Trying-different-architectures.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Attempt 1: `wooseok-shin/MetricGAN-plus-pytorch`"
      ],
      "metadata": {
        "id": "ItBiRaHMjui-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! git clone https://github.com/wooseok-shin/MetricGAN-plus-pytorch.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xSeVOW9Njx3V",
        "outputId": "630f0fa2-dbb7-44e4-f1e0-0b5608153986"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'MetricGAN-plus-pytorch'...\n",
            "remote: Enumerating objects: 51, done.\u001b[K\n",
            "remote: Counting objects: 100% (51/51), done.\u001b[K\n",
            "remote: Compressing objects: 100% (40/40), done.\u001b[K\n",
            "remote: Total 51 (delta 23), reused 29 (delta 9), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (51/51), 24.49 KiB | 2.23 MiB/s, done.\n",
            "Resolving deltas: 100% (23/23), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install -r MetricGAN-plus-pytorch/requirements.txt\n",
        "! pip install torch==1.7.1+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pGcP8eAdoYa2",
        "outputId": "efa72a08-4694-41e7-a1d6-1985a085a744"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from -r MetricGAN-plus-pytorch/requirements.txt (line 1)) (4.66.4)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from -r MetricGAN-plus-pytorch/requirements.txt (line 2)) (1.4.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from -r MetricGAN-plus-pytorch/requirements.txt (line 3)) (1.25.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from -r MetricGAN-plus-pytorch/requirements.txt (line 4)) (1.11.4)\n",
            "Collecting pesq (from -r MetricGAN-plus-pytorch/requirements.txt (line 5))\n",
            "  Downloading pesq-0.0.4.tar.gz (38 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: soundfile in /usr/local/lib/python3.10/dist-packages (from -r MetricGAN-plus-pytorch/requirements.txt (line 6)) (0.12.1)\n",
            "Requirement already satisfied: librosa in /usr/local/lib/python3.10/dist-packages (from -r MetricGAN-plus-pytorch/requirements.txt (line 7)) (0.10.2.post1)\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.10/dist-packages (from soundfile->-r MetricGAN-plus-pytorch/requirements.txt (line 6)) (1.16.0)\n",
            "Requirement already satisfied: audioread>=2.1.9 in /usr/local/lib/python3.10/dist-packages (from librosa->-r MetricGAN-plus-pytorch/requirements.txt (line 7)) (3.0.1)\n",
            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.10/dist-packages (from librosa->-r MetricGAN-plus-pytorch/requirements.txt (line 7)) (1.2.2)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.10/dist-packages (from librosa->-r MetricGAN-plus-pytorch/requirements.txt (line 7)) (4.4.2)\n",
            "Requirement already satisfied: numba>=0.51.0 in /usr/local/lib/python3.10/dist-packages (from librosa->-r MetricGAN-plus-pytorch/requirements.txt (line 7)) (0.58.1)\n",
            "Requirement already satisfied: pooch>=1.1 in /usr/local/lib/python3.10/dist-packages (from librosa->-r MetricGAN-plus-pytorch/requirements.txt (line 7)) (1.8.2)\n",
            "Requirement already satisfied: soxr>=0.3.2 in /usr/local/lib/python3.10/dist-packages (from librosa->-r MetricGAN-plus-pytorch/requirements.txt (line 7)) (0.3.7)\n",
            "Requirement already satisfied: typing-extensions>=4.1.1 in /usr/local/lib/python3.10/dist-packages (from librosa->-r MetricGAN-plus-pytorch/requirements.txt (line 7)) (4.12.2)\n",
            "Requirement already satisfied: lazy-loader>=0.1 in /usr/local/lib/python3.10/dist-packages (from librosa->-r MetricGAN-plus-pytorch/requirements.txt (line 7)) (0.4)\n",
            "Requirement already satisfied: msgpack>=1.0 in /usr/local/lib/python3.10/dist-packages (from librosa->-r MetricGAN-plus-pytorch/requirements.txt (line 7)) (1.0.8)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0->soundfile->-r MetricGAN-plus-pytorch/requirements.txt (line 6)) (2.22)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from lazy-loader>=0.1->librosa->-r MetricGAN-plus-pytorch/requirements.txt (line 7)) (24.1)\n",
            "Requirement already satisfied: llvmlite<0.42,>=0.41.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.51.0->librosa->-r MetricGAN-plus-pytorch/requirements.txt (line 7)) (0.41.1)\n",
            "Requirement already satisfied: platformdirs>=2.5.0 in /usr/local/lib/python3.10/dist-packages (from pooch>=1.1->librosa->-r MetricGAN-plus-pytorch/requirements.txt (line 7)) (4.2.2)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from pooch>=1.1->librosa->-r MetricGAN-plus-pytorch/requirements.txt (line 7)) (2.31.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.20.0->librosa->-r MetricGAN-plus-pytorch/requirements.txt (line 7)) (3.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.1->librosa->-r MetricGAN-plus-pytorch/requirements.txt (line 7)) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.1->librosa->-r MetricGAN-plus-pytorch/requirements.txt (line 7)) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.1->librosa->-r MetricGAN-plus-pytorch/requirements.txt (line 7)) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.1->librosa->-r MetricGAN-plus-pytorch/requirements.txt (line 7)) (2024.7.4)\n",
            "Building wheels for collected packages: pesq\n",
            "  Building wheel for pesq (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pesq: filename=pesq-0.0.4-cp310-cp310-linux_x86_64.whl size=262962 sha256=10737af7262ef191f945943c17bede35c81f4e697ccae8714ddf7dfa0cb1aaaf\n",
            "  Stored in directory: /root/.cache/pip/wheels/c5/4e/2c/251524370c0fdd659e99639a0fbd0ca5a782c3aafcd456b28d\n",
            "Successfully built pesq\n",
            "Installing collected packages: pesq\n",
            "Successfully installed pesq-0.0.4\n",
            "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement torch==1.7.1+cu110 (from versions: 1.11.0, 1.11.0+cpu, 1.11.0+cu102, 1.11.0+cu113, 1.11.0+cu115, 1.11.0+rocm4.3.1, 1.11.0+rocm4.5.2, 1.12.0, 1.12.0+cpu, 1.12.0+cu102, 1.12.0+cu113, 1.12.0+cu116, 1.12.0+rocm5.0, 1.12.0+rocm5.1.1, 1.12.1, 1.12.1+cpu, 1.12.1+cu102, 1.12.1+cu113, 1.12.1+cu116, 1.12.1+rocm5.0, 1.12.1+rocm5.1.1, 1.13.0, 1.13.0+cpu, 1.13.0+cu116, 1.13.0+cu117, 1.13.0+cu117.with.pypi.cudnn, 1.13.0+rocm5.1.1, 1.13.0+rocm5.2, 1.13.1, 1.13.1+cpu, 1.13.1+cu116, 1.13.1+cu117, 1.13.1+cu117.with.pypi.cudnn, 1.13.1+rocm5.1.1, 1.13.1+rocm5.2, 2.0.0, 2.0.0+cpu, 2.0.0+cpu.cxx11.abi, 2.0.0+cu117, 2.0.0+cu117.with.pypi.cudnn, 2.0.0+cu118, 2.0.0+rocm5.3, 2.0.0+rocm5.4.2, 2.0.1, 2.0.1+cpu, 2.0.1+cpu.cxx11.abi, 2.0.1+cu117, 2.0.1+cu117.with.pypi.cudnn, 2.0.1+cu118, 2.0.1+rocm5.3, 2.0.1+rocm5.4.2, 2.1.0, 2.1.0+cpu, 2.1.0+cpu.cxx11.abi, 2.1.0+cu118, 2.1.0+cu121, 2.1.0+cu121.with.pypi.cudnn, 2.1.0+rocm5.5, 2.1.0+rocm5.6, 2.1.1, 2.1.1+cpu, 2.1.1+cpu.cxx11.abi, 2.1.1+cu118, 2.1.1+cu121, 2.1.1+cu121.with.pypi.cudnn, 2.1.1+rocm5.5, 2.1.1+rocm5.6, 2.1.2, 2.1.2+cpu, 2.1.2+cpu.cxx11.abi, 2.1.2+cu118, 2.1.2+cu121, 2.1.2+cu121.with.pypi.cudnn, 2.1.2+rocm5.5, 2.1.2+rocm5.6, 2.2.0, 2.2.0+cpu, 2.2.0+cpu.cxx11.abi, 2.2.0+cu118, 2.2.0+cu121, 2.2.0+rocm5.6, 2.2.0+rocm5.7, 2.2.1, 2.2.1+cpu, 2.2.1+cpu.cxx11.abi, 2.2.1+cu118, 2.2.1+cu121, 2.2.1+rocm5.6, 2.2.1+rocm5.7, 2.2.2, 2.2.2+cpu, 2.2.2+cpu.cxx11.abi, 2.2.2+cu118, 2.2.2+cu121, 2.2.2+rocm5.6, 2.2.2+rocm5.7, 2.3.0, 2.3.0+cpu, 2.3.0+cpu.cxx11.abi, 2.3.0+cu118, 2.3.0+cu121, 2.3.0+rocm5.7, 2.3.0+rocm6.0, 2.3.1, 2.3.1+cpu, 2.3.1+cpu.cxx11.abi, 2.3.1+cu118, 2.3.1+cu121, 2.3.1+rocm5.7, 2.3.1+rocm6.0)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for torch==1.7.1+cu110\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! wget https://github.com/wooseok-shin/MetricGAN-plus-pytorch/releases/download/v1.weights/PESQ-GAN_trial1.pth"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ASma_T2Vo8QC",
        "outputId": "642ede3f-fff0-4209-b85d-6ef08c4c5303"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-23 07:01:15--  https://github.com/wooseok-shin/MetricGAN-plus-pytorch/releases/download/v1.weights/PESQ-GAN_trial1.pth\n",
            "Resolving github.com (github.com)... 140.82.113.3\n",
            "Connecting to github.com (github.com)|140.82.113.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/647613146/185c53f2-9025-4bb7-bc82-8fdcc382304c?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=releaseassetproduction%2F20240723%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240723T070115Z&X-Amz-Expires=300&X-Amz-Signature=b0f74ce064afc7ce6b4eab9a5f70910e58adf6a13be5dc1c38bf9335101bdeec&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=647613146&response-content-disposition=attachment%3B%20filename%3DPESQ-GAN_trial1.pth&response-content-type=application%2Foctet-stream [following]\n",
            "--2024-07-23 07:01:15--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/647613146/185c53f2-9025-4bb7-bc82-8fdcc382304c?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=releaseassetproduction%2F20240723%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240723T070115Z&X-Amz-Expires=300&X-Amz-Signature=b0f74ce064afc7ce6b4eab9a5f70910e58adf6a13be5dc1c38bf9335101bdeec&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=647613146&response-content-disposition=attachment%3B%20filename%3DPESQ-GAN_trial1.pth&response-content-type=application%2Foctet-stream\n",
            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.109.133, ...\n",
            "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 26116959 (25M) [application/octet-stream]\n",
            "Saving to: ‘PESQ-GAN_trial1.pth’\n",
            "\n",
            "PESQ-GAN_trial1.pth 100%[===================>]  24.91M  74.2MB/s    in 0.3s    \n",
            "\n",
            "2024-07-23 07:01:16 (74.2 MB/s) - ‘PESQ-GAN_trial1.pth’ saved [26116959/26116959]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! mkdir checkpoint_folder\n",
        "! mkdir checkpoint_folder/mahtas_exp_1\n",
        "! mkdir checkpoint_folder/mahtas_exp_1/model"
      ],
      "metadata": {
        "id": "bJPVlzUbrhbd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! mv PESQ-GAN_trial1.pth checkpoint_folder/mahtas_exp_1/model/best_model.pth"
      ],
      "metadata": {
        "id": "_eBU9FL6rZsL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! wget https://datashare.ed.ac.uk/bitstream/handle/10283/2791/clean_trainset_28spk_wav.zip?sequence=2&isAllowed=y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fSLPw-WY6iwx",
        "outputId": "aefd455d-6bf8-42f1-8336-fc64237e0f1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-22 19:28:10--  https://datashare.ed.ac.uk/bitstream/handle/10283/2791/clean_trainset_28spk_wav.zip?sequence=2\n",
            "Resolving datashare.ed.ac.uk (datashare.ed.ac.uk)... 129.215.67.172\n",
            "Connecting to datashare.ed.ac.uk (datashare.ed.ac.uk)|129.215.67.172|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 200\n",
            "Length: 2486057279 (2.3G) [application/zip]\n",
            "Saving to: ‘clean_trainset_28spk_wav.zip?sequence=2’\n",
            "\n",
            "clean_trainset_28sp 100%[===================>]   2.31G   576KB/s    in 34m 40s \n",
            "\n",
            "2024-07-22 20:02:52 (1.14 MB/s) - ‘clean_trainset_28spk_wav.zip?sequence=2’ saved [2486057279/2486057279]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! wget https://datashare.ed.ac.uk/bitstream/handle/10283/2791/noisy_trainset_28spk_wav.zip?sequence=6&isAllowed=y"
      ],
      "metadata": {
        "id": "Ruxkd3uE6pGD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! wget https://datashare.ed.ac.uk/bitstream/handle/10283/2791/clean_testset_wav.zip?sequence=1&isAllowed=y"
      ],
      "metadata": {
        "id": "xSo3C6g965fz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe30a44c-3a02-44e0-e34a-6bf32b055bc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-23 07:01:40--  https://datashare.ed.ac.uk/bitstream/handle/10283/2791/clean_testset_wav.zip?sequence=1\n",
            "Resolving datashare.ed.ac.uk (datashare.ed.ac.uk)... 129.215.67.172\n",
            "Connecting to datashare.ed.ac.uk (datashare.ed.ac.uk)|129.215.67.172|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 200\n",
            "Length: 154332064 (147M) [application/zip]\n",
            "Saving to: ‘clean_testset_wav.zip?sequence=1’\n",
            "\n",
            "clean_testset_wav.z 100%[===================>] 147.18M  10.0MB/s    in 24s     \n",
            "\n",
            "2024-07-23 07:02:05 (6.13 MB/s) - ‘clean_testset_wav.zip?sequence=1’ saved [154332064/154332064]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! wget https://datashare.ed.ac.uk/bitstream/handle/10283/2791/noisy_testset_wav.zip?sequence=5&isAllowed=y"
      ],
      "metadata": {
        "id": "wmHgjlVM68Cg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec6422f2-f500-45b0-f221-314bb84507ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-23 07:02:36--  https://datashare.ed.ac.uk/bitstream/handle/10283/2791/noisy_testset_wav.zip?sequence=5\n",
            "Resolving datashare.ed.ac.uk (datashare.ed.ac.uk)... 129.215.67.172\n",
            "Connecting to datashare.ed.ac.uk (datashare.ed.ac.uk)|129.215.67.172|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 200\n",
            "Length: 170578854 (163M) [application/zip]\n",
            "Saving to: ‘noisy_testset_wav.zip?sequence=5’\n",
            "\n",
            "noisy_testset_wav.z 100%[===================>] 162.68M  10.2MB/s    in 26s     \n",
            "\n",
            "2024-07-23 07:03:03 (6.36 MB/s) - ‘noisy_testset_wav.zip?sequence=5’ saved [170578854/170578854]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! mkdir data\n",
        "! mkdir data/VCTK_DEMAND\n",
        "! mkdir data/VCTK_DEMAND/train\n",
        "! mkdir data/VCTK_DEMAND/train/clean\n",
        "! mkdir data/VCTK_DEMAND/train/noisy\n",
        "! mkdir data/VCTK_DEMAND/test\n",
        "! mkdir data/VCTK_DEMAND/test/clean\n",
        "! mkdir data/VCTK_DEMAND/test/noisy"
      ],
      "metadata": {
        "id": "1BajWdlv9gSp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! unzip clean_testset_wav.zip?sequence=1 -d data/VCTK_DEMAND/train/clean"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VQ0thzuo7av7",
        "outputId": "240bcd04-1dd6-44bc-eba7-085bf98d72cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  clean_testset_wav.zip?sequence=1\n",
            "   creating: data/VCTK_DEMAND/train/clean/clean_testset_wav/\n",
            "  inflating: data/VCTK_DEMAND/train/clean/clean_testset_wav/p232_001.wav  \n",
            "  inflating: data/VCTK_DEMAND/train/clean/clean_testset_wav/p232_002.wav  \n",
            "  inflating: data/VCTK_DEMAND/train/clean/clean_testset_wav/p232_003.wav  \n",
            "  inflating: data/VCTK_DEMAND/train/clean/clean_testset_wav/p232_005.wav  \n",
            "  inflating: data/VCTK_DEMAND/train/clean/clean_testset_wav/p232_006.wav  \n",
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            "  inflating: data/VCTK_DEMAND/train/clean/clean_testset_wav/p257_427.wav  \n",
            "  inflating: data/VCTK_DEMAND/train/clean/clean_testset_wav/p257_428.wav  \n",
            "  inflating: data/VCTK_DEMAND/train/clean/clean_testset_wav/p257_429.wav  \n",
            "  inflating: data/VCTK_DEMAND/train/clean/clean_testset_wav/p257_430.wav  \n",
            "  inflating: data/VCTK_DEMAND/train/clean/clean_testset_wav/p257_431.wav  \n",
            "  inflating: data/VCTK_DEMAND/train/clean/clean_testset_wav/p257_432.wav  \n",
            "  inflating: data/VCTK_DEMAND/train/clean/clean_testset_wav/p257_433.wav  \n",
            "  inflating: data/VCTK_DEMAND/train/clean/clean_testset_wav/p257_434.wav  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! mv data/VCTK_DEMAND/train/clean/clean_testset_wav/* data/VCTK_DEMAND/train/clean/"
      ],
      "metadata": {
        "id": "2d_fou3jC8pK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! rm -r data/VCTK_DEMAND/train/clean/clean_testset_wav"
      ],
      "metadata": {
        "id": "NKiE9Ai0aARN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! cp data/VCTK_DEMAND/train/clean/* data/VCTK_DEMAND/test/clean"
      ],
      "metadata": {
        "id": "S6RqkC42DU7y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! unzip noisy_testset_wav.zip?sequence=5 -d data/VCTK_DEMAND/train/noisy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aL2W2kAyaBuZ",
        "outputId": "33b87abc-c18e-4ebd-eb32-7a6b8d854c3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  noisy_testset_wav.zip?sequence=5\n",
            "   creating: data/VCTK_DEMAND/train/noisy/noisy_testset_wav/\n",
            "  inflating: data/VCTK_DEMAND/train/noisy/noisy_testset_wav/p232_001.wav  \n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! mv data/VCTK_DEMAND/train/noisy/noisy_testset_wav/* data/VCTK_DEMAND/train/noisy/"
      ],
      "metadata": {
        "id": "pZT6fOaVaPj-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! rm -r data/VCTK_DEMAND/train/noisy/noisy_testset_wav"
      ],
      "metadata": {
        "id": "FDe90lMeaX_a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! cp data/VCTK_DEMAND/train/noisy/* data/VCTK_DEMAND/test/noisy"
      ],
      "metadata": {
        "id": "Gg-NnkWqCwLg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ! unzip clean_trainset_28spk_wav.zip?sequence=2 -d data/VCTK_DEMAND/train/noisy"
      ],
      "metadata": {
        "id": "9Fl-t0Oj-CsH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ! unzip clean_trainset_28spk_wav.zip?sequence=2 -d data/VCTK_DEMAND/test/clean"
      ],
      "metadata": {
        "id": "IomsxqiW-Fmq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ! unzip clean_trainset_28spk_wav.zip?sequence=2 -d data/VCTK_DEMAND/test/noisy"
      ],
      "metadata": {
        "id": "raTPkHuj-KeD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_train = '''\"\"\"\n",
        "PyTorch-style pipeline of MetricGAN+\n",
        "Original authors: Szu-Wei Fu 2021\n",
        "Github repo: https://github.com/speechbrain\n",
        "\n",
        "Reimplemented: Wooseok Shin\n",
        "Modifications:\n",
        " - PyTorch-style implementation\n",
        " - Provide other target metrics (CSIG, CBAK, COVL)\n",
        "\"\"\"\n",
        "import os\n",
        "import time\n",
        "import json\n",
        "import shutil\n",
        "import random\n",
        "import numpy as np\n",
        "import soundfile as sf\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchaudio\n",
        "\n",
        "from tqdm import tqdm\n",
        "from glob import glob\n",
        "from pathlib import Path\n",
        "from os.path import join as opj\n",
        "\n",
        "from model import Generator, Discriminator\n",
        "from dataloader import create_dataloader\n",
        "from metric_functions.get_metric_scores import get_pesq_parallel, get_csig_parallel, get_cbak_parallel, get_covl_parallel\n",
        "from signal_processing import get_spec_and_phase, transform_spec_to_wav\n",
        "fs = 16000\n",
        "\n",
        "class Trainer:\n",
        "    def __init__(self, args, data_paths):\n",
        "        self.args = args\n",
        "        self.device = torch.device(args.device)\n",
        "        self.target_metric = args.target_metric\n",
        "        self.num_samples = args.num_of_sampling\n",
        "\n",
        "        self.train_noisy_path = data_paths['train_noisy']\n",
        "        self.train_clean_path = data_paths['train_clean']\n",
        "        self.train_enhan_path = data_paths['train_enhan']\n",
        "        self.test_noisy_path = data_paths['test_noisy']\n",
        "        self.test_clean_path = data_paths['test_clean']\n",
        "\n",
        "        self.model_output_path = data_paths['model_output']\n",
        "        self.log_output_path = data_paths['log_output']\n",
        "\n",
        "        os.makedirs(self.train_enhan_path, exist_ok=True)\n",
        "        os.makedirs(opj(self.args.output_path, self.args.exp_name, 'tmp'), exist_ok=True)\n",
        "\n",
        "        self.generator_train_paths = glob(self.train_clean_path + '/*.wav')\n",
        "        self.generator_valid_paths = []\n",
        "        for sample in self.generator_train_paths:\n",
        "            for speaker in args.val_speaker:\n",
        "                if speaker in sample:\n",
        "                    self.generator_valid_paths.append(sample)\n",
        "        self.generator_train_paths = list(set(self.generator_train_paths) - set(self.generator_valid_paths))\n",
        "        self.generator_test_paths = glob(self.test_clean_path + '/*.wav')\n",
        "        random.shuffle(self.generator_train_paths)\n",
        "\n",
        "        self.init_model_optim()\n",
        "        self.init_target_metric()\n",
        "        self.init_noisy_score()\n",
        "        self.best_scores = {'pesq':-0.5, 'csig':0, 'cbak':0, 'covl':0, 'avg':0}\n",
        "\n",
        "        with open(opj(self.log_output_path, 'log.txt'), 'a') as f:\n",
        "            f.write(f'Train MetricGAN+-{self.target_metric}\\\\n')\n",
        "            f.write(f'Train set:{len(self.generator_train_paths)}, Valid set:{len(self.generator_valid_paths)}, Test set:{len(self.generator_test_paths)}\\\\n')\n",
        "            f.write(f'Model parameters:{sum(p.numel() for p in self.G.parameters())/10**6:.3f}M\\\\n')\n",
        "\n",
        "        shutil.copy('train.py', opj(self.args.output_path, self.args.exp_name, 'train.py'))\n",
        "\n",
        "        with open(opj(self.args.output_path, self.args.exp_name, 'args.txt'), 'w') as f:\n",
        "            json.dump(args.__dict__, f, indent=2)\n",
        "\n",
        "    def init_model_optim(self):\n",
        "        self.G = Generator(causal=self.args.causal).to(self.device)\n",
        "        self.D = Discriminator().to(self.device)\n",
        "        self.MSELoss = nn.MSELoss().to(self.device)\n",
        "\n",
        "        self.optimizer_g = torch.optim.Adam(self.G.parameters(), lr=self.args.lr)\n",
        "        self.optimizer_d = torch.optim.Adam(self.D.parameters(), lr=self.args.lr)\n",
        "\n",
        "    def init_target_metric(self):\n",
        "        if self.target_metric == 'pesq':\n",
        "            self.target_metric_func = get_pesq_parallel\n",
        "        elif self.target_metric == 'csig':\n",
        "            self.target_metric_func = get_csig_parallel\n",
        "        elif self.target_metric == 'cbak':\n",
        "            self.target_metric_func = get_cbak_parallel\n",
        "        elif self.target_metric == 'covl':\n",
        "            self.target_metric_func = get_covl_parallel\n",
        "\n",
        "    def init_noisy_score(self):\n",
        "        self.noisy_set_scores = {}\n",
        "        Noised_name = glob(self.train_noisy_path + '/*.wav')\n",
        "        train_score_C_N = self.target_metric_func(self.train_clean_path, Noised_name)\n",
        "        assert len(Noised_name) == len(train_score_C_N), 'must same length'\n",
        "        for path, score in tqdm(zip(Noised_name, train_score_C_N)):\n",
        "            self.noisy_set_scores[path] = score\n",
        "\n",
        "    def load_checkpoint(self, ver='latest'):\n",
        "        checkpoint = torch.load(opj(self.model_output_path, f'{ver}_model.pth'), map_location='cuda:0')\n",
        "        self.epoch = checkpoint['epoch']\n",
        "        self.G.load_state_dict(checkpoint['generator'])\n",
        "        self.D.load_state_dict(checkpoint['discriminator'])\n",
        "        self.optimizer_g.load_state_dict(checkpoint['g_optimizer'])\n",
        "        self.optimizer_d.load_state_dict(checkpoint['d_optimizer'])\n",
        "        if ver == 'best':\n",
        "            print(f'---{self.epoch}Epoch loaded: model weigths and optimizer---')\n",
        "        else:\n",
        "            print(f'---load latest model weigths and optimizer---')\n",
        "\n",
        "    def train(self):\n",
        "        start_time = time.time()\n",
        "        self.epoch = 1\n",
        "        self.historical_set = []\n",
        "        for epoch in np.arange(self.epoch, self.args.epochs+1):\n",
        "            self.epoch = epoch\n",
        "            print(f'{epoch}Epoch start')\n",
        "\n",
        "            # random sample some training data\n",
        "            random.shuffle(self.generator_train_paths)\n",
        "            genloader = create_dataloader(self.generator_train_paths[0 : round(1*self.args.num_of_sampling)], self.train_noisy_path,\n",
        "                                          num_target_metric=1, loader='G', batch_size=self.args.batch_size, num_workers=self.args.num_workers)\n",
        "            self.train_one_epoch(genloader)\n",
        "\n",
        "        end_time = time.time()\n",
        "        with open(opj(self.log_output_path, 'log.txt'), 'a') as f:\n",
        "            f.write(f'Total training time:{(end_time-start_time)/60:.2f}Minute\\\\n')\n",
        "\n",
        "        # Best validation scores\n",
        "        self.load_checkpoint('best')\n",
        "        with open(opj(self.log_output_path, 'log.txt'), 'a') as f:\n",
        "            f.write(f'--------Model Best score--------')\n",
        "        self.evaluation(data_list=self.generator_test_paths, phase='best')\n",
        "\n",
        "    def train_one_epoch(self, genloader):\n",
        "        if self.epoch >= 2:\n",
        "            self.train_generator(genloader)\n",
        "\n",
        "        if self.epoch >= self.args.skip_val_epoch:\n",
        "            if self.epoch % self.args.eval_per_epoch == 0:\n",
        "                self.evaluation(data_list=self.generator_test_paths[0:self.args.num_of_val_sample], phase='test')\n",
        "\n",
        "        self.train_discriminator()\n",
        "\n",
        "    def train_generator(self, data_loader):\n",
        "        self.G.train()\n",
        "        print('Generator training phase')\n",
        "        for clean_mag, noise_mag, target, length in tqdm(data_loader):\n",
        "            clean_mag = clean_mag.to(self.device)  # [B, T, F]\n",
        "            noise_mag = noise_mag.to(self.device)  # [B, T, F]\n",
        "            target = target.to(self.device)\n",
        "\n",
        "            mask = self.G(noise_mag, length)\n",
        "            mask = mask.clamp(min=0.05)\n",
        "            enh_mag = torch.mul(mask, noise_mag).unsqueeze(1)\n",
        "\n",
        "            ref_mag = clean_mag.detach().unsqueeze(1)\n",
        "            d_inputs = torch.cat([ref_mag, enh_mag], dim=1)\n",
        "            assert noise_mag.size(2) == 257, 'gen'\n",
        "            assert clean_mag.size(2) == 257, 'gen'\n",
        "\n",
        "            score = self.D(d_inputs)\n",
        "            loss = self.MSELoss(score, target)\n",
        "            self.optimizer_g.zero_grad()\n",
        "            loss.backward()\n",
        "            torch.nn.utils.clip_grad_norm_(self.G.parameters(), 5.0)\n",
        "            self.optimizer_g.step()\n",
        "\n",
        "    def evaluation(self, data_list, phase='valid'):\n",
        "        print(f'Evaluation on {phase} data')\n",
        "        test_enhanced_name = []\n",
        "        self.G.eval()\n",
        "        if (phase == 'test') or (phase == 'best'):    # Test path\n",
        "            clean_path = self.test_clean_path\n",
        "            noise_path = self.test_noisy_path\n",
        "        else:                                         # phase == valid\n",
        "            clean_path = self.train_clean_path\n",
        "            noise_path = self.train_noisy_path\n",
        "\n",
        "        with torch.no_grad():\n",
        "            for i, path in enumerate(tqdm(data_list)):\n",
        "                wave_name = os.path.basename(path)\n",
        "                name = Path(wave_name).stem\n",
        "                suffix = Path(wave_name).suffix\n",
        "\n",
        "                clean_wav, _ = torchaudio.load(path)\n",
        "                noise_wav, _ = torchaudio.load(opj(noise_path, wave_name))\n",
        "                noise_mag, noise_phase = get_spec_and_phase(noise_wav.to(self.device))\n",
        "                assert noise_mag.size(2) == 257, 'eval'\n",
        "                assert noise_phase.size(2) == 257, 'eval'\n",
        "\n",
        "                mask = self.G(noise_mag)\n",
        "                mask = mask.clamp(min=0.05)\n",
        "\n",
        "                enh_mag = torch.mul(mask, noise_mag)\n",
        "                enh_wav = transform_spec_to_wav(torch.expm1(enh_mag), noise_phase, signal_length=clean_wav.size(1)).detach().cpu().numpy().squeeze()\n",
        "\n",
        "                enhanced_name=opj(self.args.output_path, self.args.exp_name, 'tmp', f'{name}#{self.epoch}{suffix}')\n",
        "\n",
        "                sf.write(enhanced_name, enh_wav, fs)\n",
        "                test_enhanced_name.append(enhanced_name)\n",
        "\n",
        "        # Calculate True PESQ\n",
        "        test_PESQ = get_pesq_parallel(clean_path, test_enhanced_name, norm=False)\n",
        "        test_CSIG = get_csig_parallel(clean_path, test_enhanced_name, norm=False)\n",
        "        test_CBAK = get_cbak_parallel(clean_path, test_enhanced_name, norm=False)\n",
        "        test_COVL = get_covl_parallel(clean_path, test_enhanced_name, norm=False)\n",
        "        test_PESQ, test_CSIG, test_CBAK, test_COVL = np.mean(test_PESQ), np.mean(test_CSIG), np.mean(test_CBAK), np.mean(test_COVL)\n",
        "\n",
        "        test_scores = {'pesq':test_PESQ, 'csig':test_CSIG, 'cbak':test_CBAK, 'covl':test_COVL}\n",
        "\n",
        "        with open(opj(self.log_output_path, 'log.txt'), 'a') as f:\n",
        "            f.write(f'Epoch:{self.epoch} | Test PESQ:{test_PESQ:.3f} | Test CSIG:{test_CSIG:.3f} | Test CBAK:{test_CBAK:.3f} | Test COVL:{test_COVL:.3f}\\\\n')\n",
        "\n",
        "        if (phase == 'valid') or (phase == 'test'):    # Test path\n",
        "            checkpoint = {  'epoch':self.epoch,\n",
        "                            'stats': test_scores,\n",
        "                            'generator': self.G.state_dict(),\n",
        "                            'discriminator': self.D.state_dict(),\n",
        "                            'g_optimizer': self.optimizer_g.state_dict(),\n",
        "                            'd_optimizer': self.optimizer_d.state_dict(),\n",
        "                            }\n",
        "\n",
        "            if test_scores['pesq'] >= self.best_scores['pesq']:\n",
        "                print('----------------------------------------')\n",
        "                print('-----------------SAVE-------------------')\n",
        "                self.best_scores = test_scores\n",
        "\n",
        "                torch.save(checkpoint, opj(self.model_output_path, f'best_model.pth'))\n",
        "                print('----------------------------------------')\n",
        "            torch.save(checkpoint, opj(self.model_output_path, f'latest_model.pth'))\n",
        "\n",
        "    def get_score(self):\n",
        "        print('Get scores for discriminator training')\n",
        "        D_paths = self.generator_train_paths[0:self.args.num_of_sampling]\n",
        "\n",
        "        Enhanced_name = []\n",
        "        Noised_name = []\n",
        "\n",
        "        self.G.eval()\n",
        "        with torch.no_grad():\n",
        "            for path in tqdm(D_paths):\n",
        "                wave_name = os.path.basename(path)\n",
        "                name = Path(wave_name).stem\n",
        "                suffix = Path(wave_name).suffix\n",
        "\n",
        "                clean_wav, _ = torchaudio.load(path)\n",
        "                noise_wav, _ = torchaudio.load(self.train_noisy_path+wave_name)\n",
        "                noise_mag, noise_phase = get_spec_and_phase(noise_wav.to(self.device))\n",
        "\n",
        "                mask = self.G(noise_mag)\n",
        "                mask = mask.clamp(min=0.05)\n",
        "                enh_mag = torch.mul(mask, noise_mag)\n",
        "                enh_wav = transform_spec_to_wav(torch.expm1(enh_mag), noise_phase, signal_length=clean_wav.size(1)).detach().cpu().numpy().squeeze()\n",
        "\n",
        "                assert noise_mag.size(2) == 257, 'get_score'\n",
        "                assert noise_phase.size(2) == 257, 'get_score'\n",
        "                assert enh_mag.size(2) == 257, 'get_score'\n",
        "\n",
        "                enhanced_name=opj(self.train_enhan_path, name+'#'+str(self.epoch)+suffix)\n",
        "                sf.write(enhanced_name, enh_wav, fs)\n",
