filter-graph: add ONNX plugin

It uses the onnxruntime library to parse the onnx file and construct a
neural network. It uses the label field to setup the plugin and how to
map the various tensors of the model to input, output, control and
notify ports.

Add an example config for how to use the silero VAD ONNX model with the
noise gate.

src/daemon/filter-chain/22-onnx-vad.conf

@@ -0,0 +1,83 @@
+context.modules = [
+{   name = libpipewire-module-filter-chain
+    flags = [ nofail ]
+    args = {
+        node.description = "ONNX Example"
+        node.name = "neural.example"
+	audio.rate = 16000
+        node.latency = "512/16000"
+        filter.graph = {
+            nodes = [
+                {
+                    type = builtin
+                    name = copy
+                    label = copy
+		}
+                {
+                    type = onnx
+                    name = onnx
+                    label = {
+			#filename = "/home/wim/src/silero-vad/src/silero_vad/data/silero_vad_half.onnx"
+			filename = "/home/wim/src/silero-vad/src/silero_vad/data/silero_vad.onnx"
+			blocksize = 512
+			input-tensors = {
+                            "input" = {
+                                dimensions = [ 1, 576 ]
+                                retain = 64
+				data = "port:input"
+                            }
+                            "state" = {
+                                dimensions = [ 2, 1, 128 ]
+                                data = "tensor:stateN"
+                            }
+                            "sr" = {
+                                dimensions = [ 1 ]
+                                data = "param:rate"
+                            }
+                        }
+                        output-tensors = {
+                            "output" = {
+                                dimensions = [ 1, 1 ]
+                                data = "control:speech"
+                            }
+                            "stateN" = {
+                                dimensions = [ 2, 1, 128 ]
+                            }
+                        }
+		    }
+		    control = {
+		    }
+		    config = {
+		    }
+                }
+                {
+                    type = builtin
+                    name = noisegate
+                    label = noisegate
+		    control = {
+                        "Open Threshold" 0.1
+                        "Close Threshold" 0.02
+		    }
+		}
+            ]
+            links = [
+                { output = "copy:Out"  input="onnx:input" }
+                { output = "copy:Out"  input="noisegate:In" }
+                { output = "onnx:speech"  input="noisegate:Level" }
+	    ]
+            inputs = [ "copy:In" ]
+            outputs = [ "noisegate:Out" ]
+        }
+
+        capture.props = {
+            node.name = "capture.neural"
+	    audio.position = [ MONO ]
+        }
+
+        playback.props = {
+            node.name = "playback.neural"
+	    audio.position = [ MONO ]
+        }
+    }
+}
+]

src/modules/module-filter-chain.c

@@ -870,6 +870,119 @@ extern struct spa_handle_factory spa_filter_graph_factory;
  *     ...
  * }
  *\endcode
+
+ * ## ONNX filters
+ *
+ * There is an optional ONNX filter available (when compiled with `libonnxruntime`)
+ * that can be selected with the `onnx` type. Use the `label` field to select
+ * the model to use and how to map the tensors to ports.
+ *
+ *\code{.unparsed}
+ * filter.graph = {
+ *     nodes = [
+ *         {
+ *             type   = onnx
+ *             name   = onnx
+ *             label = {
+ *                 filename = "..."
+ *                 blocksize = 512
+ *                 input-tensors = {
+ *                     "<name>" = {
+ *                         dimensions = [ ... ]
+ *                         #retain = 64
+ *                         data = "port:..."|"tensor:..."|"param:..."|"control:..."
+ *                     }
+ *                     ...
+ *                 }
+ *                 output-tensors = {
+ *                     "<name>" = {
+ *                         dimensions = [ ... ]
+ *                         #retain = 64
+ *                         data = "port:..."|"tensor:..."|"param:..."|"control:..."
+ *                     }
+ *                     ...
+ *                 }
+ *             }
+ *         }
+ *     }
+ *     ...
+ * }
+ *\endcode
+ *
+ * The label must contain an object with the configuration of the plugin.
+ *
+ * - `filename` the ONNX model to load. It must point to an existing onnx file.
+ * - `blocksize` the number of samples to give to the model. This depends on the model
+ *               and the input/output tensor sizes.
+ * - `input-tensors` an object of input tensors of the model and how they should be
+ *                   used. Unlisted tensors will not be used.
+ * - `output-tensors` an object of output tensors of the model and how they should be
+ *                   used. Unlisted tensors will not be used.
+ *
+ * The `input-tensors` and `output-tensors` configuration must contain an object with
+ * keys named after the tensors in the model and the value must be an object with the
+ * the following keys:
+ *
+ * - `dimensions` and array of dimensions of the tensors.
+ * - `retain` an optional key for input tensors. This will prepend the last `retain` samples
+ *            from the previous block to the input tensor. The size of the tensor should
+ *            therefore at least be blocksize + retain samples large.
+ * - `data` where the data for the tensor is comming from. There are different options
+ *          based on the value of this file, selected with a prefix:
+ *      - `port:<portname>` a new input/output port is created on the plugin with the
+ *                          name <portname> and the data for the tensor will be obtained
+ *                          or copied from/to the port data.
+ *      - `tensor:<tensorname>` the data of this tensor is copied from the given
+ *                              <tensorname>. You can use this to copy output state
+ *                              info to the input state, for example.
+ *      - `param:<paramname>` the data of this tensor is obtained from a parameter with
+ *                            <paramname>. Currently only `rate` is a valid paramname,
+ *                            which has the value of the filter samplerate.
+ *      - `control:<portname>` a new input/output control port is created and the tensor
+ *                             data will be obtained/copied from/to the control data.
+ *
+ * Here is an example of the silero VAD model:
+ *
+ *\code{.unparsed}
+ * filter.graph = {
+ *     nodes = [
+ *         {
+ *             type   = onnx
+ *             name = onnx
+ *             label = {
+ *                 filename = "/home/wim/src/silero-vad/src/silero_vad/data/silero_vad.onnx"
+ *                 blocksize = 512
+ *                 input-tensors = {
+ *                     "input" = {
+ *                         dimensions = [ 1, 576 ]
+ *                         retain = 64
+ *                         data = "port:input"
+ *                     }
+ *                     "state" = {
+ *                         dimensions = [ 2, 1, 128 ]
+ *                         data = "tensor:stateN"
+ *                     }
+ *                     "sr" = {
+ *                         dimensions = [ 1 ]
+ *                         data = "param:rate"
+ *                     }
+ *                 }
+ *                 output-tensors = {
+ *                     "output" = {
+ *                         dimensions = [ 1, 1 ]
+ *                         data = "control:speech"
+ *                     }
+ *                     "stateN" = {
+ *                         dimensions = [ 2, 1, 128 ]
+ *                     }
+ *                 }
+ *             }
+ *         }
+ *         ...
+ *    ]
+ *    ....
+ * }
+ *\endcode
  *
  * ## General options
  *