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from model import PopMusicTransformer
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
import gradio as gr
import requests
import torchtext
import zipfile
torchtext.utils.download_from_url("https://drive.google.com/uc?id=1gxuTSkF51NP04JZgTE46Pg4KQsbHQKGo", root=".")
torchtext.utils.download_from_url("https://drive.google.com/uc?id=1nAKjaeahlzpVAX0F9wjQEG_hL4UosSbo", root=".")
with zipfile.ZipFile("REMI-tempo-checkpoint.zip","r") as zip_ref:
zip_ref.extractall(".")
with zipfile.ZipFile("REMI-tempo-chord-checkpoint.zip","r") as zip_ref:
zip_ref.extractall(".")
url = 'https://github.com/AK391/remi/blob/master/input.midi?raw=true'
r = requests.get(url, allow_redirects=True)
open("input.midi", 'wb').write(r.content)
# declare model
model = PopMusicTransformer(
checkpoint='REMI-tempo-checkpoint',
is_training=False)
def inference(midi):
# generate continuation
model.generate(
n_target_bar=4,
temperature=1.2,
topk=5,
output_path='continuation.midi',
prompt=midi.name)
return 'continuation.midi'
title = "Pop Music Transformer"
description = "demo for Pop Music Transformer. To use it, simply upload your midi file, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2002.00212'>Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions</a> | <a href='https://github.com/YatingMusic/remi'>Github Repo</a></p>"
examples = [
['input.midi']
]
gr.Interface(
inference,
gr.inputs.File(label="Input Midi"),
gr.outputs.File(label="Output Midi"),
title=title,
description=description,
article=article,
examples=examples
).launch()
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