        "                Enhanced_name.append(enhanced_name)\n",
        "                Noised_name.append(self.train_noisy_path+wave_name)\n",
        "\n",
        "        # Calculate true score\n",
        "        train_scores = []\n",
        "        train_score_C_E = self.target_metric_func(self.train_clean_path, Enhanced_name)\n",
        "        train_score_C_N = [self.noisy_set_scores[path] for path in Noised_name]\n",
        "        train_score_C_C = [1.0] * self.num_samples\n",
        "        train_scores.append(train_score_C_E)\n",
        "        train_scores.append(train_score_C_N)\n",
        "        train_scores.append(train_score_C_C)\n",
        "\n",
        "        train_scores.append(Enhanced_name)\n",
        "        current_set = np.array(train_scores).T.tolist()   # [num_sampling, 4]\n",
        "\n",
        "        # Training for current list\n",
        "        random.shuffle(current_set)\n",
        "        return current_set\n",
        "\n",
        "    def subtrain_discriminator(self, data_loader, hist=False):\n",
        "        for i, (enhance_mag, noisy_mag, clean_mag, target) in enumerate(tqdm(data_loader)):\n",
        "            target = target.to(self.device)\n",
        "\n",
        "            if hist:\n",
        "                inputs = torch.cat([clean_mag, enhance_mag], dim=1).to(self.device)  # (clean, enhanced)\n",
        "                score = self.D(inputs)\n",
        "                loss = self.MSELoss(score, target[:, 0])             # target: [C-E score, C-N score, C-C score]\n",
        "                self.optimizer_d.zero_grad()\n",
        "                loss.backward()\n",
        "                torch.nn.utils.clip_grad_norm_(self.D.parameters(), 5.0)\n",
        "                self.optimizer_d.step()\n",
        "            else:\n",
        "                for j, target_mag in enumerate([enhance_mag, noisy_mag, clean_mag]):\n",
        "                    inputs = torch.cat([clean_mag, target_mag], dim=1).to(self.device)  # (clean, enhanced), (clean, noisy), (clean, clean)\n",
        "                    score = self.D(inputs)\n",
        "                    loss = self.MSELoss(score, target[:, j])\n",
        "                    self.optimizer_d.zero_grad()\n",
        "                    loss.backward()\n",
        "                    torch.nn.utils.clip_grad_norm_(self.D.parameters(), 5.0)\n",
        "                    self.optimizer_d.step()\n",
        "\n",
        "\n",
        "    def train_discriminator(self):\n",
        "        print(\"Discriminator training phase\")\n",
        "        self.D.train()\n",
        "        # Get true score of train data\n",
        "        current_set = self.get_score()\n",
        "        # Training current list\n",
        "        disc_loader = create_dataloader(current_set, self.train_noisy_path, clean_path=self.train_clean_path, loader='D',\n",
        "                                        batch_size=self.args.batch_size, num_workers=self.args.num_workers)\n",
        "        self.subtrain_discriminator(disc_loader)\n",
        "\n",
        "        random.shuffle(self.historical_set)\n",
        "\n",
        "        # Training hist list\n",
        "        train_hist_length = int(len(self.historical_set) * self.args.hist_portion)\n",
        "        train_concat_set = self.historical_set[0 : train_hist_length] + current_set\n",
        "        random.shuffle(train_concat_set)\n",
        "        disc_loader_hist = create_dataloader(train_concat_set, self.train_noisy_path, clean_path=self.train_clean_path, loader='D',\n",
        "                                             batch_size=self.args.batch_size, num_workers=self.args.num_workers)\n",
        "        self.subtrain_discriminator(disc_loader_hist, hist=True)\n",
        "\n",
        "        # Update the history list\n",
        "        self.historical_set = self.historical_set + current_set\n",
        "\n",
        "        # Training current list again\n",
        "        self.subtrain_discriminator(disc_loader)'''\n",
        "\n",
        "with open('/content/MetricGAN-plus-pytorch/train.py', 'w') as f:\n",
        "  f.write(new_train)"
      ],
      "metadata": {
        "id": "_2uM0KBmFpZm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! cd MetricGAN-plus-pytorch && python main.py --exp_name=mahtas_exp_1 --target_metric pesq --hist_portion=0.1 --output_path=/content/checkpoint_folder --epochs=1 --base_path=../data/VCTK_DEMAND"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k5IO0-Esrnca",
        "outputId": "b866a962-ce4a-41d0-e9c0-708f5137addd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/content/MetricGAN-plus-pytorch/main.py\", line 72, in <module>\n",
            "    main()\n",
            "  File \"/content/MetricGAN-plus-pytorch/main.py\", line 65, in main\n",
            "    trainer = Trainer(args, data_paths)\n",
            "  File \"/content/MetricGAN-plus-pytorch/train.py\", line 63, in __init__\n",
            "    self.init_model_optim()\n",
            "  File \"/content/MetricGAN-plus-pytorch/train.py\", line 79, in init_model_optim\n",
            "    self.G = Generator(causal=self.args.causal).to(self.device)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1173, in to\n",
            "    return self._apply(convert)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 779, in _apply\n",
            "    module._apply(fn)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/rnn.py\", line 217, in _apply\n",
            "    ret = super()._apply(fn, recurse)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 804, in _apply\n",
            "    param_applied = fn(param)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1159, in convert\n",
            "    return t.to(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py\", line 293, in _lazy_init\n",
            "    torch._C._cuda_init()\n",
            "RuntimeError: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f19v9lY_x1sr"
      },
      "source": [
        "# Attempt 2: `speechbrain/MetricGAN`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "noMlsaLSoh_x",
        "outputId": "52f9d6ae-f437-40d8-c25f-807abf339ce1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting speechbrain\n",
            "  Downloading speechbrain-1.0.0-py3-none-any.whl.metadata (23 kB)\n",
            "Collecting hyperpyyaml (from speechbrain)\n",
            "  Downloading HyperPyYAML-1.2.2-py3-none-any.whl.metadata (7.6 kB)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from speechbrain) (1.4.2)\n",
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            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.9->speechbrain)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.9->speechbrain)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.9->speechbrain)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.9->speechbrain)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.9->speechbrain)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.9->speechbrain)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.9->speechbrain)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.9->speechbrain)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
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            "Collecting nvidia-nccl-cu12==2.20.5 (from torch>=1.9->speechbrain)\n",
            "  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl.metadata (1.8 kB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.9->speechbrain)\n",
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            "  Downloading ruamel.yaml.clib-0.2.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl.metadata (2.2 kB)\n",
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            "Downloading HyperPyYAML-1.2.2-py3-none-any.whl (16 kB)\n",
            "Downloading ruamel.yaml-0.18.6-py3-none-any.whl (117 kB)\n",
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            "\u001b[?25hDownloading ruamel.yaml.clib-0.2.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (526 kB)\n",
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            "\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.5.82-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m61.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: ruamel.yaml.clib, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, ruamel.yaml, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, hyperpyyaml, speechbrain\n",
            "Successfully installed hyperpyyaml-1.2.2 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105 ruamel.yaml-0.18.6 ruamel.yaml.clib-0.2.8 speechbrain-1.0.0\n"
          ]
        }
      ],
      "source": [
        "pip install speechbrain"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uFZzDCvGotmA"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torchaudio\n",
        "from speechbrain.inference.enhancement import SpectralMaskEnhancement"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205,
          "referenced_widgets": [
            "9954d2eddb554b76928532486c7ed389",
            "f0e4cdfb3a2d47d2ac38a8c151188f6a",
            "7f7af06b3f7a4e888e6caf863685c1f9",
            "50fc4206fa37480bb45009449c26f629",
            "63bc99eb076f4128a509c8e972fcb038",
            "b331dc50f26d4af58f04927e718c25f8",
            "dc01f33e6faf438d98d3470e0fcd4e01",
            "5b97874fc61145d798faed2e1b4900fd",
            "5642242ba0274e2dbeb0a34e63d2a3be",
            "b2b13a37497840fabc26c54bf70f39ff",
            "e2d68c8cc3b64a70b27f1988fabf50f7",
            "df582a6e111b419cbfe81b7f7da1ed81",
            "6e88786334d145768eb0f77b66ecc5b3",
            "8ba9ec1e50764b05b257d4e603a8b859",
            "d573ca2ff9744076a91471da8a73ed7e",
            "6320bdb4dd434f61bd1cf85fa63f0347",
            "4de3238d1a2041c49a5dd9b8b927c248",
            "f998519705114be7827a278a2f5cf7f3",
            "a649df50356e4da7b56e36d513ba46cb",
            "09554da4faa14e5eb2ba566c143d1985",
            "f595dbbaccde4a0096a98756c8af01ea",
            "1984a1a783d9461da36ddebcc0ab2ca7"
          ]
        },
        "id": "LIIlLE0PovAs",
        "outputId": "ba666a78-40ff-4c30-bcc5-c11e894a347f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "hyperparams.yaml:   0%|          | 0.00/1.12k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9954d2eddb554b76928532486c7ed389"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "enhance_model.ckpt:   0%|          | 0.00/7.59M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "df582a6e111b419cbfe81b7f7da1ed81"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "enhance_model = SpectralMaskEnhancement.from_hparams(\n",
        "    source=\"speechbrain/metricgan-plus-voicebank\",\n",
        "    savedir=\"pretrained_models/metricgan-plus-voicebank\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dkwHZ5hQTDhj",
        "outputId": "1f54a4e8-3f3b-489d-92a1-0af4ea1c9d1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load and add fake batch dimension\n",
        "noisy = enhance_model.load_audio(\n",
        "    \"/content/drive/MyDrive/SpeechEnhancement/PersianNoisy2.m4a\"\n",
        ").unsqueeze(0)"
      ],
      "metadata": {
        "id": "e_VvdV-6UDeZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "1254d3603cf64c93a31709c17cc23e44",
            "ede53335c2264c98a68bf83f0294b543",
            "7417679ddb4048b780c376e29ed03af9",
            "1cb3bd84cd0f4750bab79a2acf54dc87",
            "62b55dbe002a42eea91ab09e5c82cfca",
            "b46570512fb444efbd00b5bc1d388aaf",
            "a109d73d718d4dc9a117014977b76925",
            "595aed092ff04fd0a516107287c62feb",
            "a633613e77414785be556cb4570b2f46",
            "5f848053e31a4ad8abb7844c46bb3aff",
            "4f9c08ec87f34a9c9237eab712f479cb"
          ]
        },
        "id": "5WpOafCIoyYS",
        "outputId": "03d62118-f497-4072-9964-e58fa8603aa7"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1254d3603cf64c93a31709c17cc23e44",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "example.wav:   0%|          | 0.00/276k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Load and add fake batch dimension\n",
        "noisy = enhance_model.load_audio(\n",
        "    \"speechbrain/metricgan-plus-voicebank/example.wav\"\n",
        ").unsqueeze(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z9tkjFeGo_T1"
      },
      "outputs": [],
      "source": [
        "# Add relative length tensor\n",
        "enhanced = enhance_model.enhance_batch(noisy, lengths=torch.tensor([1.]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s2wSDihFpBFb"
      },
      "outputs": [],
      "source": [
        "# Saving enhanced signal on disk\n",
        "torchaudio.save('enhanced.wav', enhanced.cpu(), 16000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FvifQI77pT8T"
      },
      "outputs": [],
      "source": [
        "from IPython.display import Audio, display"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cl0meCuEpYDW"
      },
      "outputs": [],
      "source": [
        "Audio('/content/drive/MyDrive/SpeechEnhancement/PersianNoisy2.m4a')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s4mIXJ8OpcTO"
      },
      "outputs": [],
      "source": [
        "Audio('enhanced.wav')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iGTvouCXhXJx",
        "outputId": "e80cdf8c-f998-47a6-d563-5793de349286"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'speechbrain'...\n",
            "remote: Enumerating objects: 81076, done.\u001b[K\n",
            "remote: Counting objects: 100% (432/432), done.\u001b[K\n",
            "remote: Compressing objects: 100% (365/365), done.\u001b[K\n",
            "remote: Total 81076 (delta 155), reused 202 (delta 55), pack-reused 80644\u001b[K\n",
            "Receiving objects: 100% (81076/81076), 86.80 MiB | 27.37 MiB/s, done.\n",
            "Resolving deltas: 100% (54425/54425), done.\n"
          ]
        }
      ],
      "source": [
        "! git clone https://github.com/speechbrain/speechbrain/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3tAjpF1chqqh"
      },
      "outputs": [],
      "source": [
        "! cd speechbrain"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jXp15h7khoF5",
        "outputId": "dbceb275-b759-4de9-8024-25e24bc67872"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ignoring SoundFile: markers 'sys_platform == \"win32\"' don't match your environment\n",
            "Requirement already satisfied: black==24.3.0 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/lint-requirements.txt (line 1)) (24.3.0)\n",
            "Requirement already satisfied: click==8.1.7 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/lint-requirements.txt (line 2)) (8.1.7)\n",
            "Requirement already satisfied: flake8==7.0.0 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/lint-requirements.txt (line 3)) (7.0.0)\n",
            "Requirement already satisfied: isort==5.13.2 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/lint-requirements.txt (line 4)) (5.13.2)\n",
            "Requirement already satisfied: pycodestyle==2.11.0 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/lint-requirements.txt (line 5)) (2.11.0)\n",
            "Requirement already satisfied: pydoclint==0.4.1 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/lint-requirements.txt (line 6)) (0.4.1)\n",
            "Requirement already satisfied: pytest==7.4.0 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/lint-requirements.txt (line 7)) (7.4.0)\n",
            "Requirement already satisfied: yamllint==1.35.1 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/lint-requirements.txt (line 8)) (1.35.1)\n",
            "Requirement already satisfied: huggingface_hub>=0.8.0 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/requirements.txt (line 2)) (0.23.5)\n",
            "Requirement already satisfied: hyperpyyaml>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/requirements.txt (line 3)) (1.2.2)\n",
            "Requirement already satisfied: joblib>=0.14.1 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/requirements.txt (line 4)) (1.4.2)\n",
            "Requirement already satisfied: numpy>=1.17.0 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/requirements.txt (line 5)) (1.25.2)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/requirements.txt (line 6)) (24.1)\n",
            "Requirement already satisfied: pandas>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/requirements.txt (line 7)) (2.0.3)\n",
            "Requirement already satisfied: pre-commit>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/requirements.txt (line 8)) (3.7.1)\n",
            "Requirement already satisfied: pygtrie<3.0,>=2.1 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/requirements.txt (line 9)) (2.5.0)\n",
            "Requirement already satisfied: scipy<1.13.0,>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/requirements.txt (line 10)) (1.12.0)\n",
            "Requirement already satisfied: sentencepiece>=0.1.91 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/requirements.txt (line 11)) (0.1.99)\n",
            "Requirement already satisfied: torch>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/requirements.txt (line 13)) (2.3.1+cu121)\n",
            "Requirement already satisfied: torchaudio>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/requirements.txt (line 14)) (2.3.1+cu121)\n",
            "Requirement already satisfied: tqdm>=4.42.0 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/requirements.txt (line 15)) (4.66.4)\n",
            "Requirement already satisfied: transformers>=4.30.0 in /usr/local/lib/python3.10/dist-packages (from -r speechbrain/requirements.txt (line 16)) (4.42.4)\n",
            "Requirement already satisfied: mypy-extensions>=0.4.3 in /usr/local/lib/python3.10/dist-packages (from black==24.3.0->-r speechbrain/lint-requirements.txt (line 1)) (1.0.0)\n",
            "Requirement already satisfied: pathspec>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from black==24.3.0->-r speechbrain/lint-requirements.txt (line 1)) (0.12.1)\n",
            "Requirement already satisfied: platformdirs>=2 in /usr/local/lib/python3.10/dist-packages (from black==24.3.0->-r speechbrain/lint-requirements.txt (line 1)) (4.2.2)\n",
            "Requirement already satisfied: tomli>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from black==24.3.0->-r speechbrain/lint-requirements.txt (line 1)) (2.0.1)\n",
            "Requirement already satisfied: typing-extensions>=4.0.1 in /usr/local/lib/python3.10/dist-packages (from black==24.3.0->-r speechbrain/lint-requirements.txt (line 1)) (4.12.2)\n",
            "Requirement already satisfied: mccabe<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from flake8==7.0.0->-r speechbrain/lint-requirements.txt (line 3)) (0.7.0)\n",
            "Requirement already satisfied: pyflakes<3.3.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from flake8==7.0.0->-r speechbrain/lint-requirements.txt (line 3)) (3.2.0)\n",
            "Requirement already satisfied: docstring-parser-fork>=0.0.5 in /usr/local/lib/python3.10/dist-packages (from pydoclint==0.4.1->-r speechbrain/lint-requirements.txt (line 6)) (0.0.9)\n",
            "Requirement already satisfied: iniconfig in /usr/local/lib/python3.10/dist-packages (from pytest==7.4.0->-r speechbrain/lint-requirements.txt (line 7)) (2.0.0)\n",
            "Requirement already satisfied: pluggy<2.0,>=0.12 in /usr/local/lib/python3.10/dist-packages (from pytest==7.4.0->-r speechbrain/lint-requirements.txt (line 7)) (1.5.0)\n",
            "Requirement already satisfied: exceptiongroup>=1.0.0rc8 in /usr/local/lib/python3.10/dist-packages (from pytest==7.4.0->-r speechbrain/lint-requirements.txt (line 7)) (1.2.2)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from yamllint==1.35.1->-r speechbrain/lint-requirements.txt (line 8)) (6.0.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub>=0.8.0->-r speechbrain/requirements.txt (line 2)) (3.15.4)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub>=0.8.0->-r speechbrain/requirements.txt (line 2)) (2024.6.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub>=0.8.0->-r speechbrain/requirements.txt (line 2)) (2.31.0)\n",
            "Requirement already satisfied: ruamel.yaml>=0.17.28 in /usr/local/lib/python3.10/dist-packages (from hyperpyyaml>=0.0.1->-r speechbrain/requirements.txt (line 3)) (0.18.6)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.1->-r speechbrain/requirements.txt (line 7)) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.1->-r speechbrain/requirements.txt (line 7)) (2024.1)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.1->-r speechbrain/requirements.txt (line 7)) (2024.1)\n",
            "Requirement already satisfied: cfgv>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from pre-commit>=2.3.0->-r speechbrain/requirements.txt (line 8)) (3.4.0)\n",
            "Requirement already satisfied: identify>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from pre-commit>=2.3.0->-r speechbrain/requirements.txt (line 8)) (2.6.0)\n",
            "Requirement already satisfied: nodeenv>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from pre-commit>=2.3.0->-r speechbrain/requirements.txt (line 8)) (1.9.1)\n",
            "Requirement already satisfied: virtualenv>=20.10.0 in /usr/local/lib/python3.10/dist-packages (from pre-commit>=2.3.0->-r speechbrain/requirements.txt (line 8)) (20.26.3)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.9.0->-r speechbrain/requirements.txt (line 13)) (1.13.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.9.0->-r speechbrain/requirements.txt (line 13)) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9.0->-r speechbrain/requirements.txt (line 13)) (3.1.4)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9.0->-r speechbrain/requirements.txt (line 13)) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9.0->-r speechbrain/requirements.txt (line 13)) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9.0->-r speechbrain/requirements.txt (line 13)) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9.0->-r speechbrain/requirements.txt (line 13)) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9.0->-r speechbrain/requirements.txt (line 13)) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9.0->-r speechbrain/requirements.txt (line 13)) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9.0->-r speechbrain/requirements.txt (line 13)) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9.0->-r speechbrain/requirements.txt (line 13)) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9.0->-r speechbrain/requirements.txt (line 13)) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9.0->-r speechbrain/requirements.txt (line 13)) (2.20.5)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9.0->-r speechbrain/requirements.txt (line 13)) (12.1.105)\n",
            "Requirement already satisfied: triton==2.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9.0->-r speechbrain/requirements.txt (line 13)) (2.3.1)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.9.0->-r speechbrain/requirements.txt (line 13)) (12.5.82)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.30.0->-r speechbrain/requirements.txt (line 16)) (2024.5.15)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.30.0->-r speechbrain/requirements.txt (line 16)) (0.4.3)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.30.0->-r speechbrain/requirements.txt (line 16)) (0.19.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=1.0.1->-r speechbrain/requirements.txt (line 7)) (1.16.0)\n",
            "Requirement already satisfied: ruamel.yaml.clib>=0.2.7 in /usr/local/lib/python3.10/dist-packages (from ruamel.yaml>=0.17.28->hyperpyyaml>=0.0.1->-r speechbrain/requirements.txt (line 3)) (0.2.8)\n",
            "Requirement already satisfied: distlib<1,>=0.3.7 in /usr/local/lib/python3.10/dist-packages (from virtualenv>=20.10.0->pre-commit>=2.3.0->-r speechbrain/requirements.txt (line 8)) (0.3.8)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.9.0->-r speechbrain/requirements.txt (line 13)) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub>=0.8.0->-r speechbrain/requirements.txt (line 2)) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub>=0.8.0->-r speechbrain/requirements.txt (line 2)) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub>=0.8.0->-r speechbrain/requirements.txt (line 2)) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub>=0.8.0->-r speechbrain/requirements.txt (line 2)) (2024.7.4)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.9.0->-r speechbrain/requirements.txt (line 13)) (1.3.0)\n",
            "\u001b[31mERROR: speechbrain/requirements.txt is not a valid editable requirement. It should either be a path to a local project or a VCS URL (beginning with bzr+http, bzr+https, bzr+ssh, bzr+sftp, bzr+ftp, bzr+lp, bzr+file, git+http, git+https, git+ssh, git+git, git+file, hg+file, hg+http, hg+https, hg+ssh, hg+static-http, svn+ssh, svn+http, svn+https, svn+svn, svn+file).\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "! pip install -r speechbrain/requirements.txt\n",
        "! pip install -e speechbrain/requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bhgYuNyqqRw0"
      },
      "outputs": [],
      "source": [
        "# -*- coding: utf-8 -*-\n",
        "\"\"\"\n",
        "Data preparation.\n",
        "\n",
        "Download and resample, use ``download_vctk`` below.\n",
        "https://datashare.is.ed.ac.uk/handle/10283/2791\n",
        "\n",
        "Authors:\n",
        " * Szu-Wei Fu, 2020\n",
        " * Peter Plantinga, 2020\n",
        "\"\"\"\n",
        "\n",
        "import json\n",
        "import logging\n",
        "import os\n",
        "import shutil\n",
        "import string\n",
        "import tempfile\n",
        "import urllib\n",
        "\n",
        "import torchaudio\n",
        "from torchaudio.transforms import Resample\n",
        "\n",
        "from speechbrain.dataio.dataio import read_audio\n",
        "from speechbrain.utils.data_utils import download_file, get_all_files\n",
        "\n",
        "logger = logging.getLogger(__name__)\n",
        "LEXICON_URL = \"http://www.openslr.org/resources/11/librispeech-lexicon.txt\"\n",
        "TRAIN_JSON = \"train.json\"\n",
        "TEST_JSON = \"test.json\"\n",
        "VALID_JSON = \"valid.json\"\n",
        "SAMPLERATE = 16000\n",
        "TRAIN_SPEAKERS = [\n",
        "    \"p226\",\n",
        "    \"p287\",\n",
        "    \"p227\",\n",
        "    \"p228\",\n",
        "    \"p230\",\n",
        "    \"p231\",\n",
        "    \"p233\",\n",
        "    \"p236\",\n",
        "    \"p239\",\n",
        "    \"p243\",\n",
        "    \"p244\",\n",
        "    \"p250\",\n",
        "    \"p254\",\n",
        "    \"p256\",\n",
        "    \"p258\",\n",
        "    \"p259\",\n",
        "    \"p267\",\n",
        "    \"p268\",\n",
        "    \"p269\",\n",
        "    \"p270\",\n",
        "    \"p273\",\n",
        "    \"p274\",\n",
        "    \"p276\",\n",
        "    \"p277\",\n",
        "    \"p278\",\n",
        "    \"p279\",\n",
        "    \"p282\",\n",
        "    \"p286\",\n",
        "]\n",
        "# Lexicon missing entries\n",
        "# cspell:disable\n",
        "MISSING_LEXICON = {\n",
        "    \"CRUCIALLY\": \"K R UW SH AH L IY\",\n",
        "    \"PAEDOPHILES\": \"P EH D OW F AY L S\",\n",
        "    \"MR\": \"M IH S T ER\",\n",
        "    \"BBC\": \"B IY B IY S IY\",\n",
        "    \"EUPHORIC\": \"Y UW F AO R IH K\",\n",
        "    \"RACISM\": \"R EY S IH S M\",\n",
        "    \"MP\": \"EH M P IY\",\n",
        "    \"RESTRUCTURING\": \"R IY S T R AH K CH ER IH NG\",\n",
        "    \"OSAMA\": \"OW S AH M AH\",\n",
        "    \"GUITARIST\": \"G IH T AA R IH S T\",\n",
        "    \"BLUESHE\": \"B L UW SH IY\",\n",
        "    \"FLANKER\": \"F L AY N K ER\",\n",
        "    \"SADDAM\": \"S AA D AA M\",\n",
        "    \"COVERUP\": \"K UH V ER UH P\",\n",
        "    \"FBI\": \"EH F B IY AY\",\n",
        "    \"PREEMPTIVE\": \"P R IY EH M P T IH V\",\n",
        "    \"FOURYEAR\": \"F AO R Y IY R\",\n",
        "    \"XRAY\": \"EH K S R AY\",\n",
        "    \"TALIBAN\": \"T AE L IH B AA N\",\n",
        "    \"SUPERIMPOSITION\": \"S UW P ER IH M P OW S IH SH AH N\",\n",
        "    \"GUIDELINES\": \"G AY D L AY N S\",\n",
        "    \"FINALISED\": \"F AY N AH L AY Z D\",\n",
        "    \"HALFTIME\": \"H AE F T AY M\",\n",
        "    \"WINGERS\": \"W IH NG ER Z\",\n",
        "    \"GM\": \"J IY EH M\",\n",
        "    \"MCGREGOR\": \"M AH K G R EH G AO R\",\n",
        "    \"TWODAY\": \"T UW D EY\",\n",
        "    \"DATABASE\": \"D EY T AH B EY S\",\n",
        "    \"TELECOM\": \"T EH L AH K AO M\",\n",
        "    \"SHORTTERM\": \"SH AO R T ER M\",\n",
        "    \"SHORTFALL\": \"SH AO R T F AH L\",\n",
        "    \"MCCALL\": \"M AH K AH L\",\n",
        "    \"HEADTEACHER\": \"H EH D T IY CH ER\",\n",
        "    \"TAKEOVER\": \"T EY K OW V ER\",\n",
        "    \"ONETHIRD\": \"W AH N TH ER D\",\n",
        "    \"TV\": \"T IY V IY\",\n",
        "    \"SCREENPLAY\": \"S K R IY N P L EY\",\n",
        "    \"YUGOSLAV\": \"Y UW G OW S L AA V\",\n",
        "    \"HIBS\": \"HH IH B Z\",\n",
        "    \"DISPOSALS\": \"D IH S P OW S AH L Z\",\n",
        "    \"MODERNISATION\": \"M AA D ER N AH Z EY SH AH N\",\n",
        "    \"REALLIFE\": \"R IY L AY F\",\n",
        "    \"ONEYEAR\": \"W AH N Y IY R\",\n",
        "    \"GRASSROOTS\": \"G R AE S R UW T S\",\n",
        "    \"ARNIE\": \"AH R N IY\",\n",
        "    \"PARTTIME\": \"P AH R T AY M\",\n",
        "    \"SHORTLIST\": \"SH AO R T L IH S T\",\n",
        "    \"OUTPERFORMED\": \"OW T P ER F AO R M D\",\n",
        "    \"LONGTERM\": \"L AO NG T ER M\",\n",
        "    \"DAYTODAY\": \"D EY T UW D EY\",\n",
        "    \"MCPHERSON\": \"M AH K F ER S AH N\",\n",
        "    \"OUTSOURCING\": \"OW T S AO R S IH NG\",\n",
        "    \"FULLSCALE\": \"F UH L S K EY L\",\n",
        "    \"SERGIO\": \"S ER J IY OW\",\n",
        "    \"HENMAN\": \"HH EH N M AA N\",\n",
        "    \"MCLEOD\": \"M AH K L IY AO D\",\n",
        "    \"TIMESCALE\": \"T AY M S K EY L\",\n",
        "    \"REFURBISHMENT\": \"R IY F UH R B IH SH M AH N T\",\n",
        "    \"LINEUP\": \"L AY N UH P\",\n",
        "    \"DOWNBEAT\": \"D OW N B IY T\",\n",
        "    \"MANDELA\": \"M AE N D EH L AH\",\n",
        "    \"UNDERAGE\": \"UH N D ER EY J\",\n",
        "    \"MCNAUGHTON\": \"M AH K N AW T AH N\",\n",
        "    \"MICKELSON\": \"M IH K L S AH N\",\n",
        "    \"THREEQUARTERS\": \"TH R IY K AO R T ER Z\",\n",
        "    \"WEBSITE\": \"W EH B S AY T\",\n",
        "    \"BLUEITS\": \"B L UW IH T S\",\n",
        "    \"CEASEFIRE\": \"S IY S F AY R\",\n",
        "    \"FULLTIME\": \"F UH L T AY M\",\n",
        "    \"DOCHERTY\": \"D AH K ER T IY\",\n",
        "    \"RUNNERUP\": \"R UH N ER AH P\",\n",
        "    \"DOWNTURN\": \"D OW N T ER N\",\n",
        "    \"EUROS\": \"Y ER OW S\",\n",
        "    \"FOOTANDMOUTH\": \"F UH T AE N D M OW TH\",\n",
        "    \"HIGHLIGHTED\": \"HH AY L AY T AH D\",\n",
        "    \"MIDFIELD\": \"M IH D F IY L D\",\n",
        "    \"MCKENZIE\": \"M AH K EH N Z IY\",\n",
        "    \"BENCHMARK\": \"B EH N CH M AA R K\",\n",
        "    \"MCCONNELL\": \"M AH K AW N EH L\",\n",
        "    \"UPGRADING\": \"UH P G R EY D IH NG\",\n",
        "    \"BLUNKETT\": \"B L UH N K AH T\",\n",
        "    \"RETHINK\": \"R IY TH IH N K\",\n",
        "    \"UPBEAT\": \"AH P B IY T\",\n",
        "    \"TELECOMS\": \"T EH L AH K AO M Z\",\n",
        "    \"APARTHEID\": \"AH P AH R T HH AY D\",\n",
        "    \"AIRDRIE\": \"EY R D R IY\",\n",
        "    \"RETHINK\": \"R IY TH IH N K\",\n",
        "    \"HELPLINE\": \"HH EH L P L AY N\",\n",
        "    \"CLEARCUT\": \"K L IY R K UH T\",\n",
        "}\n",
        "# cspell:enable\n",
        "\n",
        "\n",
        "def prepare_voicebank(\n",
        "    data_folder, save_folder, valid_speaker_count=2, skip_prep=False\n",
        "):\n",
        "    \"\"\"\n",
        "    Prepares the json files for the Voicebank dataset.\n",
        "\n",
        "    Expects the data folder to be the same format as the output of\n",
        "    ``download_vctk()`` below.\n",
        "\n",
        "    Arguments\n",
        "    ---------\n",
        "    data_folder : str\n",
        "        Path to the folder where the original Voicebank dataset is stored.\n",
        "    save_folder : str\n",
        "        The directory where to store the json files.\n",
        "    valid_speaker_count : int\n",
        "        The number of validation speakers to use (out of 28 in train set).\n",
        "    skip_prep: bool\n",
        "        If True, skip data preparation.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    None\n",
        "\n",
        "    Example\n",
        "    -------\n",
        "    >>> data_folder = '/path/to/datasets/Voicebank'\n",
