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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='./result/continuation.midi', | |
| prompt=midi.name) | |
| return './result/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() | |