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README.md
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---
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title: 'VampNet: Music Generation with Masked Transformers'
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emoji: 🤖
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sdk_version: 3.36.1
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app_file: app.py
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pinned: false
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duplicated_from: hugggof/vampnet
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---
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# VampNet
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---
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<<<<<<< HEAD
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title: 'VampNet: Music Generation with Masked Transformers'
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=======
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title: "VampNet: Music Generation with Masked Transformers"
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>>>>>>> main
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emoji: 🤖
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colorFrom: gray
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colorTo: gray
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sdk_version: 3.36.1
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app_file: app.py
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pinned: false
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<<<<<<< HEAD
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duplicated_from: hugggof/vampnet
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=======
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>>>>>>> main
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---
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# VampNet
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app.py
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coarse_ckpt="./models/vampnet/coarse.pth",
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coarse2fine_ckpt="./models/vampnet/c2f.pth",
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codec_ckpt="./models/vampnet/codec.pth",
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device="cuda" if torch.cuda.is_available() else "cpu",
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)
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with gr.Row():
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with gr.Column():
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gr.Markdown("# VampNet
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gr.Markdown("""## Description:
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This is a demo of
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You can control the extent and nature of variation with a set of manual controls and presets.
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Use this interface to experiment with different mask settings and explore the audio outputs.
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""")
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gr.Markdown("""
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## Instructions:
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1. You can start by uploading some audio, or by loading the example audio.
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2. Choose a preset for the vamp operation, or manually adjust the controls to customize the mask settings.
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3. Click the "generate (vamp)!!!" button to
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4. Optionally, you can add some notes and save the result.
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5. You can also use the output as the new input and continue experimenting!
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""")
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"beat_mask_downbeats": False,
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},
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"slight periodic variation": {
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"periodic_p": 5,
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"onset_mask_width": 5,
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"beat_mask_width": 0,
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"beat_mask_downbeats": False,
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},
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"moderate periodic variation": {
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"periodic_p": 13,
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"onset_mask_width": 5,
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"beat_mask_width": 0,
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"beat-driven variation": {
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"periodic_p": 0,
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"onset_mask_width": 0,
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"beat_mask_width":
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"beat_mask_downbeats": False,
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},
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"beat-driven variation (downbeats only)": {
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"periodic_p": 0,
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"onset_mask_width": 0,
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"beat_mask_width": 50,
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"beat_mask_downbeats": True,
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},
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"beat-driven variation (downbeats only, strong)": {
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"periodic_p": 0,
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"onset_mask_width": 0,
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minimum=0,
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maximum=128,
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step=1,
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value=
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)
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label="temperature",
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minimum=0.0,
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maximum=10.0,
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value=
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)
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coarse_ckpt="./models/vampnet/coarse.pth",
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coarse2fine_ckpt="./models/vampnet/c2f.pth",
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codec_ckpt="./models/vampnet/codec.pth",
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wavebeat_ckpt="./models/wavebeat.pth",
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device="cuda" if torch.cuda.is_available() else "cpu",
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)
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with gr.Row():
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with gr.Column():
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gr.Markdown("# VampNet")
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gr.Markdown("""## Description:
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This is a demo of VampNet, a masked generative music model capable of doing music variations.
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You can control the extent and nature of variation with a set of manual controls and presets.
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Use this interface to experiment with different mask settings and explore the audio outputs.
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""")
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gr.Markdown("""
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## Instructions:
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1. You can start by uploading some audio, or by loading the example audio.
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2. Choose a preset for the vamp operation, or manually adjust the controls to customize the mask settings. Click the load preset button.
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3. Click the "generate (vamp)!!!" button to generate audio. Listen to the output audio, and the masked audio to hear the mask hints.
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4. Optionally, you can add some notes and save the result.
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5. You can also use the output as the new input and continue experimenting!
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""")
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"beat_mask_downbeats": False,
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},
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"slight periodic variation": {
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<<<<<<< HEAD
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"periodic_p": 5,
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"onset_mask_width": 5,
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"beat_mask_width": 0,
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"beat_mask_downbeats": False,
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},
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"moderate periodic variation": {
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=======
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>>>>>>> main
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"periodic_p": 13,
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"onset_mask_width": 5,
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"beat_mask_width": 0,
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"beat-driven variation": {
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"periodic_p": 0,
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"onset_mask_width": 0,
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"beat_mask_width": 20,
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"beat_mask_downbeats": False,
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},
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"beat-driven variation (downbeats only, strong)": {
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"periodic_p": 0,
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"onset_mask_width": 0,
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minimum=0,
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maximum=128,
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step=1,
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value=13,
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)
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label="temperature",
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minimum=0.0,
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maximum=10.0,
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value=1.8
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)
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