voice_cloning.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x1ayhw2sHwkb",
        "outputId": "85777b3a-c53e-49fa-a129-e313aa529967"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
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            "Installing collected packages: xxhash, dill, multiprocess, huggingface-hub, datasets\n",
            "Successfully installed datasets-2.13.1 dill-0.3.6 huggingface-hub-0.16.4 multiprocess-0.70.14 xxhash-3.2.0\n"
          ]
        }
      ],
      "source": [
        "pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "epONyu4V5H4I",
        "outputId": "f14fed33-6a09-42ed-fbea-8a0c944fac51",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install librosa"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j251me-t5H4J",
        "outputId": "ac375f92-cfd7-4849-cb1c-2a74260b31db",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torchaudio in /usr/local/lib/python3.10/dist-packages (2.0.2+cu118)\n",
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          ]
        }
      ],
      "source": [
        "pip install torchaudio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nn27ullf5H4J",
        "outputId": "7ac0d726-93f7-4835-9165-56cbdac9408d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: soundfile in /usr/local/lib/python3.10/dist-packages (0.12.1)\n",
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          ]
        }
      ],
      "source": [
        "!pip install soundfile"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3_9hTQa6hB62"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import torchaudio\n",
        "import pandas as pd\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oySqJ84uhqT6"
      },
      "outputs": [],
      "source": [
        "\n",
        "audio_dir = './data'\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LAwdQAjaivMe"
      },
      "outputs": [],
      "source": [
        "audio_id = []\n",
        "language = []\n",
        "audio = []\n",
        "raw_text = []\n",
        "normalized_text = []\n",
        "gender = []\n",
        "speaker_id = []\n",
        "is_gold_transcript = []\n",
        "accent = []\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "for filename in os.listdir(audio_dir):\n",
        "    if filename.endswith('.wav'):  # Adjust the file extension if needed\n",
        "        audio_path = os.path.join(audio_dir, filename)\n",
        "        raw_text1 = \"hi, \" + os.path.splitext(filename)[0]  # Implement a function to extract the text from the filename or any other source\n",
        "        #print(filename)\n",
        "        language1 = 1   # Provide the language value\n",
        "        normalized_text1 = \"hi, \" + os.path.splitext(filename)[0]  # Implement a function to normalize the transcribed text\n",
        "        normalized_text1 = normalized_text1.replace(\"-\",\" \")\n",
        "        print(normalized_text1)\n",
        "        gender1 = 'female'  # Provide the gender value\n",
        "        speaker_id1 = 1  # Implement a function to obtain the speaker ID\n",
        "        is_gold_transcript1 = False  # Provide the value indicating if the transcript is gold\n",
        "        accent1 = 'eng-in'  # Provide the accent value\n",
        "        audio_id.append(filename)\n",
        "        language.append(language1)\n",
        "        #audio.append(waveform)\n",
        "        #print(waveform.numpy())\n",
        "        raw_text.append(raw_text1)\n",
        "        normalized_text.append(normalized_text1)\n",
        "        gender.append(gender1)\n",
        "        speaker_id.append(speaker_id1)\n",
        "        is_gold_transcript.append(is_gold_transcript1)\n",
        "        accent.append(accent1)\n",
        "        audio.append({\n",
        "        #     'audio_id': filename,\n",
        "        #     'language': language,\n",
        "              'path': audio_path,\n",
        "              #'array':waveform.numpy(),\n",
        "              'sampling_rate':16000\n",
        "        #     'raw_text': raw_text,\n",
        "        #     'normalized_text': normalized_text,\n",
        "        #     'gender': gender,\n",
        "        #     'speaker_id': speaker_id,\n",
        "        #     'is_gold_transcript': is_gold_transcript,\n",
        "        #     'accent': accent\n",
        "          })\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7JwEZOwc3zDM"
      },
      "outputs": [],
      "source": [
        "data = {\n",
        "    'audio_id': audio_id,\n",
        "    'language': language,\n",
        "    'audio': audio,\n",
        "    'raw_text': raw_text,\n",
        "    'normalized_text': normalized_text,\n",
        "    'gender': gender,\n",
        "    'speaker_id': speaker_id,\n",
        "    'is_gold_transcript': is_gold_transcript,\n",
        "    'accent': accent\n",
        "}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pftPmOYB0Ho-"
      },
      "outputs": [],
      "source": [
        "from datasets import Dataset,Audio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U9Q7xXtTzfZj"
      },
      "outputs": [],
      "source": [
        "dataset1 = Dataset.from_dict(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s9OVRpbYmPMf",
        "outputId": "4df8455d-0959-4cd4-be8c-dfe16427fd43"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['audio_id', 'language', 'audio', 'raw_text', 'normalized_text', 'gender', 'speaker_id', 'is_gold_transcript', 'accent'],\n",
              "    num_rows: 16441\n",
              "})"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Aw009m8d41F",
        "outputId": "9581a6cc-2561-43cf-aee3-a648c897922a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "datasets.arrow_dataset.Dataset"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "type(dataset1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U2K2YgYXJ3nJ"
      },
      "outputs": [],
      "source": [
        "dataset1 = dataset1.cast_column('audio', Audio(sampling_rate=16000))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "6OPBYcKj5BAx",
        "outputId": "e649df4f-1b0d-4bc5-f0cd-24b7a3335246"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'hi, bhavya'"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset1[1]['normalized_text']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5xB8fh-ht4kK"
      },
      "source": [
        "## Install required packages\n",
        "\n",
        "We install Transformers from GitHub since not all the SpeechT5 features we need have been merged into an official release yet.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xJe8TF3atyqL",
        "outputId": "4b585dba-7dbc-46fa-b12f-133787d8f9a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: datasets in /root/miniconda3/envs/tts/lib/python3.9/site-packages (2.13.0)\n",
            "Requirement already satisfied: soundfile in /root/miniconda3/envs/tts/lib/python3.9/site-packages (0.12.1)\n",
            "Requirement already satisfied: speechbrain in /root/miniconda3/envs/tts/lib/python3.9/site-packages (0.5.14)\n",
            "Requirement already satisfied: numpy>=1.17 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from datasets) (1.24.0)\n",
            "Requirement already satisfied: pyarrow>=8.0.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from datasets) (12.0.1)\n",
            "Requirement already satisfied: dill<0.3.7,>=0.3.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from datasets) (0.3.6)\n",
            "Requirement already satisfied: pandas in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from datasets) (2.0.2)\n",
            "Requirement already satisfied: requests>=2.19.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from datasets) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from datasets) (4.65.0)\n",
            "Requirement already satisfied: xxhash in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from datasets) (3.2.0)\n",
            "Requirement already satisfied: multiprocess in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from datasets) (0.70.14)\n",
            "Requirement already satisfied: fsspec[http]>=2021.11.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from datasets) (2023.6.0)\n",
            "Requirement already satisfied: aiohttp in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from datasets) (3.8.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0.0,>=0.11.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from datasets) (0.15.1)\n",
            "Requirement already satisfied: packaging in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from datasets) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from datasets) (6.0)\n",
            "Requirement already satisfied: cffi>=1.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from soundfile) (1.15.1)\n",
            "Requirement already satisfied: hyperpyyaml in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from speechbrain) (1.2.1)\n",
            "Requirement already satisfied: joblib in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from speechbrain) (1.2.0)\n",
            "Requirement already satisfied: scipy in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from speechbrain) (1.10.1)\n",
            "Requirement already satisfied: sentencepiece in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from speechbrain) (0.1.99)\n",
            "Requirement already satisfied: torch>=1.9 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from speechbrain) (2.0.1)\n",
