initial commit

app.py

@@ -0,0 +1,70 @@
+import torch
+import librosa
+import numpy as np
+from transformers import (
+    AutoFeatureExtractor,
+    AutoModelForAudioClassification,
+)
+import gradio as gr
+
+MODEL_ID = "aitf-komdigi/KomdigiITS-86M-DFK-DeepfakeAudioClassification"
+
+feature_extractor = AutoFeatureExtractor.from_pretrained(
+    MODEL_ID
+)
+
+model = AutoModelForAudioClassification.from_pretrained(
+    MODEL_ID
+)
+
+model.eval()
+
+
+def predict(audio):
+    if audio is None:
+        return "No audio uploaded"
+
+    sr, waveform = audio
+
+    waveform = waveform.astype(np.float32)
+
+    if waveform.ndim > 1:
+        waveform = waveform.mean(axis=1)
+
+    waveform = librosa.resample(
+        waveform,
+        orig_sr=sr,
+        target_sr=16000,
+    )
+
+    inputs = feature_extractor(
+        waveform,
+        sampling_rate=16000,
+        return_tensors="pt",
+    )
+
+    with torch.no_grad():
+        logits = model(**inputs).logits
+
+    score = torch.sigmoid(logits).item()
+
+    prediction = "Fake" if score >= 0.5 else "Real"
+
+    return {
+        "Real": round(1.0 - score, 4),
+        "Fake": round(score, 4),
+    }
+
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Audio(
+        sources=["upload", "microphone"],
+        type="numpy",
+    ),
+    outputs=gr.Label(),
+    title="Audio Deepfake Detection",
+    description="Detect whether an audio clip is real or AI-generated.",
+)
+
+demo.launch()

requirements.txt

@@ -0,0 +1,6 @@
+gradio
+transformers
+torch
+librosa
+soundfile
+accelerate