Whilst this seems cool - I'm struggling to understand the real world use cases.
> Machine Learning (ML): Pedalboard makes the process of data augmentation for audio dramatically faster and produces more realistic results ... Pedalboard has been thoroughly tested in high-performance and high-reliability ML use cases at Spotify, and is used heavily with TensorFlow.
What are the actual use cases internally at Spotify and for the public here?
> Applying a VST3® or Audio Unit plugin no longer requires launching your DAW, importing audio, and exporting it; a couple of lines of code can do it all in one command, or as part of a larger workflow.
I wonder how many content creators are more comfortable with Python than with a DAW or Audacity?
> Artists, musicians, and producers with a bit of Python knowledge can use Pedalboard to produce new creative effects that would be extremely time consuming and difficult to produce in a DAW.
Googling "how to add reverb" yields Audacity as the first option. A free, open source tool available on Linux+Win+Mac. In what world is it easier to do this in Python for Artists, musicians and producers?
As a music producer that's well versed in Python myself (even if I hadn't switched to producing almost entirely out-of-the-box and on modular/hardware synths) I'd much rather just apply basic effects like these in a DAW/Audacity, where accessing and patching a live audio stream is much easier than figuring out how to do that in Python and only being able to apply effects to .wav files rather than live audio.
On the ML front (which is probably their primary motivation) it's pretty useful for the kind of things Spotify is interested. As a basic example, say you want to train a model to classify songs by genre. If you have say, a country song, adding a bit of reverb or compression to it will not change what genre it sounds like. So augmenting their training data with small transformations such as these can make their models more robust to these transformations. Obviously, this has to been judiciously, e.g, if you add tons of distortion and reverb to a country song it might sound like some experimental noise and not country. This kind of thing also can help with duplicate detection, song recommendations, playlist generation, autotagging, etc.
As for creators, maybe not a large fraction of music creators are coders, but there's certainly an intersection in that venn diagram, though I have no idea how large it is. And I imagine this could be used to create other tools that don't require coding.
Clearly, most of the time it makes more sense to apply FX interactively in your DAW of choice, but I find it useful to programmatically modify audio sometimes. For example, I've written quick scripts using sox and other tools to normalize/resample audio, as well as slice loops. I could see being able to add other fx such as compression or maybe even reverb programatically could be occasionally useful.
I don’t think I would. The amount of music produces and musicians I know the majority of them are relatively poor at ‘tech’ and definitely not coders/programmers/software engineers. They definitely know their way around tools, but not coders.
For machine learning on audio data, it is often (always?) useful to modify the original dataset to make the model more general.
A way to do such manipulation that is both convenient to use from Python (a major programming language in the field and well tied in to the major frameworks) and performant is extremely welcome.
While I think this is awesome (from the perspective of audio production pipelining requirements), I can't really see the need to use a VST to apply basic audio alterations for the stated ML purpose, since there are already many native DSP libraries that can apply reverb, distortion, delay, convolution, etc. to an audio signal.
Not a lot of creators are necessarily comfortable with Python or other coding, but there are definitely people (including me) interested in whatever can be done programmatically with a DAW quality, without a DAW.
This opens possibilities such as version control, collaboration via PR, the regular coding workflow etc.
(I am dabbling with music and Elixir + Rust at the moment, and definitely interested by what Pedalboard brings, including programmatic VST hosting etc).
There are plenty of standalone plugin hosts, so just write a plugin (JUCE offers a perfectly fine framework and workflow for that, as do some others like DPF), load it into a standalone plugin host, done.
> Applying a VST3® or Audio Unit plugin no longer requires launching your DAW, importing audio, and exporting it; a couple of lines of code can do it all in one command, or as part of a larger workflow.
Is also not really true. There are plenty of scriptable VST hosts, and libraries. BASS (the library) for instance has been around for ages and I've used it to host VSTs in script workflows.
I'm a bit perplexed why you quoted a statement saying what it's used for with "what are the actual use cases..." lol.
This useful for me, both for ML and for adding effects through sounds. Would much rather use python than a DAW. I know enough signal processing to prefer running code I can inspect rather than using some opaque GUI.
> I wonder how many content creators are more comfortable with Python than with a DAW or Audacity?
This opens it up potential for a simple GUI. For a basic user, drag and drop an audio file and flip virtual switches. Or, easier integration into a mobile "podcast creator" app.
Some years ago when Ardour (a crossplatform FLOSS DAW) was being sponsored by SSL (famous for their large scale mixing consoles), I got to attend a meeting designed to float and discuss "blue sky" ideas.
Somebody who had been with the company for a long time predicted that the broadcast world was going to end up demanding a box with just 3 buttons:
[ That was worse ]
[ That was better ]
[ Try something else ]
Everybody laughed, but everybody also knew that this was indeed the direction that audio engineering was going to go in.
One of my favourite things with Winamp ~20 years ago was the ability to stack DSP/sound plugins and then output to WAV. It was a weird but great way to quickly create CD-ready tracks that were crossfaded with effects (eg speed or stereo separation or vocal removal) etc. I was basically 'batch processing' through a GUI without even realizing it.
> Machine Learning (ML): Pedalboard makes the process of data augmentation for audio dramatically faster and produces more realistic results ... Pedalboard has been thoroughly tested in high-performance and high-reliability ML use cases at Spotify, and is used heavily with TensorFlow.
What are the actual use cases internally at Spotify and for the public here?
> Applying a VST3® or Audio Unit plugin no longer requires launching your DAW, importing audio, and exporting it; a couple of lines of code can do it all in one command, or as part of a larger workflow.
I wonder how many content creators are more comfortable with Python than with a DAW or Audacity?
> Artists, musicians, and producers with a bit of Python knowledge can use Pedalboard to produce new creative effects that would be extremely time consuming and difficult to produce in a DAW.
Googling "how to add reverb" yields Audacity as the first option. A free, open source tool available on Linux+Win+Mac. In what world is it easier to do this in Python for Artists, musicians and producers?
As a music producer that's well versed in Python myself (even if I hadn't switched to producing almost entirely out-of-the-box and on modular/hardware synths) I'd much rather just apply basic effects like these in a DAW/Audacity, where accessing and patching a live audio stream is much easier than figuring out how to do that in Python and only being able to apply effects to .wav files rather than live audio.