use a process specific copy of model

examples/parallel_example.ipynb

@@ -14,7 +14,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -q torchaudio\n",
+    "# !pip install -q torchaudio\n",
     "SAMPLING_RATE = 16000\n",
     "import torch\n",
     "from pprint import pprint\n",
@@ -67,17 +67,27 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import multiprocessing\n",
+    "\n",
+    "vad_models = dict()\n",
+    "\n",
     "def init_model(model):\n",
-    "  global vad_model\n",
-    "  vad_model = model\n",
+    "  pid = multiprocessing.current_process().pid\n",
+    "  model, _ = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
+    "                                model='silero_vad',\n",
+    "                                force_reload=False,\n",
+    "                                onnx=False)\n",
+    "  vad_models[pid] = model\n",
     "\n",
     "def vad_process(audio_file: str):\n",
-    "    global vad_model\n",
+    "    \n",
+    "    pid = multiprocessing.current_process().pid\n",
+    "    \n",
     "    with torch.no_grad():\n",
     "        wav = read_audio(audio_file, sampling_rate=SAMPLING_RATE)\n",
     "        return get_speech_timestamps(\n",
     "            wav,\n",
-    "            vad_model,\n",
+    "            vad_models[pid],\n",
     "            0.46,  # speech prob threshold\n",
     "            16000,  # sample rate\n",
     "            300,  # min speech duration in ms\n",
@@ -132,8 +142,7 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.15"
-  },
-  "orig_nbformat": 4
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 2