A GPU-optional modular synthesizer in pytorch, 16200x faster than realtime, for audio ML researchers.

Last update: Jan 02, 2023

Overview

torchsynth

The fastest synth in the universe.

Introduction

torchsynth is based upon traditional modular synthesis written in pytorch. It is GPU-optional and differentiable.

Most synthesizers are fast in terms of latency. torchsynth is fast in terms of throughput. It synthesizes audio 16200x faster than realtime (714MHz) on a single GPU. This is of particular interest to audio ML researchers seeking large training corpora.

Additionally, all synthesized audio is returned with the underlying latent parameters used for generating the corresponding audio. This is useful for multi-modal training regimes.

Documentation

Installation

pip3 install torchsynth

Note that torchsynth requires PyTorch version 1.8 or greater.

Listen

If you'd like to hear torchsynth, check out synth1K1, a dataset of 1024 4-second sounds rendered from the Voice synthesizer, or listen on SoundCloud.

Citation

If you use this work in your research, please cite:

@inproceedings{turian2021torchsynth,
	title        = {One Billion Audio Sounds from {GPU}-enabled Modular Synthesis},
	author       = {Joseph Turian and Jordie Shier and George Tzanetakis and Kirk McNally and Max Henry},
	year         = 2021,
	month        = Sep,
	booktitle    = {Proceedings of the 23rd International Conference on Digital Audio Effects (DAFx2020)},
	location     = {Vienna, Austria}
}

Comments

Device modifications

Some updates to make sure things are on the correct device.

Things I learned while doing this -- having 0d tensors on the GPU does not necessarily lead to a speed up. I think creating a scalar tensor on the GPU is slower than just using a native Number type for most operations and comparisons. Especially if those tensors are different dtypes. Having parameter ranges on the GPU did not improve performance and actually resulted in worse performance (~ 15ms on batch size of 64) . Same with have the buffer size and batch size on the GPU. Having sample rate as a float did help a little bit though. Some of the assertions are slow. Especially the one I marked in the Parameters.

opened by jorshi 8
Updating torch.range to torch.arange

torch.range is now deprecated and should be replaced with torch.arange, which is consistent with pythons built-in range. torch.range also was producing errors with high-valued ranges, see #377

Merge #380 into this first.

opened by jorshi 7
fixed buffer_size for renders
Introducing buffer_size, which is also a global default BUFFER_SIZE.

Now SynthModule and TorchSynthModule have this property, and a method to_buffer_size(). I've placed this at the return for every foward/call:

def forward # or npyforward... # ... out_ = # previous output return self.to_buffer_size(out_)

There might be a cleaner way of doing this, like making a "pre-forward" method, and then having "forward" always be: return self.to_buffer_size(self.pre_forward) or some such thing, but that seems confusing and over-engineered.

I fixed up all of module.py, and adapted all of torchmodule.py that currently exists.

Lmk.
opened by maxsolomonhenry 7
Torch ADSR + SineVCO
This PR is diff'ed against #37 for better understanding.

In this PR, I am converting ADSR and SineVCO to torch. I make sure that torch and numpy modules give the same values on forward. Gradients are computed in example.py but not in unit tests. Why are we getting nan gradient for alpha?

There's a lot to hate in this port.

~~In general, before we address the nitty-gritty (below), I think the biggest issue to consider is that I don't know if standard ADSR will be differentiable on the adsr parameters, since it involves a lot of discontinuities and padding. I think we have two options~~ ~~1) don't make anything differentiable and just focus on rendering speed. this is quite lame, of course~~ ~~2) work on the subproblem of just creating a differentiable ADSR. ignore our complicated abstractions for now, and just focus on differentiable ADSR, maybe to minimize a simple l2 distance. maybe we have to use splines?~~

Update: It turns out I can differentiate through ADSR parameters? Except alpha which is nan. Why? Check out example.py

Here's some TODO:

examples.py should have a torch section doing the exact thing as above, but with the torch versions.

Internally, we want to be storing all nn.Parameter in the 0/1 range, not the human readable range. Since that is what will be used for backprop.

