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import os
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import torch
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import librosa
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import numpy as np
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import gradio as gr
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from sonics import HFAudioClassifier
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MODEL_IDS = {
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"SpecTTTra-α (5s)": "awsaf49/sonics-spectttra-alpha-5s",
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"SpecTTTra-β (5s)": "awsaf49/sonics-spectttra-beta-5s",
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"SpecTTTra-γ (5s)": "awsaf49/sonics-spectttra-gamma-5s",
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"SpecTTTra-α (120s)": "awsaf49/sonics-spectttra-alpha-120s",
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"SpecTTTra-β (120s)": "awsaf49/sonics-spectttra-beta-120s",
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"SpecTTTra-γ (120s)": "awsaf49/sonics-spectttra-gamma-120s",
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}
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model_cache = {}
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def load_model(model_name):
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"""Load model if not already cached"""
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if model_name not in model_cache:
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model_id = MODEL_IDS[model_name]
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model = HFAudioClassifier.from_pretrained(model_id)
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model = model.to(device)
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model.eval()
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model_cache[model_name] = model
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return model_cache[model_name]
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def process_audio(audio_path, model_name):
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"""Process audio file and return prediction"""
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try:
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model = load_model(model_name)
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max_time = model.config.audio.max_time
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audio, sr = librosa.load(audio_path, sr=16000)
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chunk_samples = int(max_time * sr)
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total_chunks = len(audio) // chunk_samples
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middle_chunk_idx = total_chunks // 2
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start = middle_chunk_idx * chunk_samples
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end = start + chunk_samples
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chunk = audio[start:end]
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if len(chunk) < chunk_samples:
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chunk = np.pad(chunk, (0, chunk_samples - len(chunk)))
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with torch.no_grad():
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chunk = torch.from_numpy(chunk).float().to(device)
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pred = model(chunk.unsqueeze(0))
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prob = torch.sigmoid(pred).cpu().numpy()[0]
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real_prob = 1 - prob
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fake_prob = prob
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return {
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"🎵 Real": float(real_prob),
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"🤖 Fake": float(fake_prob)
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}
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except Exception as e:
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return {"❌ Error": str(e)}
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def predict(audio_file, model_name):
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"""Gradio interface function"""
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if audio_file is None:
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return {"⚠️ Message": "Please upload an audio file"}
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return process_audio(audio_file, model_name)
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css = """
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:root {
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--primary-color: #6366f1;
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--secondary-color: #8b5cf6;
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--accent-color: #ec4899;
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--background-color: #f8fafc;
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--text-color: #1e293b;
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--border-radius: 10px;
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}
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.gradio-container {
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background-color: var(--background-color);
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}
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.gr-button {
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background: linear-gradient(90deg, var(--primary-color), var(--secondary-color));
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border: none !important;
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color: white !important;
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border-radius: var(--border-radius) !important;
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}
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.gr-button:hover {
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background: linear-gradient(90deg, var(--secondary-color), var(--accent-color));
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transform: translateY(-2px);
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box-shadow: 0 10px 20px rgba(0,0,0,0.1);
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transition: all 0.3s ease;
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}
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.gr-form {
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border-radius: var(--border-radius) !important;
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border: 1px solid #e2e8f0 !important;
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box-shadow: 0 4px 12px rgba(0,0,0,0.05) !important;
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}
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.footer {
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margin-top: 20px;
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text-align: center;
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font-size: 0.9em;
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color: #64748b;
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}
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.gradient-text {
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background: linear-gradient(90deg, var(--primary-color), var(--accent-color));
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-webkit-background-clip: text;
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-webkit-text-fill-color: transparent;
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background-clip: text;
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text-fill-color: transparent;
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}
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.logo-container {
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display: flex;
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justify-content: center;
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margin-bottom: 1rem;
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}
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.header-container {
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text-align: center;
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margin-bottom: 2rem;
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padding: 1.5rem;
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background: rgba(255, 255, 255, 0.8);
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border-radius: var(--border-radius);
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box-shadow: 0 4px 15px rgba(0, 0, 0, 0.05);
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}
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.resource-links {
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display: flex;
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justify-content: center;
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gap: 1rem;
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flex-wrap: wrap;
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margin-bottom: 1.5rem;
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}
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.resource-link {
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display: inline-block;
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padding: 0.5rem 1rem;
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background: white;
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border-radius: var(--border-radius);
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color: var(--primary-color);
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text-decoration: none;
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box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);
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transition: all 0.2s ease;
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}
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.resource-link:hover {
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transform: translateY(-2px);
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box-shadow: 0 4px 8px rgba(0, 0, 0, 0.15);
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}
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.label-container {
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border-radius: var(--border-radius);
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overflow: hidden;
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box-shadow: 0 4px 12px rgba(0,0,0,0.05);
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}
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"""
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with gr.Blocks(css=css) as demo:
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gr.HTML(
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"""
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<div class="header-container">
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<div class="logo-container">
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<img src="https://i.postimg.cc/3Jx3yZ5b/real-vs-fake-sonics-w-logo.jpg"
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style="max-width: 180px; border-radius: 15px; box-shadow: 0 4px 12px rgba(0,0,0,0.1);">
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</div>
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<h1 class="gradient-text">🎵 SONICS: Synthetic Or Not - Identifying Counterfeit Songs 🤖</h1>
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<h3>ICLR 2025 [Poster]</h3>
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<p style="font-size: 1.1em; color: #64748b; margin: 15px 0;">
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Detect if a song is real or AI-generated with our state-of-the-art models.
