Spaces:
Running
Running
Ashley Kleynhans
commited on
Commit
•
8e10a53
1
Parent(s):
10b60b3
Allow gradio command line arguments to be specified (#50)
Browse files
app.py
CHANGED
@@ -7,10 +7,10 @@ LICENSE file in the root directory of this source tree.
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"""
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from tempfile import NamedTemporaryFile
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import torch
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import gradio as gr
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from audiocraft.models import MusicGen
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-
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from audiocraft.data.audio import audio_write
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@@ -61,90 +61,150 @@ def predict(model, text, melody, duration, topk, topp, temperature, cfg_coef):
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return waveform_video
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gr.
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)
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with gr.Row():
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submit = gr.Button("Submit")
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with gr.Row():
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model = gr.Radio(["melody", "medium", "small", "large"], label="Model", value="melody", interactive=True)
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with gr.Row():
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duration = gr.Slider(minimum=1, maximum=30, value=10, label="Duration", interactive=True)
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with gr.Row():
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topk = gr.Number(label="Top-k", value=250, interactive=True)
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topp = gr.Number(label="Top-p", value=0, interactive=True)
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temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
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cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
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with gr.Column():
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output = gr.Video(label="Generated Music")
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submit.click(predict, inputs=[model, text, melody, duration, topk, topp, temperature, cfg_coef], outputs=[output])
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gr.Examples(
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fn=predict,
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examples=[
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[
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"An 80s driving pop song with heavy drums and synth pads in the background",
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"./assets/bach.mp3",
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"melody"
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],
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[
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"A cheerful country song with acoustic guitars",
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"./assets/bolero_ravel.mp3",
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"melody"
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],
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[
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"90s rock song with electric guitar and heavy drums",
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None,
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"medium"
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],
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[
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"a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
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"./assets/bach.mp3",
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"melody"
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],
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[
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"lofi slow bpm electro chill with organic samples",
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None,
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"medium",
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],
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],
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inputs=[text, melody, model],
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outputs=[output]
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)
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You can generate up to 30 seconds of audio.
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We present 4 model variations:
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1. Melody -- a music generation model capable of generating music condition on text and melody inputs. **Note**, you can also use text only.
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2. Small -- a 300M transformer decoder conditioned on text only.
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3. Medium -- a 1.5B transformer decoder conditioned on text only.
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4. Large -- a 3.3B transformer decoder conditioned on text only (might OOM for the longest sequences.)
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When using `melody`, ou can optionaly provide a reference audio from
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which a broad melody will be extracted. The model will then try to follow both the description and melody provided.
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You can also use your own GPU or a Google Colab by following the instructions on our repo.
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See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
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for more details.
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"""
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)
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"""
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from tempfile import NamedTemporaryFile
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import argparse
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import torch
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import gradio as gr
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from audiocraft.models import MusicGen
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from audiocraft.data.audio import audio_write
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return waveform_video
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def ui(**kwargs):
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with gr.Blocks() as interface:
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gr.Markdown(
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"""
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# MusicGen
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This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
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presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
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<br/>
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<a href="https://huggingface.co/spaces/musicgen/MusicGen?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
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<img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
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for longer sequences, more control and no queue.</p>
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"""
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)
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with gr.Row():
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with gr.Column():
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with gr.Row():
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text = gr.Text(label="Input Text", interactive=True)
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melody = gr.Audio(source="upload", type="numpy", label="Melody Condition (optional)", interactive=True)
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with gr.Row():
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submit = gr.Button("Submit")
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with gr.Row():
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model = gr.Radio(["melody", "medium", "small", "large"], label="Model", value="melody", interactive=True)
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with gr.Row():
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duration = gr.Slider(minimum=1, maximum=30, value=10, label="Duration", interactive=True)
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with gr.Row():
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topk = gr.Number(label="Top-k", value=250, interactive=True)
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topp = gr.Number(label="Top-p", value=0, interactive=True)
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temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
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cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
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with gr.Column():
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output = gr.Video(label="Generated Music")
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submit.click(predict, inputs=[model, text, melody, duration, topk, topp, temperature, cfg_coef], outputs=[output])
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gr.Examples(
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fn=predict,
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examples=[
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[
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"An 80s driving pop song with heavy drums and synth pads in the background",
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"./assets/bach.mp3",
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"melody"
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],
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[
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"A cheerful country song with acoustic guitars",
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"./assets/bolero_ravel.mp3",
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"melody"
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],
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[
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"90s rock song with electric guitar and heavy drums",
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None,
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"medium"
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],
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[
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"a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
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"./assets/bach.mp3",
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"melody"
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],
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[
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"lofi slow bpm electro chill with organic samples",
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None,
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"medium",
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],
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],
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inputs=[text, melody, model],
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outputs=[output]
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)
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gr.Markdown(
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"""
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### More details
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The model will generate a short music extract based on the description you provided.
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You can generate up to 30 seconds of audio.
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We present 4 model variations:
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1. Melody -- a music generation model capable of generating music condition on text and melody inputs. **Note**, you can also use text only.
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2. Small -- a 300M transformer decoder conditioned on text only.
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3. Medium -- a 1.5B transformer decoder conditioned on text only.
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4. Large -- a 3.3B transformer decoder conditioned on text only (might OOM for the longest sequences.)
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+
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When using `melody`, ou can optionaly provide a reference audio from
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which a broad melody will be extracted. The model will then try to follow both the description and melody provided.
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+
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You can also use your own GPU or a Google Colab by following the instructions on our repo.
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See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
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for more details.
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"""
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)
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# Show the interface
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launch_kwargs = {}
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username = kwargs.get('username')
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password = kwargs.get('password')
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server_port = kwargs.get('server_port', 0)
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inbrowser = kwargs.get('inbrowser', False)
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share = kwargs.get('share', False)
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server_name = kwargs.get('listen')
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launch_kwargs['server_name'] = server_name
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if username and password:
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launch_kwargs['auth'] = (username, password)
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if server_port > 0:
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launch_kwargs['server_port'] = server_port
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if inbrowser:
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launch_kwargs['inbrowser'] = inbrowser
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if share:
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launch_kwargs['share'] = share
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interface.launch(**launch_kwargs)
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if __name__ == "__main__":
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# torch.cuda.set_per_process_memory_fraction(0.48)
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--listen',
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type=str,
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default='127.0.0.1',
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help='IP to listen on for connections to Gradio',
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)
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parser.add_argument(
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'--username', type=str, default='', help='Username for authentication'
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)
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parser.add_argument(
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'--password', type=str, default='', help='Password for authentication'
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)
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parser.add_argument(
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'--server_port',
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type=int,
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default=0,
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help='Port to run the server listener on',
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)
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parser.add_argument(
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'--inbrowser', action='store_true', help='Open in browser'
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)
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parser.add_argument(
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'--share', action='store_true', help='Share the gradio UI'
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)
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args = parser.parse_args()
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ui(
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username=args.username,
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password=args.password,
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inbrowser=args.inbrowser,
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server_port=args.server_port,
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share=args.share,
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listen=args.listen
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)
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