The modparameter abstraction needs to be cleaned up, since each nn.Parameter stores its own value. I think the cleanest thing is a TorchModParameter which inherits from nn.Parameter but adds some helper methods around it.

torch.linspace doesn't have an endpoint param.

gradients should be a unit test
opened by turian 7
Randomize modparameter

This allows you to create randomized parameters for the synth. I'd like this for unit-testing numpy vs torch.

Depends upon #33

What's weird is that the note_on_duration is sometimes disregarded and you get like 16 second samples. Is this because of the one-hit thing @maxsolomonhenry ? That seems like a problem to me. You should be able to hillclimb the synth by setting random params to try to get a similar audio of the same duration :\

opened by turian 6
added convenience import

You can take this or leave it. I've been exploring a bit the python package structure.

in essence, this allows for the example.py import:

from ddspdrum.module import ADSR # etc...

to become:

from ddrpdrum import ADSR # etc...

opened by maxsolomonhenry 6
SynthConfig as non-Tensors

https://github.com/turian/torchsynth/pull/267https://github.com/turian/torchsynth/pull/267 should be merged first for a smaller diff

I am trying to simplify the synth configuration, and it will be a sequence of linear PRs. Here is one that turns most of the configs back into Python native values.

@jorshi can you profile quickly to make sure this is okay before we merge?

opened by turian 5
Fm vco

Made FmVCO class, which basically has to process the modulation signal in a slightly different way. Typically the mod signal is applied in midi-space (log frequency), but FM operates on modulations in Hz space. Also changed the modulation depth to reflect the classic 'modulation index' of FM literature. It's a bit more intuitive this way.

Had to slightly refactor VCO to make this smooth.

Note, this is only work on the numpy modules. torchmodule.py would have to be updated accordingly.

opened by maxsolomonhenry 5
TorchParameter

Parameters for TorchSynthModules that have an internal range from 0 to 1 and can hold a ParameterRange object to convert to and from a user specified range

opened by jorshi 5
Profiling script

Not sure if we want to include this, but this has been mega helpful for me in profiling. Also used this with line profiler to look at line-by-line profiles https://github.com/pyutils/line_profiler

opened by jorshi 4
Reproducibility Issue

If you received an Error or Warning regarding reproducibility while using torchsynth please leave a comment here with details about your CPU architecture and what random results you got.

opened by jorshi 4
[Snyk] Fix for 2 vulnerabilities
This PR was automatically created by Snyk using the credentials of a real user.

Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

Changes to the following files to upgrade the vulnerable dependencies to a fixed version:

docs/requirements.txt

⚠️ Warning

unofficial-pt-lightning-sphinx-theme 0.0.27.4 requires sphinx, which is not installed. sphinx-rtd-theme 1.1.1 requires sphinx, which is not installed. librosa 0.7.2 requires scikit-learn, which is not installed. librosa 0.7.2 requires resampy, which is not installed. librosa 0.7.2 requires numba, which is not installed. ipython 5.10.0 requires pygments, which is not installed. ipython 5.10.0 requires simplegeneric, which is not installed.

Vulnerabilities that will be fixed

By pinning:

Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- | 551/1000
Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
SNYK-PYTHON-SETUPTOOLS-3180412 | setuptools:
39.0.1 -> 65.5.1
| No | No Known Exploit | 551/1000
Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
SNYK-PYTHON-WHEEL-3180413 | wheel:
0.30.0 -> 0.38.0
| No | No Known Exploit

(*) Note that the real score may have changed since the PR was raised.

Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

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opened by turian 1
[Snyk] Fix for 2 vulnerabilities
This PR was automatically created by Snyk using the credentials of a real user.

Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

Changes to the following files to upgrade the vulnerable dependencies to a fixed version:

requirements.txt

⚠️ Warning

unofficial-pt-lightning-sphinx-theme 0.0.27.4 requires sphinx, which is not installed. sphinx-rtd-theme 1.1.1 requires sphinx, which is not installed. librosa 0.7.2 requires scikit-learn, which is not installed. librosa 0.7.2 requires resampy, which is not installed. librosa 0.7.2 requires numba, which is not installed. ipython 5.10.0 requires pygments, which is not installed. ipython 5.10.0 requires simplegeneric, which is not installed.