            "Requirement already satisfied: torchaudio in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from speechbrain) (2.0.2)\n",
            "Requirement already satisfied: pycparser in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from cffi>=1.0->soundfile) (2.21)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from aiohttp->datasets) (23.1.0)\n",
            "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from aiohttp->datasets) (3.1.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from aiohttp->datasets) (6.0.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from aiohttp->datasets) (4.0.2)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from aiohttp->datasets) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from aiohttp->datasets) (1.3.3)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: filelock in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from huggingface-hub<1.0.0,>=0.11.0->datasets) (3.12.2)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from huggingface-hub<1.0.0,>=0.11.0->datasets) (4.6.3)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from requests>=2.19.0->datasets) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from requests>=2.19.0->datasets) (2.0.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from requests>=2.19.0->datasets) (2023.5.7)\n",
            "Requirement already satisfied: sympy in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.9->speechbrain) (1.12)\n",
            "Requirement already satisfied: networkx in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.9->speechbrain) (3.1)\n",
            "Requirement already satisfied: jinja2 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.9->speechbrain) (3.1.2)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.9->speechbrain) (11.7.99)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.9->speechbrain) (11.7.99)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu11==11.7.101 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.9->speechbrain) (11.7.101)\n",
            "Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.9->speechbrain) (8.5.0.96)\n",
            "Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.9->speechbrain) (11.10.3.66)\n",
            "Requirement already satisfied: nvidia-cufft-cu11==10.9.0.58 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.9->speechbrain) (10.9.0.58)\n",
            "Requirement already satisfied: nvidia-curand-cu11==10.2.10.91 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.9->speechbrain) (10.2.10.91)\n",
            "Requirement already satisfied: nvidia-cusolver-cu11==11.4.0.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.9->speechbrain) (11.4.0.1)\n",
            "Requirement already satisfied: nvidia-cusparse-cu11==11.7.4.91 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.9->speechbrain) (11.7.4.91)\n",
            "Requirement already satisfied: nvidia-nccl-cu11==2.14.3 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.9->speechbrain) (2.14.3)\n",
            "Requirement already satisfied: nvidia-nvtx-cu11==11.7.91 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.9->speechbrain) (11.7.91)\n",
            "Requirement already satisfied: triton==2.0.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.9->speechbrain) (2.0.0)\n",
            "Requirement already satisfied: setuptools in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.9->speechbrain) (67.8.0)\n",
            "Requirement already satisfied: wheel in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.9->speechbrain) (0.38.4)\n",
            "Requirement already satisfied: cmake in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from triton==2.0.0->torch>=1.9->speechbrain) (3.26.4)\n",
            "Requirement already satisfied: lit in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from triton==2.0.0->torch>=1.9->speechbrain) (16.0.6)\n",
            "Requirement already satisfied: ruamel.yaml<=0.17.28,>=0.17.8 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from hyperpyyaml->speechbrain) (0.17.28)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from pandas->datasets) (2023.3)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from pandas->datasets) (2023.3)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: six>=1.5 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
            "Requirement already satisfied: ruamel.yaml.clib>=0.2.7 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from ruamel.yaml<=0.17.28,>=0.17.8->hyperpyyaml->speechbrain) (0.2.7)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from jinja2->torch>=1.9->speechbrain) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from sympy->torch>=1.9->speechbrain) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets soundfile speechbrain\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HQCuMz3fwuHa",
        "outputId": "e8bff96d-7f1e-4360-8734-eb158fa0ffff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/huggingface/transformers.git\n",
            "  Cloning https://github.com/huggingface/transformers.git to /tmp/pip-req-build-0o7jfy22\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-req-build-0o7jfy22\n",
            "  Resolved https://github.com/huggingface/transformers.git to commit c5454eba9eac00a3e7d0a46a3d25aacd43187f1e\n",
            "  Installing build dependencies ... \u001b[?25ldone\n",
            "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
            "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
            "\u001b[?25hRequirement already satisfied: filelock in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from transformers==4.31.0.dev0) (3.12.2)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.14.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from transformers==4.31.0.dev0) (0.15.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from transformers==4.31.0.dev0) (1.24.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from transformers==4.31.0.dev0) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from transformers==4.31.0.dev0) (6.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from transformers==4.31.0.dev0) (2023.6.3)\n",
            "Requirement already satisfied: requests in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from transformers==4.31.0.dev0) (2.31.0)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from transformers==4.31.0.dev0) (0.13.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from transformers==4.31.0.dev0) (0.3.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from transformers==4.31.0.dev0) (4.65.0)\n",
            "Requirement already satisfied: fsspec in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.14.1->transformers==4.31.0.dev0) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.14.1->transformers==4.31.0.dev0) (4.6.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from requests->transformers==4.31.0.dev0) (3.1.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from requests->transformers==4.31.0.dev0) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from requests->transformers==4.31.0.dev0) (2.0.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from requests->transformers==4.31.0.dev0) (2023.5.7)\n"
          ]
        }
      ],
      "source": [
        "!pip install git+https://github.com/huggingface/transformers.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NfX4YNbs_HKP",
        "outputId": "01a0e425-f914-429e-aee2-2471e851d17c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: accelerate in /root/miniconda3/envs/tts/lib/python3.9/site-packages (0.20.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from accelerate) (1.24.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from accelerate) (23.1)\n",
            "Requirement already satisfied: psutil in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from accelerate) (6.0)\n",
            "Requirement already satisfied: torch>=1.6.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from accelerate) (2.0.1)\n",
            "Requirement already satisfied: filelock in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.6.0->accelerate) (3.12.2)\n",
            "Requirement already satisfied: typing-extensions in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.6.0->accelerate) (4.6.3)\n",
            "Requirement already satisfied: sympy in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.6.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.6.0->accelerate) (3.1)\n",
            "Requirement already satisfied: jinja2 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.6.0->accelerate) (3.1.2)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.6.0->accelerate) (11.7.99)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.6.0->accelerate) (11.7.99)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu11==11.7.101 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.6.0->accelerate) (11.7.101)\n",
            "Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.6.0->accelerate) (8.5.0.96)\n",
            "Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.6.0->accelerate) (11.10.3.66)\n",
            "Requirement already satisfied: nvidia-cufft-cu11==10.9.0.58 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.6.0->accelerate) (10.9.0.58)\n",
            "Requirement already satisfied: nvidia-curand-cu11==10.2.10.91 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.6.0->accelerate) (10.2.10.91)\n",
            "Requirement already satisfied: nvidia-cusolver-cu11==11.4.0.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.6.0->accelerate) (11.4.0.1)\n",
