ADD application file

app.py

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+import gradio as gr
+from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2Processor
+import torch
+import torchaudio
+from io import BytesIO
+
+# Hugging Face Model Hub'dan modelni yuklash
+model_name = "Mrkomiljon/voiceGUARD/wav2vec2_finetuned_model"  # Hugging Face Model Hub'dagi modelning to'liq nomi
+model = Wav2Vec2ForSequenceClassification.from_pretrained(model_name)
+processor = Wav2Vec2Processor.from_pretrained(model_name)
+model.eval()
+
+# Device setup
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Define label mapping
+id2label = {
+    0: "diffwave",
+    1: "melgan",
+    2: "parallel_wave_gan",
+    3: "Real",
+    4: "wavegrad",
+    5: "wavnet",
+    6: "wavernn"
+}
+
+# Define the prediction function
+def predict_audio(file):
+    target_sample_rate = 16000  # Model's expected sample rate
+    max_length = target_sample_rate * 10  # 10 seconds in samples
+
+    try:
+        # Load the audio file
+        audio_bytes = file.read()
+        waveform, sample_rate = torchaudio.load(BytesIO(audio_bytes))
+
+        # Resample if the sample rate doesn't match the model's expected rate
+        if sample_rate != target_sample_rate:
+            resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=target_sample_rate)
+            waveform = resampler(waveform)
+
+        # Truncate or pad the waveform to ensure consistent input length
+        if waveform.size(1) > max_length:
+            waveform = waveform[:, :max_length]  # Truncate
+        elif waveform.size(1) < max_length:
+            waveform = torch.nn.functional.pad(waveform, (0, max_length - waveform.size(1)))  # Pad
+        if waveform.ndim > 1:
+            waveform = waveform[0]
+
+        # Process the audio file
+        inputs = processor(
+            waveform.squeeze().numpy(),
+            sampling_rate=target_sample_rate,
+            return_tensors="pt",
+            padding=True
+        )
+        input_values = inputs["input_values"].to(device)
+
+        # Perform inference
+        with torch.no_grad():
+            logits = model(input_values).logits
+            probabilities = torch.nn.functional.softmax(logits, dim=-1)
+            predicted_label = torch.argmax(probabilities, dim=-1).item()
+            confidence = probabilities[0, predicted_label].item()
+
+        # Map label to class name
+        class_name = id2label.get(predicted_label, "Unknown Class")
+
+        return {
+            "Class": class_name,
+            "Confidence": round(confidence * 100, 2)
+        }
+    except Exception as e:
+        return {"error": f"Error processing the audio file: {str(e)}"}
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_audio,
+    inputs=gr.Audio(type="file"),
+    outputs=[
+        gr.Label(label="Predicted Class"),
+        gr.Label(label="Confidence")
+    ],
+    title="Audio Classification with Wav2Vec2",
+    description="Upload an audio file to classify it into one of the predefined categories."
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+gradio
+torchaudio
+transformers
+torch