        "    >>> save_folder = 'exp/Voicebank_exp'\n",
        "    >>> prepare_voicebank(data_folder, save_folder)\n",
        "    \"\"\"\n",
        "    if skip_prep:\n",
        "        return\n",
        "    # Setting output files\n",
        "    save_json_train = os.path.join(save_folder, TRAIN_JSON)\n",
        "    save_json_valid = os.path.join(save_folder, VALID_JSON)\n",
        "    save_json_test = os.path.join(save_folder, TEST_JSON)\n",
        "\n",
        "    # Check if this phase is already done (if so, skip it)\n",
        "    if skip(save_json_train, save_json_test, save_json_valid):\n",
        "        logger.info(\"Preparation completed in previous run, skipping.\")\n",
        "        return\n",
        "\n",
        "    train_clean_folder = os.path.join(\n",
        "        data_folder, \"clean_trainset_28spk_wav_16k\"\n",
        "    )\n",
        "    train_noisy_folder = os.path.join(\n",
        "        data_folder, \"noisy_trainset_28spk_wav_16k\"\n",
        "    )\n",
        "    train_txts = os.path.join(data_folder, \"trainset_28spk_txt\")\n",
        "    test_clean_folder = os.path.join(data_folder, \"clean_testset_wav_16k\")\n",
        "    test_noisy_folder = os.path.join(data_folder, \"noisy_testset_wav_16k\")\n",
        "    test_txts = os.path.join(data_folder, \"testset_txt\")\n",
        "\n",
        "    # Setting the save folder\n",
        "    if not os.path.exists(save_folder):\n",
        "        os.makedirs(save_folder)\n",
        "\n",
        "    # Additional checks to make sure the data folder contains Voicebank\n",
        "    check_voicebank_folders(\n",
        "        train_clean_folder,\n",
        "        train_noisy_folder,\n",
        "        train_txts,\n",
        "        test_clean_folder,\n",
        "        test_noisy_folder,\n",
        "        test_txts,\n",
        "    )\n",
        "\n",
        "    logger.debug(\"Creating lexicon...\")\n",
        "    lexicon = create_lexicon(os.path.join(data_folder, \"lexicon.txt\"))\n",
        "    logger.info(\"Creating json files for noisy VoiceBank...\")\n",
        "\n",
        "    logger.debug(\"Collecting files...\")\n",
        "    extension = [\".wav\"]\n",
        "    valid_speakers = TRAIN_SPEAKERS[:valid_speaker_count]\n",
        "    wav_lst_train = get_all_files(\n",
        "        train_noisy_folder, match_and=extension, exclude_or=valid_speakers\n",
        "    )\n",
        "    wav_lst_valid = get_all_files(\n",
        "        train_noisy_folder, match_and=extension, match_or=valid_speakers\n",
        "    )\n",
        "    wav_lst_test = get_all_files(test_noisy_folder, match_and=extension)\n",
        "\n",
        "    logger.debug(\"Creating json files for noisy VoiceBank...\")\n",
        "    create_json(\n",
        "        wav_lst_train, save_json_train, train_clean_folder, train_txts, lexicon\n",
        "    )\n",
        "    create_json(\n",
        "        wav_lst_valid, save_json_valid, train_clean_folder, train_txts, lexicon\n",
        "    )\n",
        "    create_json(\n",
        "        wav_lst_test, save_json_test, test_clean_folder, test_txts, lexicon\n",
        "    )\n",
        "\n",
        "\n",
        "def skip(*filenames):\n",
        "    \"\"\"\n",
        "    Detects if the Voicebank data_preparation has been already done.\n",
        "    If the preparation has been done, we can skip it.\n",
        "\n",
        "    Arguments\n",
        "    ---------\n",
        "    *filenames : tuple\n",
        "        List of paths to check for existence.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    bool\n",
        "        if True, the preparation phase can be skipped.\n",
        "        if False, it must be done.\n",
        "    \"\"\"\n",
        "    for filename in filenames:\n",
        "        if not os.path.isfile(filename):\n",
        "            return False\n",
        "    return True\n",
        "\n",
        "\n",
        "def remove_punctuation(a_string):\n",
        "    \"\"\"Remove all punctuation from string\"\"\"\n",
        "    return a_string.translate(str.maketrans(\"\", \"\", string.punctuation))\n",
        "\n",
        "\n",
        "def create_lexicon(lexicon_save_filepath):\n",
        "    \"\"\"\n",
        "    Creates the lexicon object, downloading if it hasn't been done yet.\n",
        "\n",
        "    Arguments\n",
        "    ---------\n",
        "    lexicon_save_filepath : str\n",
        "        Path to save the lexicon when downloading\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    lexicon : dict\n",
        "        Mapping from word string to list of phonemes.\n",
        "    \"\"\"\n",
        "    if not os.path.isfile(lexicon_save_filepath):\n",
        "        download_file(LEXICON_URL, lexicon_save_filepath)\n",
        "\n",
        "    # Iterate lexicon file and add the first pronunciation in the file for\n",
        "    # each word to our lexicon dictionary\n",
        "    lexicon = MISSING_LEXICON\n",
        "    delayed_words = {}\n",
        "    for line in open(lexicon_save_filepath):\n",
        "        line = line.split()\n",
        "        phns = \" \".join(p.strip(\"012\") for p in line[1:])\n",
        "\n",
        "        # Don't add words with punctuation until we can be sure they won't\n",
        "        # overwrite words without punctuation.\n",
        "        clean_word = remove_punctuation(line[0])\n",
        "        if clean_word != line[0] and clean_word not in delayed_words:\n",
        "            delayed_words[clean_word] = phns\n",
        "        elif clean_word == line[0] and clean_word not in lexicon:\n",
        "            lexicon[clean_word] = phns\n",
        "\n",
        "    # Add words with punctuation if they won't overwrite non-punctuated words\n",
        "    for word, phns in delayed_words.items():\n",
        "        if word not in lexicon:\n",
        "            lexicon[word] = phns\n",
        "\n",
        "    return lexicon\n",
        "\n",
        "\n",
        "def create_json(wav_lst, json_file, clean_folder, txt_folder, lexicon):\n",
        "    \"\"\"\n",
        "    Creates the json file given a list of wav files.\n",
        "\n",
        "    Arguments\n",
        "    ---------\n",
        "    wav_lst : list\n",
        "        The list of wav files.\n",
        "    json_file : str\n",
        "        The path of the output json file\n",
        "    clean_folder : str\n",
        "        The location of parallel clean samples.\n",
        "    txt_folder : str\n",
        "        The location of the transcript files.\n",
        "    lexicon : dict\n",
        "        Mapping from word string to list of phonemes.\n",
        "    \"\"\"\n",
        "    logger.debug(f\"Creating json lists in {json_file}\")\n",
        "\n",
        "    # Processing all the wav files in the list\n",
        "    json_dict = {}\n",
        "    for wav_file in wav_lst:  # ex:p203_122.wav\n",
        "        # Example wav_file: p232_001.wav\n",
        "        noisy_path, filename = os.path.split(wav_file)\n",
        "        _, noisy_dir = os.path.split(noisy_path)\n",
        "        _, clean_dir = os.path.split(clean_folder)\n",
        "        noisy_rel_path = os.path.join(\"{data_root}\", noisy_dir, filename)\n",
        "        clean_rel_path = os.path.join(\"{data_root}\", clean_dir, filename)\n",
        "\n",
        "        # Reading the signal (to retrieve duration in seconds)\n",
        "        signal = read_audio(wav_file)\n",
        "        duration = signal.shape[0] / SAMPLERATE\n",
        "\n",
        "        # Read text\n",
        "        snt_id = filename.replace(\".wav\", \"\")\n",
        "        with open(os.path.join(txt_folder, snt_id + \".txt\")) as f:\n",
        "            word_string = f.read()\n",
        "        word_string = remove_punctuation(word_string).strip().upper()\n",
        "        phones = [\n",
        "            phn for word in word_string.split() for phn in lexicon[word].split()\n",
        "        ]\n",
        "\n",
        "        # Remove duplicate phones\n",
        "        phones = [i for i, j in zip(phones, phones[1:] + [None]) if i != j]\n",
        "        phone_string = \" \".join(phones)\n",
        "\n",
        "        json_dict[snt_id] = {\n",
        "            \"noisy_wav\": noisy_rel_path,\n",
        "            \"clean_wav\": clean_rel_path,\n",
        "            \"length\": duration,\n",
        "            \"words\": word_string,\n",
        "            \"phones\": phone_string,\n",
        "        }\n",
        "\n",
        "    # Writing the json lines\n",
        "    with open(json_file, mode=\"w\") as json_f:\n",
        "        json.dump(json_dict, json_f, indent=2)\n",
        "\n",
        "    logger.info(f\"{json_file} successfully created!\")\n",
        "\n",
        "\n",
        "def check_voicebank_folders(*folders):\n",
        "    \"\"\"Raises FileNotFoundError if any passed folder does not exist.\"\"\"\n",
        "    for folder in folders:\n",
        "        if not os.path.exists(folder):\n",
        "            raise FileNotFoundError(\n",
        "                f\"the folder {folder} does not exist (it is expected in \"\n",
        "                \"the Voicebank dataset)\"\n",
        "            )\n",
        "\n",
        "\n",
        "def download_vctk(destination, tmp_dir='tmp', device=\"cpu\"):\n",
        "    \"\"\"Download dataset and perform resample to 16000 Hz.\n",
        "\n",
        "    Arguments\n",
        "    ---------\n",
        "    destination : str\n",
        "        Place to put final zipped dataset.\n",
        "    tmp_dir : str\n",
        "        Location to store temporary files. Will use `tempfile` if not provided.\n",
        "    device : str\n",
        "        Passed directly to pytorch's ``.to()`` method. Used for resampling.\n",
        "    \"\"\"\n",
        "    dataset_name = \"noisy-vctk-16k\"\n",
        "    if tmp_dir is None:\n",
        "        tmp_dir = tempfile.gettempdir()\n",
        "    final_dir = os.path.join(tmp_dir, dataset_name)\n",
        "\n",
        "    if not os.path.isdir(tmp_dir):\n",
        "        os.mkdir(tmp_dir)\n",
        "\n",
        "    if not os.path.isdir(final_dir):\n",
        "        os.mkdir(final_dir)\n",
        "\n",
        "    prefix = \"https://datashare.is.ed.ac.uk/bitstream/handle/10283/2791/\"\n",
        "    noisy_vctk_urls = [\n",
        "        prefix + \"clean_testset_wav.zip\",\n",
        "        prefix + \"noisy_testset_wav.zip\",\n",
        "        prefix + \"testset_txt.zip\",\n",
        "        prefix + \"clean_trainset_28spk_wav.zip\",\n",
        "        prefix + \"noisy_trainset_28spk_wav.zip\",\n",
        "        prefix + \"trainset_28spk_txt.zip\",\n",
        "    ]\n",
        "\n",
        "    zip_files = []\n",
        "    for url in noisy_vctk_urls:\n",
        "        filename = os.path.join(tmp_dir, url.split(\"/\")[-1])\n",
        "        zip_files.append(filename)\n",
        "        if not os.path.isfile(filename):\n",
        "            print(\"Downloading \" + url)\n",
        "            with urllib.request.urlopen(url) as response:\n",
        "                with open(filename, \"wb\") as tmp_file:\n",
        "                    logger.info(\"... to \" + tmp_file.name)\n",
        "                    shutil.copyfileobj(response, tmp_file)\n",
        "\n",
        "    # Unzip\n",
        "    for zip_file in zip_files:\n",
        "        logger.info(\"Unzipping \" + zip_file)\n",
        "        shutil.unpack_archive(zip_file, tmp_dir, \"zip\")\n",
        "        os.remove(zip_file)\n",
        "\n",
        "    print(\"here1\")\n",
        "    # Move transcripts to final dir\n",
        "    shutil.move(os.path.join(tmp_dir, \"testset_txt\"), final_dir)\n",
        "    shutil.move(os.path.join(tmp_dir, \"trainset_28spk_txt\"), final_dir)\n",
        "    print(\"here2\")\n",
        "\n",
        "    # Downsample\n",
        "    dirs = [\n",
        "        \"noisy_testset_wav\",\n",
        "        \"clean_testset_wav\",\n",
        "        \"noisy_trainset_28spk_wav\",\n",
        "        \"clean_trainset_28spk_wav\",\n",
        "    ]\n",
        "\n",
        "    downsampler = Resample(orig_freq=48000, new_freq=16000).to(device)\n",
        "\n",
        "    for directory in dirs:\n",
        "        logger.info(\"Resampling \" + directory)\n",
        "        dirname = os.path.join(tmp_dir, directory)\n",
        "\n",
        "        # Make directory to store downsampled files\n",
        "        dirname_16k = os.path.join(final_dir, directory + \"_16k\")\n",
        "        if not os.path.isdir(dirname_16k):\n",
        "            os.mkdir(dirname_16k)\n",
        "\n",
        "        # Load files and downsample\n",
        "        for filename in get_all_files(dirname, match_and=[\".wav\"]):\n",
        "            signal, rate = torchaudio.load(filename)\n",
        "            downsampled_signal = downsampler(signal.view(1, -1).to(device))\n",
        "\n",
        "            # Save downsampled file\n",
        "            torchaudio.save(\n",
        "                os.path.join(dirname_16k, filename[-12:]),\n",
        "                downsampled_signal.cpu(),\n",
        "                sample_rate=16000,\n",
        "            )\n",
        "\n",
        "            # Remove old file\n",
        "            os.remove(filename)\n",
        "\n",
        "        # Remove old directory\n",
        "        os.rmdir(dirname)\n",
        "\n",
        "    logger.info(\"Zipping \" + final_dir)\n",
        "    final_zip = shutil.make_archive(\n",
        "        base_name=final_dir,\n",
        "        format=\"zip\",\n",
        "        root_dir=os.path.dirname(final_dir),\n",
        "        base_dir=os.path.basename(final_dir),\n",
        "    )\n",
        "\n",
        "    logger.info(f\"Moving {final_zip} to {destination}\")\n",
        "    shutil.move(final_zip, os.path.join(destination, dataset_name + \".zip\"))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! wget https://datashare.is.ed.ac.uk/bitstream/handle/10283/2791/clean_testset_wav.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-xgx1N07Daql",
        "outputId": "7450b633-30a9-4796-8e70-83111ee26e1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-28 11:15:08--  https://datashare.is.ed.ac.uk/bitstream/handle/10283/2791/clean_testset_wav.zip\n",
            "Resolving datashare.is.ed.ac.uk (datashare.is.ed.ac.uk)... 129.215.67.128\n",
            "Connecting to datashare.is.ed.ac.uk (datashare.is.ed.ac.uk)|129.215.67.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://datashare.ed.ac.uk/bitstream/handle/10283/2791/clean_testset_wav.zip [following]\n",
            "--2024-07-28 11:15:09--  https://datashare.ed.ac.uk/bitstream/handle/10283/2791/clean_testset_wav.zip\n",
            "Resolving datashare.ed.ac.uk (datashare.ed.ac.uk)... 129.215.67.172\n",
            "Connecting to datashare.ed.ac.uk (datashare.ed.ac.uk)|129.215.67.172|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 200\n",
            "Length: 154332064 (147M) [application/zip]\n",
            "Saving to: ‘clean_testset_wav.zip’\n",
            "\n",
            "clean_testset_wav.z 100%[===================>] 147.18M  10.9MB/s    in 18s     \n",
            "\n",
            "2024-07-28 11:15:28 (8.04 MB/s) - ‘clean_testset_wav.zip’ saved [154332064/154332064]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! wget https://datashare.is.ed.ac.uk/bitstream/handle/10283/2791/noisy_testset_wav.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tiuQlCcMDkf-",
        "outputId": "d848751b-af2b-473e-ba99-3f042aee1285"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-28 11:15:38--  https://datashare.is.ed.ac.uk/bitstream/handle/10283/2791/noisy_testset_wav.zip\n",
            "Resolving datashare.is.ed.ac.uk (datashare.is.ed.ac.uk)... 129.215.67.128\n",
            "Connecting to datashare.is.ed.ac.uk (datashare.is.ed.ac.uk)|129.215.67.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://datashare.ed.ac.uk/bitstream/handle/10283/2791/noisy_testset_wav.zip [following]\n",
            "--2024-07-28 11:15:39--  https://datashare.ed.ac.uk/bitstream/handle/10283/2791/noisy_testset_wav.zip\n",
            "Resolving datashare.ed.ac.uk (datashare.ed.ac.uk)... 129.215.67.172\n",
            "Connecting to datashare.ed.ac.uk (datashare.ed.ac.uk)|129.215.67.172|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 200\n",
            "Length: 170578854 (163M) [application/zip]\n",
            "Saving to: ‘noisy_testset_wav.zip’\n",
            "\n",
            "noisy_testset_wav.z 100%[===================>] 162.68M  10.2MB/s    in 21s     \n",
            "\n",
            "2024-07-28 11:16:01 (7.69 MB/s) - ‘noisy_testset_wav.zip’ saved [170578854/170578854]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! wget https://datashare.is.ed.ac.uk/bitstream/handle/10283/2791/testset_txt.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jo_xRml8Dpc5",
        "outputId": "787c44b2-5644-4259-f04c-813fce9ab1fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-28 11:16:10--  https://datashare.is.ed.ac.uk/bitstream/handle/10283/2791/testset_txt.zip\n",
            "Resolving datashare.is.ed.ac.uk (datashare.is.ed.ac.uk)... 129.215.67.128\n",
            "Connecting to datashare.is.ed.ac.uk (datashare.is.ed.ac.uk)|129.215.67.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://datashare.ed.ac.uk/bitstream/handle/10283/2791/testset_txt.zip [following]\n",
            "--2024-07-28 11:16:11--  https://datashare.ed.ac.uk/bitstream/handle/10283/2791/testset_txt.zip\n",
            "Resolving datashare.ed.ac.uk (datashare.ed.ac.uk)... 129.215.67.172\n",
            "Connecting to datashare.ed.ac.uk (datashare.ed.ac.uk)|129.215.67.172|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 200\n",
            "Length: 440277 (430K) [application/zip]\n",
            "Saving to: ‘testset_txt.zip’\n",
            "\n",
            "testset_txt.zip     100%[===================>] 429.96K   232KB/s    in 1.8s    \n",
            "\n",
            "2024-07-28 11:16:14 (232 KB/s) - ‘testset_txt.zip’ saved [440277/440277]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dSelA2OOq2wo"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.makedirs('vctk_data', exist_ok=True)\n",
        "# download_vctk('vctk_data')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! unzip clean_testset_wav.zip -d vctk_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TxC0TVsKDycF",
        "outputId": "64df280a-f474-4ea0-dfab-4b177069b18a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  clean_testset_wav.zip\n",
            "   creating: vctk_data/clean_testset_wav/\n",
            "  inflating: vctk_data/clean_testset_wav/p232_001.wav  \n",
            "  inflating: vctk_data/clean_testset_wav/p232_002.wav  \n",
            "  inflating: vctk_data/clean_testset_wav/p232_003.wav  \n",
            "  inflating: vctk_data/clean_testset_wav/p232_005.wav  \n",
            "  inflating: vctk_data/clean_testset_wav/p232_006.wav  \n",
            "  inflating: vctk_data/clean_testset_wav/p232_007.wav  \n",
            "  inflating: vctk_data/clean_testset_wav/p232_009.wav  \n",
            "  inflating: vctk_data/clean_testset_wav/p232_010.wav  \n",
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            "  inflating: vctk_data/clean_testset_wav/p232_012.wav  \n",
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            "  inflating: vctk_data/clean_testset_wav/p257_313.wav  \n",
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            "  inflating: vctk_data/clean_testset_wav/p257_416.wav  \n",
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            "  inflating: vctk_data/clean_testset_wav/p257_424.wav  \n",
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            "  inflating: vctk_data/clean_testset_wav/p257_426.wav  \n",
            "  inflating: vctk_data/clean_testset_wav/p257_427.wav  \n",
            "  inflating: vctk_data/clean_testset_wav/p257_428.wav  \n",
            "  inflating: vctk_data/clean_testset_wav/p257_429.wav  \n",
            "  inflating: vctk_data/clean_testset_wav/p257_430.wav  \n",
            "  inflating: vctk_data/clean_testset_wav/p257_431.wav  \n",
            "  inflating: vctk_data/clean_testset_wav/p257_432.wav  \n",
            "  inflating: vctk_data/clean_testset_wav/p257_433.wav  \n",
            "  inflating: vctk_data/clean_testset_wav/p257_434.wav  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! unzip noisy_testset_wav.zip -d vctk_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lLHRI4bSD8J_",
        "outputId": "6b1d1af6-9f23-485d-def2-efde63aa7264"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  noisy_testset_wav.zip\n",
            "   creating: vctk_data/noisy_testset_wav/\n",
            "  inflating: vctk_data/noisy_testset_wav/p232_001.wav  \n",
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            "  inflating: vctk_data/noisy_testset_wav/p257_331.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_332.wav  \n",
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            "  inflating: vctk_data/noisy_testset_wav/p257_349.wav  \n",
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            "  inflating: vctk_data/noisy_testset_wav/p257_365.wav  \n",
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            "  inflating: vctk_data/noisy_testset_wav/p257_370.wav  \n",
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            "  inflating: vctk_data/noisy_testset_wav/p257_373.wav  \n",
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            "  inflating: vctk_data/noisy_testset_wav/p257_379.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_380.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_381.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_382.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_383.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_384.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_385.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_386.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_387.wav  \n",
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            "  inflating: vctk_data/noisy_testset_wav/p257_397.wav  \n",
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            "  inflating: vctk_data/noisy_testset_wav/p257_400.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_401.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_402.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_403.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_404.wav  \n",
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            "  inflating: vctk_data/noisy_testset_wav/p257_406.wav  \n",
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            "  inflating: vctk_data/noisy_testset_wav/p257_408.wav  \n",
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            "  inflating: vctk_data/noisy_testset_wav/p257_413.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_414.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_415.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_416.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_417.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_418.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_419.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_420.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_421.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_422.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_423.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_424.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_425.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_426.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_427.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_428.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_429.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_430.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_431.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_432.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_433.wav  \n",
            "  inflating: vctk_data/noisy_testset_wav/p257_434.wav  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! unzip testset_txt.zip -d vctk_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TO16BbjUD-gS",
        "outputId": "ec01a0f2-4862-42bc-d024-d46c6d892e86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  testset_txt.zip\n",
            "   creating: vctk_data/testset_txt/\n",
            "  inflating: vctk_data/testset_txt/p232_001.txt  \n",
            "   creating: vctk_data/__MACOSX/\n",
            "   creating: vctk_data/__MACOSX/testset_txt/\n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_001.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_002.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_002.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_003.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_003.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_005.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_005.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_006.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_006.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_007.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_007.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_009.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_009.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_010.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_010.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_011.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_011.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_012.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_012.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_013.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_013.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_014.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_014.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_015.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_015.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_016.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_016.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_017.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_017.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_019.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_019.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_020.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_020.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_021.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_021.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_022.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_022.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_023.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_023.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_024.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_024.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_025.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_025.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_027.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_027.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_028.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_028.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_029.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_029.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_030.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_030.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_031.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_031.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_032.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_032.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_033.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_033.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_034.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_034.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_035.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_035.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_036.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_036.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_037.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_037.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_038.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_038.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_039.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_039.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_040.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_040.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_041.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_041.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_042.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_042.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_043.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_043.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_044.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_044.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_045.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_045.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_046.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_046.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_047.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_047.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_048.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_048.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_049.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_049.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_050.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_050.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_051.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_051.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_052.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_052.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_053.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_053.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_054.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_054.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_055.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_055.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_056.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_056.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_057.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_057.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_058.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_058.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_059.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_059.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_060.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_060.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_061.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_061.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_062.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_062.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_063.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_063.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_064.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_064.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_065.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_065.txt  \n",
            "  inflating: vctk_data/testset_txt/p232_066.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p232_066.txt  \n",
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            "  inflating: vctk_data/testset_txt/p257_432.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p257_432.txt  \n",
            "  inflating: vctk_data/testset_txt/p257_433.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p257_433.txt  \n",
            "  inflating: vctk_data/testset_txt/p257_434.txt  \n",
            "  inflating: vctk_data/__MACOSX/testset_txt/._p257_434.txt  \n",
            "  inflating: vctk_data/__MACOSX/._testset_txt  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! ls vctk_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PW-pHjKNECSa",
        "outputId": "86ed02d7-6024-4141-96bc-cc4548fbc5c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "clean_testset_wav  clean_trainset_28spk_wav  __MACOSX  noisy_testset_wav  testset_txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! cp -r vctk_data/clean_testset_wav vctk_data/clean_testset_wav_16k"
      ],
      "metadata": {
