Upload audio/DF_Arena_1B_V_1/feature_extraction_antispoofing.py with huggingface_hub

audio/DF_Arena_1B_V_1/feature_extraction_antispoofing.py

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+from transformers import SequenceFeatureExtractor
+import numpy as np
+import torch
+
+class AntispoofingFeatureExtractor(SequenceFeatureExtractor):
+    def __init__(
+        self,
+        feature_size=1,
+        sampling_rate=16000,
+        padding_value=0.0,
+        return_attention_mask=True,
+        **kwargs
+    ):
+        super().__init__(
+            feature_size=feature_size,
+            sampling_rate=sampling_rate,
+            padding_value=padding_value,
+            **kwargs
+        )
+        self.return_attention_mask = return_attention_mask
+    
+    def __call__(self, audio, sampling_rate=None, return_tensors=True, **kwargs):
+        audio = self.pad(audio, 64600)
+        audio = torch.Tensor(audio)
+        return {
+            "input_values": audio
+            
+        }
+
+    def pad(self, x, max_len):
+        x_len = x.shape[0]
+        if x_len >= max_len:
+            return x[:max_len]
+        num_repeats = int(max_len / x_len)+1
+        padded_x = np.tile(x, (1, num_repeats))[:, :max_len][0]
+        return padded_x