Vulnerabilities that will be fixed

By pinning:

Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- | 551/1000
Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
SNYK-PYTHON-SETUPTOOLS-3180412 | setuptools:
39.0.1 -> 65.5.1
| No | No Known Exploit | 551/1000
Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
SNYK-PYTHON-WHEEL-3180413 | wheel:
0.30.0 -> 0.38.0
| No | No Known Exploit

(*) Note that the real score may have changed since the PR was raised.

Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

Check the changes in this PR to ensure they won't cause issues with your project.

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opened by turian 1
Bump black from 22.6.0 to 22.12.0
Bumps black from 22.6.0 to 22.12.0.

Release notes

Sourced from black's releases.

22.12.0

Preview style

Enforce empty lines before classes and functions with sticky leading comments (#3302)

Reformat empty and whitespace-only files as either an empty file (if no newline is present) or as a single newline character (if a newline is present) (#3348)

Implicitly concatenated strings used as function args are now wrapped inside parentheses (#3307)

Correctly handle trailing commas that are inside a line's leading non-nested parens (#3370)

Configuration

Fix incorrectly applied .gitignore rules by considering the .gitignore location and the relative path to the target file (#3338)

Fix incorrectly ignoring .gitignore presence when more than one source directory is specified (#3336)

Parser

Parsing support has been added for walruses inside generator expression that are passed as function args (for example, any(match := my_re.match(text) for text in texts)) (#3327).

Integrations

Vim plugin: Optionally allow using the system installation of Black via let g:black_use_virtualenv = 0(#3309)

22.10.0

Highlights

Runtime support for Python 3.6 has been removed. Formatting 3.6 code will still be supported until further notice.

Stable style

Fix a crash when # fmt: on is used on a different block level than # fmt: off (#3281)

Preview style

... (truncated)

Changelog

Sourced from black's changelog.

22.12.0

Preview style

Enforce empty lines before classes and functions with sticky leading comments (#3302)

Reformat empty and whitespace-only files as either an empty file (if no newline is present) or as a single newline character (if a newline is present) (#3348)

Implicitly concatenated strings used as function args are now wrapped inside parentheses (#3307)

Correctly handle trailing commas that are inside a line's leading non-nested parens (#3370)

Configuration

Fix incorrectly applied .gitignore rules by considering the .gitignore location and the relative path to the target file (#3338)

Fix incorrectly ignoring .gitignore presence when more than one source directory is specified (#3336)

Parser

Parsing support has been added for walruses inside generator expression that are passed as function args (for example, any(match := my_re.match(text) for text in texts)) (#3327).

Integrations

Vim plugin: Optionally allow using the system installation of Black via let g:black_use_virtualenv = 0(#3309)

22.10.0

Highlights

Runtime support for Python 3.6 has been removed. Formatting 3.6 code will still be supported until further notice.

Stable style

Fix a crash when # fmt: on is used on a different block level than # fmt: off (#3281)

... (truncated)

Commits

2ddea29 Prepare release 22.12.0 (#3413)

5b1443a release: skip bad macos wheels for now (#3411)

9ace064 Bump peter-evans/find-comment from 2.0.1 to 2.1.0 (#3404)

19c5fe4 Fix CI with latest flake8-bugbear (#3412)

d4a8564 Bump sphinx-copybutton from 0.5.0 to 0.5.1 in /docs (#3390)

2793249 Wordsmith current_style.md (#3383)

d97b789 Remove whitespaces of whitespace-only files (#3348)

c23a5c1 Clarify that Black runs with --safe by default (#3378)

8091b25 Correctly handle trailing commas that are inside a line's leading non-nested ...

ffaaf48 Compare each .gitignore found with an appropiate relative path (#3338)

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dependencies
opened by dependabot[bot] 1
[Snyk] Security upgrade protobuf from 3.20.1 to 3.20.2
Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

Changes to the following files to upgrade the vulnerable dependencies to a fixed version:

docs/requirements.txt

⚠️ Warning

unofficial-pt-lightning-sphinx-theme 0.0.27.4 requires sphinx, which is not installed. sphinx-rtd-theme 1.1.1 requires sphinx, which is not installed. librosa 0.7.2 requires scikit-learn, which is not installed. librosa 0.7.2 requires resampy, which is not installed. librosa 0.7.2 requires numba, which is not installed. ipython 5.10.0 requires simplegeneric, which is not installed. ipython 5.10.0 requires pygments, which is not installed.

