `Python 3`, but has not been tested. Moreover, we rely on several third-party libraries, listed in [`requirements.txt`](requirements.txt). They can be installed with

```bash
$ pip install -r requirements.txt
```

As our experiments are coded in PyTorch, no additional library is required to run them on GPU (provided you already have CUDA installed).

#### RenderMan

For people interested in the research aspects of this repository, if you want to try new models or evaluate variations of the existing ones, you will need at one point to render the correponding audio. We rely on the great [RenderMan](https://github.com/fedden/RenderMan) library to batch generate audio output from synthesizer presets.

### Usage

The code is mostly divided into two scripts `train.py` and `evaluate.py`. The first script `train.py` allows to train a model from scratch as described in the paper. The second script `evaluate.py` allows to generate the figures of the papers, and also all the supporting additional materials visible on the [supporting page](https://acids-ircam.github.io/flow_synthesizer)) of this repository.

#### train.py arguments
```

```

## Pre-trained models

Note that a set of pre-trained models are availble in the `code/results`  folder.

### Models details

As discussed in the paper, the very large amount of baseline models implemented did not allow to provide all the parameters for reference models (which are defined in the source code). However, we provide these details inside the documentation page in the [models details section](https://acids-ircam.github.io/flow_synthesizer/#models-details)