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.

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.

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.

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

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 :\

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...

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?

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.

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

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

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.

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

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.

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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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

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.

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

For more information: 🧐 View latest project report

🛠 Adjust project settings

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Bumps black from 22.6.0 to 22.12.0.

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

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

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.

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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Learn how to fix vulnerabilities with free interactive lessons:

🦉 Learn about vulnerability in an interactive lesson of Snyk Learn.

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

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.

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

For more information: 🧐 View latest project report

🛠 Adjust project settings

📚 Read more about Snyk's upgrade and patch logic

Learn how to fix vulnerabilities with free interactive lessons:

🦉 Learn about vulnerability in an interactive lesson of Snyk Learn.

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

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.

For more information: 🧐 View latest project report

🛠 Adjust project settings

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Learn how to fix vulnerabilities with free interactive lessons:

🦉 Regular Expression Denial of Service (ReDoS) 🦉 Regular Expression Denial of Service (ReDoS)