Create app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2Processor
+import torch
+import torchaudio
+
+model_name = "Mrkomiljon/voiceGUARD/wav2vec2_finetuned_model"
+model = Wav2Vec2ForSequenceClassification.from_pretrained(model_name)
+processor = Wav2Vec2Processor.from_pretrained(model_name)
+model.eval()
+
+def classify_audio(audio_file):
+    waveform, sample_rate = torchaudio.load(audio_file)
+    if sample_rate != 16000:
+        resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)
+        waveform = resampler(waveform)
+    if waveform.size(1) > 16000 * 10:
+        waveform = waveform[:, :16000 * 10]
+    elif waveform.size(1) < 16000 * 10:
+        waveform = torch.nn.functional.pad(waveform, (0, 16000 * 10 - waveform.size(1)))
+    if waveform.ndim > 1:
+        waveform = waveform[0]
+    inputs = processor(waveform.numpy(), sampling_rate=16000, return_tensors="pt")
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        predicted_label = logits.argmax(dim=-1).item()
+    return predicted_label
+
+interface = gr.Interface(
+    fn=classify_audio,
+    inputs=gr.Audio(source="upload", type="filepath"),
+    outputs="label",
+    title="Audio Classifier",
+    description="Upload an audio file to classify its label as AI-generated or Real."
+)
+interface.launch()