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acids-rave-2.1.6
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مشاهده بیشترتوضیحات
RAVE: a Realtime Audio Variatione autoEncoder
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | acids-rave-2.1.6 |
| نام | acids-rave |
| نسخه کتابخانه | 2.1.6 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Antoine CAILLON |
| ایمیل نویسنده | caillon@ircam.fr |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/acids-rave/ |
| مجوز | - |
# RAVE: Realtime Audio Variational autoEncoder
Official implementation of _RAVE: A variational autoencoder for fast and high-quality neural audio synthesis_ ([article link](https://arxiv.org/abs/2111.05011)) by Antoine Caillon and Philippe Esling.
If you use RAVE as a part of a music performance or installation, be sure to cite either this repository or the article !
## Previous versions
The original implementation of the RAVE model can be restored using
```bash
git checkout v1
```
## Installation
Install RAVE using
```bash
pip install acids-rave
```
You will need **ffmpeg** on your computer. You can install it locally inside your virtual environment using
```bash
conda install ffmpeg
```
<!-- Detailed instructions to setup a training station for this project are available [here](docs/training_setup.md). -->
## Colab
A colab to train RAVEv2 is now available thanks to [hexorcismos](https://github.com/moiseshorta) !
[](https://colab.research.google.com/drive/1ih-gv1iHEZNuGhHPvCHrleLNXvooQMvI?usp=sharing)
## Usage
Training a RAVE model usually involves 3 separate steps, namely _dataset preparation_, _training_ and _export_.
### Dataset preparation
You can know prepare a dataset using two methods: regular and lazy. Lazy preprocessing allows RAVE to be trained directly on the raw files (i.e. mp3, ogg), without converting them first. **Warning**: lazy dataset loading will increase your CPU load by a large margin during training, especially on Windows. This can however be useful when training on large audio corpus which would not fit on a hard drive when uncompressed. In any case, prepare your dataset using
```bash
rave preprocess --input_path /audio/folder --output_path /dataset/path (--lazy)
```
### Training
RAVEv2 has many different configurations. The improved version of the v1 is called `v2`, and can therefore be trained with
```bash
rave train --config v2 --db_path /dataset/path --name give_a_name
```
We also provide a discrete configuration, similar to SoundStream or EnCodec
```bash
rave train --config discrete ...
```
By default, RAVE is built with non-causal convolutions. If you want to make the model causal (hence lowering the overall latency of the model), you can use the causal mode
```bash
rave train --config discrete --config causal ...
```
Many other configuration files are available in `rave/configs` and can be combined. Here is a list of all the available configurations
<table>
<thead>
<tr>
<th>Type</th>
<th>Name</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan=5>Architecture</td>
<td>v1</td>
<td>Original continuous model</td>
</tr>
<tr>
<td>v2</td>
<td>Improved continuous model (faster, higher quality)</td>
</tr>
<tr>
<td>discrete</td>
<td>Discrete model (similar to SoundStream or EnCodec)</td>
</tr>
<tr>
<td>onnx</td>
<td>Noiseless v1 configuration for onnx usage</td>
</tr>
<tr>
<td>raspberry</td>
<td>Lightweight configuration compatible with realtime RaspberryPi 4 inference</td>
</tr>
<tr>
<td rowspan=3>Regularization (v2 only)</td>
<td>default</td>
<td>Variational Auto Encoder objective (ELBO)</td>
</tr>
<tr>
<td>wassertein</td>
<td>Wassertein Auto Encoder objective (MMD)</td>
</tr>
<tr>
<td>spherical</td>
<td>Spherical Auto Encoder objective</td>
</tr>
<tr>
<td rowspan=1>Discriminator</td>
<td>spectral_discriminator</td>
<td>Use the MultiScale discriminator from EnCodec.</td>
</tr>
<tr>
<td rowspan=1>Others</td>
<td>causal</td>
<td>Use causal convolutions</td>
</tr>
</tbody>
</table>
### Export
Once trained, export your model to a torchscript file using
```bash
rave export --run /path/to/your/run (--streaming)
```
Setting the `--streaming` flag will enable cached convolutions, making the model compatible with realtime processing. **If you forget to use the streaming mode and try to load the model in Max, you will hear clicking artifacts.**
## Pretrained models
Several pretrained streaming models [are available here](https://acids-ircam.github.io/rave_models_download). We'll keep the list updated with new models.
## Where is the prior ?
The prior model was an experimental feature from RAVEv1 and has been removed from this repository. **However**, we will release a new improved version of the prior soon (very soon in fact).
## Discussion
If you have questions, want to share your experience with RAVE or share musical pieces done with the model, you can use the [Discussion tab](https://github.com/acids-ircam/RAVE/discussions) !
## Demonstration
### RAVE x nn~
Demonstration of what you can do with RAVE and the nn~ external for maxmsp !
[](https://www.youtube.com/watch?v=dMZs04TzxUI)
### embedded RAVE
Using nn~ for puredata, RAVE can be used in realtime on embedded platforms !
[](https://www.youtube.com/watch?v=jAIRf4nGgYI)
نیازمندی
| مقدار | نام |
|---|---|
| >=1.2.0 | absl-py |
| >=0.5.0 | einops |
| - | gin-config |
| >=1.4.0 | GPUtil |
| >=0.9.2 | librosa |
| >=1.23.3 | numpy |
| >=1.7.7 | pytorch-lightning |
| >=6.0 | PyYAML |
| >=1.1.2 | scikit-learn |
| >=1.9.1 | scipy |
| >=1.12.1 | torch |
| >=4.64.1 | tqdm |
| >=1.0.1 | udls |
| >=2.4.1 | cached-conv |
| >=1.3.4 | nn-tilde |
| - | torchaudio |
| - | tensorboard |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.9 | Python |
نحوه نصب
نصب پکیج whl acids-rave-2.1.6:
pip install acids-rave-2.1.6.whl
نصب پکیج tar.gz acids-rave-2.1.6:
pip install acids-rave-2.1.6.tar.gz