        "id": "lwR_Az1RHgkT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! cp -r vctk_data//noisy_testset_wav vctk_data/noisy_testset_wav_16k"
      ],
      "metadata": {
        "id": "q6ys0v9oHrMj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! cp -r vctk_data/clean_testset_wav vctk_data/clean_trainset_28spk_wav_16k"
      ],
      "metadata": {
        "id": "WQ4SCZNwEFJJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! cp -r vctk_data/noisy_testset_wav vctk_data/noisy_trainset_28spk_wav_16k"
      ],
      "metadata": {
        "id": "1SPkIIwQEYjS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! cp -r vctk_data/testset_txt vctk_data/trainset_28spk_txt"
      ],
      "metadata": {
        "id": "gBDl-qe7EbnZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "Of7G7lzKOvuH",
        "outputId": "dff818e6-d9d8-4e3d-b3af-72be3727621a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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/v//b/4P/Z/wUA/f8PACEADwAGABMAKgAzADMAHAAlAD4AJAAxAEMAOgA2ADEAQQBUABsAIgAuABYABgD9/wkAIwAiABQAFQAYABgAGAAaAB4APQAYABsALQBVAGkAYwB4AGUARwBBAD0ARwCCAHUAaAB3AGAAfwCXAJQAwQCqAIsAgABtAI0AnQCbAIgAeQB3AFsAbgCMAKgAkQB/AI8AbACDAJ8AkAC8AKEAjQCZAH0AxADOAJoAwQDIALQAqgCdAKYAmgCdAKkAoQCZAJoAdABpAHUAZABnAGgAVABQADsANwA4AB8AEgAUAAMA8//k/+n//P/v/+z/7f/m/97/zv/C/7D/xf+o/8T/wv+9/77/yf/s/+X/3v/w//f/8v/t//z/9f/0/+v/6P/n/97/3//8/9T/7f/2//X/7v/X//P/9P/U/93/1//b/9f/pv+1/87/1P+4/4v/if9s/2b/Zv9k/4L/bP9Z/2P/a/93/33/d/+a/37/Z/9e/3H/gv94/27/cP9h/0r/Xf9m/1X/Q/8o/1H/Vf8n/xz/Kf8v/07/QP9U/2L/Yf9G/07/VP9X/1L/O/8v/z3/F/8N/xH/H/86/xb/E/8X/y//Ov8+/1j/Tv8m/0f/Sf9f/4D/cf+P/4H/fv+X/3f/ev+D/3j/iP9z/1L/Zv9n/3X/gP+H/4b/j/+G/57/o/+F/4r/Yv9//3r/Yv+B/33/eP+Z/5v/m/+K/4P/kv+t/63/u/+t/6D/mf+X/5H/tP+5/6P/nf+R/4P/sf+y/8//uf+0/67/cf9o/4z/nv+7/67/cv9//4L/hP+p/6L/rP+a/4j/rP+2/87/2v/I/9D/1v/M/9T/uf/O/9D/6//V/7P/0f+7/7//zP+g/6D/of+u/57/ov+Z/4b/j/+v/7f/w//Z/7v/vf/K/9v/6P/p/+j/6//V/9P/6f/z/9f/8P8FAAEA/v8dAB8AEwAiACcAHgAxACUAKABQAE0AGAD0/wcAIwAqACcAHAAkAC8AHwAPAP//2f/w//L/5f/9/93/6f/v/83/2f/P/8z/4v/D/8v/wv/W/9P/1v/0/wQAEAABAPL/FAACAAYABgAHACQADwAoADoAKQA1AEwANQA0ADsAMwA3ADsAHQAmAEkATABMAFoAVQA+ADUAMQBMAC0ABgAmADoAUAAaAAoACgAWABgAGQAXAEsAKAAjADQAPgBQADUANwBZAEAAIAATACwARQBLADMALQAqAD0AHwAwAGcAWABOAC8ADwAZAA0AKgBIAF8AcABNAFgAUgBeAHIAeQBSAEwAMQArADEAMABSAEcAMAARAAIAEQAQAA8AGgAYABIA5v/8/xIAHgASACAAHQD+//7/6/8BAPj/BAD5/wcAGAA4ADEAJgAwAEIARABgAGYAUgA6ACkAVQBkAEIAbgBYAF4AUAAxAFoAXABWADsALgBFADcAIwA9AC8AJgBDADgAOwAhABoAQQBbAEUAPAAyADYAJwAMABEAEwAcAPP/1v/8/wMA///9/+n/7P/s/+//4v/k/9X/4P8PADIANgA0ADIAWQBdAEMAQQBOADEAKAAsAEkAQgAkACAAMgAmACoAQgBvAG0AXwBxAIkAkQCxAKMA0QDNANMAxADiAPYA2wDMALIAxwDPANIA1wDfAMUApwCfAK0AwwDUANUAqQC6AM0AugDgAOMA+ADmAAQBIgH7AOsA+gD4AOsA1QDPAO8A9QAKASEBHwH8ABQBCwEYASwBJAEnATQBQQEeAR0BLAEfAR4BNQEfARcB5gDBAN0A+QDxAPYACQErAR4BIgEfARwBLQElAf0A+wDrAP0AHAEwASwBLQEGAQIBCwE3ATABJQECAQcBFAEnAS0BLwEbAfgA4AD9ABkBAQH7APkA5gDyAO0A9wAuARgB7gD0AM4AxgDYAMsABQHnALUAwgCuAMMArQCcAKwAowB0AHwAXwBbAIgAfgCBAHcAYwB8AIIAcQCOAIwAggCcAHwAfgCLAIkAkQCGAKoAxwDEAM8AqQC4AI8AgACOAJEAiAB0AIQAhwBQAFMAagB6AHkAVwBAAIEAhQCXAJMAegBqAE8AOgBoAHUAbwBjADoAMwAWACMAHAD//xcARgBNABsAGAA0ACEAOABCADsANwAuACwATQBcAFgAhACOAHwAegB0AE4AbQB4AFoAXwBVAEsAMQBJAGsAaAByAEoAHgBNAFgAYwBSAGUAUQBCACoACAAjADoAEwAOABkA9//r/9T/zv/R/8P/2//M/8H/xv+h/5//s/++/8H/1v/h/+f/1f/i/+7/4f/Q/87/0P/C/9b/2f/q//j/5P/x/+D/r/+2/9j/4v/j/93/5f/0//H/HAAKABkADAD0/+T/6f/o/83/5P/F/+P/0f/K//D/yP/A/9//EgAJAPD/9v8IAB0AKQAeACQAQwACAAoAJQALACYAFwD3/wkADAD7/9r/AwDt/8//0f/K/93/1//f/+n/8//2//T/9v/O/+b/0f/L/7r/t//H/5//p//c/73/1v/H/3b/fv+N/4v/ef98/37/gv9+/3v/dP+V/6b/hP9y/1z/TP9t/3X/ZP9b/17/O/8i/zH/OP86/yD/D/8u/zT/Iv8O/yb/J/9W/3X/if+D/2H/av9o/2H/ff91/4H/Vf9A/0b/S/9Y/4H/lf9p/0f/Wv9a/2P/Uf9x/2r/cf9S/z3/Rv85/zT/Ov8t/y7/Q/9b/2P/gP9t/z//F//1/iz/V/83/3n/Vf9N/1P/Of84/1b/eP+T/23/jv9V/27/l/9w/5T/m/91/3r/nf+V/6X/fv9I/3P/Xv+K/5b/fv+z/6D/f/+y/6X/pP+P/3//aP9l/2H/a/96/0r/bf9w/3z/jf9z/37/rv+e/2z/Zf9M/0v/T/8r/0X/Zf84/0//av9B/0D/Q/9k/1n/W/9r/2f/cv9a/1D/c/9W/3X/mf+l/5X/hv9s/1z/af9m/1n/WP9G/0f/Pf8h/0n/Nf81/3b/dP9U/0j/W/9d/zP/a/+C/3X/iP9q/27/jP99/0T/Rv9g/3H/ff9e/0L/Pv8N//7+Nv8o/y//Uf8X//j+0/6//s/+Av8s/yr/2P7U/tP+/v7j/s3+1f7E/tP+2/7J/q/+3f7U/qD+0v6i/sn+7P7x/gr/3f77/gv/Af8L/+/+Bv/h/sb+1/7q/tr+3P7X/sv+2/7p/s3+qP65/sD+2/66/sz++P7k/uD+zf7C/r/+vP7W/tP+z/6x/rn+yP7F/sH+/P7n/tz+//4B//T+6P4H/wL/Hv8c/wP/8/7h/u/+//4R/wz/7f7t/t/+xP7u/t7+w/4Y/zH/L/8L/83+/f4W///+Df8w/zn//f7u/u/+Ev9P/zT/Tv89/zL/KP8w/zz/M/9y/5n/cv95/0f/Uf8+/1D/iv+R/4L/r/+b/5r/l/+b/47/k/98/3T/t/+4/5r/iv+f/7r/1P/c/7n/qv+6/7v/xf/e/+z/9f/g/7v/8v81ADYAPgBOAHYAUAA/AEIASgB4AHwAdAB5AG4AjQCBAKEArgByAGAAWgA3AGwAXgBCAGQAUAAYACsAMQAkAAoAEQA5AFcATgAlAA4AHgBUAFEAPQAqADEASwBjAHgAdgBDADoAJwBSAHAAjwCZAIAAaQBxAIkAhwCLAJgAdgB1AGQAhgCeAF4AdABmAEgAUABHAEYARQBFADoALAAxAFcAjACKAG4AdgBjAHQAkQBxAG0AYwA7AGcAggBxAH0AWgB0AFAAJABDAD8ASABQAC8ATQBHACIAKAA8AGgAeACGAIEAeACnAIEAdACKAGYAVwCBAKwAuQCjAKEAegBoAFYAYQBkAHkAjAChAIgAfQCVAHwAlgCWAHsAlgC+AJUAhQCQAJUAcgBuAHMAZABwAHMAhQCbAGIASgBSAFgAcwCHAI0AlACRAHwAbwBjAH4AfwCVAIMAdgBZAGUAaABsAFIAXABtAGoAUABKAEQAVQBXAFEARwAVAG0AkwBjAGcANQBLAHQAiQBpAJsAswCfAGsAkgChAJsAwgDAAMMAwABuAHQAuwDXAOEAGAHKAIsArgDdAPYA/QAbAdYAxgC3AK4A7gAQASEB9QDTAMQA1gDMAJ8A8QAdAQAB2QD5AP4A+QAeAdoA8QAQAdAA/wATAewA/AAOAT8BNgHoAPIAGwEGAeMAIgEjAfgA2wDXAPcA+wDuAAkB9gD7ACABzQCVAK4A1ADZAMoA9AAVAQwBCAHyAPoA2wDCAPwACgEMAR8BDAERARMBKgEzAQ0BEQEhAR4BHAEPAQEBMQEZAQgBMgFAAVYBVgEPATEBUAFEATgBJQEGARoBDAELAfQA9QAjASQBDAEgARIBFQEcAQgBRAFJATcBAAEaASUBIgEqARQB+AAsATIBFwH9ANoA+wALAeYA3gDdAAEBAAEKAQIB5gAHAfEA/gApAfwA7wDHANUAAQETASsB/ADiAOcAqwClAMgA6wD7ALsA4ADPAPEALgEiAQ4B6QDvABEBIgFZAVsBGgH4AO4A9ADrAO4AAAEKAe0AygDTAOcA2gCoANgA4gDUANoAtwCdAL0ABQEHAfUAzwC9AM8AvADLAMAAtgCsAKoAuACjAIcAmADHANsAugCUAHcAXwBkAHkAfACUAG0AXABpAGsAZwCEAJgAegBbAE4AWQBYAHcAZABLAF0ARgA5AB4AIgD7/xoADgDq/wQAMQAfABsAGAD4/yQAMQAhABEAJAA6ADUAMgAgACwALgAgAP//3v/c/9T/uf/j//f/1v/8/0AASwAuAAUA8/8cAA8A+P8cADAAAwDy/93/4P/k//H/5f/M/6T/qf98/2//mv+Z/4r/gf9w/3f/b/9I/2b/bP9Q/y7/T/9J/zX/WP9M/z3/KP8V/wH/E/9I/zD/Of9U/zP/Pf9J/2T/f/9P/xb/I/9p/3b/Xf82/yz/Iv9J/0//e/9l/zL/TP81/zr/M/8u/zf/M/8q/xb/Of9Z/z7/Rv9c/0L/Lv8o/w7/LP8I//P+Ev87/zr/Ev/r/gj/Lf8u/zD/Av/4/gD/AP8V/yn/Kf8B/wz/Kf86/0n/MP8u/+/+3/4A/w3/HP8U/wT/wf66/tf+2/7f/rz+yP7U/sz+4f7L/q/+4f7D/sf+/v7b/tX+4f6w/nv+jv6u/qT+kf6V/rr+3/6+/q/+4P4S//r+6f7Z/s7+rf6+/rn+r/6w/rj+s/62/r7+vf6g/nH+af6C/qj+qf6F/qn+pP6R/ov+nf6N/oz+pP6p/nf+lP6z/pr+iv6e/nz+gP6I/oz+iv6Q/oT+ev57/qT+pf6x/pP+of6j/r/+v/67/rn+2v72/tH+yP7G/tT+0P7n/hf/A//U/pD+kv64/qn+n/6p/rn+wP62/r7+sP6j/rn+t/68/p7+jv62/sr+rf6L/oP+mv6w/sf+0f7U/r3+tf62/s3+rv6n/rf+z/7Z/qb+f/6w/tH+xv6b/pf+jf6T/rb+0f7O/s/+x/72/gv/Bv/8/hb/Af/j/tf+5/4L/+r+9P7m/vX+Rf8w/97+yf7K/v3+Bf/t/uL+0v6p/q7+m/7F/uL+z/7K/r3+yP7O/sj+5f6r/oj+nv6z/sv+0/6s/sr+4v7l/tj+9/4I/+f+7f74/tj+/P7j/vb+K/8r/y//RP9C/xf/B/8W/yj/Wv9l/13/af9K/yn/+f4Z/z//O/8x//v+8f7f/s7+Bv/u/uX+6P7P/t/+8v73/t/++P4s/0n/R/8V/yj/f/+N/1j/O/9C/2T/d/9x/4X/gP9j/27/i/+T/3v/af9j/0T/UP9b/0n/ff+Y/2z/Uf+B/4v/hf+I/4L/Yv94/3P/lf+n/67/tv+o/8H/yP+u/83/7v+z/7r/0v/e/+P//P8QABAA/f/p/+X/4v/v//b/AgATAA0AAwD3/wQACQAmADkACQASADkAHgDv/97/BQAIAPX/8/8DABcAGwAWAAcAEwDt//H/8P8XADwANAAPAAEADwA7AAgA9v/5/xYAKgAUAAgACwALABAAAQAFACoAEgAAABsAFwBKAEsACgAHACMAHgD3//X/EQATAPL/AwAnACcALwAmAEEANgAOABoASABNADcAMQBUAGkAaQAzAEAARwA/AE4APwA/AE8AJAAnADcAYgBsAEwATABcAHMAcgCAAJoAmQBoAFYAiQCPAIUAsQCdAF8AbQBuAJIAbQBnAKQAlQCbAJMAjACLAHMAkADDAKcAdAByAIQAkQCYAGsAdABkAEYATABtAJIAWQA5AEoAWAA+ADcAYgB6AG0AXgBfAHkAgAB2AHIAgQB0AE4AOABWAF4AXQBwAEoASwBeAFkAagBYAEQAQABIAFQAYwBeAFoAZQByAIIAlACXAHgAcwB7AHAAhABoAD4AYwBqAE4ANwBGAG8AegBlAFIATQBgAHAAkwCRAJ8AfABrAJIAfwCOAKEAtgDBAK8AtwCmAKQAtQC+AMMAzAC7AMkA3AC+ALAA0QDZANQA1gDUAMcA6gDeANsA+gAEARQB6QABAQQB8AAJAQgB/gD3AOEA9AD0ANwA1QDNAOgA1wDQAN0A2ADrAPkA6gDnAOsA4gDbANgA1wDfANQAvQC1ALQAvACpALIAtgCeAJEAewBpAIQArACQAIwAtACxALAAsACpAMIAwACjAKEAtAC0ALAAvQDpAPcA6gD3APIA+wAHAf4A+QAAAfQAAQESARkBEAEEAQQBEAEmATsBOgE+ATMBLQEtAUUBYwFWATsBKwErATQBQgE7ASwBOQEqAfoAAwH5APYA5ADzAP4A6wDgANYA6ADpAPMA4gDRANYA0ADcANYA3wD3AAEB/gD6AOwA2wDpAPwA/QDxANQA3QDuAP8A9wD3AA4BCgEIARoBFgEWARkBIAEVAQUBGgEsAUoBOwEqATUBNgE+AT0BOAFLATIBMgFIAVYBWQFoAY4BbgFtAYgBaAFoAVcBRgFHATwBLQEmAS8BPAEcAfYAEQEiASUBHgEUAQUB5wDPAMMA0gDSAMoAuAC6ANQAvQDIAMMAtwDBAMcA3gDYAMAApQCgAKMAnQCwALcAtgDDAMkA0wDMAMoAwgDWAOUA5QDgAM4A0wDXANUA3gCtAJ8ArgCcAJ0AjgCDAIUAewB9AJ0AsgCKAHYAWwBVAFoATwBVAGcAXgBeAEEAPABNAFoAWAA+ACkAEwAcADUAOAA+AC4ALgA4ACcASgBNAFIAZQBJAEEASAA5ADgAQgBFAD8AMAAgABsAJgAxAD0APAAuACoANQAxADUAOwBPAE0ANwA0AEsAQwA3ADMAQQA1ADAAIQAhACYAMQAsACkAPABBADIANAAoABYA/f8HABcAGQAQAPb/8//8/wMA+f/w/+n/+P/j/8r/y//R/8X/u//M/77/qP+g/6P/tv/C/83/xP+5/6r/qv+o/7D/rv+a/5H/mP97/3b/h/+I/5D/bf9a/1T/T/9V/2P/af9t/2b/XP9c/0//Sv9O/1T/Wf8//y//OP88/0n/Of86/y//K/81/zf/QP9G/zv/S/9I/0//Yv9W/1b/bP9q/2//ef+S/53/n/+T/4T/dv9y/2X/Y/9s/1z/TP8z/zP/R/9C/zf/Qv85/zn/N/8w/0r/Rv9O/1b/PP9E/z//UP9g/1L/P/8q/yT/G/8S/xb/CP/y/t/+7/4G/xP/Ff8R/wf///76/vv+Bf8B/yP/Gf/v/vT+B/8X/xj/Ef8b/x3/DP/8/hf/Mf8Z/xD/Iv84/zj/Lf8p/zP/QP9E/0L/R/9C/0L/Of8q/xz/G/8h/yH/Kf8i/wX/7/7m/vv+//4B/+r+4/7r/uz+3f7p/vX+9f7j/s3+4f7d/s7+0P7S/s/+v/63/tP+7f7p/tb+zf7I/tL+zv7R/tb+vP6y/sj+2v7i/tz+4v7n/gH/A//k/uD+6/7o/u7+4v7N/r7+x/7S/uj+2/66/sj+xP6x/sH+w/7O/sf+sf6y/rb+pP6X/pz+qv61/qr+lf6k/rr+wf65/q7+sP6t/qb+qv6q/rn+t/7G/tn+5f7X/tj+4P7m/uD+4/4A/xL/Cf8R/xT/C/8W/xL/FP8X/x7/F/8U/xr/J/8//zz/Gf8N/wn/FP8g/yP/Hv8M/wP/Cf8W/yf/NP8q/yz/MP80/zj/Ov83/yX/M/9S/07/Vf9G/0H/XP9r/33/iv+U/5P/g/+E/4L/hv+O/5L/kP+n/6P/kv+N/5f/nf+k/6n/pf+w/8X/0f/l/+7/7v/j/9v/4P/h/+7/8v/j/9r/2//e/+j/3P/U/9n/0P/I/7n/r/+4/8T/1P/K/8j/wP+4/8j/yP/E/7j/r/+j/6X/rf+5/73/uP+z/7n/sP+x/6z/rf+//8z/yf/J/83/2f/h/+///v/0/+7/6P/S/9X/4P/r//L/6P8GAAYA6f/n/+T/8//y/8//1P/U/9f/3v/l//z/9P/r/+7/3P/h/+b/8//o/9//1f/K/9D/0P/a/97/wf+l/4z/kv+M/4z/mf+Q/4r/fv92/4r/k/+g/53/ff9v/3P/df+H/4r/jv+H/3v/df96/4T/hf98/3L/bf9t/3j/iv+Q/4b/k/+O/4L/hP9+/4z/kP95/3n/bP9z/27/Wv9v/3D/dv9g/0r/S/9T/1v/Z/93/4D/g/9//4X/lP+n/7P/tv+s/5b/lv+e/6D/uv+7/7z/uf+q/6z/vv/E/8L/zP/5/wAA9f/y/wYABwD2//j////w/9//8P/p/9n/3P/T/8v/yv/M/8b/vv+4/7L/uf++/7z/s//B/9P/z//V/+D/6v/o/9z/6f/m/97/4//1////AQAKAAsAFAAyADcAMAAhABIAGwAiAC8APQBgAGAASABWAF8AcABuAG4AcQBfAGkAXQBrAIUAhwCdAJ4AkQCjAKYAoQCvALIArQClAKgAvgCtAMcA1gDEAMIAsQC6ALgAtQC9AKwAqgCqAKQAvQDCAL0AxADCAM0AwgDFANoA5ADqANsA1ADVANkA7QD5AAcB6gDeAOcA3wDdAOEA8ADwAOIA2wDSAOgA9gDnAMoAwwDLAMsAygDQAMoAuACrALsA2QDkAMwAtQC5AMIAwgDGAM4AwwDDAMoA1QDWANsA7wDnAOAA2wDhAPcAFAH+ANwA3gDaANUA1QDcAN0A1gC6ALsAwAC8ALIArgCwAKwAowCiAJwAoACTAH8AbABoAGgAYQBaAGAAYwBSAE4AVgBLAFsAbABTAGAAaQBwAI0AoQCtAJsAkgCcAJgAoQCaAJIAmgCKAIsAigCPAKYAlgCSAKMAnQCZAHgAdgB9AHsAigB+AHkAgQBzAEwAJQAjADQALQAoAC4ACABNAJUAEQDF/+f/WQAnAJMAuwDM/5z/PwCUAMEAoACHAFMA9P9NAJMAkgCCAH0AVwA3ACQATACDAJsAgQA8ACMAOQBXAIMAkwBKACsAFQBVAIUAeQCAAEIAJgBLAFgAYABZADsANAApAEQAfQBYAFwAcgBMAFcAXwBSAGYAVABcAGAAbgBpAHwAiQB2AFkAYABzAHoAgQCGAH8ApACqAJsAxQCrAJoAoQCcAKwAwAC3ALsArACiALQAtQDHAMAAqwCMAIkAogC4AMUAvACxAKQAkgCaAH4AiwCTAJ4ApACiAGoAZQB5AIEAlwCDAHAAWgA1AEsAWgBWAEoASwBYAHIAcwBZAE8AWgBLAFgASgBuAGoAawCAAHgAfQByAHcAnQCNAJAAgACMAK4AwQC7ALIApgC7ANkAwwDbAOYA4AD0APgABgH5ABYBNAEgAUIBQwFCATkBNwFWATUBNAFNAUcBHQEuAUQBLwEhAS4BPgEzAScBIAEqASoBCwEWASUBNQE7ASIBMgE6AT0BNAFJAUgBYAF8AVwBUQFyAWYBVAFdAV4BYwFFATUBRwE1AToBKwEPASYBNwFJATsBHwEaARgBAQEMAfsA/gDqAOoAFgHiANgA0QDZANQAzQDcAMwAyAC5ALQAtgCpAJgAmgCdAJYAkQB9AHQAaABsAHcAawBnAFwAaQBjAFQAYABeAGUAgwBHAFMAfABVAGIAYQBoAGkAhgB5AGYAcQByAE8AQAAnACQAQQArABoAEQASAD8AIgD0/x0A+v8AAP//6f/s////EwAIAPr//P8MAAgAAwDm/9//AQDW/8P/zP+9/87/vP/W/6D/sv+x/7T/q/+o/7T/mf+e/7L/jP+N/47/ef9+/27/df9W/2b/jP9j/2b/aP9j/3//ZP9R/1r/ef93/1j/M/9R/33/eP+I/5T/df9x/3f/iP9c/1j/fv9d/2j/cP9x/4f/iv9m/17/gv98/3z/c/98/3f/g/9f/2z/nf+K/7j/hP+a/4j/W/99/43/ef+X/5n/nv+n/4r/kf+Q/5H/rv+u/6n/z//D/7X/xf+2/9T/1v/V/+3//f/1/+j/8v8xACcA4v/0////8f8SABcAFQAhACMAMAANAAcAMAAtADgARwAsAEQAGwAhAFYAYwBeAEoAQQBYAE0AYwCIAG0AQgBtAE8AbgB1AI8ArgBrAH4AjACOAIgAlwB6AH4AmwC+AKMAnACYAKQAvQClAIwAjQB8AHcAiwCKAIgAjQB5AH0AeQB0AHMAQQAwAEUAWABzAGYAQwAzAFEAUABOAEAAIwAyADMAKgBJADcAPwA5ACMAGAAWABIAIwBaADEANwAyAPL/OQBAAEoAQgAuAEEAUQA6ACsAUABTAF8AWgCLAGoAOABrAC0AKwA7ADAAGQAeABMA/f8JAAkA5P8HAOf/+v/b//j/7P/U//b/CADr/+7/7P/B/83/2P/P/6T/wf/h/67/z/+o/6v/u/+m/47/nP+s/5j/df9b/27/e/+M/6D/iP+L/4D/h/90/5H/hP9x/2//cP9s/2b/f/9u/13/if93/1r/a/9P/3L/ff9s/3v/Y/9n/2D/X/9c/3b/Xv9n/53/ff+L/37/c/9z/2r/hv9e/07/X/89/2H/Z/9F/2P/TP9K/27/TP9S/2P/Ov9g/zX/ZP9q/2v/gf9g/3b/Tv9k/4b/av+H/4z/ZP97/5v/xP/D/7j/y//X/9H/tP/H/8P/yf/u/9T/u//I/9r/2f/6/9D/9/8KAPv/NwAsACAAAgAUACcAGwAsADUARQAuAEIANQAaAEYAPgAfABwAUgAjACgAMwA8AEAAPABsAF0AUQBOAE8ATABzAHwAWgB4AEoAPQBCADMAPQA2AE4ATQAaACUAFgA0ADUAJQA/ACQAOQA+ADIATAAuACwAMAAtAGQAFwAkAD0ALgA4AD0ALAAWAB4AJQAlAAAAJAAxACkAOQAxADIA9P8EAPf/6/8qAPr///8FAAsADQAMADoA+P8LAC8AFgAcACsAGQAfAC4AGgAnAE4AWwA/AEYAcABSAFEAgABdAGQAnACFAKMAmQCEAIEAgQCMAI4AlQCYAF4AQABWAIsAawBMAGoAUABYAEoAKgA1AFAAOQAKABUAKQAYAFcAWwAZADsAOABAADIAOQAuACAASQAeAEIAWQAZAFIAawA+AG8AfgCOAGwAhABrAFEAjQBwAGsAdQBmAHkASgBQAF4AVABMAFUASgA5AE0ANwBbAD0AJQBhAEwAKgAsAB8A+f8uABQA6P/l/+P/9P/G/+P/8v/y/+b/w//N/wsADQAHAL3/0v/n/+v/4v///xkACQAXABQA5P/3/9T/+v8YABYAFwAmABgACgC9/67/+v/i/wIA1f8GAOn/+f/1/9n/AQD6/9//FgDc/83/0//j/+z/0P+h/wYA1P/6//D/xP8CABEA1//c////4f/t/+T/2P/u//T/9//8/+P/AgC9/+n/CQDf/wkAKAAkACkAFgBCAAIADwA3AP//IQD5/wIAFwDx/8b/4v/1/83/5v/p/wcAy//B/+v/wv/M/8z/tv93/4//5P/D/57/pP95/3n/gf9r/3j/d/+g/4D/fP9z/4n/n/+M/3r/cf93/4X/F/9j/5//S/+F/4H/ov+L/3r/fv9o/3f/ev+a/6D/kf+i/7z/2f+y/7D/x/+W/6T/0v/F/5//kP+l/8v/gv+c/7j/r/+7/7z/wP/V/+v/6v/w//n/5v/p/+D/GQAnABMAMAD2/+3/IAAIACIAOQA0ABcAYABiAD0AWAB+AGUAbwA2AHkAQgA3AGgAXgCZAIsAXgBuAJcAdwBiAI0AXABeAIAAeAB1AJYAoACfAK0AjQBqAHMAWgByAGQAZgBIAJEAhwBSAEkAWgBKAGwAWgBLAHMAhABqAEAAMgAkAFoAYwBgAHIAhQCAAGoAbQBwAHgAfgCSAH8AjgDBAL8AcgCFAJAAlwCCAHEAsQCnAKUAuACGAGcAwACvAH8AgwCLALcAYwBqALUAegCGAIYAfAB0AHQAcwBdAFgAWQAkAPz/LwAuADsAQQBNAEgANwBTABIAJgAqAAAAJAAbAEEAPQA9AGkATwA4AFoANwBOAE4AMABlAHMAaQBgAHEAdwBDAFAAHwAIADMAPwArAFYAgwBlAEoATgBDADYAWgA5ACoAEgARACoAYQAtADUALAAuAB0AOQBWAC0A+/8GABoAPgAhAE4ALgAaAAsAGgAzAFgAQQAqAFcANAA3AFMAIgAfAGQAXwBcAGUAeQCCAHUAWwBsAIwAhQB6AEwAJwBYAFkAXABXAFQAUwBoAFgAUAA6ADwAQABAADQAXQB0AGAAVQBuAE8AZQBFACQAVQAuABMAUwBSAEAAQAApAFsAXABQAFgAGwAYADwAKAA7AHcARABzAJQAWQB1AKsAtQB4AG0AkQDIALwAwgDGAPAAzwDEAM0A3QDqAN4A8QAUAQ4B+QAEAfEA/QADAQoBIAEbARcB7wD8AP8ADQHsAOQACAH7AA0BEAHfAAEBFwHKAMgA5wDXALoA0AD1APoA7ADlAL8A7gABAeAA3QDJAKsA3wDMAMIA3AD7ABkB/AAQAQYB8gDpAPsACAEhATMBBgFHATkBTQEYASQBFwHFAPIA7wDuAN4ArwD/APUAuwC4AO8A/gDyAKwAlQC2ALsA2ACwAJEA1wDAAJkArwCVAE8AVgA/AC4AWwBmAFoASwBoAF4ALwBWAEgAEQBVACUAQgBzADgAZgB3ADgAMgA+ACcAOwBCAEcAXgB0AFEAggBQAFEAQgApAEUARgAIABsACwAeABkA5v/2/wsA0v+3/7j/sP/l/9P/zf/K/63/wf+v/7T/r/+O/6T/j/9l/1r/WP9Y/1H/af9j/1b/WP9n/0P/Rf8Y/0//Qv9X/1f/YP9l/0b/Tf9R/1X/SP9a/23/lf+T/4v/b/9a/0D/Rf9T/0L/Yf9X/3P/Wf95/2z/T/9p/2f/a/82/y3/Gv8w/1L/SP93/3L/Qv9m/2b/R/8x/yv/KP9V/1H/R/9Z/1f/XP83/2j/Y/8h/zD/R/8g/x//Nv82/zr/X/8g/xj/Ov8P/xr/Ev8u/wr/Of8g///+Pv8x/+j++v5B/wv/T/9R/yX/Fv8t/0v/P/8G/0n/H/8z/3j/P/9G/3v/cP8//3L/Zv8b/wf/Mv8T/zj/Tf88/4L/b/9a/1n/Mv8m/y7/Kf9G/0T/Q/9O/yz/DP8X/xz/RP8R/wz/Hv8u/17/S/8e/zv/Ov9Z/3z/bP+W/7f/fv+E/7P/jv+2/6n/sv+X/4n/hP+q/9X/vP/O/7H/wv+2/9P/u/+7//b/w/8EAMf/s//U/5r/5v/z/8b/8//0/8f/sv+Y/5X/2f/S/8f/0//l/97/zP/E/7n/jP+J/37/kP+6/5//if+9/8X/2v/n/8n/1//A/7z/1v8IAAUA7f/8//j/EQAGAPP/LQAMAOP/y//D/zkAEwANAAIA9//0/+H/0v/o/xIADQASACoA6f/z//D/6f/3/97///8IACIAwP/Q/yoA4//O/7b/vv/m/6D/1P+//8L/3/98/7n/sv/L/9z/0P/V/8j/5v/z/7T/yf/B/4z/hP+a/4f/1//T/8P/7//n/+r/DAD0/6//xP/X/w0A/f8VAPz/1v8BAPD/4v/Q/6X/wP+4/3j/kP+5/+T/zv/P/7L/hf9p/4D/V/9w/3D/oP90/5//m/+D/8T/t/+D/2f/of+j/6P/sP+r/8X/tP+5/8T/t//T/9r/zv++/5//pP/F/+P/1f/Y/8b/sv+g/8D/tv+o/+f/s/+I/5j/pP9t/4T/i/+M/3D/c/+x/7n/u/+0/6D/sv+I/5X/pP+E/57/pP+H/6X/nv+p/7z/lf9//4f/W/9K/3T/av8+/27/Rf8t/yz/S/9e/xb/Of9M/23/hv9I/zX/Tv83/zj/T/9v/3z/Zv98/27/a/+D/3b/hf+N/2T/bf96/3b/h/9o/3j/VP9f/0X/Tv9l/3H/dP94/3L/cv9s/3P/gv+M/4b/pv+q/7n/oP99/7v/pv+b/6P/s//F/6//vv/r/87/zP+5/7X/nv+x/8j/s/+7//r/0P+d/6b/mf+5/5j/rf/B/8//2//C/+j/+/8OAAUA7//q/yUADwAFACIAFQAwADgAKQAQABwALwArAB0ANAAPABsAJQAZAB8AKwAjAEoANQBNACsAKQA/AFMAJwAJACgANQBfAEYAGgBSACsAQwBaABMALQA1AAAAMQBRAGUAOwATAF8AfQBTACcA+v8tAEIAJAArAEEAWABYAFoAKgALAPz/5P/L/woASwAyACAAIQAaAPj/HADm/xEAGQD9/wcA2f/n/zgACAAIAO7/DAAuANv/BAD2/83/0v/3//b/4v/s/+r/2P/l/9///P/p/8T/pP/N/8P/vP+s/+L/7v/E/8D/1v+T/5f/u/+u/6n/ov+d/8P/3v/C/93/1v+k/7X/o//E/7z/sf/r/9P/1P/G/7b/of+B/77/xf+C/53/uv+//3b/f/+Z/4z/tP+L/3T/uv+Q/6X/mP+G/5P/m/+V/3z/if9q/33/gP98/2//Zv97/5z/lv91/3H/e/9r/7f/u/92/4n/mP+0/3z/fv+Q/6f/e/+P/3r/Z/+v/6L/lf+V/2X/i//A/9n/2v/S/5T/nv+0/5T/pv+v/87/q/+y/5L/Z/+O/7L/bf+J/3r/g/+2/6//s/+D/4X/tv+c/5j/kf/C/5T/ff+W/5H/7/+7/6X/8P+0/7b/zf+5/+r/9f/u/9H/wP/n/+//2f/J/7j/7P/M/9b/3//m/xMABgDg/97/GwD5/xIA4P/S//L/EwAFAPz/DAAjAD4AQADk////AQD7/+7/9f8SAPn/LQApAP3/MwBFADYAMQADAA8AOQBVAEUAJQAqAEoAaABTAG8ATwA/ADEAEgAdABgASABgADoATwBIADMAHwAbAE0AbwBvAF8APABqAEkAKwBaAHIAaAB/AD8AVgB2AJwAgwAlAGwAdAAyAFQAdwCIAJQAmwBWADIAfwBXAFgAYQBZAGYAcgB3AG0APwCEAFwAcwCFAIIAmQB+AF4AeQBvAGUAkgB4AGMAaABcAIcAhQB0AGUAmABTADMAeAB3AHYAbwBjAKoAogCNAGoAXQBwAFwAaAByAJAAfQB5AIUAfgCAAGwAigBzAHwAoQCXAJcAYwBqAFQAXQB+AEAAYwBsAHAAkwB8ADsAUQBJAHwAdQCLAIIAlwCBAJsAgwB8AEoAXwBDAG0AgQBGAFEAXwBsAEEATQBQAEIAdQCHAFIAEQBDAA4AGwA7ACoA4f/2/yEAIwD+////QQDz/+T//v/9//3/9v8mAEkAEgAAADYAGgAAAA4A2//n/xYAOwCv/+7/NQAKACYA8//T/+P/JgAGAB8ATgDT/6H/0v8gABMAEQDR/7P/pf+l/8T/NQAaAKz/5v8CAN//JAAFAPr/DgBIAEoAMAAcABAA/v9KAG4AKQAeAD4AbQBoADkATgBZAGQAQgAaAEQASwD1/wsABgDB/yMA3v+0/9f/zf+C/6L/xf+r/7b/lv+k/9T/wv/F/4T/uf+2/7X/tv+p/5f/jf+c/6v/pv+x/6j/uf+L/7z/7v/3/+3/uP/G/7D/pf8VABAAJAANALP/pv+7/4n/xf/E/9f/yP+u/4D/iP+8/6r/af+W/4D/Y/+t/9z/gf9x/3//b/9A/37/bP+O//H/of+d/3b/cP+y/5H/e//D/7X/3P/Z//X/pf+a/6X/q//B/6//zf+6/8b/v//r/wUA4v/n/+r/2v8nABwA2f+p////CACx/9//HQA2ADUAHwDr/+H/FADd/5z/2P8vAPj/3/8AAA4AGAAAAPb/LgATALz///+GAF4ASwBBAP3/CgDc/wUA4/8uABwA/v8NAC0AXgBXAC0A2f8aAAoA/v8rAG0AZwBVAEkARABFAIoAaQA4AF0A/P8GAGsAUgAkAGQAewBEAEkAXwBrAHsAQgA6AD8AUQA2AD4AbgBFAFgAKQBUAEcANQAwACkANAAsADQARAAsAL//2P8LAB0ARwAYAD4ATQDx/wsAMAAJADAAKAANABoAHgBDAEIAWAA7AA0AKAAJAOv/GQAHADEAIADf//T/3f8QAAQAEQBhACsADAAMAAkAHAADAMX/2v8EAOX/BQAsAE8A/P/Z/9r/wP/v//P/6/8GAAcA/P80ABwAJwAEAMT/9P8jAC8AHQA9ACAAHgAgABkAMgAiANz/GAARAND/AgBRAAkAIwDf/4T/9f/7/+3/FAAMAN3/2f/C/9//NQAoAMn//v/w/+z/zP/b/yMA9f/6/wAAEgDl/8v/0//6/4n/cf/k/8b/3P/9/77/kv/P/7v/eP/Q/wQAzv/G/woA3v+g/6D/tv/G/8f/xf/m/7//yf/Q/4L/c/+a/7j/oP+q/3//0v+Y/zP/jP+M/9b/mP+L/7T/Yv+C/4j/cP/E/6v/lf+3/7r/ff9b/5j/Xf+F/3n/Z//u/kj/nP9W/2P/ov+W/4n/Rv9+/57/wP+g/8H/hP96/7f/gv+o/6f/bv91/43/n/+c/4//Uv+A/5D/Zv9y/6T/rP+P/7T/q//S/43/W/8b/0n/cP+P/93/ev82/4D/uf+N/5D/ff9b/8b/jv+T/8j/HADP/43/v/+S/5X/nf/e/8X/tv+m/3z/kP+y/9H/vP/S/4H/W/9x/7r/AAACAMb/mv+p/7//vP/X/7n/u//S/9D/zP/p//r/BgD3/+j/NADn/+//ov+j/77/qf+z/8T/WgDJ/93/0/+v/wQAMwAEAMD/4/+h/63/JgD3/0EAUQC+/9b/9f8XADMA0/8kAMf/IwD0/xAADQDV/9n/+f/z/1sAZAAzABAA+f8vAIoASQAiADcAMAATAEkAHwACACIAPQD8/ywA6P8UAOT/7v/7//3/MQA/AOb/CwBFAHAASgD9////CgAsAD0ApABBAEsAHwDw/yEAIQAtABUA0f/M/9r/OAChAEUA+v/l/5z/3v8rADsAVgBYAFYAJgADAAsAsf/Z//b/5v/6//X/PwDp/6b/6P8OAO//7v/V/9b/wv9x/9L/FgC7/6D/rv/T/6T/tP/d/9r/8f/6/7L/AgDu/8f/fv+S/9v/lP+A/5H/nf+7/63/1v9n/2r/fP/n/7P/hf/V/3P/hv+M/0j/sf/S/3r/cP+y/2f/cv9p/0H/m/+F/1X/Ff+R/4j/lP+j/1//oP/y/5P/c/95/3z/b/9U/33/fv81/5v/wP/O/33/of9M/y//Pf9A/yr/Q/+R/5z/if9u/zP/g/+T/1n/q/9J/1f/Tv+s/6n/xf+Q/4H/d/93/yr/YP9k/2X/hP+h/5f/yv+T/3H/Sv9t/5z/4v/L/3r/E/9f/6L/s//H/8z/hv93/1j/WP9//9T/JgCj/wMA6P/b/77/zf/H/+j/z//U/+X/9P/T/9r/s/+L/6n/1v/p/x0AGADU/+b/QgAVAA0ALwDN/xsAIQD8/9X/MwAoAEEAAgDS/zAAbgCHAFsA+P/w/x4AJgCoAOEAyABhAI8AJAA1AHQAawAJAD0AVgBjAK8AcwBPAI0ARABeAHEAcQBsAIsA0wB5AK8ADwHzABgBzwCgAFkAgQC4ANsAogB+AJMAxwDPAMAA8QCpAJkAdwBCANEAzAC8APwAmgDXAJ0AtQANARABggCHALgAXACPAM4ABAH3AOcA8wC3AJ8AvAD5AOwAZgCYAFkAjAD+ADIB4ADQALgAHwChALQAtQC3AJkAsQDkALsAlgCwAPsAfgCsAKUAfwDmAJsAdQCDAI8AkABlAGsAygCWAOUAhgClAF4AkgClAEAAwgDgAOIAwgDmAHEAQACeAIcAswClAMMAiQA3AJEAsQAlAb4AaACpALUACwHJAAgBEgF3AKIAdQBVANsA5ACEAJEAugBDAFoA6wDEAHUAxQCdAIgA1QD5ALoAbgCxANkA7QDEAHkAawDCAFYAcgDsAO8AHwHCAJwAugDlAA8BfgBxAOUApwCVAH0AFAHCANcAAgGtAJoAmQChALoAlQC3AKwArgCgAOQAygCgAP0AjgBYAJ4AuQDeAPUA7ADDAKwA/wApAU0BHAGNAJ4AJgHNAAkByADLALEAewAwAbMA5QDOAK0AigD9ABoB9ADXAMQA3ADJALEArQDQACgB4ADZAJMA2QDnAMEA8ADyAAkBKgEXAfsA9gCfAMsAvwClAPcACQHlAOkALQEIARUB+QCeADIAlwCFAKUAIwGSAHAAxAD3AIUAzADaALEAjQApAeEA+gDtAJ8AogCSAKUAMgAvAKUAnQCbAJkARAA1AIUAtgCaAH0AdABYAEEAQwBvADEAeAC3AFoAFQD0/1oAKwCMAJUAUABPAE8A8P8SACQACgAwAAwAMwARAPz/AQA6AD8AXAAmABMAHQDI/zkATAA5ACwAPQBMABUAhv/o/6//rf83APP/BAD7/+j/0/+d/7f/mf/L/7j/BgB7AHoABgAGAOH/TgBgAP7/EAAWAMz/DwAzAFcA2P91/9b/8P/f/+b//P8zADsALgD2/wYA+P9lAJQAcgAoABgA8f8+ADsAAgD0/8j/ewAgAC0A///P/1cADACB/04ARQA8AIQAFABCAK0A4gB+ADAAlgBnADcA7v9PAKcAXgAlAGIA2AASAPr/LgBfADQASQBYAM7/w/8yAGcAQABaAE0AcQAEAGYAVQAuANf/HACiALUAYgBR/0D/GgAuABMAEACNAEQAQQAUAE4AgABdANj/sf/Y/1YAgwBBANf/0/93AAwA3f8hACkAbv9f/xMAGwAzAFQA1f8RANv/5/8kAO//ov/X/4T/Yv8eAIH/LP8yAIz/Lf+p/3j/p/85AP7/af8KALT/bwCI/7n/AABr/4P/jf+8/8j/zv+R/w8AHwB9/0z/AP85/zr/Yf8I/wb/Vf8M/17/4v/J/87/hP9Z/1v/d/+5/8D/Y/9l/33/7P5x/23/Vf9I/+/+6/4N/zv/8v4y/8j+f/9t/pn+TP8B/4j/VP+Y/uH+ev9Y/3n/8f6g/vr+7v4j/xP/CP8t/9v+F//h/pf+2P7Q/tb+Uv8v//f+3/7J/un+vv4e/6f+n/6R/p/+Mv/5/hr/Bv8W/93+Df+h/5r/hf4y/ur+bf5x/hD/O////qv+0v56/tD+Tf9d/wf/A/8D/yf/Wv8O/0f/cf/o/oH+af9d/9f+Tf/L/nH/d/9x/0P/xv6K/7H/K/+S/2T/Iv+Q/wn/TP/A/43/bf97/1f/WP9J/97/EwB4/5L/iP/9//3/3/8LAGb/Qv/I/9v/+f+z/24AFADC/8L/pv8JADEAtP+c/87/Yv94//7/sf+8/7//j/+b/+D//f/h/1gAMACI/2wARQCTAPIA3QC3ABwBEgHmAG0A0ACHABcAwv+8/+j/IwBRABMAuf8f/87/MQCLAP0AGwAAAA8AyP+nAHwAEwElAaEABQB9APkAsACcAI4AwQAVAEEAZwDLAMYAcAA9AKYAoADAALwAmgBwAMoAIgHpAPYAngDGANEA6QB6AawAbACNAKMA3wAbARABHgHOAHEALAC6ALIABAFiAb0AjwDfABYBFgHuAKYA1gC0ANUAGAEfASgBsQDzACUBbQA+AMUA1P92ALMAKQF0Ae8AowBDAUEBGQGXAHIAOwF8AYwAQwBRAFgBKAHQAKEAhgCMAGUALACIAKcA/wCdAAcAkgBGAagAOwAyADYADwEiAcEABQASAE4ASQBLAFkAQAC6AN0AngCFABsB5gATALz/BgCqAIUAxf/u/xIAGQA5APX/jADSAHMAJQAcACEA5P9aAEIA3f9wAGAA/P/+/93/3f+h/3L/Xv+w/7n/if9f/5P/1//I/wIAVgDs/3j/4v9XAD0AOAA7AJUAoP/y/8z/pv9MAOP/OwDl/yz/tP+2/2T/Xf+6/6v/j/94/x3/Ev+A/z//y/+m/3L/Mf9V/6f/Nv+e/yoAvP91/5b/rf86/6r/4P98/4P/fP+w/7z/DAC5/yb/+P77/in/Uf+J/8//jf9q/3//MP+E/6z/qv/o/6P/N/88/9j+Tf+T/7j/3f8gAO3/Ev+A/w4At//Y/2f/p/8NAOf/vf+F/w//+v6n/6/+pv6x/4v+A/9f/z7/lf/H/5b/lf+S/yD/u/+w/3P/pP+AAAEAxf8mADz/NQA0AID/z//b/1r/SP+K/7H/8P5h/2P/Iv9G/1//yv+X/3//XP8F/xj/m//f/8H/VQDe/23/Uf9T/1n/hP+h/3b/AgBW/0f/q/69/uD9pP4dAOb/t/+O/xn/Rf9c/3H/t//X/yv/W/9d/yX/jP8KAC0A9v9E/6L//v/c/6r/GgBFAOX/ZP/w/xoAdP8Z/4X/8/+x/7D/CQA3ALEAgQC9AFgA4v/Y/+P/ZAAxAB0A3P+8/8r/HQA/AA4Abf/9/ov/BAD0/7T/X/9C/yf/pP/w//b/XgAZAPn/cP/K/0oAn/+5/xwAkwAuAIgACAAVALr/Yf/d//n/7f8SAOD/1/8oAOj/FQAcAIf/5P/k/7z/0f/c/+f/Cf/a/tb+4v4S/6v+Vv9b/xL/qf7Q/hz/Kf/b/hj//f7h/gD/i/8+/6b/jv8Q/o3+Nv8AAPn/yf/o/2AAgP/f/vX/ef83/7H/iv9e/3r/Wf8A/7z+Ff4u/tL+Pf+R/1//b/5b/7T/If+t/yIA0f6c/uH+Xf7k/S/+fv4l/q7+0v5t/lz+DP0o/X39Hf6D/f/9EP8A/7/+Uf4Y/tD9Yf4H/lX+n/5k/lr+pP7O/6D/iP7L/8X9df2r/jf+F/7k/dD8Wf2w/X38UP2W/Qb9WPzQ+yX8IPy9/dz9mf1S/Yb8AP2E/Ab9/fzn/Hr91v0p/jL+Pv2H/Rz+7f34/Rb8BPwr/ZL+5v/A/0b+XP3y/IH9G/66/4r/kv9A/1v/mP8GADcA1f97AJn/Sf+z/0QArwCNATQB7gC3AIIAMgDy/88ANQH0ANUAYwDgAKMA9QDKATwBNQGLACoATgGMATYBkAHGAT8BTAG/AaIB7gCtAXMCEQNIAzEDMgOTAx4E7wO4BOMDywKNAy4D4QM+BNMD8QNyAyID9wKHAr4BcwJABCgGwQVNBXwDBQJnAvYC/gNTBEQEPwUtBUAFowZHB1UGyAUgBLoCPAPjAwsFuQUMBbEEDQSbA2QDrAMSBIkEYARPBOYEbgU5BQ4FFwTLA3oDrgIiApgDfAURBpgFpwRVA0IDdwNIAiwCjAIqA9ID4gNmBKwDSgM0AwoDvQONA2cDSwTCBMsEpgTNBCkEpgL3ASQCfQKrA/wDpwSKAzsDSAJ2AfEBzAFvAbACIwMCA2MD4ANbA1AClgEPAagBDAKNAgYDbQGFAQcCeAECAZQAkP9lAMcBJwI/A/YDZAOVArkBMgGtAKcAZgDM/woAXAGaAv8CvAIyAl4BBAG+AAABOAI5AgMBZAHDAT8C8QEIAUMA6f9l/8X/cP/m/uH/lgBVAGwBHAHWAOT/dP+FAK4AZAHcAcABSwJAATYAAgD0/9v/d/4T/gz/IAAeAZsAZP+9/r39Sf2E/t//GABQAdcBggHeAFMAlQApAdkAfQC4/3EAoADXAHUBsQBIAPT/9f51//b/jgB2ASkB5wCK/zz+1f05//4A7wCbALj/yv70/zMBGAE6ADcAugCqAN8AlP8r//b/qQBIAAQBQQH2/5sAyP9R/z0AFQH3AAAA3v8c/8H+fAA1ASEBQQHQAAj/Vf6m/gj/uf/NAM8AgQBNAJsA4P9z/0H/dwCxAB4AWQD8/mL+EP/n/+z/OP/G/2X+Wv4//1sA4AAkARQC5wD+AGoAMABXAMEAmgGmAd8BRACU/Rr9R/5VAPkBWgEH/+r95f2S/R//kwCTAYMBkP9S/Xb8ov0PAD0CiQHx/vf8t/yH/gwAWgIOAkQAEf8E/m/++f67/1QALAD3/7f+ZP2u/J391f7J/4UARwBl/2z+Ev41/kP+1f9WAMb/W/8a/1b+CP8F/13////3/03/KP9+/zH/Nf9q/5T+0/4c/5r9ZP3B/a798v2t/mT/IP+D/cD9H/7k/Rv/GwBsAEgA+/9E/yf/zv73/SL+HP5r/lT/oP9p/v399f5k/6YAPwHv/4z+Sf5u/rX+yf9CAOn/k/+W/u/8lfxE/kL+UP4i/pT9+v2C/pH9QP5P/5P/Lv/W/hL+G/95/xv/vv5I/jn/sv+SAH7/1v6m/vr9cP7//uP/gP/N/qL+5v5j/lD/sf+k/6D/1P4n/t39QP/D/4//V/9A/kz9Y/zd/Kf9mv++//z+o/5s/cP8xP3u/Uf+YP4M/yH/d//b/qT+Q/5C/ov/Av9N/gb+xP2q/vL/tv8M/+D+kv/v/uP+Af+R/7kAXADU//L/mv7L/Xb9Tf0v/kL+Q/7h/dn9UP1E/P38s/14/iv/Gv4F/TL9Vf69/rb+P//A/7v/+P5e/pP9g/7m/+3+Lf4d/o/+Nf/f/xQAb/9d/rv9vPx6/Cf9mv3//HH9c/4L/gL//v7U/n3/Cf+f/l//VgBq/+v+Jf/T/+f/HwBSAKP/ov5u/v7+ov/v/+b/1P/Q/7r+FP6B/n7/yf6o/rr++f0l/V79VP26/if/kv44/l79dP4c/3MAFQGKADIADv+c/iD/CQAYAIMBVwG2/zr/l/8n/rn+iv8hAIz/Zf5g/eb9fP6S/2n/Gf+C/0z/ff4H/nH/tv+dAKIAo//L/i/+ff6T/1oAkQEDARsASf8p/mf+kf/q/+3+A/+B/nH+u/66/bf+HAAg/2f/2P8C/wD/X//f/gT/c//F/qT+u//h/3cAjQFhAK7/Yf8iAMP/ov4//eb98P4yAKsBjQCG/k/9S/2c/h//4//eAP//W/6E/dj+0QB4AjoCzwDY/63/bv/4/sEAjAFnAvcAdv95/jr+Zv8XATkCWAKJAGP/DfyD+y/8tP48AMX/6v7O/Wr7dvth/Wz/ggCOADsA8P7Y/on+RgDTARwBmP9K/ur9dv/3/wQBrQH/AFv/eP5D/tP/wAGjAi4Cyf5w/Oj9CAERAVgAVgBRAM7+ovyW/BoAtQObAhT/2fws/MD+Ov/j/bj/kgG6AAf+R/zO/dIArQILArT/Vv5t/lv/8v8/AF0BlgAL/0L97/yp/VX/kgGyAFz/Pf7o/Sr/F/9Z/6YA3QGOAR8Apv9d/sz+HQBpAAMA0/+5/3v/gf4H/wABAwKc/8j9O/6f/jX/mQCnAKsAbABRAJX/Yv/y/+YApwFqAHf+iv6//VL+mv8VAEj/qv6b/lD+4f7p/0EBiwBK/p381fzz/i0BtAFoAbD/Hv9W/pn+eQDk/5z+a/57/3AAOwGQAWwBXQLS/6T+sP7a/oYA8gAlAOL9Svyi/WL/ZQDH/9b/cv+E/0X/Lf6E/7AA5//0/rn9hf6wADYBJgCX/1r/F/+v//n/d/8KAJMBDAFKAHIA2ADcAdQAJP8r/n3/n/9W/6YAov80/ob9pP47ALMBpAL9/9r+Qv79/Xr//wBfAfEAR/+hANQBLAF/ACb/Rf/1/1AAQf9y/y0ARAE0AgcA6P08/6cAegG8/wX/2P51/67/2//l/v3+Wf9YAD0AAv8L/0IA9v+d/mX9vP7uAIwBMgDxAMoBpwBa//3+VwCHArcDUgLT/Sn8zv3IAPkC1gE6AIn+nv2A/gcAegH0AS8Bzv+6/RH/mQH6AjADhwHZ/uv+Bf/4AB0CjAIgAncAcv8OAJ8A8QEcA2IB3/43/Zn9gQGlBF4D9gCm/ur+fQDTACUBqgI5AlAAFP3W+xb/5wKgBK0BXP5m/Q//rAGmAq0APf+y/qP/awAuAl4CjQLRAqoCDAFNAHz/dwBLAugBLgFp/z7/5wCdALH/s//vAbMDkgBf/ur+MgJtA+QAX/5GADcCnwEL/2/+9/+LAgcCc/9a/vj+xv9EAU0Akv8TATMCWgLcAWcASgHjAtkCMgKnAe7/awF0A2kCfAHHADwB+gKbAaH/xgDMAqwCJgBS/lv/AgNYAz3/G/3LAMgD+gLJ/9f9XP8zAR4Bov9v/2QAQgB7/wIAfwHvAiQDNgGE/40AKQMFAiwAXf+5/8ABjwDK/5wAEwDlADIB4gGCAfX/8P94AAgDZAMVACv+I/7EAOIBlv9E/4cABQEdAQr/DwCNAcgBZ/9W/i4AowH2ASQA9/6lAAcCzAIrAPL9zf5SAMYBXAC7/T/9Sf7B//L/z//H/cz8iv2IAOwBUAA7/VP8rgCyAloBAP9Z/9cBJQOwANL+qP8UAfMAMwA2/rb/egCU/oP9K/9CAEwA1/7d/Un+qf/l/67/gP9l/9r+IP9c/kb+kv9PASACQQBL/z0APgG0AID/BAAWAOL/bv+W/xwCIQKa/8v+RgGkBLIELwEg/1UArgNuBb8BCv6FAMYCLAOXAbL+rP5wAeMBXf++/Jr9wgAbAhsBeP9Y/gn+Qv9mAY4CTgGy/nT9cQACBFkC8P9k/fv+wgLkAjkAkf5u/yMCyQI8AaH/nP9kAPT/Lv/Z/34A/gCz/2f+Jf5gAMMBnwBY/2T+LP+6ANj/XP3j/Pb9lP8TAD//Hf5o/Rf+pv88AOH+0v2X/k0AwwB1/1f+kv5E/2H/Qv9Y/kf+4P7J/kj+oP2C/sX9gv4F/77/1wB2/1H+Ef7+/ogAmABB/h7+P//3AAUAK/5q/gQALgFqAGf/8/4AADECEAJ7AfQAmwDAAd4CAAO4ArkB+QKKBLYDuwFGAOcB/wKsAY8CtgIlAhIC0AKaA68C9AFXAkUDmANOAj4B1wJ/BWIDRwLmAg0DAwTSAmMClgMtAxoCbwHdARYCiwG7AL3/4/+PAf4BOACoADwAuP/gAKIAgf8iAIT/cf8x/8/+Vv49//H+1P3z/F/8oPzM/Pv7C/ux+iH7+PlZ+S351/iK+cj5HfrJ+ZL56vjt+Nv4xPmW+kv7avqa+F/5VPtG/EL7rvkl+hz7cPwh/eD8kvtz/Bf9T/4k/1f+Df7+/kT/jwFIA/sCuAFCAQIDgwYNBzgG0QQABbQGZQgoCVAIpgj1CKYKXQz/C/IL4wtmDNsNLw7NDP8Mkw3zDeMOiw3+DGsOYg1KDFoNUg1kDYkM4wodC6EMywtPCZkHuAdkB4gGsAXpA0AD2gFSADP/J/6m/MP6w/i19hr11/PR8hvynvD77krtxOx17PXrBeuA6Y3pV+kO6QHpEem26PzopOls6ZjqpezK7Mfs3u4Z8MPwe/KM8+rz+fSD9qT4kvlz+Sf6D/s5/aX+6/+N/0L/lgBFAQ8ERgXFBakFtAXOBlcIZApQCusIqgkLC7QN1Q1uDcwNKRAGEbkQqhFWE7UVBBcFF1EXiRooHj4fZx4YIJwhJh/RGF4V7xd/HSweBBftESMV4hd+FAMK8wEMAogE6AFr/Gz4Xfjb9j/yE+6C657q+eim5U7lV+aj59rnOeZj5N/jkuQp5eDl3uaJ6HvpNez17uHv6/Bg8XLyb/QY9Xr2hfko/HT8O/vd+af7RP4I/qz86vu++7/7Q/sU+xH7t/kc9jj0/vWc92r3BPYp9HPzZfMY9LP0YfU39gn2dPas9zX6K/wC/Wj9Rv6IANEDxwamCOIKIQypDmQQZBJEFQIXNxlNG+Yclh+II4smwSfAKCEoVSZzIlAfECHHJf0nQCTJHfYbIh6DHOEVDg2qB3IGTgVnA+3/7/sP+OfzOO9Y7FbruOn45Srjy+Of5P3k6+Qb4+ngF99O3gPhsuQx53Lnk+hH6nnssu1A7u/vIfIC9MT1JPiu+oz8af3D/ef8XvyC/N/8Fv4n/8r+lf0S/E77Wfsy+5X5f/eo9oL25/b/98D3j/VE9I7zt/KU8231dvaw9xv4vfiJ+r78Zf7D/7EAkgFcBC8IUAu9DYgPyhE/E1kVPxhUHLgfGyK8I3Am3CkoLh4u5ygEImEgjiVFLVIu1ieTILYfQSOMI/UbihC/CVwJDAnQBogDFACQ+9r0oe5c67vr/upY52zj5uA74cPiSuQ04oTertqV2Dvbud8N5ADmFuaB5u/mi+em6pHuO/D3793u4vHZ94j8D/0H+w76N/nk+PH5xfz9/00AVvyZ+O74aPts/AL7h/cT9TT2tPbN93f5kfgV9Wfx+PCX9IP3Kfjc99348vnG+5H9Mf+ZAQoDnwNyBZEJGw7OEL0TpRVbF/oZOxx1H2YkbClVLJQuKyyUJWAgqyBIKfgydzEvJsseSCMUK8wmKRfVC6oJjgjQBZkDLQOrAa/5x+535iDk5eQV5FfijuE04cffK9/f31XgFd+C3SLeDuG65T/qCezb7I3s1+s56zrsuPC+9fb2OvYQ9935jfs5++n5yvkx+gn6APmk+bf78/x6+V31T/N89bD37PYj9U/zp/Lm8l/0gfWV9WD1DPWD9cj3BfuE/C/7O/uT/TMBBQVgBy4JCgomDL8OrhGpFiAbvR6gITckJimALn0yszWqOUk+Yz9SNNwgUBVKHNAy3T9+NP8b/glBB0sK+wcr/1v6nPiE9JXqP+OL5ArmleIn2QjSFdf84cPqoOt65YXdvNoS4ZfsIPkS/hj70fdo95T7VQBZBh8MZg76DKIKBQyzEI8S9gxIBVMAJACDAgsCwv7n+Kzw++cz4fLgMORe5ZbjMd5u2L7VT9R21bbapN9k41vl++W45r7oh+oF7sj16PyNAlAGOwo9DRoPrxBgElQYJh+FJNMnjCgvJ7Ulcie1KsQvzjOsNQI23DUxN342oDeQOZ08ezfdHR0GUQF2EyUvci7gEHfwBuX26+bz4PFH6mToyuXs3h/Th9EW2pngB+Ed3OTdaOp49Un3fvGj60zs6PdcBmkSmRSADjYGwAKLBuULbRDhFjkZXhb5DAgCo/53AVIDzP9y++j4pfeK7wXj0Ns23QHivt+82r7YhNq72WvSFMwEz2vYid9x43HkYeXZ5fDjkeWL7V34sf9xAKAA1QFrBpoJHAv/Dk8VSRrGHIAe6R46HogclBx/IjUqri4/LEonZyZ6KUssvi45MUMzwDVkNUY02iiNEGoCMAS4Gww0tyj7CGHtTOgm8rz4sfR78dfzz/AF49PTm9Pd3NXmsemy5broxO8t8cjte+dG6A/yWP8SCoYNkQhMAJj6R/z9AqEJvxC+FS4VwA7jADT44flUAscIGQj3/yv5XPOj66zmp+QE6Ozp1+dX44neFtxy1gbR89Cv2ILiROkd5jTf+tuP3efiA+vl86b6dPzO+Un4KPrI/+EGPw28ElIWZBjoGNsX0xhkG1gggSY4KoIsqisZKlQpXCuVL8EzuDbPOMY5mTd9NzUwKRxvDcEJcBluMrswTxQc9aHodvB6+wr8G/Sh8FTvi+Xk1TjPvtRe4rHr5Ohi5Uzmuedz53vlOOf28GT9NgYbCEYDtvyW+uX+AwZ9CyQRGBUDFroSRwed/en9fQYoEBQSnApjAJf1veyT6obt2PMY9IXrMeJ13Ijb6Nik1GrVG9t84vjjttxs1XnUyNhS4Rzqy+/f8cXvye0C7870/vxgA+YIBQ2MDxYR9xD/EfcVrhuYIWQmoiizJ9oliSQyKBkuhTLIMx80SDXENIMzuTO2Ne40XCTLEtEN7RmwLjQt4xAz9Sbv9vnxAlb+DfFJ60XsluVq2OnS39ds4xzq/eQ031vfF+Jj5L/lMunk8Ln5X/41/Xn6f/gd+3QBYAdmCaILmQ7VELcO/gXz/noC+AzVFAwSowYa+kj0kfNO9bj3+fYc8eXoNuFT3urdQtz42EjYw9vx3nfdb9fS0anTOdtL45ToiulN6ADo5+kW7pz0A/yGAUgFvgdlCQ0MeQ6SEsIX6h08IpgkviQtJEMl+ChxLscxqjNpNFkzDzRiNOk1JDgYOcku2xrfEMcUsSQELzQfvQOJ9Ln3tQBQAIL0GOu565PrreC904DRTdmc49rkbt4o27/cot9h4Q3jZOjJ8FP3x/hT99D2ivgh/QwDuwbnCfkNORCIEOgLLgW4BBcMdBRDFuAPdQUe/kb7SfrM+iz8h/qu8gPpz+Ff4HDh395p2wTc8N6e3ljYn9GF0RfYqN+t4h3iwuFZ4uPjXudv7ajzwfiY+5n+HQJpBcYHsgumEaoXvhvaHPEdux+ZIjwlHCdvK7ovqjFoMfMvVzFzNCg2HDfFNtI0tip6GwMVOxpdJmUpBBn5AyX6pv12Aa38yfNf787vOewB4C3WKddT33vlleMn3jPcK94d4KTgMuM66XvwhPXm9Vr0XPRS99f84wJuBhMJyAu7De0MswixBWEIRA81FEYSgAvAA2P+wf0o/qX+ev2M97jwYOtx53PlKePa4IPgFuGr39PbuNdL1lPXnNtr4MXiVuIY4VvhVeWD6v/u9vII90/7aP6QAGgCAwaYDMYSfRdKGnwbCxz4HTci9ScnLPAt7i2+LjIymDS+Nas3IzlwOlc3MCv4HAUZqyChK/wq0hrjBq79+QDrA87/Qfdk8f7vZevO36bVgdXL3STkB+Jm2wTXmdid29fcPd/e5FbsIPF88PLt0e4V8/P5KwALBBIHeAl/CosJkgcOB3MLExP9FU8TCA49CL8GgAYpBUAEBQNNABv6cvJz66npLeoK6e/lG+Nx4FPdGdkH1q3WxdpO3ojeGdwu2k7btt6p4pbnRuxh72vx7fKr9YL6AQC0BbsKvw4IEq0T9RX3GN4c+iBiIwYnVypEKm8qnCv6LX8yRzMcMFcyuDWENicuMh89FxodSinnKWgbrQqzAhwGOAgWAVn4RvVd9XfxuubQ3FTbnuBu5JTi7N2/2+Xb9dzp3Nje/eQN64nuSO5P7OntavPr+Aj9M/+GAUwFcAhOCBQG/QVUCnAQ4hNiEQwM+gesBUAF4gZ2CJEIGQLa9+Hv9e4w8ojyJO5p6JHkpOIb35HbW9sb3vngH+Cn3KXaBtzi3iniNuZP6g3t/O357s7y1vgB/sUBiwXeCs4PdhI8FPIWThzJIH0keigHK44ttS0uLnYxcDZIOQk6gjlDOAcyCChiIMAhEy3HMaomjxQ+B+UGNwsNCZUAdvrq+Mn05ekW3p/ZV99d5qXlId8j2ZXWydd22RbcP+Fx5x/rsOrO6EDoBOxE83z6SP/TAZUD/wQYBukF1wZsCwoSgBajFdkQcQwmC0QLWwuxDfcOOgzvBIL6NvRe9c/3+fbC8nDtqeiw48feRtyh3j3jKOM937/a7dcu2T7cIeBu5QjpHukZ6DHpOe3V88L5Jf50AkIGRAi8CbwMkxLTGAke2iAnIbAiZiSkJporFy+NMsIzozLdMuwzzDS1MEEqMSUpJQQpxSlsIqsWKw7dCw0NCgsRBJr8SPfN8/DtVOco4w7jQeVm5KXg0ttl2Vnai9yL373ituWL5nXmvubf6JbtTPOs9yv6w/tY/e7/swKyBAkHLArKDa0Pig+bDXMM2Ay7DYwN7Qq9COcGRgU3Aqz8R/e89An1zfRc8bvriuWF4n3hY+H24S/ht98N3pncgdyP3drfQeMC5yXqZesf7Mztx/LT+b//WQONBYEHKAu7D28UdRkZHUIgXyMGJuQosys8Lycz1zZkN5M0CzS+Nbw2fjTiLOEmlif3KmspAiDWFKIOLg6iDecGRf6M+Lj1+fFc6/HjYOGU4nfj/eCQ3JjZsdk7253cpd7I4UzkFeaq5mrnp+r37xf1pfjX+if9HwDCAyIG7gfLCs0OEhIbExoSURFTEgoUXBODEAsOyAy8DJQJywNt/xP9Rvwq+tT1AfFn7XfqROdH5f7kHOQ84m/fgN0p3v3fnuAb4cvjhOZ96KPpfuv770P11fng/AIAzAN+BrgJhA7CE+sYjRu6HJAf8iPXJy0qQyyjL7czJTWhM2Yy+zKQNSs1xS/LKFklnCZHKHgkBhu+EeIN7A3RCxEFyvyG9+P1jvLW67XlKOMp5Kfkr+G93e/bfdzC3bbeEuC+4rrlBedg50rpFe0x8dn0N/e++Ur93gApA3EE9gVGCXQNEBCVEJ4PcA+AENcQkw+rDasMnguNCeMFJQJEADD+c/oj99T0r/Lm7jDqiebm5WbmJOVP4iXg2N8B4OjfmOC94jfl7eaz59npLe3I8M3z/fa/+0EA6ALUBIkH5AweE3EWbRgAHAYf8SLVJR0oDixuMM0yAzPnMNkwbjHiMJ8tUSkDJwIoTShQIyEa3hJNEMYQXA5NBjH9N/jp9S3yS+yN5mrkvOR/43/f09sZ2pPaUdsw3LTdQeBa4pXiTuIb5FLoqe1v8WXzXvVQ+Cn8Qf83AWQD3AYzCwUOxA7CDZMNWg+iERsSPBCJDIoL+QtmC34IUgIq/Qv8gPwH+zD2sfDB7KLqAumq5kTlRORZ4qPghd9t38TfMOAx4XvjtuaL6LboCOps7dTy4fcX+7z9BgGaBIQHKAw3EaQV3Rh7G+kdjiE9JYgnuCoDL2cxZzIqMhoxdy/XLcEqcipnLGcrpCZlH7YZdReNFKMQWAuBBXf/Mfu/96H0R/Cb6E/iueIH5cPkZ9992WHYutyl3yrf0t6F4HHjYuXQ5kzo7OsM777xTfXZ+e79N/9o/4cBuwVYCs0MAg5EDnsOhQ44D4IPbQ+TDkQMtwoICngHPQNC/+v8y/xY+yr23vCg7crrfuri55bk/+Ll4lHiDOFv4NLfNeBK4evi/eVK6ZjqJ+th7vnyufey+6X9LwEhBsQKHw4fEXYUtBhUHSIh7SPRJnkpHiyWLwcyCjNiMlcyBzO2Mk0v1So5KP0pwCplJmkdCRb2EvcRVA6jB/MAsPy++EPzeu1m6ZzmjeQ+4hTgft5v3cPbcdr12ujcSt884VXiReP75LXnZOvx7vvxy/Qx+OP78f4EAQ0DBgZ5CVAMXw5oD3gQ9RCPEBwQOBCyDzEPHA2vCvIIpgbbAjv+9fmA+IL3wfRF8InrZugj59PlT+ST4iHh6N8t3yLfR+Aj4bvhW+N45lHqB+2c7hTxEPUe+ln+DgLABccJzg0oEeUU3BipHJsgeCOSJbMonytTLjsxQjPOMwo0KTRVM5AwoCuHJ4wmNynPKCUi5RgpEaQOCQ4ECiADHP35+Of0Xe/P6fPlv+Po4bPfC97F3eTcxNpC2dzZcN3E4IjiuOPE5GzmS+mP7D3wc/OM9uX5Y/2mAJYCGQRZBhsJAQzYDXYOIQ+6D9QOTAxYC04McQxSCigFJgCx/m7+Lvy99x3z9+827hPsFOlz5rfkoOL84HngVOAG4IffJt9L4OziD+Vv5jroA+sS7xnzfvb4+ST9ogD/BLUJbw4UEnAUARcPG1cf0yIIJh8oYyrwLFUu9C/jMWwzMjPAMeIumClmJqwlQCYfJ8Yi2BnkErQPjw3zCSYEof0Z+vr2mvHy68fn0+TZ4jzhP9+x3hDe7dtJ2r3a29yQ3wjia+P85MrmyehW6+nuLPJe9d34UvwK/ywBtQJ6BEgHDQpBDO4NuA7ADocNGwwHDDIMygkJB9AEjwNuAg/+TfhX9XDzlfEB79DrTel/53rkBeID4qbitOFr4FPgS+E54x7kouTu5+Tr+u7P8RL1Evll/Of++AFHB1cMRw+fEXUU+hdwGxMdPh/XImklsiY7KKApGiw1LT4suyxtLo8vuS1eJvwf2B1FH7EiKyE8GuwRsgwDCp4HvANJ/7n6Pvea8zjunupo6A3l++LD4QniZOOO4sXfC96c38viWuWh55vpg+sx7Sjugu/98mz23fiU+57+bQH3AmcD0AOCBNgFogduCfoJoQkQB6IDEgGl/YD8hv0Y/nn8hPd88QHuPO0Q7KfqRurN6XboGua04/viS+Ta5NvlEemm683sA+1Q7QLwXPTt93X7Nv+qAqgE/AWLB3AKYg0LEP0TyxbtGLEZLxpWG6McLR/LIMohliRVJagjHyQhJZollSZlIeUZ2BfxGNscmR11FTAOgAsJCpYIYwUtAeT9TPsh99jyb/Hk7yfsj+kQ6AXpaOuq6XTm6+V35oLo5epQ7KDtAu+Z76PwEvNY9Vv2HfeA+eL7Mv62//X/OAD1/yoArgFNA5IEawPX/xX+Pv7u/h39kPif9vX2XPf89Wbyj+8V7lrsduwk7bjtg+016y/pJet/7bHtx+zk7Z7w3/IN9LH0BPZs9zD5T/z+/eYA3gRgA54CUgaxCTgL