Upload audio/DF_Arena_1B_V_1/pipeline_antispoofing.py with huggingface_hub

audio/DF_Arena_1B_V_1/pipeline_antispoofing.py

@@ -0,0 +1,42 @@
+from transformers import Pipeline
+import torch
+from .feature_extraction_antispoofing import AntispoofingFeatureExtractor
+class AntispoofingPipeline(Pipeline):
+    def __init__(self, model, **kwargs):
+        super().__init__(model=model, **kwargs)
+        self.feature_extractor = AntispoofingFeatureExtractor()
+
+    def _sanitize_parameters(self, **kwargs):
+        preprocess_kwargs = {}
+        postprocess_kwargs = {}
+        
+        if "sampling_rate" in kwargs:
+            preprocess_kwargs["sampling_rate"] = kwargs["sampling_rate"]
+        
+        return preprocess_kwargs, {}, postprocess_kwargs
+    
+    def preprocess(self, audio, sampling_rate=16000):
+        audio = self.feature_extractor(audio)['input_values']
+        inputs = {"input_values": audio}
+        
+        return inputs
+    
+    def _forward(self, model_inputs):
+        outputs = self.model(**model_inputs)
+        return outputs
+    
+    def postprocess(self, model_outputs):
+        logits = model_outputs['logits']
+        probs = torch.nn.functional.softmax(logits, dim=-1)
+        predicted_class = torch.argmax(probs, dim=-1).item()
+        confidence = probs[0][predicted_class].item()
+        
+        return {
+            "label": self.model.config.id2label[predicted_class],
+            "logits": logits.tolist(),
+            "score": confidence,
+            "all_scores": {
+                self.model.config.id2label[i]: probs[0][i].item()
+                for i in range(len(probs[0]))
+            }
+        }