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import gradio as gr
import torch
from api import FlexSED
import tempfile
import os
import spaces
# Load model once on startup
flexsed = FlexSED(device="cuda" if torch.cuda.is_available() else "cpu")
@spaces.GPU # 🚀 ensures this function runs on the GPU in Hugging Face Spaces
def run_flexsed(audio_file, event_list):
"""
Run inference using FlexSED and return prediction plot.
"""
if not audio_file:
return None
# Split events by semicolon or comma
events = [e.strip() for e in event_list.split(";") if e.strip()]
if not events:
return None
# Run inference
preds = flexsed.run_inference(audio_file, events)
# Generate visualization
output_fname = os.path.join(tempfile.gettempdir(), "flexsed_output")
flexsed.to_multi_plot(preds, events, fname=output_fname)
plot_path = f"{output_fname}.png"
return plot_path
# App layout
with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue")) as app:
# Header
gr.Markdown("""
## 🎧 FlexSED: A Flexible Open-Vocabulary Sound Event Detection System
👋 Welcome to the **FlexSED live demo** — explore **prompt-guided sound event detection** in real audio clips.
🔗 Learn more on the [FlexSED GitHub Repository](https://github.com/JHU-LCAP/FlexSED)
""")
gr.Markdown("### 🔍 Upload or choose an example below to detect sound events:")
with gr.Row():
# Left column: Inputs
with gr.Column(scale=1):
audio_input = gr.Audio(type="filepath", label="🎵 Upload Audio (.wav)")
text_input = gr.Textbox(label="Event list (semicolon-separated)", value="Male speech; Door; Dog; Laughter")
with gr.Row():
detect_btn = gr.Button("🎯 Detect", variant="primary")
clear_btn = gr.Button("🧹 Clear")
# Right column: Output
with gr.Column(scale=1):
image_output = gr.Image(label="Prediction Plot", show_label=True, elem_id="output-image")
gr.Examples(
examples=[
["example.wav", "Male speech; Door; Dog; Laughter"],
["example2.wav", "Male speech; Bee; Gunshot, gunfire"],
],
inputs=[audio_input, text_input],
label="Example Audios"
)
# Function bindings
detect_btn.click(run_flexsed, inputs=[audio_input, text_input], outputs=image_output)
clear_btn.click(lambda: (None, "Male speech; Door; Dog; Laughter"), outputs=[audio_input, text_input])
if __name__ == "__main__":
app.launch()
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