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| 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 | |
| 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) | |
| # Updated CSS with better color scheme for resource links | |
| css = """ | |
| /* Custom CSS that works with Ocean theme */ | |
| .sonics-header { | |
| text-align: center; | |
| padding: 20px; | |
| margin-bottom: 20px; | |
| border-radius: 10px; | |
| } | |
| .sonics-logo { | |
| max-width: 150px; | |
| border-radius: 10px; | |
| box-shadow: 0 4px 8px rgba(0,0,0,0.3); | |
| } | |
| .sonics-title { | |
| font-size: 28px; | |
| margin-bottom: 10px; | |
| } | |
| .sonics-subtitle { | |
| margin-bottom: 15px; | |
| } | |
| .sonics-description { | |
| font-size: 16px; | |
| margin: 0; | |
| } | |
| /* Resource links styling */ | |
| .resource-links { | |
| display: flex; | |
| justify-content: center; | |
| flex-wrap: wrap; | |
| gap: 8px; | |
| margin-bottom: 25px; | |
| } | |
| .resource-link { | |
| background-color: #222222; | |
| color: #4aedd6; | |
| border: 1px solid #333333; | |
| padding: 8px 16px; | |
| border-radius: 20px; | |
| margin: 5px; | |
| text-decoration: none; | |
| display: inline-block; | |
| font-weight: 500; | |
| box-shadow: 0 2px 4px rgba(0, 0, 0, 0.3); | |
| transition: all 0.2s ease; | |
| } | |
| .resource-link:hover { | |
| background-color: #333333; | |
| transform: translateY(-2px); | |
| box-shadow: 0 3px 6px rgba(0, 0, 0, 0.4); | |
| transition: all 0.2s ease; | |
| } | |
| .resource-link-icon { | |
| margin-right: 5px; | |
| } | |
| /* Footer styling */ | |
| .sonics-footer { | |
| text-align: center; | |
| margin-top: 30px; | |
| padding: 15px; | |
| } | |
| """ | |
| # Create Gradio interface | |
| with gr.Blocks(css=css, theme=gr.themes.Ocean()) as demo: | |
| # Title and Logo | |
| gr.HTML( | |
| """ | |
| <div class="sonics-header"> | |
| <div style="display: flex; justify-content: center; margin-bottom: 20px;"> | |
| <img src="https://i.postimg.cc/3Jx3yZ5b/real-vs-fake-sonics-w-logo.jpg" class="sonics-logo"> | |
| </div> | |
| <h1 class="sonics-title">SONICS: Synthetic Or Not - Identifying Counterfeit Songs</h1> | |
| <h3 class="sonics-subtitle">ICLR 2025 [Poster]</h3> | |
| <p class="sonics-description"> | |
| Detect if a song is real or AI-generated with our state-of-the-art models. | |
| Simply upload an audio file to verify its authenticity! | |
| </p> | |
| </div> | |
| """ | |
| ) | |
| # Resource Links - Updated with custom styling to match screenshot | |
| gr.HTML( | |
| """ | |
| <div class="resource-links"> | |
| <a href="https://openreview.net/forum?id=PY7KSh29Z8" target="_blank" class="resource-link"> | |
| <span class="resource-link-icon">📄</span>Paper | |
| </a> | |
| <a href="https://huggingface.co/datasets/awsaf49/sonics" target="_blank" class="resource-link"> | |
| <span class="resource-link-icon">🎵</span>Dataset | |
| </a> | |
| <a href="https://huggingface.co/collections/awsaf49/sonics-spectttra-67bb6517b3920fd18e409013" target="_blank" class="resource-link"> | |
| <span class="resource-link-icon">🤖</span>Models | |
| </a> | |
| <a href="https://arxiv.org/abs/2408.14080" target="_blank" class="resource-link"> | |
| <span class="resource-link-icon">🔬</span>ArXiv | |
| </a> | |
| <a href="https://github.com/awsaf49/sonics" target="_blank" class="resource-link"> | |
| <span class="resource-link-icon">💻</span>GitHub | |
| </a> | |
| </div> | |
| """ | |
| ) | |
| # 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" | |
| ) | |
| with gr.Accordion("How It Works", open=False): | |
| gr.Markdown(""" | |
| ## The SONICS classifier | |
| 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-α**: Optimized for speed | |
| - **SpecTTTra-β**: Balanced performance | |
| - **SpecTTTra-γ**: 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( | |
| """ | |
| <div class="sonics-footer"> | |
| <p>SONICS: Synthetic Or Not - Identifying Counterfeit Songs | ICLR 2025</p> | |
| <p style="font-size: 12px;">For research purposes only</p> | |
| </div> | |
| """ | |
| ) | |
| # Prediction handling | |
| submit_btn.click(fn=predict, inputs=[audio_input, model_dropdown], outputs=[output]) | |
| if __name__ == "__main__": | |
| demo.launch() |