{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b96abe95",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "#%env CUDA_VISIBLE_DEVICES=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "304d7eca",
   "metadata": {},
   "outputs": [],
   "source": [
    "colab_requirements = [\n",
    "    \"pip install librosa\",\n",
    "    \"pip install noisereduce\",\n",
    "    \"pip install soundfile\",\n",
    "\n",
    "]\n",
    "\n",
    "import sys, subprocess\n",
    "\n",
    "def run_subprocess_command(cmd):\n",
    "    # run the command\n",
    "    process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)\n",
    "    # print the output\n",
    "    for line in process.stdout:\n",
    "        print(line.decode().strip())\n",
    "\n",
    "IN_COLAB = \"google.colab\" in sys.modules\n",
    "if IN_COLAB:\n",
    "    for i in colab_requirements:\n",
    "        run_subprocess_command(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8e4f1f0",
   "metadata": {},
   "source": [
    "# Use torchgate directly as nn.module class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a0093f8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zorea\\.conda\\envs\\ml\\lib\\site-packages\\noisereduce-3.0.0-py3.10.egg\\noisereduce\\torchgate\\torchgate.py:180: UserWarning: Using padding='same' with even kernel lengths and odd dilation may require a zero-padded copy of the input be created (Triggered internally at C:\\cb\\pytorch_1000000000000\\work\\aten\\src\\ATen\\native\\Convolution.cpp:1004.)\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from noisereduce.torchgate import TorchGate as TG\n",
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "\n",
    "# Create TorchGating instance\n",
    "tg = TG(sr=8000, nonstationary=True).to(device)\n",
    "\n",
    "# Apply Spectral Gate to noisy speech signal\n",
    "noisy_speech = torch.randn(3, 32000, device=device)\n",
    "enhanced_speech = tg(noisy_speech)"
   ]
  }
 ],
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