/gqzC34PZw84EMATmRAlEVAWshMmFE8ZahaHFPwXNhYLF+Ac9RrFGN4bOxpSFZETKRICEzAWEhS7D+4OlQ1lC/kGLQSKBD8E9AF2/xf9tPri+Lz1cPNd9Mb1XfS48VLxu/H68IXwePDl8RrzUfM181XzW/QG9PTyu/TI9gH43vgg+dj5QfoK+Qb4mvln+pb5+PgE+df5A/oP9971mfaF9ov1GfXD9GT0rPP/8TnyEfQp88rz1vVz8tDz5Pcp9Or0ZPY59ur57vgn+Gf9z/lO/EX/MfxpAaEDyACQAwsF/wT4BuwG2AeBChwJzQijDLUMDwowD68MHAquE2oOCwouD1APVg22DtQQ0QzZDwIS8gs2ELEQXw7SEckOug2uD7UN7ArQDPgM3wp1C1oKIQf1CPQGIQR5BCkD8wK4AT//7f/w/mn9E/zt+uj6M/qz+f34ovnf9+32+feJ9dT1Zvmy9o30tvht92j2bvgE9oD3HfkJ95L4L/n598P3O/ek9sH3evgf9871jfhU98z2k/ck9sr10ffK9ov49/aw98n51fYv9vf7//fD9iP88/rH+Mn97Pkp+978eP6t/c78UP+eAaD/0P6dAdkAtgJNBMsCpgKqCJMDQgTmBUkHXQW3CBkFhgW/CggGvwQ3CdcEJQdMCyYBEweiCs8EkQbRBUwHLgZuBhwIpAUmBRkH6wU/Bs0Dogd+BlMCjQW7BnADKQS1BMUEogO5AVUFBwM7BdACBgJEBssCzgAhA+wCggMk/80CnwF+AdD+yv8QA1X9rwD3Ad/8LgAxAk76Of20AWz9jvoK/vj/tPtp+5j93P1d+436jf/i/FT3O/9y/xL4OPnXAJr9PPTy/MYCx/md+SQA1fpO+tv/IP7+9YwBqwI09h/9xAXs+BD6eQVs/S378QEEAOb9Xv6wAfb+MgD+ATv/YQHXAj4B9gDR/zMG5v6GAb0ETQHOACQEcwJIAygALgZoA4cAawWIBfT+hgMFBnX9iQP+BFAAfgEUATgCrQEOAOACNwFnAhMAGgMEACUAuQMgAev+uwL0/8n/tQBHAkn9LQKYAo//vQFL/0EBF/89AJMEwfzRAIgGqfpB/zEI0fmp/tUF5/zo/XsG7vwI/zYDb/vZ/EADLf3x/7kFafu/+8wEmP/H/J4EbwBk+5cDQQCT+3UGPv+Q9aIIjwCB91oJ1v81+4UD9v9G+78BhQBoAA379AQcAOT7PAGKAkz5NgNsBHf8bAGUApj5Of+vAXH+BAE7/3z+Kf/3ADT9igAjAT7/G/4WAvIAkQCBAgMAff6rAD4AB/9JAO//U/8qAzUBRPrzBrb/R/m6CcwA1PmNCrr8n/m4BbP/pfwXAx3+JP/qBVn8Jvt6CDf6Gv13CBH9agDLATH+9QIy/SgAXwN+/+X9+QKRA0L+wv80BOv/Fv/ABCv/8v7CAbf9lvyhCvj5MvqFDPD4APzvC/r1a//JCXL8zP+gAvL/agG5+mUC9gY/+qcCKAjn9pT/lAox+UX7sQhSAjb6ywTFAqf7fgN0Awv+JgQZAGgGv/1u+sUKFvq0+WQM2P1b+2AJlAAh+pUFfQTT+Br/Mgke/vb8xwaBAxT5PwMvAiP8nAbQA/7/kv94A1YBk/g0A1sH8/gcCJwFFvn/Ap4GyvcV/9oGWP4OA/z+i/15Bbr86/pBCb75if6hCmH7/fsZC1398PjaAogFef/dAHUCi/8AAjb9PAQO/tD8PQyP+1P9Twm9+Rj9Zf4e/vIGEALc+dz97QP2/l/8+f3q/kAFj//b/UAEiP10/90DwvrZ/7YHxgCL98wDPga7+0r8zv1MAGr/mwBSAHr7BgLRA0j6oABe/lcDeQBb+akAxwV7/IAB//3IAK8BT/20/Xz+mQGTAnL++vxw/0j+IAGG+GT88gYXAVb7//6LAKn+VQDB/Q//uQRs//v/AwPF+R0EIwFP+/n7zQRmA7/6NP3//7UCMv0g+0ICe/+4AAYAqv0i/r0DQf8n/Fv9pf0jA6v+n/uY/zICjAE0+9j89wNg/9QBNwBg/lP/jgLHABf3Ev/RCbb33flDD8L4/ffeBin7mPjCC5cAE/LgBrgCnPWIBSH/JPiIBugBg/oPAOQCTABnAAj6WATYA8b1ZwNxCB/wWgZwCGTwngBaCcH79vl+ASIDefofBLgBvPXRA6oFuPvk/IIIE/74+QgGNv79+0sHWfwl+9sDPAXM+xX90/98Aer9+P3ABML7ev47Bv73jP/PCan7Ef3XBVL3cQEQC9v0D/tTCu78bvt3A/r/WPxyBAf+6v8GBbkAxf/V/Bz/IQX2AjT0ZANPDKT2DAHtCgD1vQLKDLX2iP0wDGz9rPrGBcH+Qv5+BdX5hfxlCPMBsvlGBgD+K/uDBZACXfhIAiADFP+i/akBIwYi/Xj5zAdZ/ikAjgIQ/pIDqAKpAYn98/rGA6oC5f/tAVD9awSnAJj/4/3s/vz/mQRw/d7/ngnM/lf8Wwbc+jj/3Qbj+nT8hwlG/xL47QSX/173lQLmApj+OARLAeT6GARI/NsAiwRC+iECUAaW/hkClQChAi790v2fBRoDev9u/q39v//PAyv8YPpxAeACd/yY/lMDu/yGBR79IfeLBvoDJ/0TApf8sQAAAiYBnflf/7wHAP/5+bv+EgIGBf75ZPrvAskCLP8jAsv7ZvhOCNcFSu8nBHYKQPkD/1wCe/xkBJv9pP6w+8/9jQdvALH0RAC6Cbn7V/thApb/z/1qAqj5b/zvAxYBhPgd/5sEAv8B+u4Btf0g/AMDsQTt9z/9RwcF+kL4nwdq+3EAtAAj+RYFkAIP+C3+VP9DA5YAVP0Y+RYFsgPL+Yb+UP8q+YsEAQn49Cr7mQlO/Aj6TAIgAtT9av3l/Ez+JAQP+pj8UP8V/dYBM/lCAH3+S/ukBPwBUfY2+tQGsgBG9qUCBwWP9+4Aav8S/sP+W/vt/msHzfva/SoC+v/x+8MD9/6y9pMACQmO+wH0ogRJABT65v51/gYCTPqF/GEIYPYx+fwMOABI9YkDnwEy+AUEugJ/+nMB+wCY+0MBNgFx+gkCZgay9cT9NwiH94P7/wLIA7D9uviIAkIHgvl1/LgFmwA495ABkwjG+FABIwkx9xn8SQRzAAz9df8d/o7+EAJr/Kz9RQKP/+0APgBPAtb8sf59A5f+ugBS/xYDoAGG9qkAewZa/XP+kgL+/+v8rgKHAK34QQIQDVX7YPYgBLkJsPn5/P0FXvvJ+5MJbQcN8vL5/AyL/T32xwXaC0/3i/kmC3b+fviFBxz9ivxmB7j8QPpoCbH3H/mYDo37/fGVCPQKDfl8+kwCcgMA/hL9UAEHA0sC0v2f/RoHEP3V+usGwwH/9/j+oQqu/571bwNTCl/1Nfv1CRcD/PoB+7oGBQLR/O39RP9jBW79of1mCAL4QPgfDTcChu73AoYKa/o4+c7/ZgfT/1b5SgAsAEP/NQLNAt37fvzpB9wCzv6T/bUA/gGEAb39jQGTBPr+l/yQ/+YAK//2/ur/9P6yARcEkP3H+Z/8YAdzBav4egF1A1z90P2iAAL+yvzoAtkDmv6J/WcCFf9x+24DngIB/Mn8bwOZAbr8pP5P/9YBcv9K//IBfPrXAIcEU/pO/UL/2/1YBd8Cbfuk/dQFUwCU9DYDewpX+hr/pwD6/s7/CP/4/r0DGQHj+er/pAWB+w/8mwEkArb7yPcABtAG6vcz+cwHEwKU+dUCWwVt+Lb5wgQmBYr5nPviByEB6/vnA/j95vjLANkBBAJ4/t8BRAMo/Zn71P2x/YH/eQVgB8X6wv3IA+T9Mvv//cgE7gTb/MsBngP//SUBH/22+1EBMAUNBb4AhP1yAq8DLADf/Ln+1QAoBb0DDf/9AYL/BP4TAcf/1P6uAR0EdwKS/4ACJQF8/c//+QMYA2QCof9YAuIDev+YAdICQwJfAVUAtQEHAJ3+1AG9AlAAMf/sAyMC0v2sATUEYAJlAOX/awJjAiIATQAiAKcALgBmAVkAYf8AAZwAewDsAJMASQBR/0wAUwALAOoAMgAAAJMBPADO/in+Ef/C/yT+G/53/xEANQAcAMYAxv9G/jX/BwDF//j/DQCqAZ4ASP9RABoAGv/5/50A5f/q/XD8Ff4q/j/9Iv6Q/4T/cAB1AOP/U/8N/gv+fv5U/mP+Vv4l/yD/FP/t/s/+QP5M/kb+8P4a/9T9sf2j/uD+7P6D/nr/AAAk/wH/q/8q/7X/1v+FACcAoP/k/3D/vv/k/9X+9P2z/YL9Fv5R/tn9Z/4I//v+ff9I/23/pP8J/9H+4P6f/5D/Zf/R/6X/BwBbAEEAPwAmAKz/if9S/yb/Of8F/zn/ff+I/5j/4//B/3P/VP8MAIIAUADy/xkAMgAvADsAHgDg/wcALwDZ/y0AGQAKAML/8P9HAJoAiQBAAHQAawCWAKYAVwCMALwAZQBEAKwASwB1ANEASABAAGEAgwAAAdMAnwDhAEAAbgBDAHUAnQBFAF4AbwCpAMEA7ADKAAMB9gDDAO0A6QAgAeoAxQAPAcQAmQDSAAkBNwEqAf4AFwFIAUQBLgHWAM0A0AAMAXABQgEDATMBCwHNAA0BLQF4AWsB8AATAfIA5AAWAWIBdgESAZwBhAGLAOUADAG/AFcBegHGAc8BXgE2AeQAlgDGABcBXAE9AdQAqAFhAUgBwQFzAR4B4QAFATgBnQFnAVABYAHUAEUBEgFgAKAAHwGhAXwBqgHqAR4BEAFwAQ0BvgCuAMkA6gAjAR0BSgFaAYoBkAGbAVQBCwFNAaQBVgFfAS8B5ADkANQAPQFzAeMAAgERATEBDgESARMBVwF4ATkB7QDkAPUAoAAaAbQBYQEsATUBMwFVAT0BJgFWAR8BSQE8ASwBYgEQAa8A1wD8AJQA1QDFAIMArwDQAO0AHQExAU0BOAEUAQUBNAGeALYAMQEMAaUA9gApAQkBoQBxAJgAygCIAEIAOwBNAKUAsgD0ANQAAAGUAM8A0gC5AC0B7gDsAAYBzQB/AHIABgDc/yEAZABMACkA8P8uAIcAGwAUAFUA3wDJAKMAGwHqANYA3wBuADQADABVAF8AbQBqADcAJgA7ADcARQBOAEsAlAB9AFUAewBdAOj/HABAADwAPgAOABgA5/8VALIAsQCtAIwAfQB+ALkAnABbAE0AAABHAGcAZAAiADcAhQBHAHcAYQB1ABMABQB5AJAAkABuADwAtgBPABwAnf++/y8ALwBqACMAXQCAAJsAcwBOAIwAuQDv/zcAXwAiADgAawBaAEwAVQATANr/nf8mABoA3f8wAFAAGAApAPr/eQB4AAwAJwADAP7/aAA+AC4AIAD7/xgAZgDAAKsASACVAEwACQASAM3/FQANAPL/CQDy/0IAUwDy/zcAPwAnAJb/h/8kABIADABEAEIAPABCAPT/BADP/xQAbwBtABgACgABAOT/s/84ANr/iP/P/9n/8P8tAPb/QQBpAMv/c//J/3AAdwAyAFQAGADJ/8j/7/+6/zsAvP+e//7/nv+p/5P/zP/F/9j/0v/A/3b/DgBdAGAA1v8aAFgAgwBwADAA/v/L/+T/uP+Z/8P/z/+z/7X/9v/Z/6X/wv+u/9n/9//V/04AOQCBAB4ANwBBADYAJwAdABwAuv+t/+X/PgBVAPr/0v/s/xQABADW/xEAOAAWACIAXAB+ADAAIQDJ/6X/DQAaAHgAMwD2/xIAiwBSADcAIABrAHoARwATAAYAIgD9/zkAOABCAAMAbP/e/7L/5/91AEAAIgDt/xkA8f8hABoA+P/b/wUANgDp/wEAFQCx/y7/m/93/+7/HwD2/6D/hf++/yAAGQC1//z//P+y//n/VAAQAPP+OP94/wj/cv9S/83/6v+d/9j/dv8//z3/4//y/5D/ef/H/4j/kv+k/3b/t/+A/2j/Wv9O/1L/Z/9D/+n+e/7b/ij/ZP8r/0b/Nf/t/nP/k/9k/9b/eP+Z/77/4P8fAIn/Rf8M/w7/RP8f/1H/Qf95/6n/zP+V/0P/Kf8h/zL/+f5U/yf/Bv8i/yf/lP9q/yf/V/+Q/47/GP86/7X/kf/r/8v/i/9i/5f/df9y/03/Rf9O/3//kP+b/w3/Pv8u/07/ef8i/6f/Kf8t/4b/yf7r/gf/O/+Y/3X/gf9S/zj/K/8q/0r/lf9+/0n/1/94/47/2f+a/6z/z/9J/4z/lf8Q/93+pv6Q/ob+7/4H/wT/5f66/tj+Gv/g/z0A8f/C/1P/Sf85/w//Zf9o//H+3/79/lT/Uv8b/93+Uf9M/wf/1f4n/zP/XP8p/1f/Sf9v/57/rP9x/4n/Vv8w/yz/av+F/1n/Lv8l/0D/4f+8/7D/T/9P/zz/UP9N/3//3/+I/xn/g/9h/wn/L/9y/4v/g/9z/0v/G/8o/1D/tP+Z/4n/q/9O/wv/z/4l/y//Sv+i/57/f/+7/wf/Cv/J/tL+KP9N/4X/Z/8n/0r/5v4S/1j/Sf91/wX///7y/gv+7f18/Tn9w/wy/Db8ofvy+1j7o/kt+lf8ZQAWBGIFKQUwA2EA+P1V/aT+5wDeAvkDpAMcAoEA+P7j/Xb+UwA3AusB5gBM/0f94vt7+1H8If/QADAB6gBGABj/9/2b/cX+FQBzAEYAMgDZ/z/+cv15/UL+4/5H/8T/fACuAG8A0v+X/zv/3P8FAUsBuAE6Aen/jP9K/03/tP/d/30AywAZACYA2f+K/5H/WP8HAGAAugD8AGEA5v8b//H+1f4g/+P/LwAuAH//nv66/jz/7f7Z/q3+DACdAMT/wf47/1MB5wG6AJH/gQDDAYgBmACJAMQBNQF7AHf/KgD4Ae8AhP9Y/k//HgEZAAz/HACtAOQA9P/k/mb/XgAHAVQBawCMAOgBlgKjAjkBIv4d/j3/LgIxAyECxAB8AMH/Sv/L/8sA5QDb/2z/UP+s/xMA9v/fALUBfwG+AV0CMwPEAjz2ueoh8DX7KgMWBNwBMwJC/qb2X/Lc8t/4DP6xAoIG8Ac8CEIFtQH+AlsFdAlFDSYQ4QxMAv/9Jvaj6k3rjvZhBeIIywAj/tH+hP8C9zXyHPpp/zUBAAEG/r74aO8x6UrtcvES9D/6MAWOCqwCU/i39Rn4L/rf+Yr/eAoDDzIMWAOF/xsBxwFBAUwCSweQCncJpQNA/er6SvqX+Un5+viw/XQC0wBd+WnxN/LD9MH0kvml/5sDLwPK/+H93v39+yX85/9hA4UDPgLhAmoFzAODAcIARAX7CmwLTQrfCAcJmAYYAgMAEACrAikF0gY1BhEF+QVZBcwDaQHoAWH/of8DA0kDCQOMAagClwaOBVsDGwVzCaQIogXjBhgI1welB4sHyQeMCOUInQlJDFoN7gq/DPELBQRM/n3/DQRqBSgEqwOSBJgFwf8b+ar0JPG/8gD2g/nA+zf+/v4P/Bv5yvVv9Cb4gv29AbQByQLcBIEEKwJM/n//PQM1BhEGYQTfBlIHWgSdAUX+h/22/hgC4wJXAOj9Jf2b/if90Pgt9xD7vP+f/5H9pP3T/ob8zPnX+fb6Dv6fAJEDfgNiAvIARgD9AYQCVwGPA94F5Qe6BoIFqATVAt0BmADlAVkEvAN3BIwFegOSALYA2AH2AdL/tv2y/z0CkAK0AG4AYwLRA0cDLgK1Af8CuQL/AIsAJAMYBbgDTAH3/nz/iQBSAeICFAWgBbkCfwGxALb+lvz4/Nv8c/2F/ib/+QAU/gD7QPx6/D38/fqN/GYBQwNSAJj9Yf6e/GL78PmW+hT+eQCEA9IDWgJgAH7/mAEm/qD7kv3xAdIEMQIvAOr/2gDe/8H9p/3c/Af9Iv5MAeABkP+c/nD+Pv9t/Vf8hf1e/x8AZ/4r/wL/Rf/4AQsDZQLFAHEBpALCAt8B0AD1AHgBFQOKAgIBiAGxAP0AJAIGA5ECCgJ/Ak4Buf+a/1D/uv8pAfAA+/+fAz8EZAJhAuT/V/5NABACYAJaAW8BIQNgA0oBWAFLAZoAsP8N/5b/NADNAYUDsQPHA9MBhgA+ATEBbf9T/tv+eP8KAIoAwQJEBOQDTwNXAXAAg/82/1/+d/xv/TIAKAJyAp4BtAC4AVACigBn/5b+NP+MALMAvgBvAGAB8gFuA7oDYgLeALkAxADs/8v/gP/D/yYBzwGgAVUAnQAdAJr+Jf73/f7+sQCyAssBbAA+APn/LwDo//v+ef1p/bz/hgGFAa4AkgCDACIBjwFJAF3/GAAoATIAef+//3wA6AFzAXAAO/+//xEA2f5z/gP9mv3u/sn/TgBi/5P/t/+o/0f/uf5l/6T/DQC5/yL+U/7F/xH/5P55/hT+qf8OAOcAAgLxAKQA7P81AAb/A/0T/aj8qP1c/lj/RQA7AAMB2AEkAEf+Bv4O/mL9+/xq/CL9wf7G/93/NABUAFkACgHPALP/Vv7T/tP/lP9T/2L/4P/P/7P/5/98AHsAs/9X/5j+VP6k/kT/M//M/in/8f/fAEYBJwG0APT/7/9zAN3/qf+t/+MAIwE0AYsAXv+a/7v/J/9A/sv9Pf5L//P/OwBYADkA3v9F/yz+0f0V/pr+Wf/w/7j/uAGHAo0CtgKBAZ0AIABy/+L9/vzR/Sn/DgDPAGYBaQE7AXYARf80/4v+GP41/rr+y/5W/6MAIwG9AYQBKwHfABcAo/8A/4X+3v7b/mb//P+ZAPwAnAG/AQkBkQBJAKT/P/8P/zr/EACaAfQBQQEKAHEAzACDACUA3P6S/hz/WP/V/wsASgBiAScB6v/Z//n/yf+j//v+Zv8p/+b+TwBJAeEBzQHZASYCCwJ4Ac8APwD2/6P/tf+O//n/1P9PAOoAPQFoARgB2ACj//z+Wf5+/nr+df6b/1IBHAKVAooCEgK1AdAA7f8Y/9/+Rf/s/2oA1wFUAiwDbQNxA4sCsQGyAKz/Qv8s/zT/1/+yAA4BSwEaAv4B/QESAvIAmQCJ//L+0P5D/1n/4v/g/7QAigFkAewAtAA+/yv/yv6J/oP+Tv2n/Gr9+/3Y/qP+eP4G/5v/6f7k/UD9hf0Y/RH8JPyG/Mn9A/4h/gz+fP6I/mX+//1D/b78VPwl/HP8Lvxw/JL8g/2w/Sn+C/6h/b79Vf0w/Qj9/PwM/Vf9YP3X/fr93f77/5L/Pf9i/2v/VP/O/qn+vf5a/xQA4AEAAzMDPQMSA7UDUgQUBP4DOgR9BKMEkQWMBjoHRgd/B0IIqAipCGgIoAekBr4G9wbHBjYGoAW4BqAH3QfkB3QHMgdHB2MGqQTdA3cD8QIDA04DUQP6AmUDugOeA0wDPAINArkB1QAgADv/yf6M/vz9P/0c/S39YvwZ/EH70vl3+FD3U/Yb9YHz+fLp8mXyePJV8u3ytfPI89DzbfM48+Tzn/NF81/zjvMO9O/0hvU49SX2/vYK+On4nfiv+Kf4I/kp+bf5rfka+q37nfwb/e797/0p/wYAmgAYAb8CwQNyBCwFzgWwBhkHrgc8CJYJ9wopC9ULIA3RDE8O+Q6oD3MQIxH6EU8TChMsE0oTfBPmE9gThBTkFFsVoBX7FT4VeRL9DsYLDwnDBokC4f2q+RX5n/j292r2j/U+9q32t/Vg9JzzafMK8lrxcfBV8JbxZvPQ9Tn3NPi7+lL9Nv/g/3r/Av/n/of+sv2+/Ff8PPzy/ND9cP4O/wj/SP+o/qL9EPxX+lz4UPZm9Lfyv/EI8rLy//I18xv08fTh9CT0ZfNg8jvynPGo8FPwMvFQ8mD0OvYz+Pb5UPx0/Tr+wP4l//f+9v7p/oP+f/8WAaACjgTfBg8JBAuPDNkNrg6mD4kPCA8vDtYNVQ7BDl4PNhAzEp4T3RUJF1wY0hkDGrIZjhkGGX8YlBcQEwUOpAmzB04GKgIs/KH4Ufeg+In4ffZu823yp/ND9Gvyo/BV75PvjPDn737wcPIm9QD4BPux/Kb/XwIRBVwG4gXvBAIEJgSoA+sBjACK/+/+rQAyAb4AXP+C/kT+H/4l/Aj5SfbM9GDzEPE77z7uB+507pHvMPDY8aDzGPXl9aj2mvZC9pL29vax9q/2uPY49t326/ev+H/5Efpj+u/6OfuQ+3r7S/vH+637L/zq/Aj+0/5y//IA5QKEBKAGAQhRCfgKrwyKDdIOfg9ZECoRyBEdE28UtxVIFpcXyxhkGvsbAB0OHQ4deB34HDMY1xK4DFIJoAcuAv35p/Mj8Q3yN/NM8JHsMexw77zwZvBc7iLtyO1K77PuBe5d79HxLvbr+dT7NP4iA7YHdAriCjEKzQknC7sLZQkOBsMDKQLXArkCigCE/nL9S/5C/qH8Vvom+ML1xfN48UbuieyH62vqzul26tXr0O3F8ELz2/Tg9uz4Vfom/Mv89/vg+/P7+Puo+1n7cfuF+9X7a/wB/eX9t/3R/Ij80Puy+lv6//le+WD5ivnn+Zf7Rf2t/S7/eAFyA9AFTwiWCRgL3AzPDOsNdA99EPkRbRMUFHYVIxchGRUbOByLHZYdjR50H7ofwB5nGswUEg9dC/AIoAOS+nLzUvDz8BHyxO9m6/zprezZ7sLucOx767vsk+8y7w/tEO5j8f/1T/k7+n39PgMECT0MgQz6C0YMyg6ID7UNiQpDCBAGQgZ7BasChwBk/9/+1/7z/RL7LPlM9zv1D/Md8JbtruzU60/rD+qE6jnsbu4f8X7z7PWR+Fb7y/xM/qz+Gv+3/qn+B/+W/jD9Tfx3/N78uvyK/Wv+LP/5/yIAu/+c/9n+eP0o/XH8mPvA++b7jPsN/Hj93v6dAD8CAgSYBuYIPQojCwwMdw0AD68QXBHnEcwT2xXsFxwZVhocHOQexSDeIa4ilyL4HnYaaxQQEKINrgfC/mf3PvRc8yTzCvB065zqee048OvvOe2o7GTuKvDr7m/tKe538I/0fPZY9nH59v+VBawJJgsaDLYObRLrElcRVQ5uDMkLsgpMCMEEPwOgAeL/Gv8+/ZH8ovsv+sD45/aL9OPxRfD37UPs1OqS6rvrj+1b7zrw4PL99b359fz9/Uf/3QAaAjUCzQHtAIf/Mf/5/jH+9/2m/TH+4P5MALcAdAAbAMf/bv/u/rv9Uf0R/cb7OPsP+4L6n/sI/XX+VwDgAgIFZQf8Cd8K6Av2DZ4P0xCXEbISwBMLFfUV3haFGKkZ7BrjGxgdxB6uHxsfoBw7GbUVFhKRD2kKQQOn/N/4O/dw9fDxSu5Q7Qnv2fBH8E7u1+7p8Lzx6vGd8a7xbPPe9en2mPZj+fX9cwLfBfIGgAgKDNEP0hBYEMoOIg6ZDpsMWQl7BrAEzgLvAEL/IP0Z/Mf7ivoS+XX43vaL9UL0KPKR8FXvzO6d7mjuWe9p8HbyzvMS9oj42fpd/K79cf9JAHgARQBW/5j+Vf6q/R/9Sf3G/Uf+T/4l/+X/FgAlAXwBXQFjAcYAGwAE/5X+Tf6L/WP+hf9LAFIBSANnBUYH7gjTCtkMpw+2EPIQIRJOEtsSNRQ9FFwVtRZeF+cY+BmKGwAc/BzBHSQbqRitFQATOhB3CqUCQf3r+ez3m/U38cXtVO0f7zLwJe/t7tbvZ/FL84LyGvG+8cvzZ/Wn9YX1Ifgi/ET/xQH8AoAFqgnADMoNjg1oDScNFw13C7MIuwVmA6kBp/+a/T38zfpN+hP5mfdB9/z24/W79Brz4/G38V7xJPBU7yPvy++d8PTxevMR9Zj3P/l++tT7//ya/Xv+Ov6D/XD9Gf0e/TH8dPth/IL9r/2x/rv+A/8jAJ8AewDx/87/oP+p/nT+mv2F/fP9Cf4b/9sAogJ+BJEGlAjfCggNsA7pD8EQbxHzEUUSTBLFEtcTpRSVFLwVmRbMF3UZhBp+GgYZFReLFAQSAxCaCsIDSf6h+sn4NvYe8UHtYe347oXvb+4N7XPuUPE+8rbxRvFB8oP0zvXD9SL1Vfcw+xH+g/+qAAED/wbjCekKIQt6C80LMwytCugHJAbqBHICYAAS/r77jvrM+T/4U/fg9pv2HvZ09Tz0X/M+84fyG/J78UXw9PBb8srySvQs9Uj1HfcK+Sv6pfpF+378Fv0c/jr+hv0a/nP+jf7l/b79K/5r/jD/Ov+n/6IAogDfAB0AOQBdAOwAygD6/1kAlwBYAR4C9ALfA5wFtAcOCVgKNgxxDRMPJRDKEDkRRhKyEoESNxO1E0oUhhVHFl4XaRi3GSoa1xcFFYkSTRGAD3sKggPG/vX7LPpQ9+Lx6+257jDwJ++k7l7tY+798bvyBvFL8H/yzPRk9bz1UfWX9xv8yP2h/Qr+gwHNBcAHgwcgCAwJMQqZCp4I6AXUBLED2wHO/zT+R/zb+nH5B/jl9m72VPbD9AH0mfMP87byT/Ji8fbwIvG28U/y1vLn86X0f/Ws9vj2n/i0+cf5Rfo0+uD6qvvC+yz7NPs4/On7cPzg/JP8U/2T/kL+ev4q/xf+bv7//pv+7v1m/iH/9f60/zQA+gBsAasCFgQKBUMHzAgJCrgKsQtADdoN4g4jD/MOHxCKEQUS3hE9Eh4U1RT5FXgWAxepFyMWvxN5ESUPIg0ICCUCqv0o++r5yfZA8rXvXO9i8NvvYe4I7UrvQvGj8enwfvBI89v05PQb9JD0/PeX+iH8L/xC/oYBeATuBU8G7QdDCEQJnwn0BzEGVQUGBMgB2//8/VL8uvpX+Rn4LPd09nj16/TO9Cj0HfSv86nzGvPi8nfyH/PD8//zXPRc9MD1mvb29mn4L/ng+c/6U/uX++L7Zfxr/Cv9MP1R/an9u/3D/Yj9Xv0R/nn+h/61/tb+Hv8q/6f/mP+k/xMAGQDOABkCWQLKAiAEEAWHBdoGTQhaCTQK7AsCDX8NfA60DtsPmRDvENIRBRMQFEAUrBRXFlQXWhgLF/ATRBLsEN8Pxwy6BgsBjv5F/Ib5T/WJ8Mbvc/CB76DtQ+yp7e7vB/FA8B7wbvKk9CH1oPQ+9Tr3xvlR+7T7d/wR/wICOwS2BM4EIgdiCHUIyAfXBpsGogWtAxAB+P4g/mf8ZPpR+Ob2G/YE9kX1//PL83/zcvPm80PzEPOI8xX0GvSu9KD0LvVN9tb2SPdH+FL5jvl4+eP5dPrj+mn78Psc/Dz8Yvz5/Dj9s/wt/Wz9fP0s/U39ef03/XH9sv0C/iX+Hv7V/nz/zP9JAB4BGQLhAgQEEAU9BqsHZwi/CEoKywskDIsMCg34DUMPGBBIEIkRyxLdE5oUGxXQFYcXnxgYF2IU+RI0EeEPCA1lBlABhv9Y/er5ZfWd8bzwnPGu8D3uvO3C7tDwZfFZ8KvwafLY82H0DvRd9Un31PnL+rP6lvxo/zoCGgTeBCMG5QebCJAIBwhGB1UGRgXvA9IBDQBd/iH8svra+Xn4RPdm9tf1NPWq9D/0SfSw9Fn0G/Ox83n0pvQa9eD1jvZ198r3ofjH+VP6W/rq+uT6ifqG+0z80vtZ+7r7vfzf/Eb8XPxi/Qv+B/4V/gb+U/7K/pD+QP5M/pn+5/66/qH+UP8yALEAyAAlAd4CVgTuBE0FLgalByAJXQrDCs0LrAxMDkAPxw9NELkRsRJpE8MT1hRyFdgVtBY6F5IXrBXzEuQQ4Q/IDeMIQwM//3z9rvsA+Dnz6vA58ZvxJ/CB7sHu9PCf8gXyD/Eh8zr1TvaQ9eT1Gfia+hv8VfzR/bYA0gIiBFMFwAUOCOsJkQknCZYIHAjABvUE5gK/AJ7/jv3/+rD59/iq93/2RvUH9Wb1gPVe9dL1c/Z19kb2Tvb69hb4c/jm+Kv5Zfqt+3/8bP21/eT9ev47/3D/zP6s/bz9ev6P/t79UP0V/aP9KP7+/er9ov7d/uD+Z/5W/ov+r/6r/mz+Nf6o/lX/e/8JAB0B8gHMAtADyQTWBXIHowjOCUgLaAwZDSMONg9OEEoR/xHzEtQTsxSBFe8VjBYSF1YX5RdVGMcXoxWNE2gR6Q+iDS8IMQKB/4395/pH9+3y1/Hn8oby+PDc78Tw8fJe8wvzp/N+9WD3gveH96X4efq3/J393v3x/9ICgAREBZ0GIQhWCdsJuQnuCYAJdQi+BnwE/gLeAdT/qf2U+6L6CvoB+TD4qfd19y/3sPar9jP3/Pau9rD2Yveq9174zPiA+W/63vtH/LX8ev1N/uz+6v5Q/gD+R/6//fz8KfzB+278yPxm/On8jv3c/Sv+Pf6T/vf+jP9w/wr/+P71/tT+0v7h/mr/IQCyABsBsQEMAywEAQV6Be4GRwiPCd0KOgv+C78NZQ7cDqsPsRChERYSyhLoE5EUtxSRFVMWDBdcGDQYExYVFBMSgBBcDloJfAMsAGH+gfuO9xz0o/LH8nnyUPGM8LXxD/Pp88bzvPMk9Y72g/Zb9o/3dfl4+k37Wvx5/tMAtgLpA0UFCwc3CJoIwQh8CXQJEwhOBgUFzQMGAqH/mf0t/Ab78Pmg+Jn3R/cf96r2bPZh9qf2nvZ/9nz28vYS92b3tfdp+I/5pPqB+1H8jf0v/yUASADk/6z/pv+W/jD9sfur+qX6ivqT+Qj5Avk9+nP7cPt3+4D88vyA/ab96v0C/nf9S/3N/Jn8D/38/O786/25/wwB4gGrAo8EjAb9B/IIAQo0CxIM+wyxDdcN3A6gD7MP5g+pEJIRWhIzErISHBS5FHUV3RUbFYQUGhPjEM0OLgzgB0sD6/8U/VL6Pvdn9JLynPLK8sbxaPEY8m7zTfRh9Eb0V/Wq9qb2n/Zc9+T4avoH+y/89v1EAP0BEwPMBMAG4weSCG4IIAgdCPEGYwUTBKgCLQEO/z/94/vz+v/5KfmE+A34J/j/97j3Ffg++Ez4ufe69y/4/ff19yr4rvil+YL6svuU/Lf9Nv8+AAkBpwHMAU4BHgA//2T+C/1A+1v51vjd+LT4c/jM+P758/oB/Jn8Cf2S/f79Xf1f/aj9Jv2Y/Bb81Ptb/Ob8W/1b/oP/SgHNAhkE0QWGB4kIewkcCgELzgs5DHAMtwzGDC8N5gz5DOINYw6dDjwPqRCrEUESVBLZEvIT9BPNEokQbw60DAML0QcaA3L/wf3r+535Bvc49V/1wvVC9ZL0kfQT9XX1EfUV9cT1NfZz9hn2b/bM99T4dvle+sH7iP1E/6cACwLfAq0DKgQvBEkEDgRcA2kCeAE+AND+w/23/Kz72Pqm+nr6G/rY+YL5Evky+f74pPgm+Kv36fcK+Mv3zvf294T4Gfnr+dL62vv4/AD+i/7w/kf/1v56/qj9g/zb+zn6//ig+A34bPdq95j3gPj2+B/5HfrY+kT7m/uq++v7n/tN+wX7+PrZ+kP7efvD+4H8UP10/mT/QADAAeEC2wOdBEIFRQb8Bk4HmwelB9kHRghvCJ8IXwmqCScKfQohC74LMQyoDGYNFg5aDnoOkw7QDuwOKQ93D+AOCg3PC58KUAnWB9EEAAJtAA7/yf3n+wH6Uvlq+eD4/Pek9zr4afi896L38Pcv+Cf4qfcR+Or4n/kg+pX6pfva/Kf9Ov67/mD/HABGABIAkwDgAIQAr/8t/0r/Dv8n/rj9tP2+/Q79Z/xE/AH8ovtF+x775Prc+lH6QPqo+tv6LPsR+6P7Yvza/GT9yP3n/Yj+tv5m/gb+xf2o/Sv9gvzE+7T63fpo+q/5svn4+T76pPlX+RP6ePrw+e75M/pn+v/6l/qJ+k37L/tD+7P7y/tP/N38aP2//Ur+S/+m/9//hADjAYACAQKdAs8DZwMnBD0FzQRFBawFWAWGBUkFyASgBdMFmwWhBuoGUAc8CO4H5QhMCYUIUgmnCVAJVQmJCWoJmAkkCg0K6AmKCjgKIwrGCTIImQetBs0FPAT/AtoBrABEAAj/Cv4l/tD9If0J/cT8bPxs/I/7+fr0+pX62/lx+T/5ofnS+Zn5+Pml+kD7QPsH/Lb8B/1l/XX9x/0H/v/9cP3r/bb9Lv1h/Qz99Pxr/TT9Nv3G/D79Pv0//Ef8i/wq/P/7Qfsw+2P86/uE+1L7RPtI+2f7zPsX++f6ffth+pX5MvuS+t35t/pC+jX6Vvon+kj6vPof/PL7yfp+/Iv9x/xK/Rf+/v1V/a/95v0P/UD+ef7p/aX+IP9F/oL/OwAw/x4ARwGdAMgASQH7ABgBgQF9AVMCYgGLAlwD8gHbAnkC0QHQA2kDFwKCA7AD4QJCBB4EdwThBFYDXwTTBUUEZwRFBQQEcwRlBBsEcgXwBKUEdwUfBcsD8gOzBNYENARuBEUE/gKzAhEEcwT1A8kDZwTKA7MBdwI8AgoBzgGPAHkA9QApALsAPQBHAN0BPgCH/xkBxv9W/0QBmP8M/18ANP+a/gz/NP66/ij/Mv6s/sL+r/4Z/mv9OP74/XL8fPxq/c38g/xE/fD9v/z9+/79+PxW+2L9w/3i+sb76PyT+y79YfzZ+4H+kvuF+wP+vfoM/Cn/5Pvq/AL/Pv1w/u/9Dv7W/yb9qfzy/sH9Hf4f/l78eQCl/nf8SQLn/ev93AO4/vT+5v++/ev/Qf++/roBQQIu/kUA8QOO/uMAtwKf/qwAawIKACMAKgFAAJ3/LQI4Ap8BlQMzAvIAdwD8AEgANP8zAon/zABpAtj//AAEAs0BggEDAs0Bt/81AOoALwK/ADYCXQHyAFQBtwAfAMsBLgFIAcICKgFYAN4CCQFyAJwDLgGeAUIDNwH+AfgCeQJk/7gAEAKNAEcAZP/EAQwDYf+aARIB+ABcAKb/xgGYAeX+NgCuA6H/SP+IBWwAoP6+AxsA8P4/AV//8gDi/2P/tAKHATP9vABHA678YQAaA9r76gBcAHf9FwI3/xv+2gDK/i7/av/5/j/+Kv3R/8n/z/mJAYcC5/oaAdL/oPzc/4r+R/1U/4f/OP3I/tP/nf5YAln/n/ujAkH/3PqAAK8AOf1w/9n/kP04/ib/w/uLAQ3/Fv2GAnH8LvuQA/n8svvBAYD/kf7T/qP9AQHPAJD7kgHK/vr6tgIN/zz6iwHwAH76Cv9bAdv9ggLQ/Cf7dQa9+cb7MQJj+nj/wf8U/PkBvf+K//kB2v1bANUChP2Z/UwB3P2CADAAyf2c/yL+uADlAMH6mgNRAD/7GQIm/mL9WQLr/mH+jgDA/8f/7wFW/vb/IwWg/vv96wHg/rb/rP0m/pQBVv5s/+QAWv/fAt8A7v7TAR4A8P/n/9j9bP6YA4AAxPuWAz8BC/6BBXf/RAGuA2//tv2xAcf9tf/+AIb8lgO5A7D7nQPXA1r9fwB4AAf8r/x+AKX9PvupAu4DP/4cAGMGH/1qANsE9vUPAmMDf/U9BHED6vp4BdcCSv+1A1kCGP4lAPD8hv7f/z/7wv9SAuf90v4cBL8Bm/vlAOUAK/pp/l4B4PrnALkB5fxyAhUB/wP//mD+QwbV/Z78/wAb+gACHgFf+LIAOAfa/kYAQwJQAXMA9QCb+5H9yQB5/4f6NAOLACUEnAIR+p//mAdK+iv6JgWP/hX7xwFH/q7+ngbd/hb/WgOF/qYASf8q+WADCgGH+n8FZ/6K/xAFa/vqAAgJ7ft8/jkGs/y6/HMDyfs1/g4E5/6O/t4EJAN1/EgDGwLY+WwCCv/3/NMBDf/AAKD9KAX1ADT8eAWO/X38kwJA/eH+XAK3AyH8hf8ICNb7lP0GB//9e/0vBUP/x/k4BhMA7fucApUDoQE0/0sAHQMe/KUAJgCl/DsE4ALS+7H/4Abm/BX/jANJ+8wEjAGm+CcGiP8D/kEB0f4cBnT90PxNBib+CPwQCSX/MvbuCeQA3/eTCVf+5v2hB0/8yv7UBVr75/w/B2//T/wMBOL9eAALAZX6EgPMAmz51gEA//3/nQMD/b0BawI1/cMG3Pq9/DYLrP3h+yMEtgHT/tn/QwLC/y8FZv0X/xUB7v2E/zf+1f+AA8r3uAQZAhT1Ywk4/174jwjBAln4NQVdAaf6OwPxAuf5EgNLBhf6Vv+oBLD+Sfn5AKf/RgBM/ycD4/zKBKcF1fSeBLcEWfaOAtYCa/pnA5wDd/zE//UHD/0r+8b/PwSP/lj8yANR/mb/rgJc/MYB6QBp/v38QwWE/674PAkyAcf26greAp7zNgk6BcX0gAUhA3/5/QrO+BT5YhCD+Eny+w5D/YX0FQ4OAbzxkwv+BLrteggHCcLxvwemBqb0KwcnCLnznwKFC876HP0ZA+T+HAEVAKn9Rwmn/rT97gWC+wf9YQm/+I3/tgVD/tX/Xf63AUEA0f9jBdf8jP17CbT2lgIhCeH0SgFYCjP7Jv5VA5QB//46/1ABTQFv/LEDCAKy+MsDMgme+fD5MAhH/0785gGf/QMHngJ6/l38LwYWBI32Xga+BkL2GAgsA1HzAgshAx/0TgiCAej5Kgpc+Af6SBBs+1bzGg+n/iL55A1B9EcDtAwP9tP93Qln/9L+IARR++sExwPT/Vf8fAIJBef7Jf59A2n+XwiQ9sX/ewuG+E/91Ai5+5cB6Qbs+4D70Qq0/CD9QAqA/aT9xQUI/ij91wfk/J8CZQBQ/+0B0gJ9+bcG4gB3/L8FEv0s+jkNKfwb9xsQb/209MAOhP0G/R0EigD7/iQDvAE5AFn/xAb3/Er/IgOt/QQDEACeAY8Dk/r/BloEV/OfDAD/4vf4CZ0AePRVDPj/gPiIB8QD3fLaCa8A+Pu9Bfz9avhoDJL9DPYgDwP6kAMSBzP5EgefAH30MQvT+14AGAYa/EAByACfBBf/7vjRBcEG6/MHBiMKwvCcA/wJXvWP/GgM6PcX/2oEhQCy/n4DBP0S/okFTQJ99RkK4wQE9GgCZAvI9XH7JggZA4/8RP5wBlL+IP+1BHv/jPtiA2MBjvsoAE0Aif/PAgP/ivZzCX0CSvWtAuYDlfsFAQoAXQOd/378hgh7/6L4wgccAV72zwNLBuT3sgDUCqzyWAY6Bm70zAfV/X36OAi8/pL3EgRSAi/+HvhzDAn8b/h4CYv7G/tcCM79g/goBSEIl/PC/xwN3/bA/5sFFPun/ckEkfsY/34EG/1C/eEIxvp4+DYLe/5R8H4QHQK+8B8KZgIx+SkBagf59YwAKQv79EL6vw9c83P8lAp3+2n63wMNAvL50gCqAUIAz/yG/SkIGfvA+qsKuvn1+/4MzvFyBIoIie5DDDcCrPH6CC4BvvlKBlX86v5eBYb2iAOQBHf2rQNQBjL3GAF4ByP53/34A1ACkv+J/csC7AA3AA37z/sPA1YE6fd4/Y0LrPZt/w8HHvP1BkUDFvQgCjX6q/8HCQP13/0cEb7y6fzkCQH57AFDA1b6uQL+Ayf5Lf/dBr343wFJAoH3/gbH+lUDEQOw9HgJZAAz9j8Jff5N9nQMQvwA9iYP5PX0/QUHNfzN/63+Ff+iAqv3ZAZs+2MCKwOa8wIGlwa/8uAAdQva92L4uRMp9G/1BhFB9Ez8uwsK8TYFLgcb7wEKUAII8LkQK/fm+vMIlv1N+LUI5vmDAzwD9PXIA9gHcvIsB9sHPvFpAj4L9+12B58HZvOkAfwE3/uz/dr/vAJg/B38jAir+KD6awhL/Af7jgsD96H8Egb7+6X6zAea9+wAogdH9HcGYwHc8xARdfWE/AIMw/kV+qIE0P+V+17/HgJe/UcDrPaLBWMBivR7BwL9pfd2CvD3A/8vBNr56/6xAv/7CPxiCKT3zfq+Ct79+PPGDCQBMPMhCvD8CvdHBYf/DfZZBpL+I/dhBEH+h/tf/qAGyPUeAKIJU/OLBQD//vtmBHH2GwXs/pX8+/66A8/5XP9qAUf88/2lAgL9I/69A5b40AKVAHD3zwSXAIT1aggA/zf4mwa3/i34kwYX/Or8+v1LBUn7f/1tBUv92PpTAl7+tf2zAMf/m/zsAZwBl/rm/0UGmPbi/ugH/fb7/IAIivY0/8IHRfpi/iUFCfwN/ekC8v2T/c0Egvyu/vf/BwCi/T8Bo/d1BicA3/ZsCob3Jv9aBU/4tQPW/VP+QAMd+wYCH/+a/q4CLf3+/lsBCf3UAf78Qf2UAm797gF6+W0EDv4l/dMC9f5U/JkGyPtO/ooCT//A+oUDwfkcBPf+6vuPACoBFf6b/kb+ywO5/GsAFP8L/nAAbAFc/YP+9AK3/ln8KALf/Q3/+f88/rEBgQAS/TQB5v0x/2//Pv5W/8IBCP/o/s4AHv0eAPMBmPy+AFYADAHk/Mb/vP9q//n99/75AbX8rADp/nn9pgLf+78A0wM9+fcCrQAs+boEKv72+NACegPb9YwEsADG+tP+hQd292H99gqG92X9CAh//Gn7pgFsARb/p/yh/vUEBPmnA3sBzfkkBDsAaPrgAYD+9v/F/gsAgPsEA3/+4Pu5AOj/8f/6/L8AoP+f/pcD7/p+Ao0Ak/tfANL/Cv4y/zEAAP21AcP98f/S/5L70QIk/vL/pv29/xwAuf3u/uUB+fxxAFr9pgEq/k3/aP67AY/8+QGr/cAAoP3b/coAXv7b/ZgByf1G/uX/8f+R/o8AwP/u/3P/N/5GADf/gv6V/xL/sP7C/l4DF/qiAu79cQCk/g/+RwDRAKX8dgLl/hj9fAEkABz9RQMM/vz9FALf/jP/XQGp/mP/HwEV/47+DQFiABH+6gDaAJb/4gDn/v7+CwJq/c4A+wAe/S0BrQD2/n8AsP8+/1sAOP73//cAnwDS/qAAPgGD/cYAyQAu/9EAEABEAIwArP5bAUABpv1wAd7/FgCLABwAtv/u/0gAEAFG/U8CXQDN/sgBSf/Q/4kBWf7dAFQAAwCJAen9BQIXAPoAzP/0AGEBp//9AKEA0/90/1ICaAAzAWsAQQDnAZz/FAFCAY//5AAeAUz/CwG+Anz9cQIQAX7/kQHD/9kBEgB4AsMA4P+lA4L/qAC1As3+ngJiAfX/bQIJAVwCaP/5AowBmwGD/9gDvv/pAPsCkwDW/4YDHgGDAMIAugJAALMBSALq/tUD/v/3AJcCNgBdAoIBdwA9AoUBLQHPAoMAaQI3AecBIQJ5AZcBvwFWAb0B1AEdAdYB3AAnAmAARQEQAkkACwJGAfIA/gGmAAkChwF1ADoCKAGCAP4BBwG5ADkC2wD1AUsB+gCOAbEBgwEFAQ4CvQCXASQBNwFEAcsA4QHxAFEBhwGBAcIAGQFPAbMA5wDgAKUADAEDAUQBKAG3ABgBxQAXAcEAdAAHATcAfQGBABQBXAA1AacAjwDKAMIAfAA2AdcA5ADpAL0AYwC4AIIAKwB/AaT//gB5AHMAjQB7AAgAXACFAD8AMgB5AGEAagBQAD0AUQBuAKIADgCNAH0AgwDTAD4AuwCFAMoAhQBqAKoAlgDIAIwAmwCqAHkAfgBgAHoAcwCbAJ4AogDrANwAxwDhAKMACgE2ASABEwHJAAEB5AAoARIB+AAoAd8AwQDIAMIA9gDKAOoA4QDUACUB6QD2ADIBCQGkAMIA3QAPASwB5AAVAQwB9wAsAS4BPQEqARkBIAH1ACMBRwE7ARgBEgEkAfQA+QD1ACcBLAEYAVYBbgFAASUBEwEjAWQBWgFUAV0BaAFLAVYBWQF6AUIBPgFEAS0BZAFTARYBDgHyABQBIQH4AEYBUgFJATcBOwFcAWoBlgGeAZgBYQFvAT0BGAE5AXEBSAEiARkB8wD/ABkB/AANAREBCgHgALUA/gBFAX4BRQFmAX0BTgEaASgBIQFIAUEBXgEGATMB5wDyAOkAxgDoAPoApwCxANIA7gC6ANUACQHVANIAuAD9APoA/QDjAMMA8QDfAPUA1gCvAMcAnADUAOIAoQCKAIwAogCuALwAfQB+ALYApADEAJAAdwCEAHYAoQDpAKkAdgCQAFsAeABcAKAAhgByAGQAogBeAIoArwCfAFAAcQBJACAAQABTAJcAVQAOAAoA8f/Y//n/PwA0ABEAIgAsABMAAwACAPn/EAARAAsADwAaABAAEwACANP/KQCl/87/xf+a/6j/f/+c/6j/sf/n/4b/s/+f/8//8/+n/9v/nP/C/9L/lP8HAMf/q//T/6v/p/9w/3r/c/+Y/7P/xP/p/8//wP+Z/5f/xf/b/8z/5f/H/6z/oP+q/2r/Wv91/3z/bv+P/93/2P/L/9n/gf+N/7X/n/+b/8X/m/9X/4D/Wv9r/0D/LP8w/wL/CP8w/0T/av9N/zL/K/8+/yz/XP9K/2L/Y/9H/y3/8f7W/rL+B/+8/rb+Kf/V/gD//v7N/vP+5f4D/yj/9/6t/gD/N//7/vX+F//E/vD+1f7p/ub+wv66/s/+1/7M/qz+EP+v/tD+wf7I/uH+7f4P/wH/H//3/u3+zP6h/vT+zf4A/wz/Kv/k/rb+qf6+/vn+1f4C//7+qf7q/gj/4f4J/7H+t/6q/sn+Ev8Z/0b/Av/T/rH++f4o//b+Bf8H/x7/Sf8q/wz/IP/k/tv+AP+h/sj+Cf8z/+3+4P4V/w//EP/V/vf+6f7n/iT/Lf9s/4D/T/8Q/wf/SP9H/0//WP9D/yn/7P4t//n+JP8o/03/Lf9e/6r/d/+P/3P/c/+q/3X/ev9W/3n/Nf9G/0L/c/9d/97+Dv/d/vT+Kf8U/3P/R/92/53/Z/+V/zv/d/9b/0r/af+R/zz/J/86/3H///7p/hP/Hf8n/0b/Tv9k/4z/i/+o/3v/O/9b/2n/X/9q/2//cv9P/zf/Gf8N/0f/JP9Z/1r/Zf8j/+f+Mv8z/zv/WP+h/67/ZP8m/1//lf+N/wz/NP9W/4b/FP8M/5P/Of/2/ir/gv/H/5n/RP80/1n/4P49/5//cf94/3X/0P9E/0r/HP/5/ir/Fv99/4X/Vf8f/y//Pv9X/x3/JP9L/2f/o/+O/2P/Wf8X//7+JP+H/27/df91/zj/9f4y/6D/S/+c/17/if88/2n/dv91/2v/K/93/2j/Wv8Y/wP/f/9z/8H/c/9B/5P/Nf+O/2//YP9Z/3f/b/8//43/T/9Y/2H/Z/+p/2f/cP+O/5j/jf+a/2X/1P+R/0f/IP8//1n/ZP99/1b/b/98/zv/M/87/0j/m/+e/43/ef++/1n/E/9y/5r/OP80/2X/bP9k//j+Sf+L/3D/av9P/1f/xf7u/nf+6v0v/KP6C/wW/Tz+9f5u/+j/QwBPAGv/mf8mALz/4/9kAJ0A9gCIAL8AYgFNACsAygC0ADoAtgDbAC8AU/+l/gX/B/9E/xH/Rv/+/xsAqAD1/9v/PP+6/jj/Yv/k/n//E/+x/i//Uv/L/pP+X/9d/0f/gf+E/xf/Rf+AAJH/Lf9XABsAPwC0AGcA8f+mAHUAVgCP/xn/J//M/gUAXwBMAFoAyv8BANAAlwCQAF8AkQALADv/h/8xADUBCgFg/838e/w6/kf+CP9h//L+yf8iAG0ABAC//04BEgHaANIB6ACs/2cBCwEdAFEAJQDw/xMAfgDNAL3/DwAq/xH+p/5MAAH/Jf+N/yL/Y/8u/wH/PQGQ/pv/dQA8AAz/ygBlAI/+///pAFX/Mv8AANL/hv+GAEEB8//1/mQAHAHc/xIB8/+AACgB5P5zAZv/dv89ATAAl/5nAjYBuP5FAOb+/gDO/yn/HQFe/j0Ae/9y/yUAfPx6+1D9gf3H/Er8/P+q/wb/rP8MAQIBkwD6/6EAsAJ5AKYBngOGAGMCWgN8ASEB8P/c/87/pgCqAXoAM/9S/kMA9v4c/2ABBQBJ/2ED0AORAan/vvy7/Kv+qABkAJf+W/77/RUA3P7x/50AWv70/pYAEwFHAuH/iACG/wMAqAIFAewB2AN+AUMBSwF7AZn/5f7F/+kBfv/0/bH9pftj//sBLP4R/kn/V/0B/qEBRAFDAhkBMwHQAioBCAIiAmAC0AIJA4kD1wNVA0UDjgOUAS8CGgT0AKwCzQFkAOQD7QLmAGcClAIcADX/KgFT/8sAK/9s/3ICXAEzAU8BYQGXAYQBYgEUAs4ClACQAQsDOQNHA7kAyAGrAdIC6gOwAeoB7AJ3AawBbwHLAYUCfQCPALoDOAKUAUUBywDZAVMAz/9XAFP/rwFeAokAxQB8ASYAzv+1ANgBGAEVABoAVv+5/2D/IwCkARkBvwB//8n//wDsAZYBBgGuAOv/RAHG/8//QgERAeoB2QK0ATcA6QEeAW4BIwPPAN8B3wJdAdQAmQEnAroCkgIdAygC/ADcABQBJwFaAjgCAAICAgkCggOJA8EDnQPVA+ADIATqA7sEJAWBBLgEKQW8BEMEWgRxBBoF5ATNBIIDoAMwBAUDUAIyAv0BKQMlAlUAgAGXAJz/IP9s/9v/b/9C/y7/Y/8W/0r+ev4o/pT+9v0+/cz9ef2t/dH9k/2q/WP99/yq/PX8Sv0E/dP81/y9/O78c/ye+z/78vsL/I/7Svtc+/b65fre+p36Svub+8L6SftM+7n7M/y0/Hf9/f0C/y3/Bf9aAKABigL7A1gFDAd1CHkIfwiDCVsKwQq1Cw4MpA0cDroOjA+iDxgQJg8qDwIQ4g+XEE8QQhGdEeIRaxE+D48MCQd4AacAggEnAV7+0/ta++T8pvvG+Jr0U/Jq8Ynx8vOS9lv48fiw+Gj6I/z1+7/7gPtU/pcB4wOqBR8GfQUGBH4DCQMxApsAm/63/Z7+z/5c/d/61/i09772V/WZ9C30fvP187b04PWy9iT3g/Wv9Ub3svgG+lT61/o//Iv9BP5g/k7+wf16/O/5EfnI+Cb4XfYj9OfynPKH8hTy1vEO8sPyp/R/9mn4p/kH+qX6GPwf//YAyQLQBEkHhgqKDPoNxA5UD1wQJRG5EikTRxO4E0cUoBV/FswW6BfmGAwaphsDHJ0dVh8hH6kXPAgx+6n1nvmz/JH3kfNO9E/6jP3o96vuUei05xXqDvCH957+YQKTAsID3QYXCOsFlgPUBPwJQg8nETwQGg5FCZMDQv5a+kf3gPOQ8Knx4/S39mv09+877Zzsfu0w7yzxBfWh+Df8vv/PAtMDZwLtAD0BWAMrBQUGvQa5BkEG3QR+Ai3/s/sp+Fr14fO489jzn/J38d/wcvGk8b7xO/KR8bPvr+7y78Lz2/a9+CD7h/0sAYIDHgMxAnEA0v9OAUQDRQQbBEkDuQHHAAoAjf6M/Dz8Zv5DAlgH3gmhCi4LiAzqDngQABGxEvwV4hrBHtshGCSTJf8lZyU0JbUlmyJjFpkCifLc7MnxaPO57Z7o7Oj+7gTz+u695sPhc+QV7Oj2GgFFCZANgw1cDRoQ7REfEVgOJw0lEN8TuRMyD54Hof5V9l3vf+pi51DlgOQT5vPp1u387l3t6utf7RnyhfeN/A8BlQVzCf4LiQ34DE0LFwgNBeUEHQVJBLkB6f2++vT3VfUb8q/uZ+wM7CDtx+6E8OzxLfPQ85D1AfgS+/L9//8KAjUDbgMDA+8BhwAp/4r8C/qf+Ez45vje9yX2jPXx9XH2x/av9sX2effG+Jz6q/sA/IL8Lv4PAUsDFgbxCMAKfAzRDpgR3hLHEhES0xJMFEgVvRatF6cXhRg7GfEblR1eHdQdbh0THh8WjAGs7DXfm+Kz7FPwOu/c7m322gDLAeL4ge3S6hrzrv7BCS8RHxbmFy0VkRH+DXYKXAY3Al4B1wNeBhsFwP749W7udegO5JDij+T56I7vlfXX+o3+HwC2/k39Xv5VAgUHKAroC3YNjw35C1UIMAMF/ln5SPYb9VT1TPaE9mb1sfPG8u3yEvN586/0WvdY+/b+BgG5AS8BtwBQAEUAvABlAR0CUgIuAgYCyABu/jH7YPYp8v/vN/Ci8ZLyfPMP9nD4k/n4+Xv5mflx+tD8tP9DAf8AUwBuAKYBewLaAtACVARSBu4IQQtEDbgOIQ4XDdkM7A0vD5oQsRJcFdcXEhuQHeUewB6EHk8gfCC7GZ0Fue3u3tPd1OXv63nvGvI39zH+LAB8+/byI+9p9Kb/BgqnEKkU4RfgF1QUkQ2VBogBVf6u/aP+wf9Y/zj7j/Lm6FviFuHv42/otO2I81r6tADjBGsG3wUwBbwEGwVyBpgIsgoWC00JsQUwARP8DPa18HPtwuzG7sTxdfOu9Af1B/Yi+BT6Nvwr/g4AOwM+BvkHvQd0Bt0D6QCw/hX9AvyN+nb5mfnm+Z75xfhY9//1qfVH9iz4j/no+Hj2jfTK9Hr2APeq9hH4o/ly+xX8Ffyb+mX4m/f/+ZH9pP5Y/74ATAPIBJgEwwPpAoICdwOpBYEIbQqiCz4M2wxaDRoOug9sERMT6xSIF9obdB5dIPcfCB55GpETUQh9+IbpE+Kl43fpne4B85X2t/nK+9j7nPrP+Rb7IADaB18PohQ/FxQXShQKD5AIfgLZ/Rv7p/nS+Ff3ePTn743rEOhF56bo2+uB8Av2dPyOAtcHLgsBDGsLYwpXCWMIFQfdBSQEaQEy/oD6qvYQ89/v7e3C7MHtZ/CE9Dz4C/tw/UP/rAAOA1IFnAZUBxUHsQa1BSsEPQI8/zr8wvnz9w/3b/b+9TD2ovaE9933gfgq+X76IPyq/Wf+jf2I+7X4XfYn9gj2LPaB9k/3Hfij+O/4c/gg+J74ovnP+7T+qgCvAZECQAKQAVcAj/8+AL0AAAIUBBMGCQiMCTMLJAw7DCkMhw1vD0IRnxPfFTgYaxnNGaAbZh2FHWsbhhaVD7QCi/JZ5gHhg+Ne6ATtm/LB9838ewD5AZgBwwB2AV0FqAr/DxAUdhYnFtwRGQtTAy39xfkn+OH2KvXq8knxqe9S7kLtD+2k7sLxSPbH+9gB4wdbDNQOPg7YC2UJVwdhBfcChABK/hj8c/q4+E32bPM28Trw2PCe8or1Hfkk/RAB1QObBdEGDQfbBvgFvwRCA9ECqwLAAcH/pP3i+yz6v/i195z3BvhF+Vb77vxr/hj/RP+p/3f/Rf2E+lD46PYc9hf1KfXq9X720fas9gH3APd49vb20/g2+7v9uv9KAU0CogERAP3+9P34/GT8N/3e/hwAawH6AlwEeAXPBe0FrAZiCEoKYwwQD8gRpBOkFOIVwxZCF8sX4BhUGhMbLhuxGa0V8w73BCP3zenb4Q/iXecK7vf0WfsuAZwEyQXtBWwEeQM7BCgHjQs6EHgTZhSzEWsLJQTz/eX5V/eK9Sf0avNV80DzSfPu8pryAvPt9Db4i/ziAekG+wotDWMNFgwBCrcH+gTHAQP/zPxP+0z6Mfnd9z/2FPVY9EX0g/VN99X5av03AWQExgb1Bx8IeQd6BkMFewOLAQcA/P4j/q79afyo+lr5Uvgi+CD4vPhA+iX8sP2i/pj/1v8o/8n9Zfyd+0b78frX+gb7U/rf+M/3PPfb9jj2BfaO9nb3ZPmF+/z91P9VAFQATQDx/4b/8/5y/j/+5/1C/cH97P7G/xYByAKUBFMGqwfKCAEKWguZDNINwg5CEFESGxTlFKQVeBYFFx8XPBfRF+AW+xM+D3YIBf7Z8fnoB+fp6STuffJ+9+X8fQH/A74EDQQnA98CJQTjBn0KZg4vEY4Q6gwjB/wBiP6n+8D4VfXl8jHyi/Kt8yn01PPF8+D01/d5+7v+IQKiBcwIdwrTCgIKfwhoBnkDhAGg/m38ovtf+kP57/d49hj20PUu9vL2UPih+k/9VQBIA1UFQwYwBk8G9wWtBBsDqgGhAGX/8v2K/M/7j/vO+k76KfpN+sP6ivu2/Nf94P6S/00AEQEEAdEAZwCe/9D+G/4s+2P28PHI78Pv3+9R8EDy9vRU95f5XPym/i3/4f4z//3/nwAgAQMCNwJ8AI39gPtB+777h/ts+1n89v2gAFEDkQWHBpoGKgfkCKEKOQwNDrAQThM5FdgWwBfEFzMXkBZsFWkUtBNWE8QROQ9ZCyoFLP2S9HHurOzZ7Snx1/Rf+Xr9lgBKA2oECQQmA5sCYgPHBfEIJQxmDhoO0AvDCIAFPAI+/6T7S/gw9pv11fVB9oT2W/ZE9qz24/fT+dj7Y/7pALMDhgazCIMJdwmNCKgGYARMApAA4v4O/i/9XvzF+976NPpa+RX50/gT+XX6FPwi/hkAMwLBA18EngRSBNcDOwPGAnIC5QFAAbsArwAKAD//S/52/ef8f/zU/DP9Mv1v/cD95f0I/tn9Wf3O/Nz77vk1+Gf3vPc/+Hz4sPg++bn5+fkr+r76K/sb+5/7sPzf/av+G/+0/8P/t/6f/W/9U/29/Hf8CP1K/cP9vf78//cA5gHUAisEzAXHBhEIcAm+Ct4L8Az2DYUO8Q6dD4MQqhBpEIsQShDLD38PcQ9+D84OaA3VC6gJewUQAcL9M/yz+7b74ftA/Mb8C/1X/a79Af3E+5z6PfoF+278kv0n//v/zQD3AfkCtgNqA+ECFwJ6ARIBywB/ADUA9f+h/y//q/7q/TX9vPzF/GD8Evwo/E/8c/z7/Cv9QP27/QT+Kf5J/oD+ov7K/ir/h//5/xEAfwCtANQA3AC8AK4ANAANAPn/m/+N/zv/+v7l/tD+x/7h/uP+Df9C/3L/ef/A/5j/hv/u/xIAEAD6/+T//P/n/3r/Nf/f/k7+W/4k/tr9Df7I/Y39ef1M/e/87fzr/FT8z/uM+0X7wPoR+vb55Pna+Xj5avmZ+Z759/gs+Xb5kPnw+Tn6IPv4+kP6L/us/AD9E/wW+yX8c/1X/hn/lf/9//MAVwLeAwsETwT1A+UDcASXBQsHegc/BxYIuQmUC3UMoQzJDNkNKA/lDyUPZw9fEPsRehJcEWAQOhBXD4oMgAgrBT0DigEo/6v8f/sc+xX60fho9yL2fPVH9Wf0OvTj9Fv2Bfnd+oP8lf6qALACtgMBBFQEYQUXBmsGLQYIBlIGGgZSBVUEAgNMAgwBLf8x/ZX7jPrQ+QH5SvjO99D3lfdF9+f2K/eT9zr44viG+eP6Z/x3/an+cv+YAM0BcgKnArUC1QLqAr4CrALtAXABHAHd/wf/Sf6d/az85fsr+2D6PPr4+az5k/mQ+fb5S/p0+s/6BPsZ+6f7rPu8+7D7G/ux+oP6LvqV+Wr5b/k1+av4J/jT94X30fY39n/24fbv9mD34/f9+ED5gvkR+rn6TvvM+8v8uP2a/rD/EAEwAjgDAASxBDsF5gWTBuUGugfVCMEJMAq2ChgMeQ2bDQcOdg9FEEkQWBAMEWwSmBJqEgIS1BBsDgkLDgf1A0sB3P4Q/Xb7fPru+Rf5Kvj09v71kfQV88nyB/Mf9Mn12vcl+rj8Rf+KAcUC5wMIBTkFRQWTBdEFNAY2BiMGsAUPBVkEIgNSAYD/0P3B+xb6B/n09zv3zPZx9mz2rPa+9ur2P/fP95/40Pn7+qf88P1c/7YAMgI+AwYEfgSrBBIFLwWhBBgEvAMdA2QCbwFmAKD/Wf5a/Xv8XvvU+k36yPmA+ZT51/kF+n/62vpz+wT8HPyU/D/9iP0e/kP+qf4u/yv/8/5I/g791vsU+zD68fjJ92r3bfem9h72QPbq9Wz1A/U69aP1pvXn9ff2OvhT+XP6xfv3/Mr9k/6A/04A2wBiAZwCjwMjBLgENQUnBn8G6gaaB9EHWQjhCPEIjwltCncLGQyiDIwNaA7tDoEPDhAVEbURVBGsEBwQ1w7hCxkIvASAAlsA3f3O++L6avp0+TL4e/dU9s30a/O58mfyHvM/9Cf2gPgH+3n9/P8oAjADDASyBOQE/gQcBWsF2AW9BWsFywRSBGMDuAEqACL+aPzF+kD5X/hr96f2kvaf9qj21PZE93n38/dQ+Jf5ufqu+yL9e/7B/ycBlQKbAwIEgwTjBJEEUgS3A0IDjALaASYBTQA4/4H+wP3E/Nz7BPuI+mv69Pne+RL6V/rR+lj77vui/DL9o/05/pL+3P46/63/1/8CABMAzP/h/z//rv2l/Lb7+fqa+TL4gfd298P2NvbF9Rn2ifXi9GP1qPXZ9YD2vvfS+Mz5OPus/DL+Cv++/9AAvAGJAu0CfgOeBFQFwgVLBu0GUgerB7kHFwgpCDAIqghHCccJlAqJC58MeA0WDswObg8lENEQyxFnEgUT2BIyEqARdBCoDdoJpwZ5BEMCnf+3/QP9Wfwx+/j5XvnW+C/3mfXu9FD1tvXs9sj49Po6/Zj/1gH3AysFJwZeBpcG2QYPBwYHBgf3BsQGIQaIBeMEcAOXAZz/EP5m/I36gfkV+WT4+vfy91/49fgr+Wn59/nC+rn7S/xs/cX+AABMAX0C6APABHQF1AXbBaQFHgW6BDQEXAPdAh0CSgGbANb/3P4B/lr9cfzZ+3/7W/tY+4z7FfxT/AX9hv3a/T7+oP7W/kX/nf8CABAA7P/i/wMA6f+V/9/+/v32/LH7a/q3+Q75Ofdn9jf2YfYS9jn2uvYg96f24/b592/42fjp+Zn7+vzH/XH/5QC4AdQB2AHXAcoBugGiAQwCdALlAswDXwQBBT4FDQUlBR8FUgUEBi4HzQciCbIKFQ21DlMPXhAaEZIRmxELEvwSkxMLE74SLxIwEM4NDwqlBZUAqvsu+eT3Kfdo9oH2nPfD+LL5hvqY+o76Cvtr/Pb95f9EAhEFbAf+COgJPApICj4JpQYQBKEBsf92/gr95/v5+jL6tPmi+VT5yPhu+NT4iPnH+jL8tv17/+gAEQIuAycElgQsBMsDKQNcAjAC1gH+ADEAh/9O/7P+B/5R/W/8C/zk++z7L/yh/Hr9cv7z/oX/fwAZAYIBowEOAngCUAJqAkcCRgILAnwB6gBrAP//Qf+B/gT+V/3Z/KP81fyb/H78Wfxo/Kr8RfwF/Ob7sfs9+xX6IvnP+Kn4Q/gZ+Dj4nfi++D355fnc+aL5yPk4+sz64vqN+3T8v/wc/a/9kv1K/aj8GPzY+7776vtY/Dn9RP7G/0YBVwK4A/oE2wU1BgwH8gf1CCAJuwnjCskLoQyeDGoMwQwVDc4MMgxkDPUMGA11DZMOcg9RD9UOVA24CoEHGwTJ/wf6J/YJ9dj1X/YD9235n/yY/jcA5AAoAQUBEwG+Ae4ChwSzBY8GLgfBBn4FLQOXAM79E/tG+AX2//RT9Rr2WPdy+B35gvoV/HD9oP6k/7oANQJdA24EuARKBDoDQgKHAM3+4vz2+uz5UvmA+VT5Pfn/+dP6u/ta/AT9+v3o/h4AEAGMAQ0CfgKPAk8CcQFzAL3/w/48/pb9yvxa/H/87/w9/cn97v0e/m7+rv4X/xn/Vf+7/zv/K////ur+mv7e/Tf9k/wc/LL7BPuP+kn6efqr+ub65/qT+9372/lX+Cr4zfiD+Dv3Cfg1+Wr59/nG+nf74PoV+jf6IPs1+5H7afwl/Y39uf2T/e39Yv0V/Wf9fP0p/v/+JQB9AacCpQNnBO4EKAXiBCUFswVUBloHegjbCGYJQgokC+UL2wtqC2ALngsYDPgM4A1YDnIOJw8cEKIQSxAVD6EMeAlBBcUAVfwX+Jb1RPU39rr3Z/o//dj/DALdApUDIATgAwQEGQTlBMMFBgbSBeMElwNbAdD+ePwX+v/3afbr9cj28/cp+d762vz2/kEAsABVAYMCHgOLA3MD5gPUAwUDlQHZ/x3+U/xg+mj5e/hq+Nf4BPqI+wn93P2O/n7/iQBeAdsBnwHQAU4CngJbAowBPwBY/2r+bv2T/Lj7jfsN/Cv8nfyq/WH+Vf+W/87/RAAdAdUA2ABlACEA1v+Q/6n+EP4M/VD8gft4+xX7Dfsu+0H7qfuz+2L6o/l0+Zr6Evua+g/7n/y8/fr9w/2J/QH9i/ve+k76rfk/+Tr53Pm4+jT7evs4/Nz8aP1W/fv8Qv2Y/a/9MP7W/ir/g/99AFsBDQInAnYCRAMoBBUFWgXLBS8GegY4B4UH9AdCCD0IvgjtCa8K8ArBCvQKaAvcCzQMdQx7DE8NDA0PDQgNzAw2DZUMHQtVCMUEkQBI/K/4tPbk9iH42fkf/Er/4gLKBPMEmgQGBGMDVwL+AR0ChwJVA0sDnwLqAWIAW/4//Bf6pfjH9973XPn2+nj84f2q/0cBQQJOAhMCSgIkAqABfwGpAbcB0wAh/zj+ZP0a/Kn6+fla+tH6JvvF/F/+TP+O/wYAngBhAU4BKwHCAPUAnAFMAZoASwD2/zD/6/1W/eP8ivzo/Az9nf2R/h7/zf9MAKUA5gCIAHMA8v8Z/yn+mP2r/CH8nvuS+wH7avqJ+uD63/rZ+uP6yPrc+g77lvsE/ET8zfwU/UD98P10/Tr8pPvF+3z7a/oV+Xj4JPhC+Lv4+vn++mH8e/3q/gcAlv8U/3v+r/3r/Kv8Ff2+/eH+IwCqACYBxAHjAdABuAEjAm8CogJ2A9gExAV1BgIH8AYPB50Gega3Br0GBgdqB1UIAwlKCfsIQgnSCfUJ0gn0CX8K5As6DIELOwuMC48LvgrHCQMJzQdfBZ7/CvwB/Rn+9/vg+Rr8CgA7AXQBiAK7A+4CkwE2AvgCvQJVAsEBtwHIAZYARv+8/lX+//xC+in6Gvyh/Bf8XPxP/lEAOQFZAVUAlf/p/6P/df/x/64ADwDn/lD/0P9I/6j9lfv++gn7d/sv/Cj9vf0Y/ib+q/45/73/mv/f/kv+af60/hL/9v7B/UT8yPt4/GT88PsG+3r6UPsV/DT8V/0A/jL+mf3I/V/+Of4A/bH7RPsl+/36DPux+pT64vqc+m76o/py+1/76Poz+zr8v/zK/F/9Q/7W/k/+Uv7B/nb++/2k/Vn9XP1N/RH9gvxc/ND8+PzX/Gz9h/42/0v/MwCDAVcCxgI1A/oCVAJTAtUCoAILAkcCTwPRA0YD3APgBAsFxgTsBNMEdwSDA2EDgQMsA2IDmgPNA9oDnwQlBcAEtwTwBAQFmAW2BpMHawc6B2sHUwcQB8sFTAWJBIMEhwXMBVsFDgUMBTIFpgSFA7sCEQK4AaEB7QF3AuoBQQLwAmIDOgJ6AXQBCAFxAMr/LwBeAPL+nv6B/zr/Pf1E/An9p/2d/Kb8/P2C/qj9s/3U/kT/cf7Q/Sb+6f1l/Q/90vy3/HX8UPxq/Gn8hPxS/Bv8jvur+pn6aPrB+cv5Ufp8+l76zPrk+or6nPqG+rn6HfvE+qb6JftA+yv7mvpq+gf6M/nd+AH5Hvmk+cv5Wfrx+qn7h/zX/LD8xvzz/Ab93Py+/Of88/xk/cj9C/5u/o7+d/6l/tr+uf7p/gj/3f7v/uv+Pv+J////mwDIALoA5QDVAMwA2QAgAY0BhQEjAsQCXgIiAnAC8wGKATYC2QK+A/oD2gR9BekELATNA8kCYAJhAloCTQIuAz4EgQRkBDME3QN1A54DAQQMBOsDugQlBgMHYwbGBQYFJgTdAwMDtwJDAyIDNwNSBBgF+QTiA5oCZwJhAoYBJgJLAzQDygOkBJwE8QTDBNMD5gLyAXsBigFjAf8B+gKaAi8CLAP8A+ED/wJ8ApUChwGFAeYCGgKoALgAZAElAv0BbgHdAKIATwEGAnQBkwAp/13+rf3K/Jj7ifpf+Y356/qG+8/7NPxv/Ln84PyH/Db8P/zV/IT9KP35/CD9TPwG+xD7rPpM+iH6Qfob+7H7/fux/L/8mPzm/Kb8fPz4/Db9Xf3A/eL9lP0s/R39Ev1S/Lb7qfve/PD9D/4O/gT+1P3Z/TD+Q/6K/YL9Uv44/ycAVQC1/4D/p//