Vulnerabilities that will be fixed

By pinning:

Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- | 499/1000
Why? Has a fix available, CVSS 5.7 | Denial of Service (DoS)
SNYK-PYTHON-PROTOBUF-3031740 | protobuf:
3.20.1 -> 3.20.2
| No | No Known Exploit

(*) Note that the real score may have changed since the PR was raised.

Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

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opened by snyk-bot 1
[Snyk] Security upgrade protobuf from 3.20.1 to 3.20.2
This PR was automatically created by Snyk using the credentials of a real user.

Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

Changes to the following files to upgrade the vulnerable dependencies to a fixed version:

requirements.txt

⚠️ Warning

unofficial-pt-lightning-sphinx-theme 0.0.27.4 requires sphinx, which is not installed. sphinx-rtd-theme 1.1.1 requires sphinx, which is not installed. librosa 0.7.2 requires scikit-learn, which is not installed. librosa 0.7.2 requires resampy, which is not installed. librosa 0.7.2 requires numba, which is not installed. ipython 5.10.0 requires simplegeneric, which is not installed. ipython 5.10.0 requires pygments, which is not installed.

Vulnerabilities that will be fixed

By pinning:

Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- | 499/1000
Why? Has a fix available, CVSS 5.7 | Denial of Service (DoS)
SNYK-PYTHON-PROTOBUF-3031740 | protobuf:
3.20.1 -> 3.20.2
| No | No Known Exploit

(*) Note that the real score may have changed since the PR was raised.

Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

Check the changes in this PR to ensure they won't cause issues with your project.

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opened by turian 1
[Snyk] Fix for 2 vulnerabilities
This PR was automatically created by Snyk using the credentials of a real user.

Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

Changes to the following files to upgrade the vulnerable dependencies to a fixed version:

docs/requirements.txt

⚠️ Warning

unofficial-pt-lightning-sphinx-theme 0.0.27.4 requires sphinx, which is not installed. sphinx-rtd-theme 1.1.1 requires sphinx, which is not installed. scipy 1.2.3 requires numpy, which is not installed. pytest-cov 2.12.1 requires coverage, which is not installed. matplotlib 2.2.5 requires numpy, which is not installed. librosa 0.7.2 requires numpy, which is not installed. librosa 0.7.2 requires scikit-learn, which is not installed. librosa 0.7.2 requires resampy, which is not installed. librosa 0.7.2 requires numba, which is not installed. ipython 5.10.0 requires simplegeneric, which is not installed. ipython 5.10.0 requires pygments, which is not installed.

Vulnerabilities that will be fixed

By pinning:

Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- | 441/1000
Why? Recently disclosed, Has a fix available, CVSS 3.1 | Regular Expression Denial of Service (ReDoS)
SNYK-PYTHON-SETUPTOOLS-3113904 | setuptools:
39.0.1 -> 65.5.1
| No | No Known Exploit | 551/1000
Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
SNYK-PYTHON-WHEEL-3092128 | wheel:
0.30.0 -> 0.38.0
| No | No Known Exploit

(*) Note that the real score may have changed since the PR was raised.

Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

Check the changes in this PR to ensure they won't cause issues with your project.

Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

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🛠 Adjust project settings

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🦉 Regular Expression Denial of Service (ReDoS) 🦉 Regular Expression Denial of Service (ReDoS)
opened by turian 1

Releases(v1.0.2)

v1.0.2(Aug 19, 2022)
This update includes some bug fixes and an update to the Signal class to enable checkpointing.

What's Changed

Adding the drum nebula to docs by @jorshi in https://github.com/torchsynth/torchsynth/pull/374

Sphinx fixs by @turian in https://github.com/torchsynth/torchsynth/pull/379

Codecov Action Fix by @jorshi in https://github.com/torchsynth/torchsynth/pull/380

Updating torch.range to torch.arange by @jorshi in https://github.com/torchsynth/torchsynth/pull/378 - torch.range, which is deprecated, was producing incorrect values for larger batch_ids passed into a synth voice.

