Show HN: RaveForce – An OpenAI Gym style toolkit for music generation experiment

RaveForce

RaveForce is a Python package that allows you to define your musical task in Python with Glicol syntax, and train an agent to do the task with APIs similar to the OpenAI Gym.

Why RaveForce

It seems that music generation researches have been dominated by MIDI generation or audio generation methods with either supervised learning via a corpus or unsupervised learning with some sample library. But let's consider a simple example: you want to train an agent to play the synth sequencer for you. The goal is to copy a famous bass line. Therefore, in each step, the agent needs to make a decision on which note to play and what kind of timbre to make. The agent can have an observation of what has been synthesised, and the reward is calculated by comparing the similarity at the moment.

Yet it can be very difficult and time-consuming to build a real-world environment (such as a music robot) to cover all the needs for electronic music. Another option is to use some built-in Python function to compose our music tasks, but still, for each task, you need to write some DSP function chains which will never be used again in practice.

A better way is to find a commonplace between our simulation and real-world music practices. Live coding is exactly such a practice where the artist performs improvised algorithmic music by writing program code in real-time.

Therefore, the final architecture is:

Agent
-> Play around the live coding code
-> Live coding engine does the non-real-time synthesis
-> Get the reward, observation space, etc.

This process should involve some deep neural network as the synthesised audio is much more difficult to process than the symbolic sequences.

Previously, SuperCollider is used for RaveForce. See paper:

Lan, Qichao, Jim Tørresen, and Alexander Refsum Jensenius. "RaveForce: A Deep Reinforcement Learning Environment for Music Generation." (2019).

@article{lan2019raveforce,
  title={RaveForce: A Deep Reinforcement Learning Environment for Music Generation},
  author={Lan, Qichao and Torresen, Jim and Jensenius, Alexander Refsum},
  year={2019}
}

Note that the implementation of this paper has been moved to the sc branch.

But due to the speed limit of non-real-time synthesis on hard disk, we switch to Glicol.

Glicol is a new live coding language that can be accessed in the browsers:

https://glicol.org

The syntax of Glicol is very similar to synth or sequencers, which perfectly fits our needs. Plus, Glicol is written in Rust and can be called in Python via WebAssembly (there are other methods but wasm is used since it shares the same format with Glicol js bindings).

How to use RaveForce

Install

This is quite straightforward: pip install raveforce

Be familiar with Glicol syntax.

Visit Glicol website to get familiar with its syntax and concept:

https://glicol.org

Python

Since we are going to define our own musical task, we should make some changes to the make method.

Let's consider the simplest example: just let the agent to play for 1 step, tweaking attack, decay and freq of a sine wave synth to simulate a kick drum.

import raveforce
import librosa

target, sr = librosa.load("YOUR_KICK_DRUM_SAMPLE", sr=None)
dur = len(target) / sr

env = gym.make(
    """
     ~env: imp 0.1 >> envperc {} {}
    
    kick_drum: sin {} >> mul ~env
    """,
    total_step=1,
    step_len=dur,
    target = target,
    action_space=[
      ["lin", 0.0001, dur-0.0001], 
      ["rel", 0, lambda x: dur-0.0001-x], # related to para 0
      ["exp", 10, 10000]
    ]
)

Then, use as a normal Gym env:

observation = env.reset()
action = env.action_space.sample()
print(action)

observation, reward, done, info = env.step(action)
plt.plot(observation) # make your own import matplotlib
print(reward, done, info)

In this example, after 2000 iterations, the rewards are quite clear that a low attack and a low freq is best to simulate a kick drum, which makes sense.

I also made an interactive example on the Google Colab, you can play around with it.

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