Implementation of the methods described in Anticipatory Music Transformer.
by John Thickstun, David Hall, Chris Donahue, and Percy Liang.
This repository provides the code for creating anticipatory training datasets, and for sampling from models trained with anticipation. It does not contain code for training these models: you may use the preprocessed datasets constructed here as input to your favorite codebase for training autoregressive transformer models. We used the Levanter codebase to train models, and include instructions here for training an Anticipatory Music Transformer with Levanter.
For additional detail about this work, see the paper. You may also be interested in this blog post.
Pretrained models are hosted by the Center for Research on Foundation Models (CRFM) on the HuggingFace Hub.
This project is licensed under the terms of the Apache License, Version 2.0.
Begin by installing the anticipation package (from the root of this repository).
pip install .
Run the following command to install dependencies.
pip install -r requirements.txt
See the Colab notebook for interactive examples of music generation using the Anticipatory Music Transformer.
Load a pretrained model using the HuggingFace Transformers package, e.g.:
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained('stanford-crfm/music-medium-800k').cuda()
Sample from this model using the custom generate function implementated by the anticipation package. You can convert generated event tokens to midi using the events_to_midi function:
from anticipation.sample import generate
from anticipation.convert import events_to_midi
length = 10 # time in seconds
events = generate(model, start_time=0, end_time=length, top_p=.98)
mid = events_to_midi(events)
mid.save('generated.mid')
Load your own MIDI and tokenize it using the midi_to_events function.
from anticipation.convert import midi_to_events
events = midi_to_events('examples/strawberry.mid')
To isolate a segment of a longer stream of events, use the ops library to clip the stream and translate the clipped segment to time zero. To isolate a melodic line, use extract_instruments:
from anticipation import ops
from anticipation.tokenize import extract_instruments
events = ops.clip(events, 41, 41+20)
events = ops.translate(events, -ops.min_time(events, seconds=False))
events, melody = extract_instruments(events, [53])
To generate an accompaniment to an isolated melody, call the generate function using the melody as control inputs. Recombine the generated accompaniment with the melody controls using ops.combine:
history = ops.clip(events, 0, 5, clip_duration=False)
accompaniment = generate(model, 5, 20, inputs=history, controls=melody, top_p=.98)
completed_events = ops.combine(accompaniment, melody)
mid = events_to_midi(completed_events)
mid.save('generated.mid')
See the Colab notebook for additional examples of infilling control using the Anticipatory Music Transformer.
See the train directory for instructions on preprocessing the Lakh MIDI dataset and using Levanter to train an Anticipatory Music Transformer.
See the humaneval directory for instructions on reproducing data used for the human evaluation results reported in the paper.
To reference this work, please cite
@article{thickstun2023anticipatory,
title={Anticipatory Music Transformer},
author={Thickstun, John and Hall, David and Donahue, Chris and Liang, Percy},
journal={arXiv preprint arXiv:2306.08620},
year={2023}
}
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