add just 16k model

hubconf.py

@@ -28,7 +28,7 @@ def silero_vad(onnx=False, force_onnx_cpu=False, opset_version=16):
     Returns a model with a set of utils
     Please see https://github.com/snakers4/silero-vad for usage examples
     """
-    available_ops = [13, 14, 15, 16]
+    available_ops = [15, 16]
     if onnx and opset_version not in available_ops:
         raise Exception(f'Available ONNX opset_version: {available_ops}')
 
@@ -43,7 +43,7 @@ def silero_vad(onnx=False, force_onnx_cpu=False, opset_version=16):
         if opset_version == 16:
             model_name = 'silero_vad.onnx'
         else:
-            model_name = f'silero_vad_op{opset_version}.onnx'
+            model_name = f'silero_vad_16k_op{opset_version}.onnx'
         model = OnnxWrapper(os.path.join(model_dir, model_name), force_onnx_cpu)
     else:
         model = init_jit_model(os.path.join(model_dir, 'silero_vad.jit'))

src/silero_vad/model.py

@@ -4,7 +4,7 @@ torch.set_num_threads(1)
 
 
 def load_silero_vad(onnx=False, opset_version=16):
-    available_ops = [13, 14, 15, 16]
+    available_ops = [15, 16]
     if onnx and opset_version not in available_ops:
         raise Exception(f'Available ONNX opset_version: {available_ops}')
 
@@ -12,7 +12,7 @@ def load_silero_vad(onnx=False, opset_version=16):
         if opset_version == 16:
             model_name = 'silero_vad.onnx'
         else:
-            model_name = f'silero_vad_op{opset_version}.onnx'
+            model_name = f'silero_vad_16k_op{opset_version}.onnx'
     else:
         model_name = 'silero_vad.jit'
     package_path = "silero_vad.data"

src/silero_vad/utils_vad.py

@@ -23,7 +23,11 @@ class OnnxWrapper():
             self.session = onnxruntime.InferenceSession(path, sess_options=opts)
 
         self.reset_states()
-        self.sample_rates = [8000, 16000]
+        if '16k' in path:
+            warnings.warn('This model support only 16000 sampling rate!')
+            self.sample_rates = [16000]
+        else:
+            self.sample_rates = [8000, 16000]
 
     def _validate_input(self, x, sr: int):
         if x.dim() == 1: