Create gradioapp.py

gradioapp.py

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+import gradio as gr
+import torch
+import torchaudio
+from transformers import AutoProcessor, AutoModelForAudioClassification
+from transformers import AutoFeatureExtractor
+
+# Load model directly
+feature_extractor = AutoFeatureExtractor.from_pretrained("ThomasR/facebook_wav2vec2-large_October_03_2023_05h34PM")
+model = AutoModelForAudioClassification.from_pretrained("ThomasR/facebook_wav2vec2-large_October_03_2023_05h34PM")
+
+label2id={'fake':0, 'real':1}
+id2label = {v:k for k,v in label2id.items()}
+
+def predict(audio_path):
+    wavform, sample_rate = sf.read(audio_path)
+    
+    inputs = feature_extractor(
+            wavform, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt", max_length=16000, truncation=True, padding=True
+        )
+    
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    
+    probabilities = torch.sigmoid(logits[0])
+    # labels is a one-hot array of shape (num_frames, num_speakers)
+    labels = (probabilities > 0.5).long()
+    
+    pred_probs = list(probabilities.tolist())
+    # index of the max score
+    idx = pred_probs.index(max(pred_probs))
+    
+    LABELS=list(id2label.values())
+    #get labels corresponding to max score
+    max_label = LABELS[idx]
+    results = {LABELS[i]: round(float(pred_probs[i]),4) for i in range(len(LABELS))}
+    
+    return results 
+
+demo = gr.Interface(fn=predict,
+                    inputs=gr.Audio(type="filepath"),
+                    outputs="label",
+                    cache_examples=False
+                    )
+
+demo.launch(debug=False)