            "Requirement already satisfied: nvidia-cusparse-cu11==11.7.4.91 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.6.0->accelerate) (11.7.4.91)\n",
            "Requirement already satisfied: nvidia-nccl-cu11==2.14.3 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.6.0->accelerate) (2.14.3)\n",
            "Requirement already satisfied: nvidia-nvtx-cu11==11.7.91 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.6.0->accelerate) (11.7.91)\n",
            "Requirement already satisfied: triton==2.0.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from torch>=1.6.0->accelerate) (2.0.0)\n",
            "Requirement already satisfied: setuptools in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.6.0->accelerate) (67.8.0)\n",
            "Requirement already satisfied: wheel in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.6.0->accelerate) (0.38.4)\n",
            "Requirement already satisfied: cmake in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from triton==2.0.0->torch>=1.6.0->accelerate) (3.26.4)\n",
            "Requirement already satisfied: lit in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from triton==2.0.0->torch>=1.6.0->accelerate) (16.0.6)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from jinja2->torch>=1.6.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from sympy->torch>=1.6.0->accelerate) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade accelerate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g_Xffd7sRlpk"
      },
      "source": [
        "Do we have a GPU?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-XLi4c9DRnxA",
        "outputId": "8f73d175-fd69-4370-c389-726967509a4f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Tue Jun 20 08:02:42 2023       \r\n",
            "+-----------------------------------------------------------------------------+\r\n",
            "| NVIDIA-SMI 460.106.00   Driver Version: 460.106.00   CUDA Version: 11.2     |\r\n",
            "|-------------------------------+----------------------+----------------------+\r\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\r\n",
            "|                               |                      |               MIG M. |\r\n",
            "|===============================+======================+======================|\r\n",
            "|   0  A100-SXM4-40GB      Off  | 00000000:00:04.0 Off |                    0 |\r\n",
            "| N/A   32C    P0    45W / 400W |    109MiB / 40536MiB |      0%      Default |\r\n",
            "|                               |                      |             Disabled |\r\n",
            "+-------------------------------+----------------------+----------------------+\r\n",
            "                                                                               \r\n",
            "+-----------------------------------------------------------------------------+\r\n",
            "| Processes:                                                                  |\r\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\r\n",
            "|        ID   ID                                                   Usage      |\r\n",
            "|=============================================================================|\r\n",
            "|    0   N/A  N/A      1760      G   /usr/lib/xorg/Xorg                 96MiB |\r\n",
            "|    0   N/A  N/A      1827      G   /usr/bin/gnome-shell               12MiB |\r\n",
            "+-----------------------------------------------------------------------------+\r\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bmdgnloAQPQ1"
      },
      "source": [
        "In case no GPU is found, from the menu choose **Runtime > Change runtime type** and set **Hardware accelerator** to **GPU**. Then restart the runtime to activate the GPU."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GB6Ur8DHuUy8"
      },
      "source": [
        "## Load the model\n",
        "\n",
        "We'll start from SpeechT5 that's already been fine-tuned for English TTS, and fine-tune it again but for a new language. For more info about the original checkpoint, you can find its model card on the [Hugging Face Hub](https://huggingface.co/microsoft/speecht5_tts)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0atziOZw6fpi",
        "outputId": "a4c72c28-a04e-484f-fb4b-06a108653d6a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: transformers in /root/miniconda3/envs/tts/lib/python3.9/site-packages (4.31.0.dev0)\n",
            "Requirement already satisfied: filelock in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from transformers) (3.12.2)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.14.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from transformers) (0.15.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from transformers) (1.24.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from transformers) (0.13.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from transformers) (0.3.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: fsspec in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.6.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from requests->transformers) (3.1.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from requests->transformers) (2.0.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from requests->transformers) (2023.5.7)\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W7spxtTGtmba"
      },
      "outputs": [],
      "source": [
        "from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech\n",
        "\n",
        "processor = SpeechT5Processor.from_pretrained(\"microsoft/speecht5_tts\")\n",
        "model = SpeechT5ForTextToSpeech.from_pretrained(\"microsoft/speecht5_tts\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-5qFwV2ouqkU"
      },
      "source": [
        "## Clean up the text\n",
        "\n",
        "The dataset may contain characters that are not in the SpeechT5 tokenizer vocabulary. Those will be converted to `<unk>` tokens. However, in Dutch a character such as `à` is used to stress syllables and so we can simply replace it with `a` without changing its meaning. The tokenizer supports some characters with diacritical marks, such as `é`, but not all of them.\n",
        "\n",
        "Note that the VoxPopuli examples have both `raw_text` and `normalized_text`, which is all lowercase. The SpeechT5 tokenizer can handle uppercase just fine, however, it doesn't have any tokens for numbers. The `normalized_text` has the numbers written out as text, so we'll use that.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B6BtRe6qXTYp"
      },
      "source": [
        "\n",
        "First we'll need to find out what the unsupported tokens are. The `SpeechT5Tokenizer` works with characters as tokens, so let's extract all distinct characters of the dataset. We write a mapping function `extract_all_chars` that concatenates the transcriptions from all examples into one long transcription and then transforms the string into a set of characters. It is important to pass the arguments `batched=True` and `batch_size=-1` to `dataset.map(...)` so that the mapping function has access to all transcriptions at once."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0KmO3JnXBs6Z"
      },
      "outputs": [],
      "source": [
        "tokenizer = processor.tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "8c819dc81c3345219c2a6d4434b57923",
            "14b280e314274a60a2900e57660495c8",
            "1dc70f9f960e4d7b943ca4b1f1112918",
            "e9ca59f6bd4948218ce51dd8fa2c7fe1",
            "deda0ee9a0e240c98d597eea95791887",
            "b265c0bd2c31442898782b419db4c87a",
            "28c88a96e5984cffad4b29e4f87c054b",
            "d2aec35336c64c0b8f52b2615fb5a0ef",
            "54a0a4f9f1ff4fc1b38c50927827e3c9",
            "a1e9f17e85e04f87ba5911795b986ca2",
            "ff62c0269fd0401db5bd946179b4c405",
            ""
          ]
        },
        "id": "uwnvx2AFuh_e",
        "outputId": "49919368-bdeb-4baa-8af6-c777e563aa9c"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/16441 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def extract_all_chars(batch):\n",
        "    all_text = \" \".join(batch[\"normalized_text\"])\n",
        "    vocab = list(set(all_text))\n",
        "    return {\"vocab\": [vocab], \"all_text\": [all_text]}\n",
        "\n",
        "vocabs = dataset1.map(\n",
        "    extract_all_chars,\n",
        "    batched=True,\n",
        "    batch_size=-1,\n",
        "    keep_in_memory=True,\n",
        "    remove_columns=dataset1.column_names,\n",
        ")\n",
        "\n",
        "dataset_vocab = set(vocabs[\"vocab\"][0])\n",
        "tokenizer_vocab = {k for k,_ in tokenizer.get_vocab().items()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HoSwxPF19XF7",
        "outputId": "6b9047c8-86e1-423b-c094-9195fe2db27b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'!',\n",
              " '\"',\n",
              " \"'\",\n",
              " '(',\n",
              " ')',\n",
              " ',',\n",
              " '-',\n",
              " '.',\n",
              " '/',\n",
              " ':',\n",
              " ';',\n",
              " '</s>',\n",
              " '<ctc_blank>',\n",
              " '<mask>',\n",
              " '<pad>',\n",
              " '<s>',\n",
              " '<unk>',\n",
              " '?',\n",
              " 'A',\n",
              " 'B',\n",
              " 'C',\n",
              " 'D',\n",
              " 'E',\n",
              " 'F',\n",
              " 'G',\n",
