OSC to supercollider

Author: victor-shepardson

clients/pytorch-osc.scd

@@ -4,7 +4,10 @@ b.sendMsg("/predictor/reset");
 
 b.sendMsg("/predictor/load", "/path/to/checkpoint");
 
+(
+t = Process.elapsedTime;
 b.sendMsg("/predictor/predict", 60+12.rand);
+)
 
 
 // To listen to another application, that application needs to send a message to the port SuperCollider is listening on. Normally the default port is 57120, but it could be something different if that port was already bound when SC started. The current default port can be retrieved with
@@ -21,20 +24,21 @@ thisProcess.openUDPPort(1121); // attempt to open 1121
 
 thisProcess.openPorts; // list all open ports
 
-// To listen to incoming messages, an OSCFunc needs to be created in SuperCollider. If the sending application has a fixed port it sends message from, you can set the OSCFunc to listen only to messages coming from that IP and port:
-
-n = NetAddr.new("127.0.0.1", 57120);    // create the NetAddr
-
-// create the OSCFunc
+// To listen to incoming messages, an OSCdef needs to be created in SuperCollider.
 
+// create the OSCdef
 (
 o = OSCdef(\test, {
     arg msg, time, addr, recvPort;
-    [msg, time, addr, recvPort].postln;
-}, '/prediction', n);
+    // [msg, time, addr, recvPort].postln;
+    [msg[1], time-t].postln;
+}, '/prediction', nil);
 )
 
 o.free;    // remove the OSCFunc when you are done.
 
+n = NetAddr("127.0.0.1", NetAddr.langPort);
+n.sendMsg('/prediction', 1)
+
+// OSCdef.trace(true)
 
-n.sendMsg('/prediction', 1)

notebook/midi.ipynb

@@ -84,8 +84,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 19 ms, sys: 14 ms, total: 33 ms\n",
-      "Wall time: 54.6 ms\n"
+      "CPU times: user 20.1 ms, sys: 14.8 ms, total: 34.9 ms\n",
+      "Wall time: 56.2 ms\n"
      ]
     },
     {
@@ -114,7 +114,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x1497e0e50>"
+       "<matplotlib.collections.PathCollection at 0x13c74c460>"
       ]
      },
      "execution_count": 7,
@@ -123,7 +123,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -145,26 +145,36 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "net = PitchPredictor(hidden_size=128, domain_size=130, num_layers=2)"
+    "net = PitchPredictor(hidden_size=128, domain_size=130, num_layers=2)\n",
+    "# net = PitchPredictor.from_checkpoint('/Users/victor/Downloads/0132.ckpt')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
+   "id": "841a5b68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net.reset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
    "id": "e383b68a",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "540 µs ± 70.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
+      "10.9 ms ± 400 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
    "source": [
     "%%timeit\n",
-    "net.reset()\n",
     "net.predict(60)"
    ]
   },

pytorch-osc/pytorch-osc.py

@@ -20,14 +20,17 @@ def predictor_handler(address, *args):
         print(f"/load {args}")
         global predictor
         predictor = PitchPredictor.from_checkpoint(*args)
+
     elif(address[2] == "predict"):
         print(f"/predict {args}")
         r = predictor.predict(*args) # sanity check args
         print(r)
         osc.send_message('/prediction', (r,))
+
     elif(address[2] == "reset"):
         print(f"/reset {args}")
         predictor.reset(*args)
+        
     else:
         print(f"PitchPredictor: Unrecognised OSC {address} with {args}")