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Simply upload an audio file to verify its authenticity!
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</p>
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</div>
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"""
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)
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gr.HTML(
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"""
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<div class="resource-links">
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<a href="https://openreview.net/forum?id=PY7KSh29Z8" target="_blank" class="resource-link">
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📄 Paper
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</a>
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<a href="https://huggingface.co/datasets/awsaf49/sonics" target="_blank" class="resource-link">
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🎵 Dataset
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</a>
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<a href="https://huggingface.co/collections/awsaf49/sonics-spectttra-67bb6517b3920fd18e409013" target="_blank" class="resource-link">
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🤖 Models
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</a>
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<a href="https://arxiv.org/abs/2408.14080" target="_blank" class="resource-link">
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🔬 ArXiv
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</a>
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<a href="https://github.com/awsaf49/sonics" target="_blank" class="resource-link">
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💻 GitHub
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</a>
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</div>
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"""
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)
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with gr.Row(equal_height=True):
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with gr.Column():
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audio_input = gr.Audio(
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label="🎧 Upload Audio File",
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type="filepath",
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elem_id="audio_input"
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)
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model_dropdown = gr.Dropdown(
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choices=list(MODEL_IDS.keys()),
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value="SpecTTTra-γ (5s)",
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label="🔍 Select Model",
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elem_id="model_dropdown"
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)
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submit_btn = gr.Button(
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"✨ Analyze Audio",
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elem_id="submit_btn"
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)
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with gr.Column():
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output = gr.Label(
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label="📊 Analysis Result",
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num_top_classes=2,
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elem_id="output",
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elem_classes="label-container"
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)
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with gr.Accordion("ℹ️ How It Works", open=False):
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gr.Markdown("""
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The SONICS classifier analyzes your audio to determine if it's an authentic song (Human created) or
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generated by AI. Our models are trained on a diverse dataset of real and AI-generated songs from Suno and Udio.
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**Models available:**
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- **SpecTTTra-γ**: Optimized for speed
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- **SpecTTTra-β**: Balanced performance
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- **SpecTTTra-α**: Highest accuracy
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**Duration variants:**
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- **5s**: Analyzes a 5-second clip (faster)
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- **120s**: Analyzes up to 2 minutes (more accurate)
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""")
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with gr.Accordion("🎬 Example Audio Files", open=True):
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gr.Examples(
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examples=[
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["example/real_song.mp3", "SpecTTTra-γ (5s)"],
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["example/fake_song.mp3", "SpecTTTra-γ (5s)"],
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],
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inputs=[audio_input, model_dropdown],
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outputs=[output],
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fn=predict,
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cache_examples=True,
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)
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gr.HTML(
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"""
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<div class="footer">
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<p>SONICS: Synthetic Or Not - Identifying Counterfeit Songs | Created by SONICS Team</p>
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<p>© 2025 - For research purposes only</p>
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</div>
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"""
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)
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submit_btn.click(fn=predict, inputs=[audio_input, model_dropdown], outputs=[output])
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if __name__ == "__main__":
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demo.launch() |