              " 'H',\n",
              " 'I',\n",
              " 'J',\n",
              " 'K',\n",
              " 'L',\n",
              " 'M',\n",
              " 'N',\n",
              " 'O',\n",
              " 'P',\n",
              " 'Q',\n",
              " 'R',\n",
              " 'S',\n",
              " 'T',\n",
              " 'U',\n",
              " 'V',\n",
              " 'W',\n",
              " 'X',\n",
              " 'Y',\n",
              " 'Z',\n",
              " '[',\n",
              " ']',\n",
              " 'a',\n",
              " 'b',\n",
              " 'c',\n",
              " 'd',\n",
              " 'e',\n",
              " 'f',\n",
              " 'g',\n",
              " 'h',\n",
              " 'i',\n",
              " 'j',\n",
              " 'k',\n",
              " 'l',\n",
              " 'm',\n",
              " 'n',\n",
              " 'o',\n",
              " 'p',\n",
              " 'q',\n",
              " 'r',\n",
              " 's',\n",
              " 't',\n",
              " 'u',\n",
              " 'v',\n",
              " 'w',\n",
              " 'x',\n",
              " 'y',\n",
              " 'z',\n",
              " '{',\n",
              " '}',\n",
              " 'æ',\n",
              " 'é',\n",
              " 'ê',\n",
              " 'œ',\n",
              " '̄',\n",
              " '—',\n",
              " '▁'}"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer_vocab"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C2u-lIk1Tvl8"
      },
      "source": [
        "Now we have two sets of characters, one with the vocabulary from the dataset and one with the vocabulary from the tokenizer. By taking the difference between these sets, we find the characters that are in the dataset but not in the tokenizer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lNV2BPKOvVuQ",
        "outputId": "c99c6470-0d00-47d2-f405-91843f213f3a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{' '}"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset_vocab - tokenizer_vocab"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "okCE9DjNvXM1"
      },
      "source": [
        "Next, we will define a function to map these characters to valid tokens and then run it on the dataset. No need to handle space, that's already replaced by `▁` in the tokenizer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "0b92cd998f1041699b898223a5b6c77a",
            "115e613dcaa34ae7b6c8814ac6e49543",
            "b8ca7c8817b549f6ba28ee1a1709d6a1",
            "18e77f8d90ac4956a349c5174ba36b06",
            "de40751d0b9b46678743e15c32860ccb",
            "b2af3edbbdbe485d89417f166f32e11e",
            "8f6ea511a3934235860f6879c9a90830",
            "db8c5efc5f5b488d8d822d480af5cef3",
            "4e863ae2a6f94d8685bab8db09a405b1",
            "f665ed1f0f254582ac4fb2cf434f3be8",
            "9340ad78859d4e62bc0c99cfb50b84fa",
            ""
          ]
        },
        "id": "ybqsbUGZvtCa",
        "outputId": "d4dcd806-1501-47e0-9a9b-5434ebe98e20"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/16441 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "replacements = [\n",
        "    (' ', ' '),\n",
        "    ('0', ' '),\n",
        "    ('1', ' '),\n",
        "    ('2', ' '),\n",
        "    ('3', ' '),\n",
        "    ('4', ' '),\n",
        "    ('5', ' '),\n",
        "    ('6', ' '),\n",
        "    ('7', ' '),\n",
        "    ('8', ' '),\n",
        "    ('9', ' '),\n",
        "    ('_', ' '),\n",
        "]\n",
        "\n",
        "def cleanup_text(inputs):\n",
        "    for src, dst in replacements:\n",
        "        inputs[\"normalized_text\"] = inputs[\"normalized_text\"].replace(src, dst)\n",
        "    return inputs\n",
        "\n",
        "dataset1 = dataset1.map(cleanup_text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HmfamiPxXf_4"
      },
      "source": [
        "## Speakers\n",
        "\n",
        "VoxPopuli is a multi-speaker dataset. We know there are 20968 examples in the dataset, but how many speakers are there and how many examples do they have?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p95Czw8TXFDj"
      },
      "outputs": [],
      "source": [
        "from collections import defaultdict\n",
        "speaker_counts = defaultdict(int)\n",
        "\n",
        "for speaker_id in dataset1[\"speaker_id\"]:\n",
        "    speaker_counts[speaker_id] += 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QdHJjHKP5H4Y",
        "outputId": "31a4de2f-97fe-4ce2-a01e-54c66d99b61b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: matplotlib in /root/miniconda3/envs/tts/lib/python3.9/site-packages (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from matplotlib) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from matplotlib) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from matplotlib) (4.40.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from matplotlib) (1.4.4)\n",
            "Requirement already satisfied: numpy>=1.20 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from matplotlib) (1.24.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from matplotlib) (23.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from matplotlib) (9.5.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from matplotlib) (3.1.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: importlib-resources>=3.2.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from matplotlib) (5.12.0)\n",
            "Requirement already satisfied: zipp>=3.1.0 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib) (3.15.0)\n",
            "Requirement already satisfied: six>=1.5 in /root/miniconda3/envs/tts/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "pip install matplotlib"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nV0FTeGX8Crw"
      },
      "source": [
        "By plotting a histogram we can get a sense of how much data there is for each speaker."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "tyJQ6rlt65ef",
        "outputId": "49bd7b3b-1ca6-41da-bf00-5b2ac3dbe142"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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zqba29qKKakyVlZUKCAhQRUWF/P39PV0OADcLTdnwy51ctH/pxEabG8CFNeT1u94rN3a7/aILAwAAaGwuvVsKAACguXL5huKqqiq9//77KikpUU1NjdO+++6776ILAwAAcIVL4Wbnzp2aMGGCTp06paqqKnXu3Fnl5eVq27atAgMDCTcAAMBjXLosNW/ePMXFxemHH35QmzZt9NFHH+nAgQOKjIzU008/7e4aAQAA6s2lcFNcXKwHHnhAXl5e8vb2VnV1tUJCQvTUU0/p0UcfdXeNAAAA9eZSuGndurXjreGBgYEqKSmRJAUEBOjgwYPuqw4AAKCBXLrnJiIiQh9//LH69OmjUaNGKS0tTeXl5frzn/+sgQMHurtGAACAenNp5WbJkiXq1q2bJGnx4sXq1KmT7rnnHh09elTLly93a4EAAAAN4dLKzZAhQxx/DgwMVF5entsKAgAAuBguf4jfmTNn9M477+ill17SiRMnJEnfffedTp486bbiAAAAGsqllZsDBw5o3LhxKikpUXV1tcaMGaMOHTroySefVHV1tXJyctxdJwAAQL24tHIzd+5cDRkyxPE5N2fdcsstys/Pd1txAAAADeXSys3WrVu1bds2+fj4OLWHhobq0KFDbikMAADAFS6t3NjtdtXW1p7T/o9//EMdOnS46KIAAABc5VK4GTt2rLKyshyPbTabTp48qfT0dE2YMMFdtQEAADSYS5elli1bptjYWA0YMEA//fSTpkyZoq+++kpdu3bV6tWr3V0jAABAvbkUbi699FLt2rVLa9as0aeffqqTJ09q+vTpmjp1qtMNxgAAAE3NpXAjSa1atdLtt9/uzloAAAAumsvhZu/evXruuef0+eefS5L69++v2bNn68orr3RbcQAAAA3l0g3Fa9eu1cCBA1VUVKSwsDCFhYVpx44dGjRokNauXevuGgEAAOrNpZWbhx9+WKmpqVq4cKFTe3p6uh5++GHddtttbikOAACgoVxauTl8+LASEhLOab/99tt1+PDhiy4KAADAVS6Fm+uuu05bt249p/2DDz7Qtddee9FFAQAAuMqly1I33nijHnnkERUVFWn48OGSpI8++khvvPGGMjIy9NZbbzn1BQAAaCo2y7Kshg7y8qrfgo/NZqvzaxo8qbKyUgEBAaqoqJC/v7+nywHgZqEpGxpt7v1LJzba3AAurCGv3y6t3NjtdpcKAwAAaGwNuuemsLBQ69evd2r705/+pCuuuEKBgYG6++67VV1d7dYCAQAAGqJB4WbhwoX6v//7P8fj3bt3a/r06YqJiVFKSor+/ve/KzMz0+1FAgAA1FeDwk1xcbGuv/56x+M1a9YoKipKK1asUHJysv74xz/q9ddfd3uRAAAA9dWgcPPDDz8oKCjI8fj999/X+PHjHY+HDh2qgwcPuq86AACABmpQuAkKCtK3334rSaqpqdGOHTscbwWXpBMnTqh169burRAAAKABGhRuJkyYoJSUFG3dulWpqalq27at04f2ffrpp+rVq5fbiwQAAKivBr0VfNGiRbr11ls1atQotW/fXv/1X/8lHx8fx/6VK1dq7Nixbi8SAACgvhoUbrp27aotW7aooqJC7du3l7e3t9P+N954Q+3bt3drgQAAAA3h0of4BQQE1NneuXPniyoGAADgYrn0xZkAAADNFeEGAAAYhXADAACMQrgBAABGIdwAAACjNItwk52drdDQUPn5+SkqKkrbt2+v17g1a9bIZrPp5ptvbtwCAQBAi+HxcJObm6vk5GSlp6drx44dCgsLU2xsrI4cOXLBcfv379eDDz7o9AnJAAAAHg83zzzzjO666y4lJSVpwIABysnJUdu2bbVy5crzjqmtrdXUqVOVkZGhnj17NmG1AACgufNouKmpqVFRUZFiYmIcbV5eXoqJiVFhYeF5xy1cuFCBgYGaPn36Lx6jurpalZWVThsAADCXR8NNeXm5amtrFRQU5NQeFBSk0tLSOsd88MEHeuWVV7RixYp6HSMzM1MBAQGOLSQk5KLrBgAAzZfHL0s1xIkTJzRt2jStWLFCXbt2rdeY1NRUVVRUOLaDBw82cpUAAMCTXPpuKXfp2rWrvL29VVZW5tReVlam4ODgc/p/88032r9/v+Li4hxtdrtdktSqVSvt3btXvXr1chrj6+srX1/fRqgeAAA0Rx5dufHx8VFkZKTy8/MdbXa7Xfn5+YqOjj6n/5VXXqndu3eruLjYsd14440aPXq0iouLueQEAAA8u3IjScnJyUpMTNSQIUM0bNgwZWVlqaqqSklJSZKkhIQE9ejRQ5mZmfLz89PAgQOdxnfs2FGSzmkHAAC/Th4PN/Hx8Tp69KjS0tJUWlqq8PBw5eXlOW4yLikpkZdXi7o1CAAAeJDNsizL00U0pcrKSgUEBKiiokL+/v6eLgeAm4WmbGi0ufcvndhocwO4sIa8frMkAgAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRmkW4yc7OVmhoqPz8/BQVFaXt27eft++KFSt07bXXqlOnTurUqZNiYmIu2B8AAPy6eDzc5ObmKjk5Wenp6dqxY4fCwsIUGxurI0eO1Nm/oKBAkydP1nvvvafCwkKFhIRo7NixOnToUBNXDgAAmiObZVmWJwuIiorS0KFD9fzzz0uS7Ha7QkJCNGfOHKWkpPzi+NraWnXq1EnPP/+8EhISztlfXV2t6upqx+PKykqFhISooqJC/v7+7nsiAJqF0JQNjTb3/qUTG21uABdWWVmpgICAer1+e3TlpqamRkVFRYqJiXG0eXl5KSYmRoWFhfWa49SpUzp9+rQ6d+5c5/7MzEwFBAQ4tpCQELfUDgAAmiePhpvy8nLV1tYqKCjIqT0oKEilpaX1muORRx5R9+7dnQLSz6WmpqqiosKxHTx48KLrBgAAzVcrTxdwMZYuXao1a9aooKBAfn5+dfbx9fWVr69vE1cGAAA8xaPhpmvXrvL29lZZWZlTe1lZmYKDgy849umnn9bSpUv1zjvvaPDgwY1ZJgAAaEE8elnKx8dHkZGRys/Pd7TZ7Xbl5+crOjr6vOOeeuopLVq0SHl5eRoyZEhTlAoAAFoIj1+WSk5OVmJiooYMGaJhw4YpKytLVVVVSkpKkiQlJCSoR48eyszMlCQ9+eSTSktL02uvvabQ0FDHvTnt27dX+/btPfY8AABA8+DxcBMfH6+jR48qLS1NpaWlCg8PV15enuMm45KSEnl5/WuB6cUXX1RNTY1+97vfOc2Tnp6uxx9/vClLBwAAzZDHP+emqTXkffIAWh4+5wYwU4v5nBsAAAB3I9wAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFGaRbjJzs5WaGio/Pz8FBUVpe3bt1+w/xtvvKErr7xSfn5