l/77/Qv/A/gD/tP+FAOoADQHJAJoAowDbAE4BuQGTAbIBeQKRA3oDnwKYAdEAFQCu/8b/DwDt/0wATgEqAqUCFwIkAUgAOABgAK//n/+yAGEBzAFUApQCbgJ5Ap8CgAIiAvABxQHgARoCgAJIAv4BggKyAhEChAHoAeQBXgEvAW8BigFqARQB3ACfAFYAqQAWAcQAeAABAbYB8QEzAoQBTQAtAEMABAAXABkA9v9kAOwAcgGwAbQBVgGWAH4AewAjAOD/af9H/9f/pwDfAHEAXQD3AEkB1QCVACMArP/j/zYAnf+c/ij+wP00/lX/V//T/iT/nf/t//b/GP8J/l39sf2i/gP/rP6n/pX+5f7k/mT9svv8+jP7F/xt/Tf+N/6L/ov/LAD9/w//u/0Z/SP92f17/hT+hP1z/VT9nv3w/T39PPwz/Pr8+v19/pn+r/6a/m/+af4h/pz9MP10/WH+Qv+U/2z/VP8R/yX/+P5B/rL9/P25/jn/jf/c//v/NgCEAHgAsv9m/2L/6P9TALUAcwBKABEBIAIDArwAh//J/vX+pf/V/4P/of8nAEUBagJUAjAB0/8X/6n/mgCyAFQAqwBZAR0C2gKtAowBVgDc/0sA6gCUABgARQBXAfoB7QFnAaUARQBfAK8AiwCOABUAAwC5AGEBKgFeAAQAcwD3AEEBPQFaAU0BcwHgAdQBYwHuAHUAjACaAKAARwDi/8v/+f8gANL/n/+T/+3/cgDgABkBJAEDAf8AxwBnABkA5/99/5f/FwAzAKj/N/8n/93+LP4n/o3+H/8WANcARQFIATQBlgA/ACQA4P8J/4b+Bv/x/y0Auf9r/yD/6P7k/rH+ev4s/q3+X//p/wUApP8o/w3/wv6p/sb+Zf5f/qn+wv7Z/vX+vP6J/ov+f/44/uz97v1f/qT+pf65/vf+dv+9/9X/vP+T/zf/Nv9i/13/If8W/xD/YP+f/2j/tf6x/lD/1P+9/2f/Nf86/3H/q/9H/77+qv6y/hL/jv+l/2n/d/+B/13/ZP82//D+7P5J/9b/BQD1/7X/2v8AANX/x//O/xQA0QAJAfEA1wB0APj/0f+v/9n/8f+t/4n/7/+GAJEAIwD4/7X/oP8ZAJ4A+QAsATUBnQHJAagBLwE7APX/HQBGAFMAZwBuAH0A4wDvAIYARAA8AD4AsAARARkBZQFpAYcBPAHVAG0ARQBnAKEA8gAaAQ4BVAGuAaIBBQF5AIAA6ABJAW4B0wDcAEABoAFpAe4AlgCfANIA4ACuAFsAWgCrAMwAuQBOABQAOABjAH8ALgCY/5b/GgB8AKIAcwAyAFUA8gAZAcAAigCEALYAzACkAOn/Ev/V/uT+O/+D/23/iv8bAMIA1QCiABYAxP+y/xkAOwBaAIwAYwBNAJgAeQDu/3H/gP+6/xsAQgA0ACEAOAA5ADMA0v9//7X/9f8jAEAARQAtAAsACADG/5P/l//m/xwAPQApAAoA9//R/5n/gP9U/0//hP+t/5j/rf/I//H/2f/k/y8AdACNAMIA1AAFAfMAwgBtAFgARwDd/5H/df+U/6//k/+//wQA+//S/+H/IwBVAIsAnQCDALEA4wDPALEAdwBeAGUAZgBkADcAHwAjABkAAAAsAD8AaQCEAHUAawAsAN//zv+0/53/iv/b/wwAJACYAK8AeQB4AHUAbgBaADoAQQBPAG8AcQCJALwAygCIAEcAQACKANkA3gDFAIMAYwAdACAAAwCq/3z/uv8vAIoA9wAIAdcAxQC6AIYAOwAWAC4AagCzAMkAnABZAJAA8gDsAGYA8//0/yUALgAGAAMAIgA7AHUAmwCKAFQAegCDAFcA8P9t/zH/fP/c/woAGwAhADgAQgAlANL/XP8U/wH/PP99/73/9f8rAHIAtwDKAJ4ATQAcAA4ADgDp/5T/jP+6/wcALABYAIYAwQD6AAsB5gC/AHcAVAAsAAkAPgCdAM4A7ADBAHAAUwBGAPP/fv9i/6D/FwB+ALwAnAB6AJ4AjgBCAPX/7/8hAHgAeAB1AEIAOQBuAGsAIQD+/9//DABbAFwALwA3AAUA8v8OAPD/wv/E/wkAMwAdAOX/vv/T/xQAIgDs/9b/CAAqAAEAvv+M/4P/if/X/xkAOwB/APkAIAHhAHEACwC7/6X/5v/V/37/kf/k/z4AXQAYAN//6f8aAC8A8f/H/77/zv/4/+7/q/+E/3n/p//+/xsA5f/N/9f/AQAPAMH/ef+A/+P/KABwAJIAgABqADgAx/+T/4T/af+8/xIAUgBnAKoA1wCTACcA8/+s/6n/j/92/3n/e/+U//z/AgDm/wgAKQBnAGoAOgDz//b/FAAZAPH/sf/W/yAAQgBEACAA/f8DABUAHgANAPz/DgBLAHMAMgACAAcAMABmAEYAIwArAHQA8wDrAIAANgDm//T/EwDl/8b/r//M/zQATAB1AEsAFwBIAGYAhQBwAEkAVwCMAHUAEQC2/37/q//6/wUALABGAHoAjQByAFcALwDm/6b/ov+Z/7j/6/8dADYAMQDn/7L/u//H/+P/mf+W/6v/6P/P/5//ZP9h/8L/tv+a/4f/u//n/77/tP+I/1D/dv+4/93/zf+T/2X/oP+e/5r/of9i/2b/kv+e/2T/QP+F/8n/rv/b//7/7P/4/+r/4/+c/1//of+Y/4b/if99/5L/x//Y/9T/tP96/6X/r/+z/5n/3P8QAB8AHgAmANr/uP+Y/33/Tv8u/z7/Yv+e/5P/2P+r/3n/eP9s/1L/k/+o/3f/XP+I/5j/cP98/1j/Z/+M/53/w/+y/+f/6f/k/5P/hP+e/5b/k//e/wMA9f+p/8X/4v/C/73/lP9X/4n/lf+E/4z/TP9d/3P/l/+l/6T/t/8WADQACgC0/2f/f/+w/8f/7P/E/xMAFwDy/zYAJAAuAEUAKwArACIAHAA4AEoAHwD9//T/vf/M/xAAPQApAEwAZgAZAO7/AwADAPH/FADu/w0APQBoAEAAYwBGAB4ANQAsAFAASwC9AK8AngBfACUA7P8SABwAGAAmACMAQwBZAI4ATgB9AGEATAAdADUA/f///0cASQBxAFkANQAwAHgAbQBvAGYAIwD4/0QAdgCJAJ8AqgBwAGkAKQAFACcAJgAKAPP/2v/5/x0AEQACAJ7/tf+L/3f/Uf9H/5D/vv8bACQACAAOAAsATQAhAAcACAD4/yAAUAAoAEUAUwA2ADsAJQAGAAgA3/+5/6f/pP+4/77/mP+r/3L/hf+b/5v/h/+C/4//sf/J/3P/dP+V/6b/rf/F/+T/WQBCAHoAcQAvABQADgDe//T/8f+s/9X/3v/4/83/w/+l/6L/kv86/x//av9y/9b/+P/O/9b/1v+t/7b/of9s/0P/Zf9l/3P/jP+v/6v/0v/j/63/qv9U/17/eP9u/0v/Uv99/43/hf+3/6v/c/9v/1r/Hv8n/1r/Sf8//2f/Tv9w/5H/i/+n/6P/xf+1//T/IQAlADIATQAtAAQAKAA0AFMAKgDu/+H/+P8uACMAMQA8ADAABgALAPz/7f/Q/5n/r/9//23/Xv9d/5D/3//t//7/CwD6/xoALgDd/5j/av+E/6X/pv/P//D/w/8bAE8ABAAYAE4ACwCR/+7/CwAaAE0AQgBFAEcADQALAKb/Y/+A/3//Wv+8/wIABgAeAC8AXgCFAGEAIABFAN//p/8gAEoAMwCEAGoAOQBkAHUAEgARAPb/wv+d/6X/7/8zACkA2/8bACgAPQADAAIAAgDS/8//6f8DAPj/1//A/7P/+/+7/9j/k/+U/+T//v8SAA4AMABcAG8AhAB0AAEA8f8RAPb/SAATAMr/EwBUAFIAQQANAAYAAAAMAPH/AwD0/97/+f/s/9z/DAAqAMv/3//z/zoAWAB5ADwAPAAhAPT/EgASAEUAEQAoAFAAfAA7ACgA5f/o/0EARwA1ABAAlwBzAC4ASgAgAP7/4/8HAC0A9P/h/wEA//8wAA0AEgAzAOn/6P/0//D/hP8NAOT/0f8mAGcAKABhAKEAGQA7AFQAUgDy/wgAjgBHAFEAKQA9AP3/SwAZAOf/4/86AAQA7f////7/v/+T/9X/2//Z/wQASAAVAPH/UAAeACEAKQAzAAMA9P8JAE8AJAC+/zcAEwDm/y8Aw//4/y0AawAPACoAOAAPAB0A+v/k/0gA1f/z/ywA9v/U/8z/x/+W/+r/BAD9/xAASABCAGoAOQA2ALEAQgAcAGoASwAdAIgAGwABAD8ACgAXABkA2v/g//T/7f8aAGMAAwAjAB4A+/9RACgASwB4AHEANQBqAIIARwD5/z0AHAArAGQAMwA8AIIAngBuAGQAiACOAH4AYwAzAGkAdAAVAGEAbgBaAFgAIwBwADEAGACQAEEATwC7AAMAXgDfAE8APQD9AE4AuQDdAE4AsQBtAGgAeABJAAMAZwAgAFYAOgBDAMEAggB2AFgAegBMAFcAzgB0ADgANACoAFsAbgCsAEYAYAByAGQAVQA8AAIAUwBKAAwAbgCTACcAbwCtAGcAfgC4AEwAfQBrAHEAjQAQAPn/HwDQ/yIATgBBADgAagAJADUAWgAUAGoAFQBVAJMAFQBgAFsA+P9NADwAAwA4AFwAGwAZADYAigBXAGYAeABFACYASwAnADYAQAAPABAANwBHAGoAjQASAPf/ZgBUACsAcABLAPv/VgDm//f/MAD0/6//KAAjAAEADQDY/97/JwDx/xwA4v+g/+f/4v+7/wcABgC8/wMAMAA7AD8A8f8VAIQAFQAHAMb/xv+0/8b/BQDk/7r/BgD3/xsAOwAKAPv/zf8AAN3/z//U/5z/z//k/wYAq//z/8z/AgAWAPH/xf/g//H/zv/Y/0kAGwAiAAgA8v8LAOP/6f/j/9v/CgADAAQACwAUABAAaQAfABoAXQDz/w4ASQD7/zwAHQDT/woA4/8lAEkAgQAIABwAEQBPAGAA1v+b/0IAvv8AAEwAJABHAGkAYADbAKcAjwCCAEUAnABvADYApwAzAEoAZwByAGMAbAAmAPz/dABDAEEAIwBFALEAcgBmAMoAhAB/AK0AkACBAH4AgACgALMAZQBBAIgAPgBCADoAEgCeAHgAsgCdAHEAiACpAJ0A4gC+AI0AKQBLAHoASQCiAFwASAChAGMAZwCPAD8AXQCoAEMAYgB9AJkASwBbAFcAZACUAMEAOgBPAIIAdgBZAG4AjwB7AHwAcwC9AJUA/P9TANAAJgCDAIIAFgBCAEUAPQBpAIEAIgArAFsAKgBUADQASwD6/6v/GAAjAAQA7f/v//H/EwD1/yQA9f8BAPL/+v92AEcAAAAQAB0ARADq/+f/AAAnAB8A1P80AL3/qf/F/7D/dP+Q/8z/dP+s/7X/PP/K/+T/oP+i/83/av+G/6P/8v+k/8//kv9g/4L/9P/N/5n/bv97/6b/n/+s/4H/p/+L/2X/lv9e/0P/hv91/zj/gP/i/lP/fP95/1z/Bf+k/1//Pv9i/6X/fP9//4P/q/9a/yv/fv9Y//3+af98/5T/Uf9T/0H/WP91/1H/X/9M/2f/Lf+D/53/rP8x/4f/gv82/zb/nP+F/3v/g/8h/1L/aP/z/jr/Yf8l/0j/Uv9b/37/Yf+L/07/bf9X/1T/U/8x/y//TP9l/33/d/+Y/2H/Vf99/6n/ZP9J/6v/hP+W/1T/hf+Q/2n/Q/+Z/2j/Vf9B/0r/MP9z/5D/pf+l/6X/vP+o/1f/gv+C/6r/x/99/5n/pP+e/8T/pP/u/zQA1f8KANf/9f8YANj/3P/g//L/9v8fAAkA7/8TABcABQAgAPv/DwDS/6z/8f////P/DgAYAPD/UgBzABYASQClAEsAhQBTAJEAhwB2AEoAjQAUAEYAqwBhAJ0AdABcAGwAIgAyAIkAaACRAJgAPAA5AHcAoQCbAEkANAA1AGEAbwBHAE4ASABdACsAMwBnAAwAbwCGAGUAKgA3ABkAdQAVAOf/KAAjAGoAcgBJADQAJQBTADUAGgAVAOr/FABQAAMA+/8FAMP/2P8BAM3//P8zAAkAGAAOAAIAAQD1/wQALAAtAOz/BwDo/9b/x/+k/6L/EADf/5L/s//L/8P/t/+D/4z/k/9q/8n/3/+B/0n/Xf+R/73/b/92/1X/T/9Q/2P/Tv92/4b/Hv87/0L/B/8Y/xH/IP8I/93+JP8q/w3/V//w/hb/If9U/zj/Hf8i/yj/O/8L/yz/Dv8o/zj/0/4W/wP/zf45/+7+E/9T/yT/Tv8g/+L+AP9K/0L/bv8y/x3/Iv8F/y7/UP85/x//Hf83/yH/Yf9Q/2b/Sf8e/0r/Nv83/33/YP9A/wr/Kf9k/2//E//Z/ij/LP9d/zj/IP9S/2P/V/+f/4j/kf+P/3P/bv+W/2z/jP+k/2j/1/+V/53/jP+a/6H/if+n/6f/lv+X/5X/kv/U/8n/zP/f/57/ov/l/+H/yP/f/9v/CQDm/9X/1v/u/5r/0P/7/83/0v8jAB8ABAAcABMAFwAKAEYAGQDf/9H/1f/U//r/z//0/xoAAgDh/9v/4f8MAP//3f/n//H/EwADAC4ADwD+/ykANAAvAOP/9P8oAAQANwDy/woAEgBHADcAzv8mACEANAAPAB0AEwBSAF4AOwBVAAIAPAA3AOL/2P/V/8v/4v8hAA0AHABDAGYALQAwAFUAbwCHAF0AQAApABYASQAaABoAOAAtAC0AHwACADcAkwA5AC0ARABfACUAXABNAD8ASgBqAEoAbAB9AFEAvACUAL8A3ACoAJkApgC0AOMAxgDAAIkAuACIAGYAWACEAHAAYwCfAGQAWgCAAKUAnQDaANgA1gDUAOkACQEXAecA4wDoAMEA3wDmAMIAjQCvALcAzwC9ANgA/ADqAAABqwDVAOgAwwC+AOQA7gCoAJsAlwAWAcgAsQDgAMUAswD9ANcA7QDgAOkAuwDXAKUAhQA7AI0A6AC9AJkAlgCiAIcAdgBdAHUAjACTAIcAsgCkAGwAegDQAHcAcQBSAL0AmQB2AJ0AhgBEAC0ARwA4AG0ASgAmADoAOAA2ADUAWwAjACwA+P/1/zgAQQBkAFUADQAZAA4AHgD3/xQAAQDL/yQAAgAMAOz/CwDk/93/5P/k/9n/sP+h/6v/n/9l/47/uP99/3P/iv/Q/8z/fP9t//b/1f+1/7n/i/92/4z/3f+j/6j/zf/E/8X/tf+r/5f/tf+h/7r/cP8w/1//UP8+/13/S/97/2P/gP+8/6T/jf9F/5r/hf9u/6//jP97/5L/cP97/8X/rv+b/5z/0f/C/6L/vv+L/6b/xf9s/4b/rv+V/6j/u/+X/8v/wv+y/8v/mv+s/6T/o/+2/9b/p/+W/9f/s//J/8P/BwAZAN//uP8RAA0ADAD///f/EADY/9H/AgD1/8//DwD8/ywAHAD8/yUADgA8AEIALwBGAFgAigBqAFEAfQBWAC0AUwBLAIEAXABcAFoAbwCkAKoAjAA1ABIAWQBhADQAYgBnAF4AgwCAAJgAtQBeAJUAhwCeAHQAogCuAIUAsQB9AF4AXwBiAE8ARwBgADsAQgBVAGYAagBTAD8ASQBkAEYAgwBoAJMAgQCeAI8AoACIAF8AnAB8AH4AkgA/AEMAWwBlAFQAKgAuAFcAbwCZAGoAVABkAKkAlgCHAFsAQQCkAKkAvQCiAKYAmQDFAFYASABHAIAAegA2AEIARgBPAC4ARgBWAGsATwAjAEAAPwBcAEoAJABDADcAbgCPAEMAawB0AGYAUQBxAEcASQBcAGYAJQAeACcAHAAkACIADQAaABgAPwBAABwAGgAmABkALgAeACEALQAqAF4APAAtAFUATQAoAOn/EgDn/0AAGAD//w0ABwC5/9z//P/e/wUABgDu/wEA3P/J/+v/t//m/9b/5P8cAAQA+P8LAP//HgD7/8n/3f/f//f/DwAIAPL/4v/F/8z/jP+k/8P/y//W/8D/0v/V/83/m//a/+j/DADx/9P/yv/X/8X/2f8DAA4A///n//T/AQD6/yIACQDv/x0A5v/7/8f/1/8FAO7/8P8GANX/6P/l/+3/9f8IALD/xP/e/9H/AgDf/9n/IQBmAFcAOgA5AEkAQwBFAFsAQQA+ADUAGQAsABsA8f/h//H/GwDr/xcAEgAZAD0ASAAWAAEAJQAUADUAEgA1ACkAFAAPABQAKAAmACMAMwA9ABsAJQD4/xoAHwAZACsABAAfAO3/xP+7/8r/BAAJAPf/6/8CANj/3v/5/9//5P8OAB8AEQAcAAkA8P8fAEgA1v8YABAA/f/k/+T/+f/j/xQAHwDr/+v/9v/h/wUAAQD8/xYA2//b/wEA7f/t/+H/kv+q/9P/8P8LAPj/BgAcAAIAGgApAC8AOgDy/+X/GgAMAAYALwAZAB4ADQAHAAEA8f/w/+H/FAAsAFIAPABKACYAKABVADwAQgAPABMAUgBSABIA9f8kADYAKAAvADgAOQAwAGQALgBJAEMAZABXAIMAYgA9AEgAOABmAFIAbgCFAEcANwBjAHQAdQBUAEYASgBlAH8AfQBEAFEAfgBoAEsAPwBNAC4AfABTAEUAbABwACYAOgAwADEAYgBUACoARwAnAEsAYwAxACQAUABoAEgADAAIAA4AHQAxACgABwADAOz/z//x/+v/BgDm//r/+//y/8L/mv+n/7L/l/96/7P/mf+p/5f/xf/f/+//nv+5/7f/iv+c/67/if+r/8n/bf9k/3T/JP+J/5P/PP8//wn/Jv8k/w7/Vv9L/0n/M/82/zT/Rv9L/1n/Xv8Y//P+Av8S/x7/DP8I//n+6/4m/+7+LP8o/yL/Av/r/vn+/f4V/0T/Lf8K/+j+JP9T/zb/H/8M//D+Bf8L/+b+Lf8I/yf/Jf8x/z3/Ff8G/zD/Cv8W/yr/I/8+//3+K/82/yL/G/9N/1j/Ov8j/wz/SP9X/1X/cf9c/2r/b/97/3n/Z/98/5T/i/+C/2v/Zv+4/3n/gf9s/1L/hf9R/23/if+J/4j/m/+I/6n/pP+0/3//k/+x/8b/wP+k/7r/y/+K/4j/jf+c/8L/ov+O/7r/s/+w/6L/r//M/9L/r/+c/9X/u/+o/6//qP/e/6X/of+u/8r/qf/E//v/3f/w/8H/rP/H/+b/2//g/5v/nP97/73/xv/N/wgA5/+5/93/7P/n/9r/v//M/8D/wP/Z/7f/xP/r/+T/5v/z/7n/tf/X/6r/wP+c/7r/3P9y/5n/0f/O/8n/3P+n/83/0f+P/wcAzf+q/8z/sf/n/7z/r//6/53/qP+x/6//sf/F/5D/Wv+X/7z/v/+V/7L/CACS/33/x/+2/3n/g//C/53/O/+m/6j/tv+f/1f/X/9t/2H/YP9T/3v/Tf9N/2X/Cv9c/5D/Zv93/07/Vv9A/0f/gf+H/1H/iP+Q/13/Pv8r/zH/O/82/yH/Jf+E/1X/H/8w/0H/GP8j/0j/Iv8b/zr/cP8r/yr/Jf8P/zT/Tv8m/1j/bf8V/yr/B/8Y/xf/6/4P/yT/Df8O/wH/+f4O/9z+Vf9P//X+Cv9G/yv/ef8E//j+a/+V/0P/NP8y/0//Wf8G/2T/Pv9L/xX/Xv9j/2P/Yf9X/3j/m/+u/2X/qv+r/7D/bv9y/2X/jv+p/6f/hv/S/wwAt/8UAKH/m//Z//f/+v/V//n/RABXACYAXgBVADIABQBvADEAEAA0AOz/PwAjAEgAUwBsADMARAA8AH4AKgA9AIAAlADGAFIApQC5AMEAsACUAHoAhwCHALUAbQCJAJ4AUACaAJkA5ADHAHUAtQDPAHYA1gCxAMsAqAB6AIYAfwCTAI8AuwB5AIoAjgBcADkAhAA9AF8AaAB+AJYAfwBwAIcAqQDAAIcAigCYAHkAyAAoAJIATwAXAHwANQCuAD8ARQB1AFYApwBAAEoAhwBRAIwAmQBtAJsAPgB2AGYAVgBYADAAWwB6AFwAEQBoADgAHQBeAFcAQgBUAD4AfwBaAEUAUAA4AGQAhABeAGgAhQBTAEcATgAfAHcAVgBwAH8AJQBgAKIAqgDUAHAAhwBZAG0AJgBxAJEAVQBQACEAkABAAGgAOQA8AHcAfQCsAIoAUgCeAFEAhgB4ADgAhgAaAE8AawCFAHgAgABcADcAWwBJAFYAfQBlAC4AhgBNAIIAmgBVAEwAZACNAIsAXQBuAH0ArgC8ACsAhQBOAFUAnQBHAGYAUQBMAFUAUABsAHIAUACCAKgAmACbAFQAhgAmAEgASwAoAJQAVABVAGcAUABTAGAAFQBUABsAQQBZAA8AOwAgAEQAWQBDAG8AlwCEAJYAdwByAD0ALwAcAA4AXABIAEcABQApAB8AKQBWABwARwBcAEgAcwBTAF8ATgAwAGkAQAA0AEEAPQApAF0AWAA+ABUAewA0AGAANABdAE4AFgCjADcAkwBSACcAewAvAFkAmQBZAJEAcQB2AJwARgApAFQAVgBqAFkAVwBqAIsAsAB3ALcAoQB3AJ8AiAChAK0ARwDLAGsAnABxAJUAsgCSAJAAfwDAALMAiACBAJsAbgCiAJMA0AC8AJYAnwDXAMMAqgC4ALYAtACkAL0AsQDAALMAvQCJAM4AkAC7AJsAoADJAM4AlACvAIsAOwC9AJoAxwDQAM4ArgDdAKMAoQC3AKAApACXAIcAcgCCAIQAiACGAJ8AZQCSAIAAhgCFAKkAZwBwAKUAagCFAEcAbwAuAHgAfQBYAF8AVgBqAJYATAA+AFoAcwBmAGQAggBLAE4AGwBcAB8ARgDZ/zQARABYAFIAawAVAE8AQABCAEsAOQBjAA8AJQAmADoANgBUAAEA/P8kAAEAPwAFACwABAA1AAUAFgAjAB8AawAZACkAOAA3ADEAHQDx/ycA5v8LAAQANwACABcABQD1/xIA7v8gAEkAEgAWADIAJgATANT/AwDp/wkABAAbAPL/7f/n////BAAoAOH/FwAGAAoAEwDt/xYA7P8GAAUA6//1/+z/+P///8r/5/+w/9L/3f/h//v/3f/G/wsAx/+j/9T/zP8CAO7/7P/t/+v/6P/s/9f/CADn/+z/1P/U/7T/nf/n/7b/CwDD/wcAxf/T/93/z/+0/+b/sP/2/+H/5f8HAN7/x//S/7n/6f/c/+P/7v/q/+P/5P/1/+3/AgDe/wUA1f/X/+r/9//X/zIA2v/o/xYACAAhACMAFwD6/+z/CwAQAPr/BwANAPz/BwAIABoAEAAFABEAHAAYAB8AAAAOAPD/HwDX/wkAAAAFACEAHQATAEMAMgAiAA0ANQDr/ycA9/8BADwAKQAbAC8AGgAyAEAAPgAvAE4AQgAjAE0AFQAZAP3/7P/d//f/1/8iAC8ATgBFAB8APwAWABMAJgArACsASwBBAEMAMgAWACoAFAAaABEA+P8fADYADAAgACYAJwA/AB8AXwApAC8ARQAcADwASAAZADIAJgAjACsANwANAC8ABgAJACAAKwAgABAAQgA7AEQASwBeAF0AVQBGAFAAHwBBADUAJABCAD8ARwAxAEUALQBQAGIAWQBjAE0AQwBYAEkASQATAD8AMwAwACwAQgBDADcANABRADsAQQA4ADcAQgArABoAHwAyAFoAMwA1AEEAIAApAA0AGAAKABoAAwDz/wMA6v/7/wwA6v/s/wUA9P/3/+//2P/Z/97/6P/j/8v/x/++/9z/yP/A/9z/uP/W/+j/0//e/73/w//H/8//xv+s/7P/uv+0/6//s/+l/5//wP+r/6D/i/+a/57/r/+f/6T/s/+l/4v/rP+w/6H/q/+o/5f/uv/F/6n/zv+0/7b/yP+1/7r/xv+0/6//vv/D/7z/z//z/7n/t//D/63/uf/a/7f/0f/Z/+3/xf/R/97/y//J/9L/5P8HAP//8P/t/wcABQD0/+3/EQAAABkACAANAOr/9P8AAOr/HwAaAAwALwAJABQAJgAvAD0AOABKADYAUgB6AGUAUgBGAC0AGwBCAD4AIwA7ADIAKgAYACMAKQAWADgANgBIACIANAAtADAAUABAADwANgAyADMAQwBcAE0AOgAlACkAOgAgADQAEwAaAP3/CAAcAP3//v/v/+//+f8HAAUA8v8MAAAAGAAIABEABwDf/wEA/v/m/wUA5v/u/9D/xf/S/8P/uv+U/67/tf+s/8T/vf/m/9H/3v/X/9L/tv/B/8P/qv+U/5z/h/+Y/73/kv+x/6D/sP+Z/6H/gf+D/5b/ff+C/5j/iv9r/4f/pv+W/4r/nP+L/3j/i/+f/3z/qf+x/4T/qP+Z/4f/cv+J/5n/dP+f/4H/UP+Q/4b/d/+d/43/hP9+/5T/mP+t/7X/lv+d/6H/nf+O/7P/iv+C/8b/vv+l/7H/nv/H/5n/tv+Z/7T/t/+m/6f/wf+1/7b/3P++/83/ov/I/7L/3f+z/8H/z//X/77/3P+2/8f/z//F/+//3//l/+T/3v/T/9X/4//Y//X//P+2/8//4v/Z//T/6v/i/93/2v/s//r/9v/1/+P/2/8AAMv/3P/V/9b/y//r/93/4v8KAN//8v/6/+j/3v8HAP7/zf/2/+z/8P8ZAPT/7P8CAO//BQDy/woA9f/9/wkA3P8DACAA/P/2/wYADwDy/xUABgAFAB0AHwAQAAwAMAAzAA4A7v8XAAEAGgA0AA4ACgADAAUAHgAGABoABgD1/yoADQAbACAAHwAfACAAIwAPABQAFwAGACQAHgD8//7/EQAYABEAGAATACEAIADz//n/6P/8//T//v8gAOb/4/8UAP//9f8LAPX//f/f/+L/AwDx/9//6P/N/7//zv/R/+D/1/+6/9b/wP/t/97/u//I/8D/x//A/8n/y//g/7X/z//F/7X/qf+b/6X/sf/E/6v/kv+V/5z/if+q/6n/sv+r/6b/sP+u/57/t/+V/5H/cf+O/4r/m/+I/6f/gv+k/6D/tP+f/6f/pf+l/6//k/+X/5D/t/+L/6b/sP+2/53/s/+u/6H/p/+7/6//yP/Q/8b/2//A/9j/yf/R/9H/qP/G/6v/vv/m//L/5f/r//T/7P/i/w0AGgDl/wgA+f/4//3/EQDu/xMAEwD9/wcACAAhACEAMQBMACwALwAzAC0APAA5ADkANwBAACQALgAlADkAFQA5ADIAQwBYAFwAYQBXAHkASQBeAHAAewB2AGEAVQByAH0AYQBYAFwAXQBgAGMAWQBiAEQAZQBpAFMAawBmAE8AZgBqAEkAXQBvAF0AcQBtAGYAUgBdAFsAOQBXAGkAdgCOAH0AZgBnAHUAZgBWAGAAYABDAFwASQBOAEIAOgAvADMAMgAbAD0ALQBGADsASgBoADAAOQA8ACcAGAAjAC8ALwA+AEAAOwAvAB8ANQAqADEARwAmAFwAMABAAEEAEAAjADUAPAAgACMAIAAxADEAQAA3ACIAJwA6ABoALQBLAEYAUgBWAEgANgA9AFkAWQBPACwASABKADkAVgBYAE0APgA+AEsAUQBYAFoAZgBnAFAANwA/AEQAYABiAEcAWABDAEUAeABRAFIAWwBZAG0AdwB0AE8AXQBlAEoAVABWAFIALQBHAEkAQABpAFYAUABdAE4ARwA+AFAAXABAAFgASABWAEEATQA+AEQAPwBDAGEAVgBXADMAOAA9ADoAQQArADYAOQBLADgAPQAhABgAMQATABUAFAAMABoAHgAiABwAJgBEADgASAA3ACYAFwAdABwAJQA7ABMAAgA0ABgAIAApAC4AIQAKAAMADQAYACoAOgA9ADgALwAZABYAEwAbAAkADQAvADIAOQA9ACIAMAA1ACQAPQA3ADwAOwBCAEsAUwBNADYAOQAlACsANgBFAE8AWABCAFwASQBGAEMAOAAzAE4AcwB0AFoAbQBWAGIATgBEAFUAdwCEAHgAhgCCAHoAZQBvAFUAegB2AIUAegCTAIwAdACAAG4AewB3AHkAfgB/AHIAgQCTAJgAkgCVAKQAowCeAJEAcAByAHEAcQBbAHwAagCKAHkAfwCYAHEAagBqAHgAfgCMAKcAoQCuAIcAhwCYAIAAjQCTAJYAgABsAGoASQBYAFkAbAB6AF0AZgBuAIcAiQBzAHMAeQCCAHIAbABuAJEAnQCbAI0AcABYAEwARABRAE4AVQBHAFYAYQBzAHAAWwBRAEwAVABSAFgARQBDAGMASQA3AGEAUgBkAD8AOQA9ADYALAAnACcARAA9AC8ANgAtADgAMwA3ACsAFgAZABQANQA2AB0AIAD6//b/9v/1//v/AAAZACMAQgA2ABwAIAAXAPn/7//T//z/AgAcAC4ALgApAAoA8v/6/wMABQD6/9///f/w//r/5f/h//r//v8JAPH/+v/4/+r/2P/l/+P/5P8EAPT//v8DANn/zf/S/7z/vv/M/8L/8P/2/wEAAQD//9j/6//x/9n/6P/l/+D/4f/d/9//1P/n//P/7v/t//L///8AAN7/3f/G/8n/vf+k/8T/sP/e/+f/6//T/8r/0v/O/9L/3P/n//z/DgABAPD/5f/Q/8j/1v++/73/6f/5/wwAGgD///L/wP+y/63/of+R/63/tP+1/7r/xv+4/8P/1P+0/8X/sv+y/73/qv+1/7b/wf+//8f/y//A/6n/nv+N/5X/if+H/6X/pf+W/6z/pf+H/23/Yf9a/1//YP9t/4D/a/+K/4P/c/93/1//W/9y/3f/df9h/2j/SP82/0P/Pf9M/z//VP9E/zz/Wv9c/1z/Yf9W/zv/M/86/0T/Sv80/yH/L/8f/yP/Kv8k/z3/Sv9G/y//K/8j/zn/R/9W/0D/JP8S/yL/Nv8x/xX/C/8e/0n/P/87/yX/Ov82/zL/KP8g/wb/A/8T/yj/If8c/yn/Nv81/xX/Hf8k/x//H/8Q/wv/9/4C/x//NP8u/yn/NP8p/zD/Kv8+/z7/P/80/z3/Pv8//zn/Uv9D/z7/Mv8d/yD/Hv8i/wz/A/8M/zf/Yv93/43/a/9J/yP/G/8R/x//OP9c/1r/Uf9W/0z/Tv8+/1L/MP8p/xb/J/9M/1H/J/8j/yT/I/9D/yb/N/82/1L/Vf9M/0//Hv86/0r/T/9i/0f/P/9p/2n/UP9H/0L/R/91/4//l/+c/37/ff+G/2n/hf+V/57/wf+t/5j/i/9r/1L/O/9A/13/jv+U/6D/hf+E/5D/j/+S/2H/YP9p/3//f/9q/33/nP+z/53/bv9z/53/yP/O/8b/yf+z/7z/wf+m/67/rv/G/87/0v+9/7D/vv/a/9z/3//I/7T/3v/X/87/wP+z/7P/s/+e/5f/q/+7/9H/zf+l/7j/r//P/9n/3P/c/9T/5P/4//b/wv+v/5X/s//U/+j/wf/J/wMA9P/f/7v/zv/c/97/wv+x/8f/uf+5/63/sf+5/8z/uP++/6//p/+e/7T/1P/S/9b/xf/U/8b/zf/W/9v/3f+5/6T/q//F/+7/AQDr/9z/3P+z/93/2//k/wAAEAAnABAACwAaABwABAAHAPf/7//v/wAADgApAEgAQQBCABgAFwAzACsAQABJAD8ARwA+AD8AQABHAFEARgA7ADQARwBwAEwAZgBNAD0ASQBUAG8AkACLAG8AggCUAI0AdwBrAFcAYABiAI0AlwCaAI8AggB3AHYAVwBDAEgAbgB8AHAAZgB3AIoAhQCMAFcAZQB+ALgA1gCqAIIAXwBIAG8AhQBlAFkAYABxAJEApACBAHYASQBMAE8AMwBFAFgASABcAFoAggChAI0AaQB0AIYAmQCjAIcAfAB+AIgAewCEAIAAkgCwAKIAowB2AF0AZAB5AKIAmgCbAJcAnACuAKMAYQBcAHoAiQB4AFEAPwBkAJYAkQB5ADQAIQBZAGwAswDaAJ8AewCEALUAvwCBAF4APgB7AKoAjAB7AEIAVABgAH0AlwBWADYASgBjAIwAjwBqAEMAWQBwAG4AQAA0AEoAgwCgAIYAbQBLACoANQA8AFcAiwBrADIAKAAyAF4AcABAAO3/4v/o/xsAgwBqACYAJwAtAJ0AmQBoADIANgBoAHcASAAdAEYAfQCOAE4AGgAVAC8AVAB6ADAA6//H/+j/8f/k/8f/vP/s//H/0v+8/8f/AwAVAAsA/f/c/8D/6/8OAB0AFwD3/w4AEgAPACEASAA1AAgAyf/C/xMAPgAoAPX/zv/c/97/zf+5/8b/2P/u/9T/rf+U/83/9v/6/wgA9f8XABgA7f/e//f/EAANAPv/zv/J//b//f8eAPv/uP/F//b/OwAoANz/q//a/xAA9/+q/3f/iv/O//3/5v/G/7T/l/+o/53/tP+7/6D/hP+Z/+r/CgAAALf/hv+K/3H/qf+n/7T/vv9z/4D/rP/i/7n/mv+c/8j/0/+W/5r/0P8PAPP/mP9h/5n/jf9p/4v/rf+x/6b/ZP9i/5D/iP+V/7b/qv9b/zv/dv+//9j/ov+c/4r/lP+p/8r/8P/l/3j/Uf+V/9L/2//O/37/lP+c/5b/u/+6/5L/hP+B/9j/vP+1/0//U//B/+H/yf9p/zP/Xv+S/8L/tP9X/xz/Q/+9/8v/nf9J/0T/Y/+t/6//av87/0n/dP+U/3r/YP92/1v/Qf9a/4j/p/+b/3z/Sf92/6v/of9k/2r/R/9H/2T/iv+//5D/Zv9l/5f/sf/H/1f/Wf9//3f/ff9m/6z/mv92/yD/QP+C/6P/rv+h/6D/k/9i/2H/df+S/4v/T/8q/1n/wf8IAOD/p/9z/5b/CQDg/7P/dv83/0//bv+W/73/lf+L/73//P8PAND/av9l/4L/yf+c/5n/pf/M/+v/zP+q/6z/rP91/1//Wf+0/9L/pf98/4P/uv/y/9X/0f/M/67/q/+M/5//zP/o/9b/u//Q/7z/zv/U/wsADgDe/6L/ff+o/83/2v+6/5j/AADV/7L/pf+5//L/6f/L/4n/mf/n/93/sf+H/3v/g/+Y/57/t/+W/1v/O/9F/7D/0f9+/1T/NP+S/4//gf+W/2r/Rv8z/0//u//N/3b/K/88/5r/sf80/w3/aP/F/wQAz/9y/4L/sf/T/9b/nf9A/0L/Nf9e/5D/jv9o/y//Uf+L/6P/jf89/yP/Nv9a/5v/qP+e/y3/uf77/nb/pP98/xz/2f4k/5H/k/9h/1D/+/4w/13/kv+W/3T/N/9V/5X/jv+D/0D/Qv83/23/nv+1/7T/a/9p/3f/Yf9//2X/gP9//3v/hv+4/8D/lP+R/33/fP/j/8j/uP+O/3//kv+4/6b/rf/H/9n/ev9z/2z/p/+d/4b/x//L/4b/Yf+v/+P/FgB7/3j/wP8NAAcA3/+q/+D/DQAEAOf/DwA6AAUAGwBCAGMAFwDM/6b/+/9hADsA7v/Y/+z/IADX//3/EwANAJf/Q/+U/xMAQgAXAO7/5/8eABUAPQAxAE0A2f/G/83/QQBiAEoA+f/2/2IAgQB1AFIAUAA+AOf/7//8/7sAfgAUAMX/QACqAFEA6f/c/x0AfQA3AEIASQB0AC4AHABfAFoAFwBS/4j/BABVADIAfv95/xUAUgD3/93/+f8zAOX/uv88AJAAGQCQ/6b/BgCUAEgA/P/g/1QAbAAlABAAMQBmAPX/NABnAIkA/P+F/7v/YwB0ABcAbv+L//r/EwDl/wcADwDP/4v/t/9OAFMAvv9f/5v/MgAKANL/0P8jAPP/lf+0//3/CwCx/2n/3/95ADcAvf/I/zIAYAAHALn/w//v//r/z//e/xsA9/+8/5b/FgAMALf/fv+a//3/OQAZAM7/1P/0/9r/6v8LAD8A+/+o/7b/DABLAMr/5//d/+T/CQDs/9f/TgBMAA8A0v8JACkAFgALAOf/TwAlAAwAHQDx/2IAUAAgAOr/LgA9AOf/yf8sADYA8P+y//L/LQBLAEkAOwBUAHgATQAWAF4AhAB4ACUABABHAIoAcAA/AGoAYQBqAE4AdgDEAJEAVQBeAKoAswCeAEsALAB0AJwAYQBWAE8AUAAhAE0AXgBvAEUA6f/Q/zsAkQBoACgADAA2AFMAWABfAFcAjwBRAOj/OACpAL4AjAAqAGYAZgCYAK0AvwCZAF4AGwAaAJUAtQBNAPb/AQB0AHQAIgAhABkA7v/j/xIAUQB7AFgA6f8EAGsAcQBOABsAGwA5ADMANgCIAFUAXAAjAOv/YgCUAF8A3P/F/ycAKQAKAJz/hv+V/5H/uv/m/wIAzf+F/6D//v9bAOn/r/+h/+v/7P8FAOv/x/+t/4z/y/8TAD4AKADI/5j/uv/r/8D/Xv9D/17/jP+y/63/0v+a/13/UP+K/5v/dP8M/wz/av/x//T/pP9t/37/gP+C/6//1f+x/3r/Xv+N/93/5f+2/3z/f/+Z/8T/q/+v/5L/jv9a/0T/av+P/3P/Qf9p/8X/2v90/37/lf+r/6L/2v/g/8H/tf+g/+j/AwD6/8j/o//T/9f/EgAVAOX/vf+j/4//lf/h/9H/s/99/5P/uv8pABwAKAAfANv/8/8RADoAXQBMANT/u//v/wgAAQABANz/xP/J/5P/+P8gABsA4v+p/9b/AAD4/+L/6P/z/y8AHQD+/9r/CwBRABsAJwAXAA0ACgD7/xEAMgA7AFwAVQCQAF0AIQAiAA4ACgAuAP7/4//7/yMAaAA3AA8AOAA1AHMAdwA+ABAAGwA9AGYAhQBwACsATABVAEMAXABpAEMASABPAFYAXQBxACMAJwAOADwAUgBoAG0AgABbAFsAsABtAJAAdQBfAEsAQABnAH8AhwChAGcATgAlAFMAVgCLAHAANgAoAFgAxwDhAL0AfQA7AFsAeAB/AJwANQBHAFAAWQCZAHEAlACHAHkAhACMAIAAjAChAJEAqQCqAIYAfwCyAKkAnQCdAG4ApQDhABgBAQHKANEA8QAmARsB3wC5AHoAmADIAMEA4QC5AKsAyQDDANEAXQBRACsAGwA1AEsAbQBAACAAPQAwAGYARABoAEUAPgArAFwAaQBZAGUAeQBvAGcAgwCrAMEAXwBPAEcAZwC3AK4AqgC5AJ4ApgDaAL4ADAHQAHgAGQBGAE4AZwBuAGoATAA9AB8AFQAYAPf/EQDO/7n/yP/9/+b/8//p/xEANgAuAB4AKAA3APX/yP/P//H/7v/W/9n/+/8lADQA6//V/6b/r/+4/w4ABgDx//3/5v8CAA4ADAAMAMn/nv+M/7f/sv+g/5n/xP/l/8n/zf/R/8//lP+g/5f/bv9k/2r/j/+Y/1z/hv9h/1X/bf9//3T/Wv9C/xH/Kf89/1X/Uv9q/4T/ff92/4T/eP+K/33/kP9u/57/kP+e/8n/4v+c/3L/Xv9d/zX/GP8+/03/V/9d/1L/Wv82/0L/ev+I/6j/f/9A/y//Nv9M/17/cf9j/07/YP+B/3X/cf9x/0P/HP8e/zP/Mv8w/zr/c/82/yz/Jf8w/xT/Of9K/0f/PP8F/zv/cP+J/33/bv9X/43/b/+C/6T/qf+V/3z/lf+6/6b/n/+N/3L/e/99/4T/Uf8y/zL/Mv9Q/1z/Zv92/0z/OP9T/2b/N/8h/0D/U/91/2H/Vf9h/0L/U/+H/3r/d/9c/2X/Zv+u/9H/6f/M/7H/0P/2/+//4f/4//v/AQAfADAAGwAiABEAGgASAAAA5//U/8T/rv+s/67/lf9V/zT/YP91/33/ov+F/1j/fv+e/9H/3P+e/4f/n//R/9X/3f/G/8n/of+1//r////u/97/vf+6/63/yv/a/8j/o/+G/3P/jv/B/+L/2/+o/27/nf/X/+j/w/+i/8n/rv+2/7z/v//G/6f/i/+P/8P/wP/S/+H/zv+6/5H/kv+a/5f/df+D/63/gP9k/3//if9//3z/lv+6/6P/nf+//9z/3/+i/7H/o/+R/5z/vv/a/+n/uf+p/5z/p/+z/7//3f+8/33/qP/C/8n/wP/A/+n/y/+i/6//6v8SAB4AHgAUAA4ADgDh/wUAMwAaAOz/6v/+/xoANgAtAAEAEgDu/9L/AAAWACEAMQAGAPH/LwBgACcAFQAoACMACwD//xQANAAdAAkA8f80AFsAUgA+ABIAPQBAACEAOABKACEA4/8pAFcAXABOAEoAFQAOABAAOwBWADkALQBXAKcAuADCAJsAaQBrAHsAigCDAIkAWwBRAEgAdQCFAGMARAAWAAEANQAhAAsA9f/S/8X/7P8rAFAAYgA9ACUACQA3AE8AUAAvADEALQBWAJ0ArQCNAJ0AkQB7AD8AEAAiAAcAJQBIAFYAXgBXAD8ATABXAG8ATQBHADkAMABXAGgAYgBTADIAPQBFAGEAfABDACcAKwBEAFwAXwBfAFEAGAARAEIASwBBAA8A9/8HADIAUQBQACoACwDu/ysATwBbAGkALAAgAGUAXQCGAJEAdQBnAC4ATwB3AGUAOAA+AEkAQQBDACkAJAA6ACYAHAAqAP3/2v+s/8T/GAAWACYAJgDs/x4AOQAnACwACwDY/+r/JwBfAFsAYAA6AE8AfwCcAKQAfwAzAAYAQgB8AKcAbgBNAFwAhgCoAJcAhwBOAB4ANgBiAIIAgwA1AD4AcwDMAOMAmQBvAGYAewCiAK8AlwBhAGIArgDmAPUAywC9AMMAmgDJAPUADQH9AA8BKgFRAUwBGwEPAQAB9AAJASgBHgHhALEAvgDqAMcA5wDNAJQAtgDiAPAA8ADqAMsAzwDnAOkA9AAHATABNgFXAVYBagF4AUcBXwFvAYwBgQFiATwBTQFOAXgBjgFiATEB5QD6AD0BVAFPARkBzAC+AA8BMAFIASIB7gDwAAQBIQFTAVgBWwErAU4BcwFkAXYBbgFbAU8BZQFyAWcBWgFRAVABaQFsATQBJQEaAUwBbgF5AVEBGgEOAQ8BJAFLAVMBEAG3ANMAJgFKAXYBOwEIAdsA3gAmAUkBHQHTALQA3gAdATUBNgEYAeIAzAADAS4BTAFQAQkBxgDiAD0BVwE0ASEBDQEJASgBQgFNAUUBKwEpAREBJQEeAfkAEAE0AXABbQFVATMB/gAFARgBKwEzASMBBAHqAAABPAEoARQB1gCLAIwApwDHAOIA2ADnAN0AwwDZAOsA2gDcAJ0AkQCVAJ4AuwCyAIoAhgCCAJQArQCmAIMAbAByAJgAsADCANIA2wDdAAcBIgEKAfIA5wC8AM0A7gANAS8BLwHsALcArAChALQAlgBZABcA8/8BACoAawCbAHsAXwBZAEsAPgAxAAcAHwBCAGEAbgCDAJUAoQB4AG8AYQBfAGoAQABMAIkAjgCNAJIAkQCYAJIAfwB9AHMAWwBNAFgAVQBiAHwAkACgAJ8AmwBzAE8AVABSAEgANwAwADIALABDAGMAYwBJAEAARgApAC8ARgBOAGgAZwBXAFcAUwBFADYAPgBTAGwAjACoAKoAnQCAAHYAaQBVAEkAVQBUAFUAXgBOADIAKgAkAP3/AADq/+f/FQAxADcAIgAWAAgANABcAEMANAAeACcANgAwAEAAQgAlAB4AAQD+/yQAKgAJAO3/5P/a//T/5v/K/6z/m/+o/8z/6f/k/9X/uf+8/8b/0//G/7v/1//1/wkAAAAKAPb/DgAjACYAHwD+/wAAAgDs/8z/sf+7/5//i/+C/1f/X/9+/4j/lP+A/5H/wf+//67/qP+i/8b/2f/E/7//zf/r/woAAQAGAPf/BgATAAEABAAMABcA/P/U/6r/t//g//P/+v/g/6r/jP+l/9j/7v/O/6P/bP+R/7j/3//3/wgA7f/g/8H/sf/N/8P/yf/U/8r/1//0/+7/7P/O/7T/sP+3/8r/of+D/4v/kv+B/6//yv/J/7b/pf+U/7L/uv+L/3j/S/9i/5L/uv/c/9D/tv/R/8H/yv/H/63/oP+m/6H/sP+n/6T/kP+D/3//bv91/1j/O/85/yj/E/8b//P+5f78/hf/Hv8Y//L+6f7t/tH+zP7R/tD+1f7k/v3+Jf9C/zT/F//j/sX+nf6A/pL+ov6X/nr+cf6Q/qX+wf6y/nX+bP5s/kr+PP4+/jP+U/5Y/mz+hP6C/or+mv6B/lL+M/4X/g3+E/4d/jT+PP5E/lb+cv50/lj+Jv4U/gP+Af70/fn9K/5C/lX+b/52/pH+e/5E/lH+VP5S/lD+S/5S/mP+Ov4t/j3+Sv5P/kH+Ov4n/i3+Q/5U/kn+TP5e/nr+jv6y/tD+zf6k/nT+aP5K/mP+df6I/nz+eP6O/qL+vv7C/pj+Yv4q/kr+Yv6f/tn+9P4K/wz/Ef8m/zj/Of8i/zP/Lf8g/yH/Tv+O/5z/f/9t/3D/ZP9m/2//Vf9N/1v/Vv9z/5L/jP+l/8X/lf9N/w7/+f5M/3T/mf+s/7n/wv/O/9b/yf/Q/6P/l/+S/6T/sP/a/woA+v/b/8L/0v/k//X/8f/d/9X/5f/v//z//f8KACEAKAAtACUAOgA2AEAAVgAyACcAEgARAAoA7f/8/+X/BwAkABMABQACAA8AFQArACwAJgAiAAoABQAXADYAMgBfAE0AUABtAGYAWQBOAEEAVQBMAEsAWQBgAGIAZwA7AC8AQABUADwAPQBLAGoAkACLAIEAVQAuABgAQABmAEsAPgA8AEoAcgCfAKIAmgCJAGgAcgB0AI0AUgA3ADsANAA3AF8AfABfAHUAbACAAIAAbABDAEsAZwByAH4AfQBoAFQANwBOAGcAUABWAF4AQwBDAE0AhQBeAE4AMgAzAAcA+P8cABkAFwAyACcAHwAdAAcA9v/o/8T/uP/C/8f/y//p/w8AIQAtAP3/AAAGAP3/6v/C/8b/4v/2/xYAIgApACUANgAaABwAAgAXACEAGgAUABcAFAAsADwANAD+/+f/3v/1/9b/1v/A/9P/5f/k/+b/CQAJABQAFwAEAAgA/v8EABkAEwAWAFMAVQB9AJsAdwBuAGsAQwA6AFMATwBVAGUAagCWAJQAWACOAIIAdgBkAE4ATABFADoASwBeAEwAZgBuAKMAhgB+AHoAmQCBAIMAnAC1AK0AmACgAMUA3wDOAOIA3ADWAPQA+QD3AOQAugDLAPgAAAH0AO0A0wD/ABkBKwEZAe8A1ADiAAkB/ADqAMsAsADHALcA2wDvAMwAwgCrAK0AuQDFAK4AmACkAMIA2QDeAPEA+AABAf4A7QDWANQA0wDGAM4ADQEFAR8B8wDjAO0A6wAPAQsB5QCpAJ8AxgC6AM4ApQCsAJ4AtADCALUArACYAJwAjgCQAGsAiQBuAHAAdQCVAKQAqACUALMAoACjAKwAtgCzAKMAmACTAKwAoACYAIYAegBuAIcAhQBqAE4AVQCXAKUAvACxAJ0AswCwAKQAnQCMAF0AfwBiAGoAugDPALMAmQC7AK0AgACjAJIAegBmAF8APgB5AJgAsgB8AJYAkQB1AIIAoABpAEgAdwBbAKEA1gDXAK4AmwB3AGUAdACOAGkAVwBRAIMAYwBYAGUAbwBxAIQAmACXAHAAagB7AGEAWwBZAHYAhwCNAHYAewClAIMAfQBWAIQAXABdAG4AaQBqAGAAUgBsAJUAjgCKAE8ALwA0AFQAgQBdAFYAcgBZAIgAzQDJANYA4QCxAIAAhwDAAMcAvgCjAKoAyAD5APAA7gDtALQAvACiALcAggBhAI0AfQCAAJ0AmgC0AJEAXwBgAGoAfQBxAG4AXgCHAGwAjgCYAHQAcwBqAHsAXwCGAEcAagBQAFAAUgA8AFsAZgBlAHgAigBpAEYAUQBzAFoAVgBZAGoAUQBGAEcAUwBjAGEARgA2ABkAKQBUAF8AOQARAPf//P8wACgAFAATAAAAGwAZAA8A///T/8r/5//r/93/q//Z/+j/1//g/7D/7P+9/8D/wf/S/8D/jf+R/3r/dP9P/43/qv+T/2T/o/+g/8v/f/91/4b/aP9x/17/Sv9Y/1X/TP90/4D/k/99/5H/Yv+D/5b/cP9d/3P/aP9L/z7/LP8o/0H/Kf8a/0f/HP8f//n+8P7k/gX/B/8L/wr/BP///jb/TP8T/xn/LP8t/9L+/f7//iD/LP80/yv/EP///i7/Jf/0/vf+7/4R/wT/Cf/5/gT/Qv9W/y//G/8F/wD/PP9D/zb/DP8G//3+Ov8j/yr/Fv8j/xr/Iv9G/z3/PP8t/yz/M/9c/4H/WP8R/zT/LP88/2X/Zf88/0P/V/+D/5L/r/9+/2T/cv9v/2X/sf+V/4n/o/+f/+P/z/+q/6//ov+P/8X/2v+y/6r/lf++/7r/tf+w/9D/g/+E/5//4//b/7j/5//Y//3/HAA6ACgACQATABwAIQA0ADoADwAWAAsAFgBFACgAQQAmAPX/NgA4AC8AQQCBAEkANQBHAFMASwBPAGUAGQATAA8AHAAwADQAZgAuACwANwA+AFIAYwBlAHEAfACIALIAigCNAIoAmQCBAIkAZgBBAE4ATwBnAG4AWQAvAGIANAAuAD8ATgAjABEAMgBJABgAKwBMADcABQAZACcASgBCACQALQA9ADEAOABuADUAIgBDAF8ARgAsAF0AKwAPAC4AFwAUAA8A3//4//P/4v/X/x0AAwArAPP/6f8CAOv/z//4//H/+P/f//L//f/L/7H/uv/T/8b/tf90/47/l/9q/2D/fP+2/5v/cP+V/4//lv99/53/ef9s/3T/l/91/4v/n/+6/53/lf+F/5D/m/+X/3//dv+Z/4D/ff+a/4P/c/+O/2P/T/9B/yf/QP9H/1j/Gf9G/1T/Wf9H/2z/Xf9q/3r/WP9s/0n/fv9O/1//dP9z/2f/cv+A/3T/ef9a/4//cv9n/2b/Sf9u/2//ZP9h/07/cf97/3//k/+d/4P/vf+N/7r/vf+v/53/pP+1/7H/xv+6/7H/l//B/+3/AADU/87/GgD2/9v/DQD0/+L/1f/M/7n/9P/5/8f/nf/T/7b/1P/Y/8j/4//Z/+v/yP+z/8H/AQDS/+v/5v8IAOn/4f+9/8z/nf/j//X/z//Y/9f/6f/Z////+P8FANb/+v/v/xoADQARAAIAFgAIAOz/BAD5//L/wP+H/6n/rP+b/8D/3v++/6X/nf/3/+z/xv/J/+f/5f/d/ykAJQDg/wEA4v/q/9X/yf/5/5//kv+//7b/qP+T/6L/tP+L/7f/2//S/+H/v////+b/xf/P/9L/yf8BAPD/5P/p/7P/1//I/7P/q/+u/5b/qf+X/9H/sv+P/8L/pf+f/7D/iv+K/53/mf9v/0z/gv+T/3v/xf+j/4j/aP+N/6P/kP/C/77/uf+5/6n/k//D/+D/qv+l/6H/iv9S/4f/Zf9p/4r/af9l/1j/UP9z/6v/av+X/4f/fv+1/9v/r/+1/6v/uf/O/6P/l/+W/4v/pP/W/9T/wv+g/4P/df+O/6f/jf92/23/Q/8t/w//Pv91/z3/QP9p/0b/fv9V/4n/mP9I/yb/ZP92/4//Zf+l/6H/rf+K/7T/x/+e/8L/mf+4/8f/xv+W/8z/1P+S/8f/uP+l/5L/j/+V/8j/nP/P/+T/wf/f/6D/1f/S/9P/zf/h/+r/6v8QAAAAEgA+AAMADgAiACYAMQD+/woA3f8fAFYAHgAKABcAGAAHAPD/HgAlADYABAAXADoAaQA7AFkAZABBAFoAdAAyAFwATgBYAIQAYgBVAKMAhABuAG0AaQBRAF0AXwBLAEMAgABnAFkAYwBbAG4AaACIAHEAkwCEAKcAxQCUADUAhgCfAGYAbACCAJgAdQB1AG4AjgCRAIAAtgB3AIUAkACJAJ0AlgB2AG4AjgCkAHUAdQCnAHQAdwBiAGoAjwBmAHkAjwCIAMUAvwCcAKgAjgCXAKYAsQCkAJcAcgBsAIgAcABsALoArQCrAKAAtQCFAH4AhAC0AJ4AiQCAAIcAlQCeAMUArQB0ALoAqgBSAHwAhACmAFgAbgCzAJUAdgB4AHwAfwBRAJoArwCNAFMAigCUAGEAawClAHoALQCDAIkAigCBAIwAlQB4AF4AdAA5ADIAPwBPAH4AXgBdAJIAOwBaAE0AUwA8ADUAJAAPADcAKwBBADsASwA9AAkABgAPAPr//P/x//f/0v/S/9H/3v/C/83/vv/Q/63/lP+K/8D/xP/1/+z/2//i/+D/+v/g/+7/OAASAOL/2//0/w8AAwAJAAcA6//q/+j/+/8JABAABwAAANz/uv/d/7n//v/Y/6H/1/+6/7X/vP/D/3v/cP9u/4b/jf+N/3r/0v/O/63/t//M/9z/h/+k/6P/nP+Y/73/3P+w/6j/7P/8/wsA6f/D/9D/mv+R/9T/0P///97/xP/y/xAA/P/+/wsA8v+4/6P/qf+h/6n/rf+8/77/q/+g/8L/s//u/9L/v/92/+f/vP+6/+P/8f/m/9r/7v/Q/8j/lP+n/37/rP/Q/8r/6v/+/+r/4/8CAN7/vv+9/7D/s//H/+L/0f/o//L/HAAJANP/3P/P//j/4f/q/+r/yP8SAAIA5//c//D/2f+j//b/DgD5//P/EAAsABEA8P8mAAMAy/+u/6T/8/++/63/wP/X/+T/zP8FAAYA5f8gAAwACgDa/w0AAADv/8X/+f/c//P/JQAFABQA///j/wYA4v8PAAMAIQA7AOf/AwBGABIAHwD4/+r/AwDs//r/BQAKAML/3f8PANf/5f/J/+b/CQDy/+//FwAJANz/AQAsAA8AAQAjABoA+//g/yUADQDA/6D/uv+3/6r/vv+x/97/x/+q/8j/1f/p/9r/6/8BAMX/+P/0/8X/wf/T/+j/5v+d/8L/zf+h/6f/zf+l/8f/zf/u/9j/wv/H/+3/\" type=\"audio/x-wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Audio('/content/tmp/noisy-vctk-16k/clean_trainset_28spk_wav_16k/p226_001.wav')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "5nBshIO0PL8B",
        "outputId": "ea335762-250f-44d2-a4bc-8e224276ad9e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "                <audio  controls=\"controls\" >\n",
              "                    <source 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\" type=\"audio/x-wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Audio('/content/tmp/noisy-vctk-16k/noisy_trainset_28spk_wav_16k/p226_001.wav')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h-1r_YdBPdQg",
        "outputId": "2947ac4f-2fa7-4c24-bc20-2a46d4788f92"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Please call Stella.  \n"
          ]
        }
      ],
      "source": [
        "cat '/content/tmp/noisy-vctk-16k/trainset_28spk_txt/p226_001.txt'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cegk5PEqU9yv"
      },
      "outputs": [],
      "source": [
        "os.makedirs('chkpt', exist_ok=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qg7QqVotVMJ7",
        "outputId": "ff995981-8f67-4ce7-b48c-5ebad1e570de"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-28 11:20:29--  https://huggingface.co/speechbrain/metricgan-plus-voicebank/resolve/main/enhance_model.ckpt\n",
            "Resolving huggingface.co (huggingface.co)... 3.163.189.114, 3.163.189.90, 3.163.189.74, ...\n",
            "Connecting to huggingface.co (huggingface.co)|3.163.189.114|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7586021 (7.2M)\n",
            "Saving to: ‘enhance_model.ckpt’\n",
            "\n",
            "enhance_model.ckpt  100%[===================>]   7.23M  16.2MB/s    in 0.4s    \n",
            "\n",
            "2024-07-28 11:20:30 (16.2 MB/s) - ‘enhance_model.ckpt’ saved [7586021/7586021]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "! wget https://huggingface.co/speechbrain/metricgan-plus-voicebank/resolve/main/enhance_model.ckpt\n",
        "! mv enhance_model.ckpt chkpt/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "79JOvVlxV4HX",
        "outputId": "a9423969-217e-4ccb-8fde-3ca4f2f56446"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-28 11:20:35--  https://huggingface.co/speechbrain/metricgan-plus-voicebank/resolve/main/config.json\n",
            "Resolving huggingface.co (huggingface.co)... 3.163.189.114, 3.163.189.90, 3.163.189.74, ...\n",
            "Connecting to huggingface.co (huggingface.co)|3.163.189.114|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 56 [text/plain]\n",
            "Saving to: ‘config.json’\n",
            "\n",
            "\rconfig.json           0%[                    ]       0  --.-KB/s               \rconfig.json         100%[===================>]      56  --.-KB/s    in 0s      \n",
            "\n",
            "2024-07-28 11:20:35 (30.0 MB/s) - ‘config.json’ saved [56/56]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "! wget https://huggingface.co/speechbrain/metricgan-plus-voicebank/resolve/main/config.json\n",
        "! mv config.json chkpt/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TH06JfHed2oX",
        "outputId": "3fe8e8f2-62e0-46c7-83b5-e9b8edc6b517"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/speechbrain/recipes/Voicebank/enhance/MetricGAN\n"
          ]
        }
      ],
      "source": [
        "cd  recipes/Voicebank/enhance/MetricGAN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CixOomF9fi5w",
        "outputId": "b5dc3a51-01ad-4be5-ddc5-d4fd7bd6ad3f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pesq\n",
            "  Downloading pesq-0.0.4.tar.gz (38 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: pesq\n",
            "  Building wheel for pesq (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pesq: filename=pesq-0.0.4-cp310-cp310-linux_x86_64.whl size=262964 sha256=e9b18a7d9fd5644b70f840ae97eae518da4c3100e6d5b3030ec9cb3bc9a77283\n",
            "  Stored in directory: /root/.cache/pip/wheels/c5/4e/2c/251524370c0fdd659e99639a0fbd0ca5a782c3aafcd456b28d\n",
            "Successfully built pesq\n",
            "Installing collected packages: pesq\n",
            "Successfully installed pesq-0.0.4\n"
          ]
        }
      ],
      "source": [
        "! pip install pesq"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XJmmAT7Gd828",
        "outputId": "e97eee83-17fd-427d-f96d-7f669324e487"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2024-07-28 11:33:54.208608: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-07-28 11:33:54.208662: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-07-28 11:33:54.210572: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-07-28 11:33:54.218339: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2024-07-28 11:33:55.289110: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "Downloading http://www.openslr.org/resources/11/librispeech-lexicon.txt to /content/vctk_data/lexicon.txt\n",
            "librispeech-lexicon.txt: 5.63MB [00:01, 2.92MB/s]                \n",
            "Traceback (most recent call last):\n",
            "  File \"/content/speechbrain/recipes/Voicebank/enhance/MetricGAN/train.py\", line 589, in <module>\n",
            "    datasets = dataio_prep(hparams)\n",
            "  File \"/content/speechbrain/recipes/Voicebank/enhance/MetricGAN/train.py\", line 551, in dataio_prep\n",
            "    datasets[dataset] = sb.dataio.dataset.DynamicItemDataset.from_json(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/speechbrain/dataio/dataset.py\", line 401, in from_json\n",
            "    return cls(data, dynamic_items, output_keys)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/speechbrain/dataio/dataset.py\", line 153, in __init__\n",
            "    static_keys = list(self.data[self.data_ids[0]].keys())\n",
            "IndexError: list index out of range\n"
          ]
        }
      ],
      "source": [
        "! cd  speechbrain/recipes/Voicebank/enhance/MetricGAN && python train.py hparams/train.yaml --data_folder=/content/vctk_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GLRXpIQnxxuU"
      },
      "source": [
        "# Attempt 3: `microsoft/DNS-Challenge`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Heq9Yarhs3jP",
        "outputId": "4855bdba-810e-4e63-95f9-8ffe35df5610"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'DNS-Challenge'...\n",
            "remote: Enumerating objects: 1284985, done.\u001b[K\n",
            "remote: Counting objects: 100% (619/619), done.\u001b[K\n",
