import os
import torch
import librosa
import numpy as np
import gradio as gr
from sonics import HFAudioClassifier
# Model configurations
MODEL_IDS = {
"SpecTTTra-α (5s)": "awsaf49/sonics-spectttra-alpha-5s",
"SpecTTTra-β (5s)": "awsaf49/sonics-spectttra-beta-5s",
"SpecTTTra-γ (5s)": "awsaf49/sonics-spectttra-gamma-5s",
"SpecTTTra-α (120s)": "awsaf49/sonics-spectttra-alpha-120s",
"SpecTTTra-β (120s)": "awsaf49/sonics-spectttra-beta-120s",
"SpecTTTra-γ (120s)": "awsaf49/sonics-spectttra-gamma-120s",
}
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_cache = {}
def load_model(model_name):
"""Load model if not already cached"""
if model_name not in model_cache:
model_id = MODEL_IDS[model_name]
model = HFAudioClassifier.from_pretrained(model_id)
model = model.to(device)
model.eval()
model_cache[model_name] = model
return model_cache[model_name]
def process_audio(audio_path, model_name):
"""Process audio file and return prediction"""
try:
model = load_model(model_name)
max_time = model.config.audio.max_time
# Load and process audio
audio, sr = librosa.load(audio_path, sr=16000)
chunk_samples = int(max_time * sr)
total_chunks = len(audio) // chunk_samples
middle_chunk_idx = total_chunks // 2
# Extract middle chunk
start = middle_chunk_idx * chunk_samples
end = start + chunk_samples
chunk = audio[start:end]
if len(chunk) < chunk_samples:
chunk = np.pad(chunk, (0, chunk_samples - len(chunk)))
# Get prediction
with torch.no_grad():
chunk = torch.from_numpy(chunk).float().to(device)
pred = model(chunk.unsqueeze(0))
prob = torch.sigmoid(pred).cpu().numpy()[0]
real_prob = 1 - prob
fake_prob = prob
# Return formatted results with emojis
return {
"🎵 Real": float(real_prob),
"🤖 Fake": float(fake_prob)
}
except Exception as e:
return {"❌ Error": str(e)}
def predict(audio_file, model_name):
"""Gradio interface function"""
if audio_file is None:
return {"⚠️ Message": "Please upload an audio file"}
return process_audio(audio_file, model_name)
# Custom CSS for styling
css = """
:root {
--primary-color: #6366f1;
--secondary-color: #8b5cf6;
--accent-color: #ec4899;
--background-color: #f8fafc;
--text-color: #1e293b;
--border-radius: 10px;
}
.gradio-container {
background-color: var(--background-color);
}
.gr-button {
background: linear-gradient(90deg, var(--primary-color), var(--secondary-color));
border: none !important;
color: white !important;
border-radius: var(--border-radius) !important;
}
.gr-button:hover {
background: linear-gradient(90deg, var(--secondary-color), var(--accent-color));
transform: translateY(-2px);
box-shadow: 0 10px 20px rgba(0,0,0,0.1);
transition: all 0.3s ease;
}
.gr-form {
border-radius: var(--border-radius) !important;
border: 1px solid #e2e8f0 !important;
box-shadow: 0 4px 12px rgba(0,0,0,0.05) !important;
}
.footer {
margin-top: 20px;
text-align: center;
font-size: 0.9em;
color: #64748b;
}
.gradient-text {
background: linear-gradient(90deg, var(--primary-color), var(--accent-color));
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
background-clip: text;
text-fill-color: transparent;
}
.logo-container {
display: flex;
justify-content: center;
margin-bottom: 1rem;
}
.header-container {
text-align: center;
margin-bottom: 2rem;
padding: 1.5rem;
background: rgba(255, 255, 255, 0.8);
border-radius: var(--border-radius);
box-shadow: 0 4px 15px rgba(0, 0, 0, 0.05);
}
.resource-links {
display: flex;
justify-content: center;
gap: 1rem;
flex-wrap: wrap;
margin-bottom: 1.5rem;
}
.resource-link {
display: inline-block;
padding: 0.5rem 1rem;
background: white;
border-radius: var(--border-radius);
color: var(--primary-color);
text-decoration: none;
box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);
transition: all 0.2s ease;
}
.resource-link:hover {
transform: translateY(-2px);
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.15);
}
.label-container {
border-radius: var(--border-radius);
overflow: hidden;
box-shadow: 0 4px 12px rgba(0,0,0,0.05);
}
"""
# Create Gradio interface
with gr.Blocks(css=css) as demo:
# Title, Subtitle, and Logo
gr.HTML(
"""
"""
)
# Resource Links
gr.HTML(
"""
"""
)
# Main Interface
with gr.Row(equal_height=True):
with gr.Column():
audio_input = gr.Audio(
label="🎧 Upload Audio File",
type="filepath",
elem_id="audio_input"
)
model_dropdown = gr.Dropdown(
choices=list(MODEL_IDS.keys()),
value="SpecTTTra-γ (5s)",
label="🔍 Select Model",
elem_id="model_dropdown"
)
submit_btn = gr.Button(
"✨ Analyze Audio",
elem_id="submit_btn"
)
with gr.Column():
# Define output before using it in Examples
output = gr.Label(
label="📊 Analysis Result",
num_top_classes=2,
elem_id="output",
elem_classes="label-container"
)
with gr.Accordion("ℹ️ How It Works", open=False):
gr.Markdown("""
The SONICS classifier analyzes your audio to determine if it's an authentic song (Human created) or
generated by AI. Our models are trained on a diverse dataset of real and AI-generated songs from Suno and Udio.
**Models available:**
- **SpecTTTra-γ**: Optimized for speed
- **SpecTTTra-β**: Balanced performance
- **SpecTTTra-α**: Highest accuracy
**Duration variants:**
- **5s**: Analyzes a 5-second clip (faster)
- **120s**: Analyzes up to 2 minutes (more accurate)
""")
# Add Examples section after output is defined
with gr.Accordion("🎬 Example Audio Files", open=True):
gr.Examples(
examples=[
["example/real_song.mp3", "SpecTTTra-γ (5s)"],
["example/fake_song.mp3", "SpecTTTra-γ (5s)"],
],
inputs=[audio_input, model_dropdown],
outputs=[output],
fn=predict,
cache_examples=True,
)
# Footer
gr.HTML(
"""
"""
)
# Prediction handling
submit_btn.click(fn=predict, inputs=[audio_input, model_dropdown], outputs=[output])
if __name__ == "__main__":
demo.launch()