+GjRokDZu3NhElQIAgObO4+EmNzdXycnJSk9P144dOxQWFqbY2FgdOXKkzv7btm3T5MmTNX36dO3cuVM333yzbr75Zn322WdNXDkAAGiObJZlWZ4sICoqSkOHDtXzzz8vSbLb7QoJCdGcOXOUkpJyTv/4+HhVVVVp/fr1jrbhw4crPDxcOTk5v3i8yspKBQQEqKKiQv7+/u57IgCahdCUDY029/6lExttbgAX1pDX71ZNVFOdampqVFRUpNTUVEebl5eXYmJiVFhYWOeYwsJCJScnO7XFxsZq3bp1dfavrq5WdXW143FFRYWkf54kAOaxV59qtLn5dwPwnLO/f/VZk/FouCkvL1dtba2CgoKc2oOCgvTFF1/UOaa0tLTO/qWlpXX2z8zMVEZGxjntISEhLlYN4NcqIMvTFQA4ceKEAgICLtjHo+GmKaSmpjqt9Njtdh07dkxdunSRzWbzYGXNQ2VlpUJCQnTw4EEu0zUiznPT4Dw3Dc5z0+Fc/4tlWTpx4oS6d+/+i309Gm66du0qb29vlZWVObWXlZUpODi4zjHBwcEN6u/r6ytfX1+nto4dO7petKH8/f1/9b84TYHz3DQ4z02D89x0ONf/9EsrNmd59N1SPj4+ioyMVH5+vqPNbrcrPz9f0dHRdY6Jjo526i9JmzdvPm9/AADw6+Lxy1LJyclKTEzUkCFDNGzYMGVlZamqqkpJSUmSpISEBPXo0UOZmZmSpLlz52rUqFFatmyZJk6cqDVr1uiTTz7R8uXLPfk0AABAM+HxcBMfH6+jR48qLS1NpaWlCg8PV15enuOm4ZKSEnl5/WuBacSIEXrttdc0f/58Pfroo+rTp4/WrVungQMHeuoptGi+vr5KT08/59Id3Ivz3DQ4z02D89x0ONeu8fjn3AAAALiTxz+hGAAAwJ0INwAAwCiEGwAAYBTCDQAAMArh5lcgOztboaGh8vPzU1RUlLZv337B/sePH9esWbPUrVs3+fr6qm/fvtq4cWMTVdtyNfQ8Z2VlqV+/fmrTpo1CQkI0b948/fTTT01Ubcu0ZcsWxcXFqXv37rLZbOf9TrmfKygo0NVXXy1fX1/17t1bq1atavQ6W7qGnuc333xTY8aM0SWXXCJ/f39FR0fr7bffbppiWzBX/j6f9eGHH6pVq1YKDw9vtPpaMsKN4XJzc5WcnKz09HTt2LFDYWFhio2N1ZEjR+rsX1NTozFjxmj//v3661//qr1792rFihXq0aNHE1fesjT0PL/22mtKSUlRenq6Pv/8c73yyivKzc3Vo48+2sSVtyxVVVUKCwtTdnZ2vfp/++23mjhxokaPHq3i4mLdf//9mjFjBi+8v6Ch53nLli0aM2aMNm7cqKKiIo0ePVpxcXHauXNnI1fasjX0PJ91/PhxJSQk6Prrr2+kygxgwWjDhg2zZs2a5XhcW1trde/e3crMzKyz/4svvmj17NnTqqmpaaoSjdDQ8zxr1izrt7/9rVNbcnKydc011zRqnSaRZP3tb3+7YJ+HH37Yuuqqq5za4uPjrdjY2EaszCz1Oc91GTBggJWRkeH+ggzVkPMcHx9vzZ8/30pPT7fCwsIata6WipUbg9XU1KioqEgxMTGONi8vL8XExKiwsLDOMW+99Zaio6M1a9YsBQUFaeDAgVqyZIlqa2ubquwWx5XzPGLECBUVFTkuXe3bt08bN27UhAkTmqTmX4vCwkKnn4skxcbGnvfnAvew2+06ceKEOnfu7OlSjPPqq69q3759Sk9P93QpzZrHP6EYjae8vFy1tbWOT3s+KygoSF988UWdY/bt26d3331XU6dO1caNG/X111/r3nvv1enTp/llOg9XzvOUKVNUXl6ukSNHyrIsnTlzRjNnzuSylJuVlpbW+XOprKzUjz/+qDZt2nioMrM9/fTTOnnypCZNmuTpUozy1VdfKSUlRVu3blWrVrx8XwgrN3Bit9sVGBio5cuXKzIyUvHx8XrssceUk5Pj6dKMUlBQoCVLluiFF17Qjh079Oabb2rDhg1atGiRp0sDLsprr72mjIwMvf766woMDPR0Ocaora3VlClTlJGRob59+3q6nGaP6Gewrl27ytvbW2VlZU7tZWVlCg4OrnNMt27d1Lp1a3l7ezva+vfvr9LSUtXU1MjHx6dRa26JXDnPCxYs0LRp0zRjxgxJ0qBBg1RVVaW7775bjz32mNP3qcF1wcHBdf5c/P39WbVpBGvWrNGMGTP0xhtvnHM5EBfnxIkT+uSTT7Rz507Nnj1b0j//M2pZllq1aqVNmzbpt7/9rYerbD74F9RgPj4+ioyMVH5+vqPNbrcrPz9f0dHRdY655ppr9PXXX8tutzvavvzyS3Xr1o1gcx6unOdTp06dE2DOBkqLr3tzm+joaKefiyRt3rz5vD8XuG716tVKSkrS6tWrNXHiRE+XYxx/f3/t3r1bxcXFjm3mzJnq16+fiouLFRUV5ekSmxcP39CMRrZmzRrL19fXWrVqlbVnzx7r7rvvtjp27GiVlpZalmVZ06ZNs1JSUhz9S0pKrA4dOlizZ8+29u7da61fv94KDAy0nnjiCU89hRahoec5PT3d6tChg7V69Wpr37591qZNm6xevXpZkyZN8tRTaBFOnDhh7dy509q5c6clyXrmmWesnTt3WgcOHLAsy7JSUlKsadOmOfrv27fPatu2rfXQQw9Zn3/+uZWdnW15e3tbeXl5nnoKLUJDz/Nf/vIXq1WrVlZ2drZ1+PBhx3b8+HFPPYUWoaHn+d/xbqnzI9z8Cjz33HPWZZddZvn4+FjDhg2zPvroI8e+UaNGWYmJiU79t23bZkVFRVm+vr5Wz549rcWLF1tnzpxp4qpbnoac59OnT1uPP/641atXL8vPz88KCQmx7r33XuuHH35o+sJbkPfee8+SdM529twmJiZao0aNOmdMeHi45ePjY/Xs2dN69dVXm7zulqah53nUqFEX7I+6ufL3+ecIN+dnsyzWwAEAgDm45wYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAhli8eLFGjBihtm3bqmPHjvUe9/nnn+vGG29UQECA2rVrp6FDh6qkpOScfpZlafz48bLZbFq3bl2dc33//fe69NJLZbPZdPz4cZeeR32OcyGEGwD4/6677jrdf//9ni4DuKDrrrtOq1atqnNfTU2N/uM//kP33HNPvef75ptvNHLkSF155ZUqKCjQp59+qgULFsjPz++cvllZWbLZbBecb/r06Ro8eHC9j1+X+hznQgg3ANzqjjvukM1mO2cbN26cp0sDjJeRkaF58+Zp0KBB9R7z2GOPacKECXrqqacUERGhXr166cYbb1RgYKBTv+LiYi1btkwrV64871wvvviijh8/rgcffLDO/f/93/+tq6++Wn5+furZs6cyMjJ05syZBh/nlxBuALjduHHjdPjwYadt9erVni4LwL+x2+3asGGD+vbtq9jYWAUGBioqKuqcS0GnTp3SlClTlJ2dreDg4Drn2rNnjxYuXKg//elP8vI6N15s3bpVCQkJmjt3rvbs2aOXXnpJq1at0uLFixt0nPog3ABwO19fXwUHBzttnTp1UkFBgXx8fLR161ZH36eeekqBgYEqKyuTJOXl5WnkyJHq2LGjunTpohtuuEHffPONo//+/ftls9n0+uuv69prr1WbNm00dOhQffnll/r44481ZMgQtW/fXuPHj9fRo0cd4+644w7dfPPNysjI0CWXXCJ/f3/NnDlTNTU1530e1dXVevDBB9WjRw+1a9dOUVFRKigocOw/cOCA4uLi1KlTJ7Vr105XXXWVNm7c6MYzCTSuI0eO6OTJk1q6dKnGjRunTZs26ZZbbtGtt96q999/39Fv3rx5GjFihG666aY656murtbkyZP1+9//XpdddlmdfTIyMpSSkqLExET17NlTY8aM0aJFi/TSSy/V+zj11eqiRgNAA5y9p2XatGnatWuX9u3bpwULFuiNN95QUFCQJKmqqkrJyckaPHiwTp48qbS0NN1yyy0qLi52+t9genq6srKydNlll+nOO+/UlClT1KFDB/3hD39Q27ZtNWnSJKWlpenFF190jMnPz5efn58KCgq0f/9+JSUlqUuXLk7/c/y52bNna8+ePVqzZo26d++uv/3tbxo3bpx2796tPn36aNasWaqpqdGWLVvUrl077dmzR+3bt2/ck4hfnSVLlmjJkiWOxz/++KM++ugjzZ4929G2Z8+e84aKC7Hb7ZKkm266SfPmzZMkhYeHa9u2bcrJydGoUaP01ltv6d1339XOnTvPO09qaqr69++v22+//bx9du3apQ8//NDp9622tlY//fSTTp06pXfeeecXj1Nvnv1ScgCmSUxMtLy9va127do5bYsXL7Ysy7Kqq6ut8PBwa9KkSdaAAQOsu+6664LzHT161JJk7d6927Isy/r2228tSdbLL7/s6LN69WpLkpWfn+9oy8zMtPr16+dUV+fOna2qqipH24svvmi1b9/eqq2ttSzLskaNGmXNnTvXsizLOnDggOXt7W0dOnTIqZ7rr7/eSk1NtSzLsgYNGmQ9/vjjDT1FQIN8//331ldffeXYhg0bZj355JNObadPn3Ya8+qrr1oBAQG/OHd1dbXVqlUra9GiRU7tDz/8sDVixAjLsixr7ty5ls1ms7y9vR2bJMvLy8saNWqUZVmWFRYWZnl5eTn2e3l5WZIsb29vKy0tzbIsy/Lz8zun7rNbbW1tvY5TX6zcAHC70aNHO62YSFLnzp0lST4+PvrLX/6iwYMH6/LLL9ezzz7r1O+rr75SWlqa/vd//1fl5eWO/1mWlJRo4MCBjn4/fzfG2VWfn99EGRQUpCNHjjjNHRYWprZt2zoeR0dH6+TJkzp48KAuv/xyp767d+9WbW2t+vbt69ReXV2tLl26SJLuu+8+3XPPPdq0aZNiYmJ02223XfS7RIB/17lzZ8fvjyS1adNGgYGB6t2790XP7ePjo6FDh2rv3r1O7V9++aXjdyIlJUUzZsxw2j9o0CA9++yziouLkyStXbtWP/74o2P/xx9/rDvvvFNbt25Vr169JElXX3219u7de96663Oc+iLcAHC7du3aXfAf3m3btkmSjh07pmPHjqldu3aOfXFxcbr88su1YsUKde/eXXa7XQMHDjzn3pjWrVs7/nz2LaP/3nY2GLni5MmT8vb2VlFRkby9vZ32nb30NGPGDMXGxmrDhg3atGmTMjMztWzZMs2ZM8fl4wIXo6SkRMeOHVNJSYlqa2tVXFwsSerdu7fj7+2VV16pzMxM3XLLLZKkhx56SPHx8frNb36j0aNHKy8vT3//+98d95edvW/u31122WW64oorJMkRYM4qLy+XJPXv39/xeTtpaWm64YYbdNlll+l3v/udvLy8tGvXLn322Wd64okn6nWc+uKGYgBN6ptvvtG8efO0YsUKRUVFKTEx0RFCvv/+e+3du1fz58/X9ddfr/79++uHH35w27F37drl9L/Ljz76SO3bt1dISMg5fSMiIlRbW6sjR46od+/eTtvP/wEOCQnRzJkz9eabb+qBBx7QihUr3FYv0FBpaWmKiIhQenq6Tp48qYiICEVEROiTTz5x9Nm7d68qKiocj2+55Rbl5OToqaee0qBBg/Tyyy9r7dq1GjlypFtri42N1fr167Vp0yYNHTpUw4cP17PPPnvOqqk7sHIDwO2qq6tVWlrq1NaqVSt16tRJt99+u2JjY5WUlKRx48Zp0KBBWrZsmR566CF16tRJXbp00fLly9WtWzeVlJQoJSXFbXXV1NRo+vTpmj9/vvbv36/09HTNnj27zret9u3bV1OnTlVCQoKWLVumiIgIHT16VPn5+Ro8eLAmTpyo+++/X+PHj1ffvn31ww8/6L333lP//v3dVi9Ql5+/Y+/frVq16rwf8HeWZVnntN1555268847611DXXP83HXXXVdnn9jYWMXGxrrtOOdDuAHgdnl5eerWrZtTW79+/TRlyhQdOHBA69evlyR169ZNy5cv1+TJkzV27FiFhYVpzZo1uu+++zRw4ED169dPf/zjH3Xddde5pa7rr79effr00W9+8xvHW1cff/zx8/Z/9dVX9cQTT+iBBx7QoUOH1LVrVw0fPlw33HCDpH++02PWrFn6xz/+IX9/f40bN+6ce4gAND2b5WosAoAW5I477tDx48dd+p4aAC0L99wAAACjEG4AAIBRuCwFAACMwsoNAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGCU/wfV8mzVzQUjNgAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure()\n",
        "plt.hist(speaker_counts.values(), bins=20)\n",
        "plt.ylabel(\"Speakers\")\n",
        "plt.xlabel(\"Examples\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_GKHahr8V2J"
      },
      "source": [
        "Around one-third of speakers has fewer than 100 examples. Approximately ten or so speakers have more than 500 examples. To speed up training, let's limit ourselves to speakers that have between 100 and 400 examples. This also makes the dataset more balanced."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "552067cdc4e949e6b2d47694007a78ea",
            "0b2306bf44544c7aa5cbe8fcc227a694",
            "6d788a2a0b1a4a67a51919133c344349",
            "daf97da7f4114b4caf1fc8c45d6afd0c",
            "59d8d61ab5ee4e56868c7c016547ab34",
            "279452e7ee544889afc3ca9db377bebc",
            "638170326d504f3382e2189bafa44d3f",
            "8a24e2db7ce949abaf3d85c67458ec51",
            "cf725e5d14634bef84f62936c6ed21f8",
            "8a53707a6f3c4e6d9330e64b22e39ab5",
            "55c2cc0960444f76b5ff7aea4c53da1e",
            ""
          ]
        },
        "id": "hU12-RRn8-sL",
        "outputId": "f4256e05-9ccf-40e2-fb40-a9fa2527cfa9"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Filter:   0%|          | 0/16441 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def select_speaker(speaker_id):\n",
        "    return 100 <= speaker_counts[speaker_id]\n",
        "\n",
        "dataset1 = dataset1.filter(select_speaker, input_columns=[\"speaker_id\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gB2_vznxFDFJ"
      },
      "source": [
        "How many speakers remain?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fDM9ZEf39PYe",
        "outputId": "fcd0fff6-3144-4330-e300-307dacf1f420"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(set(dataset1[\"speaker_id\"]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pZKvfkf_FL5J"
      },
      "source": [
        "And how many examples?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9TzhTrjxFPMs",
        "outputId": "7d427ccc-5675-4518-cef3-35f998c0ae53"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "16441"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(dataset1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9qbivw_W9p0Z"
      },
      "source": [
        "Just under 10k examples and ~40 unique speakers, which should be plenty."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gdI1j_XzX3Px"
      },
      "source": [
        "Note: Even better would be to include the lengths of the examples. It's quite possible that speakers with few examples have plenty of audio available if these examples are relatively long. But it takes a while to scan through the entire dataset to find this information (since that requires loading and decoding each audio file), which is why we're skipping it here."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-OL3s6YUv9ND"
      },
      "source": [
        "## Speaker embeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QsmonrEW1IlH"
      },
      "source": [
        "To allow the TTS model to distinguish between multiple speakers, we'll need to create a speaker embedding for each example. The speaker embedding is simply an additional input into the model that captures a particular speaker's voice characteristics.\n",