Fix floordiv by @turian in https://github.com/torchsynth/torchsynth/pull/382

Add new_empty to Signal by @turian in https://github.com/torchsynth/torchsynth/pull/384 - this enables deepcopy on torchsynth Signals and allows for checkpointing

Full Changelog: https://github.com/torchsynth/torchsynth/compare/v1.0.1...v1.0.2
Source code(tar.gz)
Source code(zip)
v1.0.1(Jun 29, 2021)
Now works with torch 1.9

Source code(tar.gz)
Source code(zip)
v1.0.0(Apr 27, 2021)
All AbstractSynth now return multi-modal tuples: (audio batch, parameter batch, is_train batch)

Batch sizes that are multiples of 32 are now supported for any reproducible output. (128 is still the default.)

More detailed documentation, including documentation fix proposed by @daisukelab.

Default voice nebula added, as well as a drum nebula.

modulation signal input on VCO and LFO is now optional

Source code(tar.gz)
Source code(zip)
v0.9.2(Apr 13, 2021)
Fixes bug that prevented importing of torchsynth without the README.md file

Pushed preliminary docs

Source code(tar.gz)
Source code(zip)
v0.9.1(Apr 11, 2021)
Memory optimizations

Preliminary docs

Source code(tar.gz)
Source code(zip)

Owner

torchsynth

The fastest synthesizer in the universe

GitHub Repository https://torchsynth.rtfd.io/

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A GPU-optional modular synthesizer in pytorch, 16200x faster than realtime, for audio ML researchers.

Related tags

Overview

torchsynth

Introduction

Installation

Listen

Citation

Comments

Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

Vulnerabilities that will be fixed

By pinning:

Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

Vulnerabilities that will be fixed

By pinning:

22.12.0

Preview style

Configuration

Parser

Integrations

22.10.0

Highlights

Stable style

Preview style

22.12.0

Preview style

Configuration

Parser

Integrations

22.10.0

Highlights

Stable style

Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

Vulnerabilities that will be fixed

By pinning:

Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

Vulnerabilities that will be fixed

By pinning:

Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

Vulnerabilities that will be fixed

By pinning:

Releases(v1.0.2)

v1.0.2(Aug 19, 2022)

What's Changed

v1.0.1(Jun 29, 2021)

v1.0.0(Apr 27, 2021)

v0.9.2(Apr 13, 2021)

v0.9.1(Apr 11, 2021)

Owner

torchsynth

RoBERTa Marathi Language model trained from scratch during huggingface 🤗 x flax community week

Pytorch reimplementation of the Vision Transformer (An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale)

Official implementation for paper: A Latent Transformer for Disentangled Face Editing in Images and Videos.

A Python library that enables ML teams to share, load, and transform data in a collaborative, flexible, and efficient way :chestnut:

This repository is an open-source implementation of the ICRA 2021 paper: Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling.

Load What You Need: Smaller Multilingual Transformers for Pytorch and TensorFlow 2.0.

Detecting Blurred Ground-based Sky/Cloud Images

Unity Propagation in Bayesian Networks Handling Inconsistency via Unity Smoothing

This repo is customed for VisDrone.

[CVPR2021 Oral] FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation.

Learning Continuous Image Representation with Local Implicit Image Function

DrWhy is the collection of tools for eXplainable AI (XAI). It's based on shared principles and simple grammar for exploration, explanation and visualisation of predictive models.

QuanTaichi evaluation suite

Supervised Contrastive Learning for Downstream Optimized Sequence Representations

Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations

✔️ Visual, reactive testing library for Julia. Time machine included.

PyTorch reimplementation of REALM and ORQA

The code written during my Bachelor Thesis "Classification of Human Whole-Body Motion using Hidden Markov Models".

This is an official implementation of CvT: Introducing Convolutions to Vision Transformers.

Circuit Training: An open-source framework for generating chip floor plans with distributed deep reinforcement learning