            "remote: Compressing objects: 100% (333/333), done.\u001b[K\n",
            "remote: Total 1284985 (delta 306), reused 563 (delta 280), pack-reused 1284366\u001b[K\n",
            "Receiving objects: 100% (1284985/1284985), 275.02 MiB | 15.44 MiB/s, done.\n",
            "Resolving deltas: 100% (680/680), done.\n",
            "Updating files: 100% (132177/132177), done.\n",
            "\n",
            "Exiting because of \"interrupt\" signal.\n",
            "warning: Clone succeeded, but checkout failed.\n",
            "You can inspect what was checked out with 'git status'\n",
            "and retry with 'git restore --source=HEAD :/'\n",
            "\n",
            "^C\n"
          ]
        }
      ],
      "source": [
        "! git clone -b interspeech2020/master  https://github.com/microsoft/DNS-Challenge.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8P9-XWUiwNRT"
      },
      "outputs": [],
      "source": [
        "! mkdir training_set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IaNc32wnv15F"
      },
      "outputs": [],
      "source": [
        "!  cp -r DNS-Challenge/datasets/clean training_set/clean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eV-z6je7xUx9"
      },
      "outputs": [],
      "source": [
        "! mkdir dns/training_set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PXE7xweZs5al"
      },
      "outputs": [],
      "source": [
        "! mv DNS-Challenge/datasets/noise dns/training_set/noisy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3VDljMdFqdrA",
        "outputId": "dbc488cb-0bf1-4854-d64f-268a048052f9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'cleanunet'...\n",
            "remote: Enumerating objects: 46, done.\u001b[K\n",
            "remote: Counting objects: 100% (42/42), done.\u001b[K\n",
            "remote: Compressing objects: 100% (25/25), done.\u001b[K\n",
            "remote: Total 46 (delta 14), reused 31 (delta 12), pack-reused 4\u001b[K\n",
            "Receiving objects: 100% (46/46), 28.19 KiB | 1.13 MiB/s, done.\n",
            "Resolving deltas: 100% (14/14), done.\n",
            "Filtering content: 100% (2/2), 351.59 MiB | 44.45 MiB/s, done.\n"
          ]
        }
      ],
      "source": [
        "! git clone https://github.com/nvidia/cleanunet.git\n",
        "! cd cleanunet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wUhjw97YqiXN",
        "outputId": "420bb908-7098-496a-85ae-e2c01495d961"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.25.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (1.11.4)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.3.0+cu121)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.18.0+cu121)\n",
            "Requirement already satisfied: torchaudio in /usr/local/lib/python3.10/dist-packages (2.3.0+cu121)\n",
            "Collecting pesq\n",
            "  Downloading pesq-0.0.4.tar.gz (38 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting pystoi\n",
            "  Downloading pystoi-0.4.1-py2.py3-none-any.whl (8.2 kB)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (4.66.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.15.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch) (2.20.5)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n",
            "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.3.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch) (12.5.82)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (9.4.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.5)\n",
            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Building wheels for collected packages: pesq\n",
            "  Building wheel for pesq (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pesq: filename=pesq-0.0.4-cp310-cp310-linux_x86_64.whl size=262963 sha256=de8b8fd4df5af59b9fcb758b8f5cc1ac2d0579514ffcfff91db14b90d6648d1d\n",
            "  Stored in directory: /root/.cache/pip/wheels/c5/4e/2c/251524370c0fdd659e99639a0fbd0ca5a782c3aafcd456b28d\n",
            "Successfully built pesq\n",
            "Installing collected packages: pesq, pystoi\n",
            "Successfully installed pesq-0.0.4 pystoi-0.4.1\n"
          ]
        }
      ],
      "source": [
        "pip install numpy scipy torch torchvision torchaudio pesq pystoi tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yTlQJTmMrCZR",
        "outputId": "00284e0b-a38a-4638-b76b-5ab04d1f568a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/cleanunet\n"
          ]
        }
      ],
      "source": [
        "cd cleanunet/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cWNUBZazrFOm",
        "outputId": "8f7db037-7651-48a9-c622-315ad4db6de4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DNS-large-full.json  DNS-large-high.json\n"
          ]
        }
      ],
      "source": [
        "ls configs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lm-lK9dVqt_2",
        "outputId": "eb5d7810-b416-4e2a-c75d-4c0cb6367814"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "exp_path: DNS-large-high\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/cleanunet/denoise.py\", line 153, in <module>\n",
            "    denoise(gen_config[\"output_directory\"],\n",
            "  File \"/content/cleanunet/denoise.py\", line 73, in denoise\n",
            "    dataloader = load_CleanNoisyPairDataset(\n",
            "  File \"/content/cleanunet/dataset.py\", line 92, in load_CleanNoisyPairDataset\n",
            "    dataset = CleanNoisyPairDataset(root=root, subset=subset, crop_length_sec=crop_length_sec)                                                       \n",
            "  File \"/content/cleanunet/dataset.py\", line 39, in __init__\n",
            "    assert N_clean == N_noisy\n",
            "AssertionError\n"
          ]
        }
      ],
      "source": [
        "! python denoise.py -c configs/DNS-large-high.json --ckpt_iter pretrained"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nkHKrLn3x3bS"
      },
      "source": [
        "# Attempt 4: `resemble-enhance`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "07Z_Pi_IyBnu",
        "outputId": "5ea89174-68d4-4121-fa04-c7ac908374c8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting resemble-enhance\n",
            "  Downloading resemble_enhance-0.0.1-py3-none-any.whl (57 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.1/57.1 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting celluloid==0.2.0 (from resemble-enhance)\n",
            "  Downloading celluloid-0.2.0-py3-none-any.whl (5.4 kB)\n",
            "Collecting deepspeed==0.12.4 (from resemble-enhance)\n",
            "  Downloading deepspeed-0.12.4.tar.gz (1.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting librosa==0.10.1 (from resemble-enhance)\n",
            "  Downloading librosa-0.10.1-py3-none-any.whl (253 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m253.7/253.7 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting matplotlib==3.8.1 (from resemble-enhance)\n",
            "  Downloading matplotlib-3.8.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.6/11.6 MB\u001b[0m \u001b[31m23.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting numpy==1.26.2 (from resemble-enhance)\n",
            "  Downloading numpy-1.26.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.2/18.2 MB\u001b[0m \u001b[31m44.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting omegaconf==2.3.0 (from resemble-enhance)\n",
            "  Downloading omegaconf-2.3.0-py3-none-any.whl (79 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.5/79.5 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting pandas==2.1.3 (from resemble-enhance)\n",
            "  Downloading pandas-2.1.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.3/12.3 MB\u001b[0m \u001b[31m71.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting ptflops==0.7.1.2 (from resemble-enhance)\n",
            "  Downloading ptflops-0.7.1.2.tar.gz (15 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting rich==13.7.0 (from resemble-enhance)\n",
            "  Downloading rich-13.7.0-py3-none-any.whl (240 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m240.6/240.6 kB\u001b[0m \u001b[31m20.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: scipy==1.11.4 in /usr/local/lib/python3.10/dist-packages (from resemble-enhance) (1.11.4)\n",
            "Requirement already satisfied: soundfile==0.12.1 in /usr/local/lib/python3.10/dist-packages (from resemble-enhance) (0.12.1)\n",
            "Collecting torch==2.1.1 (from resemble-enhance)\n",
            "  Downloading torch-2.1.1-cp310-cp310-manylinux1_x86_64.whl (670.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m670.2/670.2 MB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torchaudio==2.1.1 (from resemble-enhance)\n",
            "  Downloading torchaudio-2.1.1-cp310-cp310-manylinux1_x86_64.whl (3.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.3/3.3 MB\u001b[0m \u001b[31m102.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[33mWARNING: typer 0.12.3 does not provide the extra 'all'\u001b[0m\u001b[33m\n",
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            "\u001b[?25hCollecting starlette<0.38.0,>=0.37.2 (from fastapi->gradio==4.8.0->resemble-enhance)\n",
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            "\u001b[?25hCollecting fastapi-cli>=0.0.2 (from fastapi->gradio==4.8.0->resemble-enhance)\n",
            "  Downloading fastapi_cli-0.0.4-py3-none-any.whl (9.5 kB)\n",
            "Collecting ujson!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0,>=4.0.1 (from fastapi->gradio==4.8.0->resemble-enhance)\n",
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            "\u001b[?25hCollecting email_validator>=2.0.0 (from fastapi->gradio==4.8.0->resemble-enhance)\n",
            "  Downloading email_validator-2.2.0-py3-none-any.whl (33 kB)\n",
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            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx->gradio==4.8.0->resemble-enhance) (1.2.1)\n",
            "Collecting httptools>=0.5.0 (from uvicorn>=0.14.0->gradio==4.8.0->resemble-enhance)\n",
            "  Downloading httptools-0.6.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (341 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m341.4/341.4 kB\u001b[0m \u001b[31m44.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting python-dotenv>=0.13 (from uvicorn>=0.14.0->gradio==4.8.0->resemble-enhance)\n",
            "  Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB)\n",
            "Collecting uvloop!=0.15.0,!=0.15.1,>=0.14.0 (from uvicorn>=0.14.0->gradio==4.8.0->resemble-enhance)\n",
            "  Downloading uvloop-0.19.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m107.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting watchfiles>=0.13 (from uvicorn>=0.14.0->gradio==4.8.0->resemble-enhance)\n",
            "  Downloading watchfiles-0.22.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m82.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hBuilding wheels for collected packages: deepspeed, ptflops, antlr4-python3-runtime, ffmpy\n",
            "  Building wheel for deepspeed (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for deepspeed: filename=deepspeed-0.12.4-py3-none-any.whl size=1290645 sha256=cded54fd725b91f1369451d4737c86ecfe783ef7b616ce495b51afb115ffa21b\n",
            "  Stored in directory: /root/.cache/pip/wheels/7b/0c/52/f464610477b069120f740202a9d84a27f9d7235cbf035c4b75\n",
            "  Building wheel for ptflops (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ptflops: filename=ptflops-0.7.1.2-py3-none-any.whl size=13213 sha256=e1ef9668e80cee7ec16b889ca30ea16cc6ee310bb91dc601318ddda4d88f05b2\n",
            "  Stored in directory: /root/.cache/pip/wheels/9d/90/07/20e8c3221349a85d63b319593e1bcbb6e0c995d2e2bcc5d775\n",
            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144554 sha256=915714a6336a65915a11a37db4405be3cd964fb5d02ca2c7bde27485a261b1bd\n",
            "  Stored in directory: /root/.cache/pip/wheels/12/93/dd/1f6a127edc45659556564c5730f6d4e300888f4bca2d4c5a88\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.2-py3-none-any.whl size=5584 sha256=5b0732f5abd9883e89c173c77c181bb4e6cb71b8bb9f85ecd9865b76833b1c38\n",
            "  Stored in directory: /root/.cache/pip/wheels/bd/65/9a/671fc6dcde07d4418df0c592f8df512b26d7a0029c2a23dd81\n",
            "Successfully built deepspeed ptflops antlr4-python3-runtime ffmpy\n",
            "Installing collected packages: pydub, ninja, hjson, ffmpy, antlr4-python3-runtime, websockets, uvloop, ujson, triton, tqdm, tomlkit, tabulate, semantic-version, python-multipart, python-dotenv, pynvml, orjson, omegaconf, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, httptools, h11, dnspython, aiofiles, watchfiles, uvicorn, starlette, rich, pandas, nvidia-cusparse-cu12, nvidia-cudnn-cu12, httpcore, email_validator, resampy, nvidia-cusolver-cu12, matplotlib, httpx, torch, librosa, gradio-client, fastapi-cli, celluloid, torchvision, torchaudio, ptflops, fastapi, deepspeed, gradio, resemble-enhance\n",
            "  Attempting uninstall: triton\n",
            "    Found existing installation: triton 2.3.0\n",
            "    Uninstalling triton-2.3.0:\n",
            "      Successfully uninstalled triton-2.3.0\n",
            "  Attempting uninstall: tqdm\n",
            "    Found existing installation: tqdm 4.66.4\n",
            "    Uninstalling tqdm-4.66.4:\n",
            "      Successfully uninstalled tqdm-4.66.4\n",
            "  Attempting uninstall: tabulate\n",
            "    Found existing installation: tabulate 0.9.0\n",
            "    Uninstalling tabulate-0.9.0:\n",
            "      Successfully uninstalled tabulate-0.9.0\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.25.2\n",
            "    Uninstalling numpy-1.25.2:\n",
            "      Successfully uninstalled numpy-1.25.2\n",
            "  Attempting uninstall: rich\n",
            "    Found existing installation: rich 13.7.1\n",
            "    Uninstalling rich-13.7.1:\n",
            "      Successfully uninstalled rich-13.7.1\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 2.0.3\n",
            "    Uninstalling pandas-2.0.3:\n",
            "      Successfully uninstalled pandas-2.0.3\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.7.1\n",
            "    Uninstalling matplotlib-3.7.1:\n",
            "      Successfully uninstalled matplotlib-3.7.1\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.3.0+cu121\n",
            "    Uninstalling torch-2.3.0+cu121:\n",
            "      Successfully uninstalled torch-2.3.0+cu121\n",
            "  Attempting uninstall: librosa\n",
            "    Found existing installation: librosa 0.10.2.post1\n",
            "    Uninstalling librosa-0.10.2.post1:\n",
            "      Successfully uninstalled librosa-0.10.2.post1\n",
            "  Attempting uninstall: torchvision\n",
            "    Found existing installation: torchvision 0.18.0+cu121\n",
            "    Uninstalling torchvision-0.18.0+cu121:\n",
            "      Successfully uninstalled torchvision-0.18.0+cu121\n",
            "  Attempting uninstall: torchaudio\n",
            "    Found existing installation: torchaudio 2.3.0+cu121\n",
            "    Uninstalling torchaudio-2.3.0+cu121:\n",
            "      Successfully uninstalled torchaudio-2.3.0+cu121\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "bigframes 1.11.0 requires tabulate>=0.9, but you have tabulate 0.8.10 which is incompatible.\n",
            "google-colab 1.0.0 requires pandas==2.0.3, but you have pandas 2.1.3 which is incompatible.\n",
            "torchtext 0.18.0 requires torch>=2.3.0, but you have torch 2.1.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed aiofiles-23.2.1 antlr4-python3-runtime-4.9.3 celluloid-0.2.0 deepspeed-0.12.4 dnspython-2.6.1 email_validator-2.2.0 fastapi-0.111.0 fastapi-cli-0.0.4 ffmpy-0.3.2 gradio-4.8.0 gradio-client-0.7.1 h11-0.14.0 hjson-3.1.0 httpcore-1.0.5 httptools-0.6.1 httpx-0.27.0 librosa-0.10.1 matplotlib-3.8.1 ninja-1.11.1.1 numpy-1.26.2 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.18.1 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105 omegaconf-2.3.0 orjson-3.10.6 pandas-2.1.3 ptflops-0.7.1.2 pydub-0.25.1 pynvml-11.5.0 python-dotenv-1.0.1 python-multipart-0.0.9 resampy-0.4.2 resemble-enhance-0.0.1 rich-13.7.0 semantic-version-2.10.0 starlette-0.37.2 tabulate-0.8.10 tomlkit-0.12.0 torch-2.1.1 torchaudio-2.1.1 torchvision-0.16.1 tqdm-4.66.1 triton-2.1.0 ujson-5.10.0 uvicorn-0.30.1 uvloop-0.19.0 watchfiles-0.22.0 websockets-11.0.3\n"
          ]
        },
        {
          "data": {
            "application/vnd.colab-display-data+json": {
              "id": "2685f42ec71c4ef3a7e54a5c8df2d378",
              "pip_warning": {
                "packages": [
                  "matplotlib",
                  "mpl_toolkits",
                  "pydevd_plugins"
                ]
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "pip install resemble-enhance --upgrade"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m8WJLyC8yyEb",
        "outputId": "2693128f-603c-4caa-bb94-e463e01d6178"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mv: cannot stat 'example.wav': No such file or directory\n"
          ]
        }
      ],
      "source": [
        "! mv example.wav in_dir/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dna50VZc9hD1"
      },
      "outputs": [],
      "source": [
        "! mkdir in_dir"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qybg_SMIyt2i"
      },
      "outputs": [],
      "source": [
        "! mkdir out_dir"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X4TiO_LBypq6",
        "outputId": "f3a180f3-620e-4a03-caaf-386fa5f2e5f6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2024-07-06 12:13:25,127] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n",
            "Processing out_dir/example.wav:   0% 0/1 [00:00<?, ?it/s]Cloning into '/usr/local/lib/python3.10/dist-packages/resemble_enhance/model_repo'...\n",
            "remote: Enumerating objects: 40, done.\u001b[K\n",
            "remote: Counting objects: 100% (12/12), done.\u001b[K\n",
            "remote: Compressing objects: 100% (11/11), done.\u001b[K\n",
            "remote: Total 40 (delta 9), reused 1 (delta 1), pack-reused 28 (from 1)\u001b[K\n",
            "Unpacking objects: 100% (40/40), 6.22 KiB | 454.00 KiB/s, done.\n",
            "\n",
            "  0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "100% 1/1 [00:07<00:00,  7.51s/it]\n",
            "Processing out_dir/example.wav: 100% 1/1 [00:32<00:00, 32.47s/it]\n",
            "🌟 Enhancement done! 1 files processed in 32.47s\n"
          ]
        }
      ],
      "source": [
        "! resemble-enhance in_dir out_dir"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UNwwX-ZVBKc4"
      },
      "source": [
        "# Attempt 5: `breizhn/DTLN`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zUSwo-4sBL-e",
        "outputId": "9459c9b4-cb6b-4b6d-f1ee-bd7ec309e32f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'DTLN'...\n",
            "remote: Enumerating objects: 333, done.\u001b[K\n",
            "remote: Counting objects: 100% (100/100), done.\u001b[K\n",
            "remote: Compressing objects: 100% (20/20), done.\u001b[K\n",
            "remote: Total 333 (delta 93), reused 80 (delta 80), pack-reused 233\u001b[K\n",
            "Receiving objects: 100% (333/333), 26.08 MiB | 31.99 MiB/s, done.\n",
            "Resolving deltas: 100% (188/188), done.\n"
          ]
        }
      ],
      "source": [
        "! git clone https://github.com/breizhn/DTLN.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nLBD5VzbBjgl"
      },
      "outputs": [],
      "source": [
        "! mkdir target_dir"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8toOBA8nBuAm"
      },
      "outputs": [],
      "source": [
        "! cd DTLN/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w4dZOex5B75k",
        "outputId": "0653e382-2e57-4198-afd5-fbd031dd96ea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "convert_weights_to_onnx.py\t   measure_execution_time.py\t real_time_processing_tf_lite.py\n",
            "convert_weights_to_saved_model.py  pretrained_model\t\t run_evaluation.py\n",
            "convert_weights_to_tf_lite.py\t   README.md\t\t\t run_training.py\n",
            "DTLN_model.py\t\t\t   real_time_dtln_audio.py\t tflite_env.yml\n",
            "eval_env.yml\t\t\t   real_time_processing_onnx.py  train_env.yml\n",
            "LICENSE\t\t\t\t   real_time_processing.py\n"
          ]
        }
      ],
      "source": [
        "! cd DTLN && ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fxh81qWbCRZO",
        "outputId": "ea96a2a6-4c07-4762-8c6c-0dd27107400e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting wavinfo\n",
            "  Downloading wavinfo-2.3.0-py3-none-any.whl (28 kB)\n",
            "Requirement already satisfied: lxml~=4.9.2 in /usr/local/lib/python3.10/dist-packages (from wavinfo) (4.9.4)\n",
            "Installing collected packages: wavinfo\n",
            "Successfully installed wavinfo-2.3.0\n"
          ]
        }
      ],
      "source": [
        "! pip install wavinfo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iL238RZqBWcJ",
        "outputId": "57e77df8-7fec-4839-c7fe-a99541facc27"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2024-07-06 12:34:55.379785: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-07-06 12:34:55.379842: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-07-06 12:34:55.381344: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-07-06 12:34:55.389568: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2024-07-06 12:34:56.583466: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "2024-07-06 12:34:58.627203: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
            "2024-07-06 12:34:58.671503: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
            "2024-07-06 12:34:58.671780: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
            "2024-07-06 12:34:58.730404: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
            "2024-07-06 12:34:58.730643: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
            "2024-07-06 12:34:58.730826: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
            "2024-07-06 12:34:58.913887: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
            "2024-07-06 12:34:58.914215: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
            "2024-07-06 12:34:58.914432: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
            "2024-07-06 12:34:58.914578: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13949 MB memory:  -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\n",
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)        [(None, None)]               0         []                            \n",
            "                                                                                                  \n",
            " lambda (Lambda)             [(None, None, 257),          0         ['input_1[0][0]']             \n",
            "                              (None, None, 257)]                                                  \n",
            "                                                                                                  \n",
            " lstm (LSTM)                 (None, None, 128)            197632    ['lambda[0][0]']              \n",
            "                                                                                                  \n",
            " dropout (Dropout)           (None, None, 128)            0         ['lstm[0][0]']                \n",
            "                                                                                                  \n",
            " lstm_1 (LSTM)               (None, None, 128)            131584    ['dropout[0][0]']             \n",
            "                                                                                                  \n",
            " dense (Dense)               (None, None, 257)            33153     ['lstm_1[0][0]']              \n",
            "                                                                                                  \n",
            " activation (Activation)     (None, None, 257)            0         ['dense[0][0]']               \n",
            "                                                                                                  \n",
            " multiply (Multiply)         (None, None, 257)            0         ['lambda[0][0]',              \n",
            "                                                                     'activation[0][0]']          \n",
            "                                                                                                  \n",
            " lambda_1 (Lambda)           (None, None, 512)            0         ['multiply[0][0]',            \n",
            "                                                                     'lambda[0][1]']              \n",
            "                                                                                                  \n",
            " conv1d (Conv1D)             (None, None, 256)            131072    ['lambda_1[0][0]']            \n",
            "                                                                                                  \n",
            " instant_layer_normalizatio  (None, None, 256)            512       ['conv1d[0][0]']              \n",
            " n (InstantLayerNormalizati                                                                       \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " lstm_2 (LSTM)               (None, None, 128)            197120    ['instant_layer_normalization[\n",
            "                                                                    0][0]']                       \n",
            "                                                                                                  \n",
            " dropout_1 (Dropout)         (None, None, 128)            0         ['lstm_2[0][0]']              \n",
            "                                                                                                  \n",
            " lstm_3 (LSTM)               (None, None, 128)            131584    ['dropout_1[0][0]']           \n",
            "                                                                                                  \n",
            " dense_1 (Dense)             (None, None, 256)            33024     ['lstm_3[0][0]']              \n",
            "                                                                                                  \n",
            " activation_1 (Activation)   (None, None, 256)            0         ['dense_1[0][0]']             \n",
            "                                                                                                  \n",
            " multiply_1 (Multiply)       (None, None, 256)            0         ['conv1d[0][0]',              \n",
            "                                                                     'activation_1[0][0]']        \n",
            "                                                                                                  \n",
            " conv1d_1 (Conv1D)           (None, None, 512)            131072    ['multiply_1[0][0]']          \n",
            "                                                                                                  \n",
            " lambda_2 (Lambda)           (None, None)                 0         ['conv1d_1[0][0]']            \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 986753 (3.76 MB)\n",
            "Trainable params: 986753 (3.76 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n",
            "None\n",
            "2024-07-06 12:35:09.886251: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8902\n",
            "example.wav processed successfully!\n"
          ]
        }
      ],
      "source": [
        "! cd DTLN && python run_evaluation.py -i ../in_dir -o ../target_dir -m ./pretrained_model/model.h5"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Attempt 6: `drethage/speech-denoising-wavenet`"
      ],
      "metadata": {
        "id": "0mwcTNbFL5CX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! git clone https://github.com/drethage/speech-denoising-wavenet.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Hb-qw7aL7DN",
        "outputId": "dec52932-a46d-4bae-d108-2d8baf95d581"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'speech-denoising-wavenet'...\n",
            "remote: Enumerating objects: 31, done.\u001b[K\n",
            "remote: Counting objects: 100% (6/6), done.\u001b[K\n",
            "remote: Compressing objects: 100% (6/6), done.\u001b[K\n",
            "remote: Total 31 (delta 1), reused 0 (delta 0), pack-reused 25\u001b[K\n",
            "Receiving objects: 100% (31/31), 58.71 MiB | 32.39 MiB/s, done.\n",
            "Resolving deltas: 100% (5/5), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install -r speech-denoising-wavenet/requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dV1D2XnyMAjj",
        "outputId": "53dae293-dfa3-4fc0-bf84-ede4d064bf5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting numpy==1.13 (from -r speech-denoising-wavenet/requirements.txt (line 1))\n",
            "  Downloading numpy-1.13.0.zip (5.0 MB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/5.0 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.9/5.0 MB\u001b[0m \u001b[31m26.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m5.0/5.0 MB\u001b[0m \u001b[31m73.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.0/5.0 MB\u001b[0m \u001b[31m49.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting scipy==0.19.0 (from -r speech-denoising-wavenet/requirements.txt (line 2))\n",
            "  Downloading scipy-0.19.0.zip (15.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.3/15.3 MB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting theano==0.9.0 (from -r speech-denoising-wavenet/requirements.txt (line 3))\n",
            "  Downloading Theano-0.9.0.tar.gz (3.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m29.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting keras==1.2.0 (from -r speech-denoising-wavenet/requirements.txt (line 4))\n",
            "  Downloading Keras-1.2.0.tar.gz (167 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m167.3/167.3 kB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting soundfile==0.9 (from -r speech-denoising-wavenet/requirements.txt (line 5))\n",
            "  Downloading SoundFile-0.9.0-py2.py3-none-any.whl.metadata (9.3 kB)\n",
            "Collecting tqdm==4.11.0 (from -r speech-denoising-wavenet/requirements.txt (line 6))\n",
            "  Downloading tqdm-4.11.0-py2.py3-none-any.whl.metadata (28 kB)\n",
            "Collecting Cython==0.25.2 (from -r speech-denoising-wavenet/requirements.txt (line 7))\n",
            "  Downloading Cython-0.25.2.tar.gz (1.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m48.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting h5py==2.7.0 (from -r speech-denoising-wavenet/requirements.txt (line 8))\n",
            "  Downloading h5py-2.7.0.tar.gz (256 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m256.5/256.5 kB\u001b[0m \u001b[31m21.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from theano==0.9.0->-r speech-denoising-wavenet/requirements.txt (line 3)) (1.16.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from keras==1.2.0->-r speech-denoising-wavenet/requirements.txt (line 4)) (6.0.1)\n",
            "Requirement already satisfied: cffi>=0.6 in /usr/local/lib/python3.10/dist-packages (from soundfile==0.9->-r speech-denoising-wavenet/requirements.txt (line 5)) (1.16.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=0.6->soundfile==0.9->-r speech-denoising-wavenet/requirements.txt (line 5)) (2.22)\n",
            "Downloading SoundFile-0.9.0-py2.py3-none-any.whl (24 kB)\n",
            "Downloading tqdm-4.11.0-py2.py3-none-any.whl (45 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.6/45.6 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hBuilding wheels for collected packages: numpy, scipy, theano, keras, Cython, h5py\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py bdist_wheel\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Building wheel for numpy (setup.py) ... \u001b[?25lerror\n",
            "\u001b[31m  ERROR: Failed building wheel for numpy\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[?25h  Running setup.py clean for numpy\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py clean\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "\u001b[31m  ERROR: Failed cleaning build dir for numpy\u001b[0m\u001b[31m\n",
            "\u001b[0m  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py bdist_wheel\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Building wheel for scipy (setup.py) ... \u001b[?25lerror\n",
