diff --git a/app.py b/app.py
index 97fa9e82..42850b6b 100644
--- a/app.py
+++ b/app.py
@@ -7,10 +7,10 @@
"""
from tempfile import NamedTemporaryFile
+import argparse
import torch
import gradio as gr
from audiocraft.models import MusicGen
-
from audiocraft.data.audio import audio_write
@@ -61,90 +61,150 @@ def predict(model, text, melody, duration, topk, topp, temperature, cfg_coef):
return waveform_video
-with gr.Blocks() as demo:
- gr.Markdown(
- """
- # MusicGen
+def ui(**kwargs):
+ with gr.Blocks() as interface:
+ gr.Markdown(
+ """
+ # MusicGen
+
+ This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
+ presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
+
+
+ 
+ for longer sequences, more control and no queue.
+ """
+ )
+ with gr.Row():
+ with gr.Column():
+ with gr.Row():
+ text = gr.Text(label="Input Text", interactive=True)
+ melody = gr.Audio(source="upload", type="numpy", label="Melody Condition (optional)", interactive=True)
+ with gr.Row():
+ submit = gr.Button("Submit")
+ with gr.Row():
+ model = gr.Radio(["melody", "medium", "small", "large"], label="Model", value="melody", interactive=True)
+ with gr.Row():
+ duration = gr.Slider(minimum=1, maximum=30, value=10, label="Duration", interactive=True)
+ with gr.Row():
+ topk = gr.Number(label="Top-k", value=250, interactive=True)
+ topp = gr.Number(label="Top-p", value=0, interactive=True)
+ temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
+ cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
+ with gr.Column():
+ output = gr.Video(label="Generated Music")
+ submit.click(predict, inputs=[model, text, melody, duration, topk, topp, temperature, cfg_coef], outputs=[output])
+ gr.Examples(
+ fn=predict,
+ examples=[
+ [
+ "An 80s driving pop song with heavy drums and synth pads in the background",
+ "./assets/bach.mp3",
+ "melody"
+ ],
+ [
+ "A cheerful country song with acoustic guitars",
+ "./assets/bolero_ravel.mp3",
+ "melody"
+ ],
+ [
+ "90s rock song with electric guitar and heavy drums",
+ None,
+ "medium"
+ ],
+ [
+ "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
+ "./assets/bach.mp3",
+ "melody"
+ ],
+ [
+ "lofi slow bpm electro chill with organic samples",
+ None,
+ "medium",
+ ],
+ ],
+ inputs=[text, melody, model],
+ outputs=[output]
+ )
+ gr.Markdown(
+ """
+ ### More details
+
+ The model will generate a short music extract based on the description you provided.
+ You can generate up to 30 seconds of audio.
+
+ We present 4 model variations:
+ 1. Melody -- a music generation model capable of generating music condition on text and melody inputs. **Note**, you can also use text only.
+ 2. Small -- a 300M transformer decoder conditioned on text only.
+ 3. Medium -- a 1.5B transformer decoder conditioned on text only.
+ 4. Large -- a 3.3B transformer decoder conditioned on text only (might OOM for the longest sequences.)
+
+ When using `melody`, ou can optionaly provide a reference audio from
+ which a broad melody will be extracted. The model will then try to follow both the description and melody provided.
+
+ You can also use your own GPU or a Google Colab by following the instructions on our repo.
+ See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
+ for more details.
+ """
+ )
- This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
- presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
-
-
-
- for longer sequences, more control and no queue.
- """
+ # Show the interface
+ launch_kwargs = {}
+ username = kwargs.get('username')
+ password = kwargs.get('password')
+ server_port = kwargs.get('server_port', 0)
+ inbrowser = kwargs.get('inbrowser', False)
+ share = kwargs.get('share', False)
+ server_name = kwargs.get('listen')
+
+ launch_kwargs['server_name'] = server_name
+
+ if username and password:
+ launch_kwargs['auth'] = (username, password)
+ if server_port > 0:
+ launch_kwargs['server_port'] = server_port
+ if inbrowser:
+ launch_kwargs['inbrowser'] = inbrowser
+ if share:
+ launch_kwargs['share'] = share
+
+ interface.launch(**launch_kwargs)
+
+if __name__ == "__main__":
+ # torch.cuda.set_per_process_memory_fraction(0.48)
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ '--listen',
+ type=str,
+ default='127.0.0.1',
+ help='IP to listen on for connections to Gradio',
)
- with gr.Row():
- with gr.Column():
- with gr.Row():
- text = gr.Text(label="Input Text", interactive=True)
- melody = gr.Audio(source="upload", type="numpy", label="Melody Condition (optional)", interactive=True)
- with gr.Row():
- submit = gr.Button("Submit")
- with gr.Row():
- model = gr.Radio(["melody", "medium", "small", "large"], label="Model", value="melody", interactive=True)
- with gr.Row():
- duration = gr.Slider(minimum=1, maximum=30, value=10, label="Duration", interactive=True)
- with gr.Row():
- topk = gr.Number(label="Top-k", value=250, interactive=True)
- topp = gr.Number(label="Top-p", value=0, interactive=True)
- temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
- cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
- with gr.Column():
- output = gr.Video(label="Generated Music")
- submit.click(predict, inputs=[model, text, melody, duration, topk, topp, temperature, cfg_coef], outputs=[output])
- gr.Examples(
- fn=predict,
- examples=[
- [
- "An 80s driving pop song with heavy drums and synth pads in the background",
- "./assets/bach.mp3",
- "melody"
- ],
- [
- "A cheerful country song with acoustic guitars",
- "./assets/bolero_ravel.mp3",
- "melody"
- ],
- [
- "90s rock song with electric guitar and heavy drums",
- None,
- "medium"
- ],
- [
- "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
- "./assets/bach.mp3",
- "melody"
- ],
- [
- "lofi slow bpm electro chill with organic samples",
- None,
- "medium",
- ],
- ],
- inputs=[text, melody, model],
- outputs=[output]
+ parser.add_argument(
+ '--username', type=str, default='', help='Username for authentication'
+ )
+ parser.add_argument(
+ '--password', type=str, default='', help='Password for authentication'
)
- gr.Markdown(
- """
- ### More details
-
- The model will generate a short music extract based on the description you provided.
- You can generate up to 30 seconds of audio.
-
- We present 4 model variations:
- 1. Melody -- a music generation model capable of generating music condition on text and melody inputs. **Note**, you can also use text only.
- 2. Small -- a 300M transformer decoder conditioned on text only.
- 3. Medium -- a 1.5B transformer decoder conditioned on text only.
- 4. Large -- a 3.3B transformer decoder conditioned on text only (might OOM for the longest sequences.)
-
- When using `melody`, ou can optionaly provide a reference audio from
- which a broad melody will be extracted. The model will then try to follow both the description and melody provided.
-
- You can also use your own GPU or a Google Colab by following the instructions on our repo.
- See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
- for more details.
- """
+ parser.add_argument(
+ '--server_port',
+ type=int,
+ default=0,
+ help='Port to run the server listener on',
)
+ parser.add_argument(
+ '--inbrowser', action='store_true', help='Open in browser'
+ )
+ parser.add_argument(
+ '--share', action='store_true', help='Share the gradio UI'
+ )
+
+ args = parser.parse_args()
-demo.launch()
+ ui(
+ username=args.username,
+ password=args.password,
+ inbrowser=args.inbrowser,
+ server_port=args.server_port,
+ share=args.share,
+ listen=args.listen
+ )
\ No newline at end of file