            "\u001b[31m  ERROR: Failed building wheel for scipy\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[?25h  Running setup.py clean for scipy\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py clean\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "\u001b[31m  ERROR: Failed cleaning build dir for scipy\u001b[0m\u001b[31m\n",
            "\u001b[0m  Building wheel for theano (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for theano: filename=Theano-0.9.0-py3-none-any.whl size=2971682 sha256=9cff7c797fc1af0b7f4f2a75077d56b62cf9fe659e0b02694735752434bc52ea\n",
            "  Stored in directory: /root/.cache/pip/wheels/e9/52/9f/dd53a6ccc3f6a3be11106575d9d029fee82f4fb27910d300ca\n",
            "  Building wheel for keras (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras: filename=Keras-1.2.0-py3-none-any.whl size=200412 sha256=1f744972e9f1ff7a3c6ebe857bd9dc2f90738a000560d5922406b2b1f87dd372\n",
            "  Stored in directory: /root/.cache/pip/wheels/51/7e/53/b5eb07b4f1c666bb1ba7a56bb74cc0d97f5cb92d851faa1428\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py bdist_wheel\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Building wheel for Cython (setup.py) ... \u001b[?25lerror\n",
            "\u001b[31m  ERROR: Failed building wheel for Cython\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[?25h  Running setup.py clean for Cython\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py bdist_wheel\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Building wheel for h5py (setup.py) ... \u001b[?25lerror\n",
            "\u001b[31m  ERROR: Failed building wheel for h5py\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[?25h  Running setup.py clean for h5py\n",
            "Successfully built theano keras\n",
            "Failed to build numpy scipy Cython h5py\n",
            "\u001b[31mERROR: ERROR: Failed to build installable wheels for some pyproject.toml based projects (numpy, scipy, Cython, h5py)\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Attempt 7: `RoyJames/room-impulse-responses`\n",
        "Investigating RIR datasets required for resemble-enhance"
      ],
      "metadata": {
        "id": "gsDYF90IWQH0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! git clone https://github.com/RoyJames/room-impulse-responses.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tpfoeb2aWSvS",
        "outputId": "7b1e6308-59be-4d6f-97f0-095897203ddc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'room-impulse-responses'...\n",
            "remote: Enumerating objects: 92, done.\u001b[K\n",
            "remote: Counting objects: 100% (92/92), done.\u001b[K\n",
            "remote: Compressing objects: 100% (77/77), done.\u001b[K\n",
            "remote: Total 92 (delta 39), reused 23 (delta 6), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (92/92), 23.76 KiB | 760.00 KiB/s, done.\n",
            "Resolving deltas: 100% (39/39), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! ./room-impulse-responses/get_miracle.sh miracle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bxkG5z3QWW5d",
        "outputId": "00ae47a0-417b-4aca-ef14-986bee29778b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading A1.h5...\n",
            "--2024-07-28 12:38:42--  https://depositonce.tu-berlin.de/bitstreams/67156d9c-224d-4d07-b923-be0240e7b48d/download\n",
            "Resolving depositonce.tu-berlin.de (depositonce.tu-berlin.de)... 141.23.71.160\n",
            "Connecting to depositonce.tu-berlin.de (depositonce.tu-berlin.de)|141.23.71.160|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://api-depositonce.tu-berlin.de/server/api/core/bitstreams/67156d9c-224d-4d07-b923-be0240e7b48d/content [following]\n",
            "--2024-07-28 12:38:43--  https://api-depositonce.tu-berlin.de/server/api/core/bitstreams/67156d9c-224d-4d07-b923-be0240e7b48d/content\n",
            "Resolving api-depositonce.tu-berlin.de (api-depositonce.tu-berlin.de)... 141.23.35.91\n",
            "Connecting to api-depositonce.tu-berlin.de (api-depositonce.tu-berlin.de)|141.23.35.91|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 200\n",
            "Length: unspecified [application/octet-stream]\n",
            "Saving to: ‘miracle/A1.h5’\n",
            "\n",
            "miracle/A1.h5           [        <=>         ]   1.00G  24.3MB/s    in 45s     \n",
            "\n",
            "2024-07-28 12:39:29 (23.0 MB/s) - ‘miracle/A1.h5’ saved [1074810584]\n",
            "\n",
            "Downloading A2.h5...\n",
            "--2024-07-28 12:39:29--  https://depositonce.tu-berlin.de/bitstreams/cbb462d7-cb28-4803-98d8-84b03aad0d5f/download\n",
            "Resolving depositonce.tu-berlin.de (depositonce.tu-berlin.de)... 141.23.71.160\n",
            "Connecting to depositonce.tu-berlin.de (depositonce.tu-berlin.de)|141.23.71.160|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://api-depositonce.tu-berlin.de/server/api/core/bitstreams/cbb462d7-cb28-4803-98d8-84b03aad0d5f/content [following]\n",
            "--2024-07-28 12:39:30--  https://api-depositonce.tu-berlin.de/server/api/core/bitstreams/cbb462d7-cb28-4803-98d8-84b03aad0d5f/content\n",
            "Resolving api-depositonce.tu-berlin.de (api-depositonce.tu-berlin.de)... 141.23.35.91\n",
            "Connecting to api-depositonce.tu-berlin.de (api-depositonce.tu-berlin.de)|141.23.35.91|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 200\n",
            "Length: unspecified [application/octet-stream]\n",
            "Saving to: ‘miracle/A2.h5’\n",
            "\n",
            "miracle/A2.h5           [        <=>         ]   1.00G  22.8MB/s    in 45s     \n",
            "\n",
            "2024-07-28 12:40:17 (22.7 MB/s) - ‘miracle/A2.h5’ saved [1074810584]\n",
            "\n",
            "Downloading D1.h5...\n",
            "--2024-07-28 12:40:17--  https://depositonce.tu-berlin.de/bitstreams/86680ee5-ae0c-4b38-8ef8-805652a21ded/download\n",
            "Resolving depositonce.tu-berlin.de (depositonce.tu-berlin.de)... 141.23.71.160\n",
            "Connecting to depositonce.tu-berlin.de (depositonce.tu-berlin.de)|141.23.71.160|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://api-depositonce.tu-berlin.de/server/api/core/bitstreams/86680ee5-ae0c-4b38-8ef8-805652a21ded/content [following]\n",
            "--2024-07-28 12:40:18--  https://api-depositonce.tu-berlin.de/server/api/core/bitstreams/86680ee5-ae0c-4b38-8ef8-805652a21ded/content\n",
            "Resolving api-depositonce.tu-berlin.de (api-depositonce.tu-berlin.de)... 141.23.35.91\n",
            "Connecting to api-depositonce.tu-berlin.de (api-depositonce.tu-berlin.de)|141.23.35.91|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 200\n",
            "Length: unspecified [application/octet-stream]\n",
            "Saving to: ‘miracle/D1.h5’\n",
            "\n",
            "miracle/D1.h5           [              <=>   ] 288.30M  23.8MB/s    in 13s     \n",
            "\n",
            "2024-07-28 12:40:33 (21.5 MB/s) - ‘miracle/D1.h5’ saved [302303864]\n",
            "\n",
            "Downloading R2.h5...\n",
            "--2024-07-28 12:40:33--  https://depositonce.tu-berlin.de/bitstreams/0fc5f5a4-a2f7-4eb7-b796-7114260e5e86/download\n",
            "Resolving depositonce.tu-berlin.de (depositonce.tu-berlin.de)... 141.23.71.160\n",
            "Connecting to depositonce.tu-berlin.de (depositonce.tu-berlin.de)|141.23.71.160|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://api-depositonce.tu-berlin.de/server/api/core/bitstreams/0fc5f5a4-a2f7-4eb7-b796-7114260e5e86/content [following]\n",
            "--2024-07-28 12:40:34--  https://api-depositonce.tu-berlin.de/server/api/core/bitstreams/0fc5f5a4-a2f7-4eb7-b796-7114260e5e86/content\n",
            "Resolving api-depositonce.tu-berlin.de (api-depositonce.tu-berlin.de)... 141.23.35.91\n",
            "Connecting to api-depositonce.tu-berlin.de (api-depositonce.tu-berlin.de)|141.23.35.91|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 200\n",
            "Length: unspecified [application/octet-stream]\n",
            "Saving to: ‘miracle/R2.h5’\n",
            "\n",
            "miracle/R2.h5           [          <=>       ]   1.00G  22.2MB/s    in 47s     \n",
            "\n",
            "2024-07-28 12:41:22 (21.9 MB/s) - ‘miracle/R2.h5’ saved [1074810584]\n",
            "\n",
            "Downloading loudspeaker.h5...\n",
            "--2024-07-28 12:41:22--  https://depositonce.tu-berlin.de/bitstreams/f16367cb-14cc-44f9-bb0b-9be3c33183d3/download\n",
            "Resolving depositonce.tu-berlin.de (depositonce.tu-berlin.de)... 141.23.71.160\n",
            "Connecting to depositonce.tu-berlin.de (depositonce.tu-berlin.de)|141.23.71.160|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://api-depositonce.tu-berlin.de/server/api/core/bitstreams/f16367cb-14cc-44f9-bb0b-9be3c33183d3/content [following]\n",
            "--2024-07-28 12:41:23--  https://api-depositonce.tu-berlin.de/server/api/core/bitstreams/f16367cb-14cc-44f9-bb0b-9be3c33183d3/content\n",
            "Resolving api-depositonce.tu-berlin.de (api-depositonce.tu-berlin.de)... 141.23.35.91\n",
            "Connecting to api-depositonce.tu-berlin.de (api-depositonce.tu-berlin.de)|141.23.35.91|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 200\n",
            "Length: unspecified [application/octet-stream]\n",
            "Saving to: ‘miracle/loudspeaker.h5’\n",
            "\n",
            "miracle/loudspeaker     [   <=>              ] 456.97K   658KB/s    in 0.7s    \n",
            "\n",
            "2024-07-28 12:41:25 (658 KB/s) - ‘miracle/loudspeaker.h5’ saved [467936]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C1ZEbtXLYO3V",
        "outputId": "babf566a-827f-4932-c5ca-cfde49cf1c7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "miracle  room-impulse-responses  sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import h5py\n",
        "with h5py.File('miracle/A2.h5', 'r') as f :\n",
        "  ir = f ['data']['impulse_response'][()]\n",
        "  fs = f ['metadata']['sampling_rate'][()]"
      ],
      "metadata": {
        "id": "6919nNmwXnMI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ir[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j6gxPW03Za_H",
        "outputId": "ac61cb21-27d7-4f23-f819-21215d988d04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.0000000e+00, -4.1893890e-07,  1.4598560e-06, ...,\n",
              "        -1.2822369e-07, -2.6960961e-07,  0.0000000e+00],\n",
              "       [ 0.0000000e+00, -1.5256103e-07,  8.6924194e-07, ...,\n",
              "         4.8150406e-07,  8.6897478e-07,  0.0000000e+00],\n",
              "       [ 0.0000000e+00, -1.8956412e-08,  2.3302466e-07, ...,\n",
              "         1.6839543e-06,  3.3870828e-07,  0.0000000e+00],\n",
              "       ...,\n",
              "       [ 0.0000000e+00, -2.2938252e-08,  2.0212092e-07, ...,\n",
              "         6.3319050e-07,  2.5132860e-07,  0.0000000e+00],\n",
              "       [ 0.0000000e+00, -2.4849803e-07,  1.7373435e-06, ...,\n",
              "         1.3964872e-06,  2.7489494e-07, -0.0000000e+00],\n",
              "       [-0.0000000e+00,  4.5821074e-07, -1.2900291e-06, ...,\n",
              "        -5.5614640e-07, -2.0284106e-07,  0.0000000e+00]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ir.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zmr_2EXmY-Ci",
        "outputId": "06857dff-fb90-47f7-c330-69b4a21c5ae6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4096, 64, 1024)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dJGWe5MBZHjW",
        "outputId": "5af9c74a-02b3-4cc5-c1cc-68b53d0a9bb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32000"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! ./room-impulse-responses/get_soundcam.sh"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CxVIm_hXZ5ba",
        "outputId": "fe6d46f8-3027-4d13-dcbf-7fe0baa15a6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-28 12:53:19--  https://stacks.stanford.edu/file/druid:xq364hd5023/3Dscans.tar.gz\n",
            "Resolving stacks.stanford.edu (stacks.stanford.edu)... 171.67.37.91\n",
            "Connecting to stacks.stanford.edu (stacks.stanford.edu)|171.67.37.91|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 127980118 (122M) [application/gzip]\n",
            "Saving to: ‘3Dscans.tar.gz’\n",
            "\n",
            "3Dscans.tar.gz      100%[===================>] 122.05M  5.60MB/s    in 22s     \n",
            "\n",
            "2024-07-28 12:53:42 (5.52 MB/s) - ‘3Dscans.tar.gz’ saved [127980118/127980118]\n",
            "\n",
            "--2024-07-28 12:53:42--  https://stacks.stanford.edu/file/druid:xq364hd5023/ConferenceRoom_preprocessed.tar.gz\n",
            "Resolving stacks.stanford.edu (stacks.stanford.edu)... 171.67.37.91\n",
            "Connecting to stacks.stanford.edu (stacks.stanford.edu)|171.67.37.91|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 51664459687 (48G) [application/gzip]\n",
            "Saving to: ‘ConferenceRoom_preprocessed.tar.gz’\n",
            "\n",
            "Room_preprocessed.t   4%[                    ]   2.31G  5.45MB/s    eta 2h 24m ^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! ./room-impulse-responses/get_but.sh but"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EVYPIXysaHUh",
        "outputId": "45d82a81-4796-4da3-d923-7b209975b2a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-28 13:03:32--  http://merlin.fit.vutbr.cz/ReverbDB/BUT_ReverbDB_rel_19_06_RIR-Only.tgz\n",
            "Resolving merlin.fit.vutbr.cz (merlin.fit.vutbr.cz)... 147.229.176.19, 2001:67c:1220:8b0::93e5:b013\n",
            "Connecting to merlin.fit.vutbr.cz (merlin.fit.vutbr.cz)|147.229.176.19|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 9308593693 (8.7G) [application/x-gzip]\n",
            "Saving to: ‘but/BUT_ReverbDB_rel_19_06_RIR-Only.tgz’\n",
            "\n",
            "l_19_06_RIR-Only.tg   3%[                    ] 277.44M  13.0MB/s    eta 11m 8s ^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! ./room-impulse-responses/get_mit.sh mit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XA_JSKr4caV5",
        "outputId": "50723c15-26d9-4e54-dd47-2b6587e9b259"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-28 13:04:21--  https://mcdermottlab.mit.edu/Reverb/IRMAudio/Audio.zip\n",
            "Resolving mcdermottlab.mit.edu (mcdermottlab.mit.edu)... 18.13.53.64\n",
            "Connecting to mcdermottlab.mit.edu (mcdermottlab.mit.edu)|18.13.53.64|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 11660005 (11M) [application/zip]\n",
            "Saving to: ‘mit/Audio.zip’\n",
            "\n",
            "Audio.zip           100%[===================>]  11.12M  18.0MB/s    in 0.6s    \n",
            "\n",
            "2024-07-28 13:04:23 (18.0 MB/s) - ‘mit/Audio.zip’ saved [11660005/11660005]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! mkdir data\n",
        "! mkdir data/rir"
      ],
      "metadata": {
        "id": "ACg93Pz2eRJN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from scipy.io import wavfile\n",
        "import numpy as np\n",
        "\n",
        "# Directory containing .wav files\n",
        "input_dir = '/content/mit/MIT_Survey'\n",
        "# Output directory for .npy files\n",
        "output_dir = '/content/data/rir'\n",
        "\n",
        "# Ensure output directory exists\n",
        "os.makedirs(output_dir, exist_ok=True)\n",
        "\n",
        "for filename in os.listdir(input_dir):\n",
        "    if filename.endswith('.wav'):\n",
        "        # Construct full file path\n",
        "        input_file_path = os.path.join(input_dir, filename)\n",
        "\n",
        "        # Read .wav file\n",
        "        sample_rate, audio_data = wavfile.read(input_file_path)\n",
        "\n",
        "        # Here you can process the audio_data if needed\n",
        "\n",
        "        # Save as .npy file\n",
        "        output_file_path = os.path.join(output_dir, f'{filename}.npy')\n",
        "        np.save(output_file_path, audio_data)\n",
        "\n",
        "        print(f'Saved {output_file_path}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FeeSFFxAeFGx",
        "outputId": "89b39e3e-2dc4-4068-ac95-2bddb800bb12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved /content/data/rir/h037_Classroom_5txts.wav.npy\n",
            "Saved /content/data/rir/h264_Hallway_MITCampus_1txts.wav.npy\n",
            "Saved /content/data/rir/h034_Classroom_6txts.wav.npy\n",
            "Saved /content/data/rir/h001_Bedroom_65txts.wav.npy\n",
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            "Saved /content/data/rir/h252_Auditorium_1txts.wav.npy\n",
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            "Saved /content/data/rir/h158_HospitalWaitingRoom_1txts.wav.npy\n",
            "Saved /content/data/rir/h199_Outside_DoorstepOfHouseInCambridge_1txts.wav.npy\n",
            "Saved /content/data/rir/h172_Outside_EntranceOfLexingtonPublicLibrary_1txts.wav.npy\n",
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Attempt 8: `denoiser`"
      ],
      "metadata": {
        "id": "HIYdgtWzlT1O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt-get install python3.7"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qf5miiOloUaI",
        "outputId": "d0067fc4-810b-4a93-ccc9-9cae5066366b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "python3.7 is already the newest version (3.7.17-1+jammy1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 45 not upgraded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.7 1"
      ],
      "metadata": {
        "id": "EV6YvWvQoaUw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python3 --version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZVicPvF6odzQ",
        "outputId": "f03ced9a-1546-4471-845a-75a8e7ef93b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python 3.7.17\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt install python3-pip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B1_k2ZNXrRAk",
        "outputId": "26e5211e-6ebd-4e74-ccb9-e04769f81079"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  python3-setuptools python3-wheel\n",
            "Suggested packages:\n",
            "  python-setuptools-doc\n",
            "The following NEW packages will be installed:\n",
            "  python3-pip python3-setuptools python3-wheel\n",
            "0 upgraded, 3 newly installed, 0 to remove and 45 not upgraded.\n",
            "Need to get 1,677 kB of archives.\n",
            "After this operation, 8,967 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 python3-setuptools all 59.6.0-1.2ubuntu0.22.04.1 [339 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 python3-wheel all 0.37.1-2ubuntu0.22.04.1 [32.0 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 python3-pip all 22.0.2+dfsg-1ubuntu0.4 [1,305 kB]\n",
            "Fetched 1,677 kB in 1s (2,204 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78, <> line 3.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package python3-setuptools.\n",
            "(Reading database ... 124859 files and directories currently installed.)\n",
            "Preparing to unpack .../python3-setuptools_59.6.0-1.2ubuntu0.22.04.1_all.deb ...\n",
            "Unpacking python3-setuptools (59.6.0-1.2ubuntu0.22.04.1) ...\n",
            "Selecting previously unselected package python3-wheel.\n",
            "Preparing to unpack .../python3-wheel_0.37.1-2ubuntu0.22.04.1_all.deb ...\n",
            "Unpacking python3-wheel (0.37.1-2ubuntu0.22.04.1) ...\n",
            "Selecting previously unselected package python3-pip.\n",
            "Preparing to unpack .../python3-pip_22.0.2+dfsg-1ubuntu0.4_all.deb ...\n",
            "Unpacking python3-pip (22.0.2+dfsg-1ubuntu0.4) ...\n",
            "Setting up python3-setuptools (59.6.0-1.2ubuntu0.22.04.1) ...\n",
            "Setting up python3-wheel (0.37.1-2ubuntu0.22.04.1) ...\n",
            "Setting up python3-pip (22.0.2+dfsg-1ubuntu0.4) ...\n",
            "Processing triggers for man-db (2.10.2-1) ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt-get install python3-distutils\n",
        "!sudo apt-get install python3-apt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6gs5I3rWsdFs",
        "outputId": "01defd59-4844-4748-a30e-c64a639de240"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "python3-distutils is already the newest version (3.10.8-1~22.04).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 45 not upgraded.\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "python3-apt is already the newest version (2.4.0ubuntu3).\n",
            "python3-apt set to manually installed.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 45 not upgraded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install denoiser"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8E6_ku2kdzRU",
        "outputId": "d7b03e00-1f82-4c45-ae36-f7a32cffa5d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting denoiser\n",
            "  Downloading denoiser-0.1.5.tar.gz (49 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting julius (from denoiser)\n",
            "  Downloading julius-0.2.7.tar.gz (59 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.6/59.6 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting hydra_core<1.0 (from denoiser)\n",
            "  Downloading hydra_core-0.11.3-py3-none-any.whl.metadata (2.2 kB)\n",
            "Collecting hydra_colorlog<1.0 (from denoiser)\n",
            "  Downloading hydra_colorlog-0.1.4-py3-none-any.whl.metadata (1.0 kB)\n",
            "Requirement already satisfied: numpy>=1.19 in /usr/local/lib/python3.10/dist-packages (from denoiser) (1.26.4)\n",
            "Collecting pystoi>=0.3.3 (from denoiser)\n",
            "  Downloading pystoi-0.4.1-py2.py3-none-any.whl.metadata (4.0 kB)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from denoiser) (1.16.0)\n",
            "Collecting sounddevice>=0.4 (from denoiser)\n",
            "  Downloading sounddevice-0.4.7-py3-none-any.whl.metadata (1.4 kB)\n",
            "Requirement already satisfied: torch>=1.5 in /usr/local/lib/python3.10/dist-packages (from denoiser) (2.3.1+cu121)\n",
            "Requirement already satisfied: torchaudio>=0.5 in /usr/local/lib/python3.10/dist-packages (from denoiser) (2.3.1+cu121)\n",
            "Collecting colorlog (from hydra_colorlog<1.0->denoiser)\n",
            "  Downloading colorlog-6.8.2-py3-none-any.whl.metadata (10 kB)\n",
            "Collecting omegaconf<1.5,>=1.4 (from hydra_core<1.0->denoiser)\n",
            "  Downloading omegaconf-1.4.1-py3-none-any.whl.metadata (2.9 kB)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from pystoi>=0.3.3->denoiser) (1.13.1)\n",
            "Requirement already satisfied: CFFI>=1.0 in /usr/local/lib/python3.10/dist-packages (from sounddevice>=0.4->denoiser) (1.16.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.5->denoiser) (3.15.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.5->denoiser) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.5->denoiser) (1.13.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.5->denoiser) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.5->denoiser) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.5->denoiser) (2024.6.1)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.5->denoiser)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.5->denoiser)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.5->denoiser)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.5->denoiser)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.5->denoiser)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.5->denoiser)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.5->denoiser)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.5->denoiser)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.5->denoiser)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-nccl-cu12==2.20.5 (from torch>=1.5->denoiser)\n",
            "  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl.metadata (1.8 kB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.5->denoiser)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.7 kB)\n",
            "Requirement already satisfied: triton==2.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.5->denoiser) (2.3.1)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.5->denoiser)\n",
            "  Downloading nvidia_nvjitlink_cu12-12.5.82-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from CFFI>=1.0->sounddevice>=0.4->denoiser) (2.22)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from omegaconf<1.5,>=1.4->hydra_core<1.0->denoiser) (6.0.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.5->denoiser) (2.1.5)\n",
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            "Downloading hydra_colorlog-0.1.4-py3-none-any.whl (4.0 kB)\n",
            "Downloading hydra_core-0.11.3-py3-none-any.whl (72 kB)\n",
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            "\u001b[?25hDownloading pystoi-0.4.1-py2.py3-none-any.whl (8.2 kB)\n",
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            "Downloading colorlog-6.8.2-py3-none-any.whl (11 kB)\n",
            "Downloading nvidia_nvjitlink_cu12-12.5.82-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m22.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hBuilding wheels for collected packages: denoiser, julius\n",
            "  Building wheel for denoiser (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for denoiser: filename=denoiser-0.1.5-py3-none-any.whl size=43820 sha256=e4f516bd84fbf5f1fda3bae809afc836641b93e44315bc1ee55f6dbd0f517510\n",
            "  Stored in directory: /root/.cache/pip/wheels/f1/57/3f/7ce0ae586d457c598b2cb275a37801ff5a705f656ba7a0215e\n",
            "  Building wheel for julius (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for julius: filename=julius-0.2.7-py3-none-any.whl size=21870 sha256=170e73e8f42ff0ac3141a7c0a58dc2ee66f1fa1eaf08bf66d44a5cefbded71f6\n",
            "  Stored in directory: /root/.cache/pip/wheels/b9/b2/05/f883527ffcb7f2ead5438a2c23439aa0c881eaa9a4c80256f4\n",
            "Successfully built denoiser julius\n",
            "Installing collected packages: omegaconf, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, colorlog, sounddevice, pystoi, nvidia-cusparse-cu12, nvidia-cudnn-cu12, hydra_core, nvidia-cusolver-cu12, hydra_colorlog, julius, denoiser\n",
            "Successfully installed colorlog-6.8.2 denoiser-0.1.5 hydra_colorlog-0.1.4 hydra_core-0.11.3 julius-0.2.7 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105 omegaconf-1.4.1 pystoi-0.4.1 sounddevice-0.4.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ! git clone https://github.com/facebookresearch/denoiser\n",
        "# ! cd denoiser\n",
        "# ! pip install -r denoiser/requirements.txt  # If you don't have cuda\n",
        "! python3 -m pip install -r denoiser/requirements_cuda.txt  # If you have cuda"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pRzyH4zFd-Z_",
        "outputId": "417539cf-6fb9-4ae7-a033-28a085b8cb9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n",
            "    \"__main__\", mod_spec)\n",
            "  File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n",
            "    exec(code, run_globals)\n",
            "  File \"/usr/lib/python3/dist-packages/pip/__main__.py\", line 29, in <module>\n",
            "    from pip._internal.cli.main import main as _main\n",
            "  File \"/usr/lib/python3/dist-packages/pip/_internal/cli/main.py\", line 9, in <module>\n",
            "    from pip._internal.cli.autocompletion import autocomplete\n",
            "  File \"/usr/lib/python3/dist-packages/pip/_internal/cli/autocompletion.py\", line 10, in <module>\n",
            "    from pip._internal.cli.main_parser import create_main_parser\n",
            "  File \"/usr/lib/python3/dist-packages/pip/_internal/cli/main_parser.py\", line 8, in <module>\n",
            "    from pip._internal.cli import cmdoptions\n",
            "  File \"/usr/lib/python3/dist-packages/pip/_internal/cli/cmdoptions.py\", line 23, in <module>\n",
            "    from pip._internal.cli.parser import ConfigOptionParser\n",
            "  File \"/usr/lib/python3/dist-packages/pip/_internal/cli/parser.py\", line 12, in <module>\n",
            "    from pip._internal.configuration import Configuration, ConfigurationError\n",
            "  File \"/usr/lib/python3/dist-packages/pip/_internal/configuration.py\", line 26, in <module>\n",
            "    from pip._internal.utils.logging import getLogger\n",
            "  File \"/usr/lib/python3/dist-packages/pip/_internal/utils/logging.py\", line 27, in <module>\n",
            "    from pip._internal.utils.misc import ensure_dir\n",
            "  File \"/usr/lib/python3/dist-packages/pip/_internal/utils/misc.py\", line 39, in <module>\n",
            "    from pip._internal.locations import get_major_minor_version\n",
            "  File \"/usr/lib/python3/dist-packages/pip/_internal/locations/__init__.py\", line 14, in <module>\n",
            "    from . import _distutils, _sysconfig\n",
            "  File \"/usr/lib/python3/dist-packages/pip/_internal/locations/_distutils.py\", line 9, in <module>\n",
            "    from distutils.cmd import Command as DistutilsCommand\n",
            "ModuleNotFoundError: No module named 'distutils.cmd'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pydub"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BGuYFn8QgmVo",
        "outputId": "0fe33b99-56db-4a72-9fbe-7b724532561b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pydub\n",
            "  Downloading pydub-0.25.1-py2.py3-none-any.whl.metadata (1.4 kB)\n",
            "Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n",
            "Installing collected packages: pydub\n",
            "Successfully installed pydub-0.25.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pydub import AudioSegment\n",
        "\n",
        "def convert_m4a_to_wav(input_file_path, output_file_path):\n",
        "    # Load the M4A file\n",
        "    audio = AudioSegment.from_file(input_file_path, format=\"m4a\")\n",
        "\n",
        "    # Export the audio in WAV format\n",
        "    audio.export(output_file_path, format=\"wav\")\n",
        "\n",
        "# Example usage\n",
        "input_file_path = \"/content/drive/MyDrive/SpeechEnhancement/PersianNoisy1.m4a\"\n",
        "output_file_path = \"/content/drive/MyDrive/SpeechEnhancement/PersianNoisy1.wav\"\n",
        "\n",
        "convert_m4a_to_wav(input_file_path, output_file_path)"
      ],
      "metadata": {
        "id": "k96Ed-dNgpRt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! python3 -m denoiser.enhance --master64 --noisy_dir=/content/drive/MyDrive/SpeechEnhancement --out_dir=./"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ri0t2EJOfluw",
        "outputId": "f40eba58-68a9-4361-8162-c3c3fa8d3acc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:denoiser.pretrained:Loading pre-trained real time H=64 model trained on DNS and Valentini.\n",
            "/usr/local/lib/python3.10/dist-packages/denoiser/audio.py:89: UserWarning: torchaudio._backend.get_audio_backend has been deprecated. With dispatcher enabled, this function is no-op. You can remove the function call.\n",
            "  if torchaudio.get_audio_backend() in ['soundfile', 'sox_io']:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n",
            "    return _run_code(code, main_globals, None,\n",
            "  File \"/usr/lib/python3.10/runpy.py\", line 86, in _run_code\n",
            "    exec(code, run_globals)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/denoiser/enhance.py\", line 155, in <module>\n",
            "    enhance(args, local_out_dir=args.out_dir)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/denoiser/enhance.py\", line 132, in enhance\n",
            "    for data in iterator:\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/denoiser/utils.py\", line 135, in __next__\n",
            "    value = next(self._iterator)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 631, in __next__\n",
            "    data = self._next_data()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 675, in _next_data\n",
            "    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py\", line 51, in fetch\n",
            "    data = [self.dataset[idx] for idx in possibly_batched_index]\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py\", line 51, in <listcomp>\n",
            "    data = [self.dataset[idx] for idx in possibly_batched_index]\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/denoiser/audio.py\", line 94, in __getitem__\n",
            "    out, sr = torchaudio.load(str(file), offset=offset, num_frames=num_frames)\n",
            "TypeError: get_load_func.<locals>.load() got an unexpected keyword argument 'offset'\n"
          ]
        }
      ]
    }
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