        "\n",
        "To create the speaker embeddings, we use the [spkrec-xvect-voxceleb](https://huggingface.co/speechbrain/spkrec-xvect-voxceleb) model from SpeechBrain. The following defines a function `create_speaker_embedding()` that takes an input audio waveform and outputs a 512-element vector containing the corresponding speaker embedding."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "smJs3_VB1Da2",
        "outputId": "b1ca7800-3583-4305-86ad-a260de4a4145"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "torchvision is not available - cannot save figures\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import torch\n",
        "from speechbrain.pretrained import EncoderClassifier\n",
        "\n",
        "spk_model_name = \"speechbrain/spkrec-xvect-voxceleb\"\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "speaker_model = EncoderClassifier.from_hparams(\n",
        "    source=spk_model_name,\n",
        "    run_opts={\"device\": device},\n",
        "    savedir=os.path.join(\"/tmp\", spk_model_name)\n",
        ")\n",
        "\n",
        "def create_speaker_embedding(waveform):\n",
        "    with torch.no_grad():\n",
        "        speaker_embeddings = speaker_model.encode_batch(torch.tensor(waveform))\n",
        "        speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2)\n",
        "        speaker_embeddings = speaker_embeddings.squeeze().cpu().numpy()\n",
        "    return speaker_embeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UczHmLsuFkC6"
      },
      "source": [
        "Note: the `speechbrain/spkrec-xvect-voxceleb` model was trained on English speech from the VoxCeleb dataset, while our training examples are in Dutch. We're assuming here that this model will still make reasonable speaker embeddings, but this may not be true. First training an X-vector model on the target speech, such as Dutch, might work better."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_a8ouSY-Fnxy"
      },
      "source": [
        "## Preparing the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dTI_2WkH-3_Z"
      },
      "source": [
        "The `prepare_dataset` function takes in a single example and uses the `SpeechT5Processor` object to tokenize the input text and load the target audio into a log-mel spectrogram. It also adds the speaker embeddings as an additional input."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mmu8f555v_Ea"
      },
      "outputs": [],
      "source": [
        "def prepare_dataset(example):\n",
        "    # load the audio data; if necessary, this resamples the audio to 16kHz\n",
        "    audio = example[\"audio\"]\n",
        "\n",
        "    # feature extraction and tokenization\n",
        "    example = processor(\n",
        "        text=example[\"normalized_text\"],\n",
        "        audio_target=audio[\"array\"],\n",
        "        sampling_rate=audio[\"sampling_rate\"],\n",
        "        return_attention_mask=False,\n",
        "    )\n",
        "\n",
        "    # strip off the batch dimension\n",
        "    example[\"labels\"] = example[\"labels\"][0]\n",
        "\n",
        "    # use SpeechBrain to obtain x-vector\n",
        "    example[\"speaker_embeddings\"] = create_speaker_embedding(audio[\"array\"])\n",
        "\n",
        "    return example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XYYxiCoC15WG"
      },
      "source": [
        "Let's verify the processing is correct by looking at a single example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GYiXtnFczVIP"
      },
      "outputs": [],
      "source": [
        "processed_example = prepare_dataset(dataset1[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dr_zXRhiGu-G"
      },
      "source": [
        "This should give us:\n",
        "\n",
        "- the tokens for the input text in `input_ids`\n",
        "- the speaker embedding in `speaker_embeddings`\n",
        "- the target spectrogram in `labels`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wkboc3EUzXtp",
        "outputId": "118bad36-9a0d-4a09-c9ae-9a4e974f9681"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['input_ids', 'labels', 'speaker_embeddings']"
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "list(processed_example.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "spFPYNxJ_gNi",
        "outputId": "c50171e4-ff00-4e3f-ff15-f13620fb3130"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'hi, kirthika nanthakumar'"
            ]
          },
          "execution_count": 81,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset1[0]['normalized_text']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y8i63i_nI3Oh",
        "outputId": "55331cb0-ba03-4e11-b0cf-58767412547b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'path': './kareena_filtered/kirthika-nanthakumar.wav',\n",
              " 'array': array([0., 0., 0., ..., 0., 0., 0.]),\n",
              " 'sampling_rate': 16000}"
            ]
          },
          "execution_count": 82,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset1[0]['audio']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nvNI_JrE2oA8"
      },
      "source": [
        "The tokens should decode into the original text, with `</s>` to mark the end of the sentence."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "uu8PgZqH07lW",
        "outputId": "c41261b1-8abe-44d5-acaf-24700ac9de35"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'hi, kirthika nanthakumar</s>'"
            ]
          },
          "execution_count": 83,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.decode(processed_example[\"input_ids\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wOdMw-xa2u_8"
      },
      "source": [
        "Speaker embeddings should be a 512-element vector:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uuybSBq32AuH",
        "outputId": "e2b6068f-1ee1-4bc8-97c9-c7bf64f5bb77"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(512,)"
            ]
          },
          "execution_count": 84,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "processed_example[\"speaker_embeddings\"].shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YzyoPE2y2zHk"
      },
      "source": [
        "The labels should be a log-mel spectrogram with 80 mel bins."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "dKu2km552GL-",
        "outputId": "ef3f6c42-01e9-48ce-cb85-935714fb7af6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure()\n",
        "plt.imshow(processed_example[\"labels\"].T)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bWMT9X6w237M"
      },
      "source": [
        "If we run a vocoder on the log-mel spectrogram, it should produce the original audio again. We'll load the HiFi-GAN vocoder from the original [SpeechT5 checkpoint](https://hf.co/microsoft/speecht5_hifigan)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2Yu-rqwa1zns"
      },
      "outputs": [],
      "source": [
        "from transformers import SpeechT5HifiGan\n",
        "vocoder = SpeechT5HifiGan.from_pretrained(\"microsoft/speecht5_hifigan\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sX-Jlw25--hS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "byzHH-Jl2XeL"
      },
      "outputs": [],
      "source": [
        "spectrogram = torch.tensor(processed_example[\"labels\"])\n",
        "with torch.no_grad():\n",
        "    speech = vocoder(spectrogram)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 61
        },
        "id": "MgovGHdT2aJv",
        "outputId": "4fe0e8c1-627c-4258-e81b-0507334434c8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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Atvzjtewyumiz5fA7PpS/3+KSr8X2hDp88bJ0cB28ekc5x4XERbuuMayr7LFdumyAbMbWESDVkw7xgl26t7vVv+7BsgFJv5G9m3wi+KV2OvkuQXEJRMwLiKUEUEJxwajAqEA0gaCDQkKqf8T9Bfz7/yaCKsQnAzTArTmsdTgukaN5bHgEcpuSm+VLdQULgra5lPIFt83CsYamRNwBa/cB7bLuCvb0PnIAs4hskRjR8IgUPzH918DRxZAEXf9Gek/5WrlY9pI39b8JyEjLY0fzgoO/bT9Uwy9GSAbNQ/jB8f8U+wP5az0tQk8GNMcbwq85ZXJw6g2hYTKyDRkehJgUiVAFjr1ossHxDz1+RzQHksX/fcqyzGsYcAT7mYG4g4YJIowciQHE3YAfQcjIKUkXwg67bTgMeJ92k3YF/hiFmEZtQ17BD/7Xv3+FGYpti4YJSATcQHx7SrlSu2v/JMP3RGCCI7pnM+ZxDWj8qRQA4FhMG1HQkEPIgVJ3LvVXe5zCqwbIxVHChDWh6dgqi7ZPvudCnYh3yxXKLsesg2a/8MMiCaNIcsAtuTv3rnbLtfq5uoGIBUEEK8IPv8u+LoNVSwiOkwrAhUJCBz3WOhI6EX42AqqFbYJhOXK05ephIddzGgs3V4FSvMlDB3m/abdyudeBNINQBNKFg/rT7U5qwHLze7NAdMSIh1sE/YNfhbvF/AWkyeMMXMUtO1y3x/m0N5m393yDf+y/ov+mgP8BVELaiDUMCEvTR6+DLH9uv3d/Rn5dPsa+4QB/ezR5+TIEpNcuRURrVNYSTMeXRa+CVHxwfF5ACUGWwvEFXD+uMlBsVDGbeNf8zgHDhQVFHQXhxn7DfcHehzVMZMl8/ws6lvm097w4lLxcfmy/k8F9wlAAqUBHBSjLrIwzyIpFYkJ2vrN9Jv4xv87Ec7+L+mLo4+Hmc+yIXJG3C9RGKAJgvym9/4BLA3jERMcxBbR6hC8J7lhzorg1vl6BlAHbgigDCoK8QP+GKs1UjyxId7/j+yH33vin+16+jr4CPc7+jT8vP6iCzAhYDCnKKEcfw/1AXn5UAOhDp0KnwO+7U2rRo0qzbMKMzIoKAMWMwtk88HzHQeuGMMZfSSJGVHmDsCZu6XNyOyc+ucBnv7++GoB1AF3B1gcjzVWNM4dHApO9WnpNeNd8W39NvsE+Dz6Lv82/+AM3RmvJDQo0R3WDq0DowLRCdkPvAVj6vacCpdc4/4ZNy86IG0ElPHj45b7sBaIH9UcByCsDXrKI8CSz5Ho0vu3AgD7c+x99I38fwN/CA8iDDiFLRYO2/pt7MnoI/krC5QJh/2b8RTwU/n0BDAaYSMQI/wT/hPoBSUIcBKgEjwOXO5WpS+co+SeHfA35xPd6K7aIeUV/+UffCebHoUeTwdY2+3EANFz6g8HufwW8urkVOV9+TEEkxBlIaIycybiDw36OOwn9DACURJ3D6f3B+pL6Tj9FQvnHbYdAh2zHtEPkA6iApgOgA/YCRPn45QloZTwOy9vPDwES+Lc48ruNQvKIssfFRfJF4L6gt1iwD3XQfldBGT46t2w4pDwaA++GYYaJx+fJaMeXgSJ9/H4eAeSEd8TXwSj6ejfEfK8DdwgjB0TIDwYEhKlC3ILExC4CxkKAslLl73GOA9wMMIYa9xu01zg0vkYGrUmThvOGukKCeQJ1TrcKABIEkL9LOB5zyPZNve+Dd4R4xDVEvUT+wraBGEHIRPQGogVUQlM8w7pde/PBx4Z2x6dFooODQ00D7UMrRkKF34OYv4QsD2UHtjLK9U+TwiOxDPJT+YGA/0ghyfSG2sSjQIm4WHYveuHDEwX1PWe2QzQ9uCJ/rsL6Qt8CbcLKwvmBzYIMBMsH3siDRNqAKTwke8C+SoPjBxxGL8NQQMQCBAPxhgRH3sPnwNoufKYxNgHIrc33PT+vzjFx+ZZAxkZdSBwGRsRogg57OPmePiMEmsWHfe/3rvWV9/d9DICWgENAe8CrQewA6EFARH1Igkm3BZkAoL1N/pH/lMMTRxZG/ELcvpjADAMTx4FIYoVW/+W5E6xY7Jp950rVihy31m1XshT8DwX/CfWJLEPkP6i9+rxlPxyDlgWeAeU4onRWNvm85sFDQQ/+tr1d/ujAeAH1A7bGM4iWR1oD9/9EfkLAmQTqh2tGxoS2AGL9kABmhPhHysbX/33uNGZ5eGEK/A1uOvUt1nKQuzzC7AX9RboCSIFHv9b+FwA2w3xE1QBQuu85C3sSfmiA8P8kPKU8wn9xAAFAPsKxB7ZJvIXtgMu+2AJIhWIGgQXkBa4CsgC9gChBnkaXRn1D+3h455guAwQsUQcHKnGfah5yZ37YCJ9K+0N1fWM7arunvpMEnUpqBdx75Ta9eLB+RoLUA6S/O/rC/D2+OH7UvpmDvQpai0qD6DuEvK+BwAhqy+mIUgPzPaa8vH9gxeFLFkmdOKemvy96wo2O40acdm2uBjGSe/hFaIfoAs3/ELzCeYm7TMOjSxGIOfx0tGp2Xj5BxfPGPsAE/Gt8vD32fmbA3Iaryx8H4H9VOq1+3IY1ytPJE4Qgv509tQEnhVwHlUZ9uC5sLHSAhXZLPH+a8XMve7kWwcXGKkJ8fCu6+v0APk/BPAYgx+OBbfdItDO6jgRFSH2DCPqROBo8PwFBQzwDtoa0yBBDyj1uvRdE3UrpyptELn3RfW8Aa4YYx5jGnYBV8zAvjPxMCUDJkHw8L5nwM/s5hg5HNX9id0o4Yj0lAJtDpIYABbB+KPbCtyI/bYe/x1+/0/h9eN2+ikPChNVDP0HjAXpARkBRArkGUYg5haQBLn9+Qf5GJwfuhiJBqf4xdUjyyT4cCAQGzPm674Zx2z2QRf7FA78oOdz6ov3lwMuCzAT+A9V9xffHN7h9t0ThhmCAQznyeZ8/cMWGRhoDzgLAQ0kDkUNoxE4FpAWPw+LBdkABQi2FC8XVgwz+XXiQ87X8BAZfRUP8ZzNqs8U79oOsQ6m/+DrROoz9icCiQNQDAMRxgFg7l3klPFoCJsUHQlj85TqzfTCCnkVwBCgCnsHCQrdDNgVzRtQGdIQzAMfBEIQSRogF6IMCP8S4mnUyuiUBC0SSP5I16rLhOSKAb8NYAMS9sfsYvA1/JEF5QxcDGcBUe/i6B3yNAMaC0MDbPds8o/41gM/D7oVFxInB1oDJRDQHVodORE2CYoFJAXSBSAOnhyxFmf46tpx0dbl5gudFX/5LNd9zpTohAuEFS8FJfRh74fykPuqAtoHPghO/WrtvegG9fUCSgd1A+H9Y/oA+2wAHgwhFA0VnA9aDJ4S4RZ7F9sPmgq0CggHbwVNB3AO2AaV5eLQ/Ob+BcQLQPVr3Zjf4fbrBqoGE/5v9RPyPfI7+4YCdweNBCv6E/FX7Vz0o/8qBVICB/51AYcHDAd2BpIKZA68DfIPCRZxGLcSAgsXCJsG8AkjC3oNCAz2/ujrdtzp6JQGuQko7j7Vu91L/ToPOAQD743rSfgZBXkG+/yS9fD10vz3/c/6o/hP91z4p/uoABgGJgoDDNoMsQwaC/kL0xLMFi4YTxZ0DaEH7AbtDh8RegOi98fxqfSd+08FW/0V4wXUeeKFBVAWSAZY6qjk7/KJAAgHLwRA+6/1gPOZ8ZP1Qf4vBeQDn/tk9dj6LgkME0gTmgxlAr3/jA45IPsh1RViCmQHywvADQcPFBQKDb7yIuEY8A8FKQjO91Lhkthf6v8ArwX0Adf6LfAX7fb3ogAMAUL/FfpC85ryIPNO+ysHTQfi90PwC/zEDHEWLBHABqoCfwr9FXsgKxqHCTEGYAygCW8MWhOcEsgF5u1c6Bb32wqHBE7z5OjD5dboePLf/nsCkvyH7w/rAvLG+14DiwPO+z3veeyb97ADKAYe/Vz2W/vuBckOghHxCy4EEgKxCcAX6h5IGOMPExBpD6YJqQR3BjsLUQTv9bfqUfNV/YP+e/uG9UHuaesw8vz5cf5r/Wz4Y/Vh9bbzjvbv+C733/N/86H2cPl1/Mz8PP7oAXAGmwlRDHIOrw0CDm0PcRJgFMcRRwx5DKYLtATCAFwMeBL4Blv3dO/M+UgEJwJc9vXsmOu18Vz6A/uQ8zTx5/Y/+rvzf+ws8gP/pAIn+n/y6fJe+vMARwICAbb+F/+DBO4L/A0bChYJIw5gFP8VJRExDEsODQ9BChUIZwxrCAb+O/O686/+vgPw/RPy0u2h8L/6n//0+bPz1fWw/GD/Kvbx7mXyHvyL/573afLY8Sz63gHNAlsAif7GAGwGbgt5CaYHbAnvDqASVhG4DJQLKwspBk0E/gvAEREHC/uw9oD8IQQVBBT75/AH8C71dvp0+rv1B/NU+Iv+Lf2a9qv2QvpG/jb/Ovsk+KH5JvsZ/BH/IQCMAIEB7QKMBDoI9QpfDb8RjRIMDL0HFgpOCnkJigtqCUYF0P/F+4H/lwSmAfT4YvR+82T1yPph+wf5tvaf9T33N/mj+mf5Qfsj++n4BPjm+eL8MP7S/oX/vP6t/oQBwgT6BUsGjgmrCp8KPQp6B4EG6QjYCGoJzQpfBpkAIgIbAzQDOAjxBIT9Vfkj+Jv3o/2dAAX7vPV59If3bPsw+8/22/a/+dr56fh6+TP6Hf0y/17+ef6q/nsA/wOFBR8CDgGcBVAJmwmwB7sFnQOFBDMHsgdECMIGfQSrA4wCXQE5AsEBWwCe/vj9kv12/f39Qvx6+wH9UP8Z/qX6YPg5+fn6yfn++V77z/qj+2z87/sQ/Zv+MABUAhwEYQKtACMCVARhBvMDeQHdAfQBfwLnAkYBm/4WA5QH+QfXBX3/9/v8/0oDgQMbAzcBc/64+0L84P5mAJD+xvxA/if/Tf7k+mX76f1E/if+wf0i/jP+ef1P/VT/pP9mAa8CZgBJ/5n/NQBXAcAAEwHvAhsCyP7F+Rz8twKeBe0C9P/G/sv/DwP3A0YBLf0T/gICTQMiAXj9Wfoy/K7/lgCm/2T98P1E/1YAlwC9AVcDWQNeAvj/zv5GAOYCJQM+ALn+0P8IATkA7f0V/FL+mgFQAaEADAD7/gX/wf9t/k39dP+0ApADoABt/MT6Yf3W/1r/CP7l/W7+Pf/e/nX9kP1DADcD3AJuAZIAhAHpA2AF/AQTBK4CqwHAARsCYAJEAq0BMwGPAIL/4P+0/xD/FP99AE0AjP40/if/Sf/J/mL+s/4B/pD9tP50/kz9/Puf/Cr+8P4M/nr7W/pD/A3+qP60/5n/Iv/GABsDBwS1AzkEVgW4BGwDugLdAuwELgbXA3QBTwAMAWAC6wFqARgAnf56/vj/EwGBAGL+hPxy/AT9hf7w/u/+uf/M/1X+A/wW+yX9RP/1/wn/Hf2w+zf7zPya/rX/TgCy/0r+Jf7t/3sCxwOBAw0DmgIoArUCTAOgAlcClAJDAkgBawAvAUMCOgJWAQcA2P87Ad4BlwBQ/w//4/98ANj/Xf7u/Qr/+//H/rD8YfwN/g4ANQAl/kL8nP0LAF8AyP6i/YT+VAC1ACn/0/3B/Wv/CQGWANb/z/9EAIkAigBTAOIAFAKBAmsB5//C/04AswDrAAUAEv9y/18A5AB5//D9zv4RAQAC1wD1/9D/1v/Y/y//x/7//2gB7ABo/5L+Rf6O/m//m/9S/7v+lf7U/lD/bf9d//H/WwArAVgBWwAQ/3v/0gDZAN7/w/40/vb+MQBYALT/r/7E/t3/UwE8AiYCGQGJ/w//WgCIAWIBVgAf//H+l/+DADABNAHfABAAiP8oAEYBSQEJAakA8f9d//D+yP7h/s7/bQBUAMf/p/7P/tH/hQC7AEMA5/+N/x4A+gD4ABkA7v56/pP/dABBAAkAoQDfAEQAxf8iAKEAIwFoARABnAAYAH0ASQE/AZUA3v///lL/YAABAWUAtP+Y/4H/gv8ZANsA9AC1AEcABQCB/xX/Wf9k/9L+LP6W/pT/u//w/w4A/f9GAPoAWQGMAXIBrADz/3wAUgHFANL/v/6l/qf/JQGWAV4A+f7R/gsAigHuAc8ArP9f/63/IwArAET/X/5r/ib/AAAUAFz/sf7q/n//0P+V/4v/EwBiAIQAawCd/2j/IQCbAHwAzf/d/wMAewDiAOsAgwBgAI8AhAAIAXcBNwEbAIX/dP8AAK0A+wDwAD8AW/9K//r/hwB6AMf/z/5W/tL+hf/Z/+b/Iv+o/Qn9ev5QAE0BDgHT/7/+Df+pAMkBgQFTAIL/FQB6AeoB0ACN/3D/DgB+ACMA1f8iAOkALQGyAKr/NP85AA4BswAy/yz+kv7v/80AlgBq/3j+hP58/2sAIQBT/8X+1/4v/4b/vf+7/4n/ef9u/1D/vP9iALcAsgCNAHYARwB+AAYBZgEKAVcAVADRAPUAmAAGAHb/Sf9D/1f/mf/o/7b/FP+S/s7+nf9ZAGwAu/8Y/xT/h/9m/z//rP8VAAQAyv9t/y3/tP+UANwADQCl/3oAewHKATsBlQBoAMgAjAHCAQcBLAAuANEAVAFzAQQB//9J/4D/VQC+AG8Arf/k/n/+bf67/rf+TP5I/qz+P/9w/2z/e//G/z0AFAD8/ycAowDJAEUAsP+Y/9r/jABeAZMBHgGEAHYA1QBGAacBuAFRAb0AcwDTAGUBPwHDADwAuf/i/zwAZwD//1v/2P6v/tz+Sv8L/zP+xP0W/s3+OP+Q/4v/U//5/j7/EAB8AGIAGQDk/8n/3/9AANYARwEJAb8A1gAIAVIBXgFZATIBNAE2AfYA5gDtAMAAXwDj/97/NgBnADAAof8b/xP/dv98/+z+fP5j/of+0P4y/yT/uP6b/un+Rf9H/0z/bP/N/0AAawDp/4n/9//uAEkB9wDdAAEBRgE7AQoB3ADTACsBZQEFAVMA+/9WAOwACQGJAPX/cf9Y/5n/sP+L/0j/wf58/qD+4P7V/oD+qP77/jT/af9c/yz/FP81/57/x//E/+D/6v/v/+z/4/8mANYANgEOAbUAtQAwAZsBtAFmAfUAuACrALAATwDi/57/o/+x/+H/7P+p/27/QP8r/xv/Lf92/3v///59/n3+zv4D/xP/8v7T/vj+a//P/97/z/8KAGoAnQBRADUAoAAiASsBzACQAKUAEQFxAWYB2gB2ALEAEgEJAbQAawBCAC8A3/+q/53/jf91/yb/yv7d/if/Kf/a/rv++/5D/2H/S/8c/wn/Ov+o/+f/2f+p/7H/tv/P/zAAnQAJAT8BEgHiABQBhAHHAZYBHQHpACABVAH7AGIAPgCGAL0AtACTADoA2f+p/4f/TP82/0n/V/8f/5H+Tv6E/vj+Ov9f/53/3P+e/1n/eP/f/xAA+P+9/8v/KABgAEYAJABbANQARAEZAdYA6gBQAZABRgENAQIBQwEaAdMApQBrAGYAPgD4/37/L/8g/yL/Kf8m/xr/Ef9M/4v/Q//4/tb+5P4t/0r/ff+B/17/W/9Y/1r/r//Z/9j/2v8zAJUAiwCCAKkA3gD1AAYBzgBuAEsAaAC2ALEAhQCAAN8ARAE0AXkArf++/0gAaQC6/+H+Sf5s/vb+Yf98/yP/xP7N/lD/vP/w/7T/ZP9X/8H/QAB1ACQAif8W/1n/YQAfARcBgAA6ACsABwASAFgAsQA/AZABIAE2ANP/YQARAXQBCQEbAKz/GABmAAUAav/q/gD/af+z/4P/2/5g/qn+Z/+m/2f/QP+N/67/ev9P/6//ZgDXAKUABACy//H/1QBXAfEALgDc/woARgBJAI0ACwEbAYwA8v/9/2IAfgA8ABgAAgBhAK0AIQBu//X+Uf+g/8X/AgCh/wH/8P5H/4f/df8f/w7/SP/S//P/lP9e/6X/NACgAKIAVQA0AFoA0ABHAQkBcgAwAEkAnwB/ANb/bv/L/3QAsgB0AO//l/+g/wQAZgCGAEYA7v+x/4T/fP9w/5P/vP/K/7H/cf9f/3f/3v8oAAUAw//Y/w8ATABcADAADQDw/0MAyQA2ARMBdgAHAGsAHgEdAZ4AbwCHAEEA8f/D/73/5//+/+r/uv9+/4v/qP/o/y0APADl/3r/ff+9/wgA8/++/5L/Wv9s/77/CADG/yP/Ov8qAOEAmQDC/2z/CwAKAVsBxQAEANL/RwCqAI0A1P+Q/+P/WgBfAOD/ov/V/zkACgBs/+j+I//E//P/w/9v/x3/Of/y/6EAUQBu/wj/bP9MAH0AVQD6/7z/AwCBANwAtABMAPP/EgDNABABpQAWACoAsgCUADwAGgApAEEAEAAAAMT/YP+c/ysAAABM/yL/f/+3/4T/bf+b/57/lf+d/5f/oP9M/zf/rf///w4A6P/E/xsATAARACcAVwDmAPsAdwANAPb/SwDdABkB0QBfACUAKQAmABIAFgBiAHwALQDV/9D/xf+S/1z/d//E/8L/Qf/y/ub+5f4c/2D/6v8pAAIA3f/M/+P/+f/4/wsACQDi/77/g/+W/9//GQBNAGUAlAB9AFIAaACAAHgAlAC/ANAAhgAMAKv/gf/f/3EArQAUAH3/aP+p/8T/xP/p////AwDN/5L/Xv9q/7b/NQBgABgA/P8hAFEAGQCl/3T/AADEAAkBfAD0/+f/+/9XAKcA6ADuAGUACwAmAPv/8f8/AMQA0QANALj/qv+Q/8X/CQBmAD0A6v+r/6j/rv9o/3v/0/8KAI3/I/90/8f/a/8y/3X/4f8PADAAUwCEAJcAWwA4AFsAhgB1AKAAjwBFACYAMQCJANUAzgDcAK0AUADe/7//HwBRAL0AnwD7/6X/m//8/yMACwCz/23/U/92/3P/Wv91/7n/kv+7//H/zv/K/2H/4/8eAPX/QgDx/8D/sf8gAIIAPQD1/xoAXwBBAAMABgBCAJQA/AC0ADgAIwAoAPL/of9L/2b/5P9EAKAAlAAOAIr/Vf8r/0T/6P8bAKr/Mv91/8H/cv9v/67/p/+h/9P/MwA3ANX/2P+7/6z/DABfAIoAIwD7/7wADADS/woABgCp/yQAGwAm/yYB9gNRBj0CHPu4+qr/nQCrAPoDBwN0/4n/FgBk/KP6Ev8eAn0BIgIWAtP9gfqM/Hb/Yf/aALoDMwO6ABv/6f6J/fz9If/k/zH/EABKAeYEvATPAIv+afvF/FP9SQK9BAIDYQNHAPr7UPpl/ub+xv5TAuEDSwEu/8L+Avw8+qD9ewDo/6cBxwJQAFv9U/0B/Yv8h/8wAZ4CGgHOAI8EpwQv/nr7hgIdA9ABuAKPA48AyP1SANr+fP5cAHYDrADH//ABbP/e/T7+IgHFABACcgDF/0gAvf0r/2oASP6I/d4AYwCH/QP72P1pAvsDvgBn/2sB7PyN/uEFRAboAHX77vr3/s/9Rf9tB8UDOf4cAywClvjr+iEFhQbdA2MFpP4a9wwC7gTd/0v9ev1o/fX79gA+Aqv8o/v1/uMA0P63/mcCfwFC/h39KwFbAOv68v0EBOgCqf5V/xD+u/2jAD8CQwI/A9cCVgCz/sb/xgAvAtIClADOALz8GP1pAiUC0v0N/qsCSf9M/Pv+pQP2Am0B1P4x/GH9yv0I/jcCfAQsAdX/PP1y+nP7Uv+//vADUgnpBbIAoAGtAo37Tv2XBD4F5QMDAngAO/1f+3j9+v3L/ZQCGAIg/c/+vQDM/YX7oQA//9P7k/wDAFwGRgIi+WD5UP5+AcgCzQOaAXz93f/iAm8AMP99AbsBdQGzAm//Sf0d/67/6gH7AVkBlAEgAIT9Zv5uAR3+0/7aAzEEegBD+MT3WQBrBhUGTAV0/dX59P8g/wcA3ANLBaYB1wEWB/YDpPuk/fj/i/wKAcUFjQEq+374pfu4AK7+kgE0BhkB9fvbAHYCyPo/+00FOQZaA2wCQvv0+VL8xvu6/CsB+gWyAzIAPf1e+1P7+vlrANMGMAhEBXv+avms9Qb4vP87BcoIFAei/v749fgR+nH+dAZECfMGPQIE+z/39ftaAZr7AwGyC1EBgPo7/8/6APWU/ckHKQfz/37+9f4y/Qf8yfkxAZYL9QPi/DX/JvtW/An/UAT2BQEEAgEj+ML76QJd/0L97AFvAt/+9f0w/33/RgGW/338RwBQA1oBH/yi/pcBmPyF/qkEJgKAAIr/B/zd/IQFMwfz+wX6IADjAfkACgJBArcB/QGqAIP+9v48/1D9Hf83BB8G5AIj/1H8QfzR/j8AbgL/AokEOQJz/tsAavw6+Yn/UgS0A38DOAA//Uv+4v1r/Nv71gByBcEChQDF+sf3oPyF/7H+vv/2AC39Df6//TT6s/q9/V7+/v3cAcADRgAR/vn9xv80A2IGsAbxAUQBnQR9BKMDMgWfBt4EbATaBMUEngUHBYoEcQUQBpUDCQL9ARQD8wAG/+j/5f2//4L+EPw094v1tvd3+a76u/Rc61vo7uzR8In02/V59Cjyt/Pg9aH3K/mt+Zv8BAS+CAAH3QaDBnAGEgoFEGMVjhWpE5MQuw6TD+sPqBBLEhYV1BK4DUcJDQZXBtkHeAd7A6oAWf/L/cv9q/rk9Zn1gvhs+i77sfk395HzH/Qe88TvTOyZ513kp+Zw9iQBNP7s8ZvnceeU8fz+mAiaCREHCQOW/rj9/P+HBMQLPxTRGgYaoA91BmsBLgejEX4XmRmAFukPqAjuAdD+OgHxB68OWQ5/B3781vSc88D5+P72AEYAU/xD+nD3UfZs9av2CPmk+WX2ue3r4YLY8NoT8HcGKAbw9ALkZOEu6XL54QQMCPEIrQeiA9D+Df2e/s0FSxFoHJ0fnxYZBxQAcwKdDCEXwBnyFucRtgv6BAP//v1bA44MuhD5CYD/DfYW9F340P0kAUwB0gBK/mH9Avs49wL3wPrG/SD71fQ56RXeNtfn4R36fgrxA5TuqN+S4pbwUQEPCoEKIwkwB8kEXQFxAU0D4AmVFXEf9h1kEFwCOf4dBp0QNhhrF3UQBAp/BcECswBNAAcE/giICiwGbfz18tnxLPtaBMgFnwEo/jH8XP3i/Yb6ovom/Pf8t/cj7cHfIdZu13rrdQhuE8H/UODs0wbij/taDUMPFwnVBOAD9QO2AZf/JwPoDDQdOCYdGswD/PUu+g4L4hn7HMEUAwr+A1EAdf5Z/8EDpAnDDB4Knf9L89jtV/S1AFcJTgnaArn8xfo1/LH8l/wQ/kP/1Pzm8VLjQNRazgTg7QRDHMcMY+U1y8fTK/P8Dz4XMQ3VAhj/wAAbAgoBfgHlCGAZiCb2IFUKc/Xh8+kEsxjmIcoZNwrRAIP+1QCXA5sFgge/Cv4JbwKS9qvuI/Ka/mMKDwsSBKX7Y/nk/U0CdAKS/0j+C/yF94LsVOAa1ArRBeaVDOse4Qfz2u7CkNPd/FAeaCK0D7v6N/Qf+x8FUApTCu0N4xm+IUIYjwIi8o/2cwo1HW4i8BbkBNX5nPpzAoIISAmnCP8IlgcN/nTxOe1p9jsHnhLHDQv/2fSs9b//fgj8CGUBovr29sbyHuod2kXM8tL29pkd+h7/9DDHUMB/4ugR4CebHEYEL/ei+eYBfgYyBSEGzxFoIoclZRJO9PnmTPaBFlYtnSc5DQ31OPFP/gYOWRFMC60FYASPArb74PNI8af55AhfEloMnPzG70by4gC6DUUPAgUk+WruNOg843HcDdWr39f/gRxaE7jnicD9wzbw2x9MLgQYhvm67ib5ageVDaMJYQioFPki2iDvCOjtVOm8AFEf9iwtH3QCbO6J8IIDNRK7EJIFBP8gA3kEBPwO8Dnu1PyjERgX5Qhs9ULso/Z6CmoWNxDh/vfwy+0679bqGeG11zPaWO/rEJwbn/8gz1C7ntmJEe0ymyaQARXpO/BFBUMRTQzTA5gHkhgkJB0XvffY4oDvPxQmL38qaQ6n8U7pUPklEUcbQRAUAPP6+f5UADH6TfPg9uwFExZ1FcEBNe3S6+//XBZvG6gLqPcM7Hrr6uvg5eLfd9015Fn4WQ6RD4vx3cqCwSXmOhq6NQAkS/sH4YzopgN5FTwVGwxxCTAUNRuEDsT1fumT+ksdFDABJPUGDO6d6dj6FxT1HSwURASa+Pv1JPbH9vr58AK4DvoSpApm+iPvIvDgAFYUqhteD1D46+nT5vXp5uaU4iviiea08OYD4gyi+W7UEMN/3UMSNzTyKc0BKOP75O7/TBa6GBsP2gjqDq0WrhEr/oHxW/r3FAsnPiF4DOL4q/HG9lwIrBZ+Fs4KFACn+Fz1EvNS9uwB/g4DFIgJuvke8aL2ewO+EPAVkRFeAgTyVupj6zrvAu2o6kvllN/E3iz0HgvhCYvnBslw0O366yVOLWESvfAZ5+j2BQ3MFpgTfw5pEH0U+A8CAXjzR/p8EeQhOh5QDUH+sfXY9eQAThKqGC4RAgMd+ej0/vLV95UByw3gEgkMtPxo8w70tv7YDUYXtRWLBQHzieiB6tPvPe7O6hroxORU3pblLfk3BwP4bNx407Lp1w0AI5YcOwVs81D0aAJoD9YTbBOMFPgUehGlBo746vVBB84dISLnEkMAc/h4+J//pwyrFU0TaQdX/ev2CPRH9X78qAehD6QMEgIo943xAfgzB2IWsxhnC3/55e5f64TqFuw57rPuH+h/3zXbkestAXMFSO501r/ZpPg1GesgkBNr+171uv3TCmQOHhDQER0WuhcnEGcBmfKT9ggNYSIYIbkONP3I9Az3FQSsFOcafhINBEv63PN49SL+WwjkDJALMAUI/DD1J/h3BD8QyBVKEC0FHvTw6CPowe9A8qzwRugK3fDTGN1/+CUI6fvY3aXTY+N4A4IZgRz0Caz6OPl6A0kLHAxQDvQSqReDGAcQ2vqn7wr84xfQJeQZ3wTf9rX0MP1QD+wZ9RUqCa4A+/gG90H8DgUxDKwLNwgLAZf6tfeE/54KzRJdEy0KV/uD7ePrqO809c/xyutB4ITVyM/q4jIC1wvc9tXYzNG0404E3BrSHUsMF/2n/cACZQVWB9wPKRe8HSsZqghG8CzsJgNdHZUiWxN3A+P3b/Y2AKYQKRaaFOoPnghC/pH1j/o+BEIO8xITEB8CX/ZM9nIAnQzLEfYRsghg+z7zovFY8dLwZu3M6gnkU9kS0ojh8fy1BLn0qNr51LbipgBZF9ganQqf/UP+ZQGjAzsHpQ7QFRgbkBmcCa/yfe1kATgWjxx7FJwIKP1K9sX8FQghEYwU7xg6EqkASfWa9yAEQg+bFuwSzAdy+nn3cP7cBMMKVA5kDPECkfZ47jzqmOuD7cDt0OaC28bS0N1u+PwAKfMQ3VfVOuN0+4cUqhjbDrD+K/0Z/Fv/PghLFBsbVxl+FGkFQPba8hYCcRTNGQoUSAkzAAT55P0ICKkOTxRTFwUUsATU+pv5dQMxD/MVRRLJBrj9IPnu/UgDbQxHDSELbwNe+irufedT6F3sce796HzhqtS43gHxm/268zPkXdue5bP8cxBiGDEL1/+/+s7/GwA9BzkO3xJTFb4TCQpo+fvySfyqD8UXjBWgC7v+9Pg5/h0IYQ5rFH0Z4BdHCtX8UPwJBN0PmhYDFMAHqPyz+j39TwFhBbAJGgjO/8j3eu996CjnqupS61/nC+J/1xPh7vLU+xPztuVp29Ti3Pr7EM8YOAsA/Xb50P83AnwIrg13D1UVaRZcDBX7wvRg/JIMVRbCEzELagC1/OUBTQiICL0OehZiGKIRyARk/e0AdwtMFbQVhwrI/fX5s/2WAlcECwKQ/TX6dfiC98vw7Olt5dXlO+gC5hHkDube9uX6TPY26wPhvuTi9XIMWhTFD9EBm/qx+J/7MgUyC7EQXBNlEvQIaf5795H6fgVHDh4TMA8KCIACIgMcBdgIfA74FEoZXBVGDX4FIQFNBN8LdA+ODasHh//W++T7jv5YAN7/lP4p+/L3N/S474DpouXs5aPlX+kR6ED0jPzj9J/st+Lm4kzuQQQAEdoOeQNF+cv4EPoCAHQExgbOCUkQ5hEGCdH+CPcf+bkEtQ7KEs8ObQfGAuoEAgUiCYQNjBIfF3YX4BB5CGQEWwTdCl4OQQ3KCVAGTQJAAIr+qfoy/Pr+FQGxAKH82vQM74/rlevI7rrugOwv8nz2l/yG9ufsy+t56dXwewDLB4ICxgHA+av20/Yd+YD7vwCEBNIHrgjT/839Xfmd+cwAGwZRCJwK2wk5CNQHAwbiBGAHdQpEDpoQVg74C8YI9gg4Cg4JoQg1CYgHZQd1CAoGfwNJAXwAmwDCAVgBEgFV/d/5VveT9Nz0Rfag+Hv4zvt7+Hb0LvKT7qjy+PeZ+zv9svsk9tz0+vOK8qv1svan90/5ovo0+Wn66fi5+ej74f3NAuoDHQRSBOYE0wQKCOQI3wiFCoIL+A3wDZ8Nrgz9C+8KvQoUC9MLOgy3Cw0L5gi4B0gHtQY5BhkGDQWeAsH/Sf7h/an9tv3U/Lz62fit93r2GvZc9vz1pvSk82/zJvJ28WHyvfJj8zPzrvMG9Jn0ofUl92L2mfb89x/5zfzQ/1sAVwBb/10A5wM+ByAJkAnyCMYKqQsIDGALsQqHC6sNgA/DD3MPPQznCowL3gwYDv0MNgpVCAgIbQjbB6gFtAMkApwBdAD9/lf8FPqq+dD5M/kJ90X1T/NX8uzyLfNu8l/vfu0P7ynwcPHs8nXxS/F28gH0qfVM9wn5CfuS/cf/EAFVAJf/cQHSAy8HDQnSCDgI3AehCNUKEA0fDcIMcAw0DD8M9Qz5DKsMigzzC1EMtwtUCkoJrQiYCP4HRQZkBGMCLwCZ/5/+0Pyy+7P62vkV+d73qfay9e30jfRa9KX0gPRG81TyO/Ld8RXy2PGK8o3zP/SA9LD0j/UX9yr5yfsY/pT/6wDMAk8EwgVQB4UIKAoFDOwMUAz+CgIKUQo3C14Mbgz1C+sK5gnPCaUJqQmOCXMJxQjSB5EGJAXtA6QD8AMYBHIDIwKSAAr/nv38+7L65fne+YX5+fj39+b1tPPl8jXzLvTf9Lz0yvNV8hry0PEI8mryhfNo9T73RPiP+MD4rvmU+y3+wwDLAiYE8wR5BeUFcQZZB8MIkgn2CS4KtAnSCFEImggmCU0JOQmfCHQHzwa4BisHhgdiBxkH8gbKBtEGiQZSBQYEwgNMBAgErwKpAIv+5vx0/Cb8nvtR+wz7rvoY+t/4ovcv90n3EfjG+Bz5ifjC92L3Lfj/+LD59frL+5/8R/1l/YP9ZP0b/nP/9gDWAXYBEQHQAFIBMgKqAqkCqwIKAxAD4gKlArQCMAMSBMIECgUEBeUEIgXMBXIGCwcjB54GwQVyBBkEuQM5A6kCDAJnAVAAX/+c/hP+U/1a/Zf9/f0M/qv9Vv1A/az9Zf4B/w7/Xf9a/yf/1/5q/jH+Jf6S/sr+rf4i/jP9y/y1/A79Rf1I/TX9Cv0N/QH9KP0v/Yv9XP6U//r/BgD2/+3/dwAtAT4CuQJ1Aj0C1gGKAZUBfgFnAZMBdwETATAAQv8D//z+VP+K/6b/qv+d/4L/wP8KAGUALgH3AbQCqAJfAv4B1QHrAa8B7QEWArIBCAE0AIP/jv4f/s/92f3s/Y39Of2o/DL8LfyO/Cz95v1Q/sr+9v4T/zr/Wf/2/4cADwEyAegAlAAxAF8AogDQAOYAaADM/zz//P4C/yf/dP+5/77/zf/o/xEARAB2AOwAqgFJAqICYgIVAhsCKgK5ArkCZwI8AtgBVAHVAB8AgP/o/pz+wf6Z/mP+7f2m/Yf9hf2H/Yn97/2k/kb/Vv9Z/z7/Fv+W/1MApwCpAHMAMQAbAOH/z/+q/5//qv+h/2//Sv9S/2T/tP8IAGgAlQCEAF8AiQD2AFoB+gFvAoECVgIDAvAB6AHuAeoBzAGiASQBvQAcAHH/Kv8K/w//N/86/+P+zP4U/yz/Bv8B/w3/S//I//3/+f++/8v/LABFAHoAZwAIANX/vf+u/33/VP8z/y3/Wf+G/1f/Xf96/8z/YQCzAO4AEwEaATUBiQEOAo8CEgODA3QDNgM9A/4C2gLcAsUCjwIIAnwB7QAaAIr/Zf8x/zb/Cf/l/o/+R/58/sf+8f4J/xD/Lf93/63/3//4/w0ABAABAPz/0f+N/yn/C//q/sD+l/47/jP+//38/S7+Sv5Q/pX+2v5Q/8f/FABIAE8AiwAVAYcB6wFcAsAC6gLnAusCrgJRAlMCWwIUAtMBJgF5AA8Aw/+e/z3/Jf8E/7j+sv6p/qv+o/6j/sD+Fv+U/9b/1v/1/8D/iv+R/5T/cf9l/0D/Ff/C/nf+Kv7Y/b/9xP3F/Z79zf3E/e/9NP6Y/un+NP+r//D/HQCSANUAEAHAAQUCXgK9AqYCdQJIAjACPQIYAvQBnQEMAZ8ACAC2/7f/qf+4//L/xv+h/3b/Pv+J/83/QACfANcA5gCFADkAFgDr/+b/AgDd/4z/Q//N/lX+Sv4l/h7+5f3b/bj9df28/dT92/0z/lv+pv73/hz/iv/s/2UA7wANAVMBfwGPAXoBgwGXAbcBoQGIAX0BKgHwAFoAIgADAPH/AABSAHUAWgA6ABsAYACGAOEARwGIAckBpwGNAXcBPQFuAXwBcAFgAckARQAAAOb/uv+d/zD/G//z/uD+yP5q/t7+D/+M/8//df+1/67/GQDCAPsATgFCAX0BrAGyAYcBYAFSAVgBYQFPAccAZABHAB8A5//g/+H/i//X//L/s/+b/5X/0v8PAEMAbwCMAD8ArQDcAOIA9wC5AM0AhABoADUAnP+H/+P/mf/P/7H/bv6r/hT+zP6T/lH//P9p/1MAD//E/9T/HgBaAVIBbAECAUcBKQH5AAMBPwE3AY0BpQHHAKgAjgAxAHoAwwCJAEsAZgAtANv/4//f/w4AdQDgAEAAAQBgAPn/DgA6ANcAhwBcADgAYf+j/13/5f6K/2f/Nv/9/tz+IP+t/pH+vv64/hP/Y/+A/2H/9/4G/2P/hv+//9//of98/5n/ev+T/6L/pf8YABUASAA7ADQAKQD5/7UAtAAvAScBAQG6AKsAPwG8AIEAYgE4AcIAMgEDAZoAsACyAPMAEAHSAJ0APwD4/wsA1v/U/yUABACiAML/0P5g/63/8v/G/1gAyf/0/mb/8P7z/9H/iv4/AM//0/4eAKr/6f40/xn/lv/2//L/dP+//zYA3P/G/2UAPgBVAQIBpACUACgA2P9yAPEAQwD0AJ3/ev/8ACgAcP9iAJoAGgAvAFoAQgA+AIEAkgACAWIA0f/NAGkBmADZAGgAFgBUAQgBVQBrAF//pQB3AMv+AADQAK//qv4eAGP/sP9DATn/nv1cAVUBM/8fAFL+pwG1Af388gFYArD+VABoAjD/KgDNAnb9IwHj/vz94QHFAZD9S/3oAdz/iQCE/8AAf/41AKAED/1j/qIETv4PACkAxgBGBQL/jP5qBPv7CQCvCawAuP3Q/94ApP+WA9r/Kv+QANT7GAFv/fL8RQJ5Ai4C5/zk/Of9fAM8AswDwwUf+/39p/7LAycCewBuBjX6z/0f/l7/PAIHABICgfmY/Zz/mwA7AGH+Vf+A/a3+Vf+xAaL+ev1NBLH/F/8m/kz9qAe1//EBhwIa923+zwRDBfj9pAFK/9v7xgH//rX/Gf1F/xYDJwLd+Vj50wF3AVkE7f8e/Kb9lf4TAywB5/xe/m3/6v5VApn+EPz1/yr9af1gBNAANPvi/nL8Bf6gAToBDABv/fX4iv7CApUAhgNB/tr8hfyx/5EA8gGfAvn/1f8o/DYABv/aA2IB0wC8BHb57vohBJ/+EQEsB1v7w/7LAbX8zf/6BF3+IACcAR0Ai/+HAD3+uQKgAWf/xAP1+nX89AM+AeP71QZ/+Rb97P/9+7QAUvxSAk3/bP/B++f7+P5bAncBpPozAir+S/z1APP/NP5J/ucAIwA+/2j99//YAKD/0AAQAUn9Iv23AogBegDn/rj+7QD2/qsAnwNY/57+rwHNAZH9gQFBAvUA9P7r//wCGP9YAGkBaALr/Q//FgLd/2j+yv9YAj7+Rf3k/pX++/03/+n/Iv6T/P388f5c/6b9Zv63/3T+T/78/fr9df4+/5L/xv6o/Tz9Vv8AAIz+FgGFAN/9B/5NADcBtwEzABQALQG0AF4BfwIDAncC7ALYAR0DVQMgAwIDBwRdBJcEDgTyAfsDPgN3BN0DeAOPAowD7AJZASYD7AC9AXsBMv+E/zX/Xv72/kP90/sd+oz5Ifn4+ML4jfX49Oby/vJh8g7yQPNe8tvxC/HP8vX1L/dA+tn4zfpA/kQCTwR4BwMJfwlMDMUNUBHQEsARkxQ/FTQUJhRcE3wUVRTLEzQPThBXDnsLdQsyCR8ICgbtBKICsP+3/hz+o/zQ+Vf3DfPM8Nftve7k6STlb9to1WXVRfI+ARjowdKJzK3lgfrJDDcHRvMa48DtFQ8JHXoWow3xAmQAZgyCG60hZhitEN0NTwhZCIkZMCHKGX4QgAZyB8EM5BaIG0UU3QY4BLAIjAfJCdkLcAl1AZ/7XPx5/t7+mQM8Aun2he8t7xX1KPWV8xjmpN2807fdW/tB+g7lRcr20pzpRP+aB7f6peZn3IX4UhDsF9wHGADJ+zr+ZQ3gGjsatAyeB3MErgTkChcaVx/nEEwD1QHvBeUT0iCpGiYKiQCzBW4NZhGVEdwNDQXf/cMAkQOPBEgGHgaJ/p/3LPkCAFICvP5j+SXx7uxg6+TseORn3FTZ7PFl/B/nYdOxzjLmwPqwBtn9r+WD3WzyKhgAF3wJkfrU+HEBexIZHaoYEwoQAfsI6AlkDEAVuhzxEMkCev8yCc0VRR5KG04GN/2RBOQTuRcLECEH7P6/AJ8EkwsdBUf/gADl/4L+Ef07AikALvvi9+v0T/I57d/rhORs29bXRurDARvyM9h4yk3cW/imCCMG3uyE3KLncg3wHwkP0/xd8h/92g4YILgcUAgl/aQCghDaENoS0xJ8DfcEUgS7DCcSEhYWFKgK4f9/A5sQehY3D80ES/+cAkAJYAt7Blj7l/pT/3IFmQLO/Zr5tvao+mL46fbU63jo098I3uPbE/GKBSnykNNDyPvgJv/EDnoDY+eE2C7rlhUCJe4MxPTH8A//IhRfIfUakQIG+sQFdhXRFWARJA/VBZAEmgi9FaATRBBjDPsHugV1CWQV8xDsBwsA2gWfC3MNfAl9AMX7nP3sBkAI+AEf+u35vPtwAKT6IfTz6qbpVefM4tza0eAR/Vz6C+RHy8TXHvOQB0AJUPI24ofiKwe3IAcVVPv68Lf8MA/THZ4cWwum+mIC9xKVGE0Uhg1rB4kBdgmyFBwYjA8OCccGFwZlCxgURhQMCGH/DgQcDewQ7wrqAe35//kSBQgMKwdA+aL0dvrwAAP/vPXJ7DfmJOaO5CHiMeED+Xf7puWGzcXVd/JyB5UL2fY64xHfTALlIBAfKPyR75D4JA1qHkofDxDX+sn+xQ/qGmIVIhBECpoCXwbkE28auBPfCoEGTgYFC/0U/xVwCkUAvALsDcgQfwuU/1j5TPpdBkYN9wJf+Gb0K/r9/9////bq6rPj5edl6TbjzN3G81b5TOZ31LjZKvFCAE0F+/em5OTjqQIYHpwVffzC8Wv6xQuGHZ8cgQlC+kACgRXEF/sRuAutCLkEbg1QFhIUogtoCeEMSwscDukRhQ+/Bv8GCguVDa0MJAfx//75z/9jBnEGFPzU9dL31Pvc/l38vvL76cfoEO3P6j/hl9YT5Lr/APff3CXN8d7R+eIJoARH7VLecO3OEu0hRgwC9WPy2gGQFvAfQxWP/7X77wzyGYcWeg5wCG4I4ArpFFIV0Q7NCHoL/w/vDgQRUQ0YCvsF4AtnDlsNTwnSAJ79LP5XBWgFAf8t9Uj2hf2L/z/9mvab7WHrBe9l8QzqbN9v2TPqSv8d8ZrY7tCh5v//lQSQ+XnkiuK+9/cSghZq/4fwdvOBClYYJhcGBzL4LQEcEw0adQ47Bn8EZQpUERAU9g8KB/0HGBBZE2oQ0AxDDK4JlgoYENgPYAviBUoCaQEgBCsG/gOx+530W/g9AAgD1/sl8jfwSPOD9n718e9S47HgjeER7zoAL/Zn3YbTiOU7/V0Hd/z96iniHe7/CkkVIAec7WXvLgAAFFIUugV4/MX9VxDfE7sMvwTMBkkOahKqECgJSAT1CAcWcBZ0C2cDLgdlD/URHBBUDFQG8wQSCWkKKQhOBHcChQFM//z9NgCKAHX/Kf62+Tv3hfgm+976gvf/8d7vS+537KXvz/ml9yDtouO85NHzA/qQ+7301+c06Wn4BwT0Ahj6Xu/99QMBGwiUBoL+rvuaA1YKwAgMBpQCUgbJC0MOJgtbBHUDFwwQEtoPCguQBeQGSQ34ET0RcAyxBwEHwQmQC/IM6AkvBoYE8gQcBl0F8ARmBGsDcQHhAJUAo/87AOIAGf9X/An7Jve+9jP69Pnn8h/xke+t7szwofE983rx/O9A8pLzw/Pg9Qf3Hvf+9gT5Lvn0+dn7pvyR/UD9vv0A/3H/TgA3AugCJgKJAoMDtgSpBegGkwcRCKgIUgrOC10LawwtDfoM+QwKDUYNVQ1FDToMtQrTCUoJfgmqCY4IUwdaBusFiQbSBVAExgIsAkgCTgEOAFj/nf7//Lf7OPuJ+jr5cvh++Df38vRl9Cz1mPRm8/fxq/DR8NPw3/FN8VjxCfKF8nPzf/Q/9Qj3h/j5+YX73PwJ/t//CwI8AvsCXwRtBcYGZQenB2gHcgfgBzQIoQhECLkHdwfFByAInQjzCOUIfwjRCAIJnAkPCs0JNgr1CaAJ8AkACp4J0wjrB54HagdzBqkFfASgAsQBDAHx/5n+c/32+y/7KPoN+S74Nfea9n/1YfQB9JTzR/MO83byWfIz8lvyjPLy8j/zV/Me9ID0bfWF9tf3/ffc+Kv55foF/Yz9Ov65/o//2ADdASkD2AO0AwcE1ASuBQ8G5QVSBl8GuwY4B4sHcQdNB7QHwAhaCXgIlAixCA8JUwmECVoJVgj6BywIEggfB7AGgAXlBIcEtgOUAzACWQF+AJb/xf7G/aT9S/2Y/N/7VvuM+mP6bfo0+nf5+/jG+Lr49vh4+Df40feQ94v3z/fg9zr3HveX9wj4hPie+Kb4//iW+Yv6sfv1+678X/3T/cn+HAAAAfsAbQHMAWgCvwKnA5EDCgPsAioDzgPDA4ADOAMVAysDqQPpA/oDUANJA6ED0AMmBC0EHQS2A7gDDwRpBC4E7gPPA5kDuwN7A1oD3QIWAgECnwEcAZMAOADG/zX/p/5M/uH9a/1x/RH9x/xv/Gb8N/wK/EH8O/wk/On7Mvw4/Pn7X/y5/Kb8wfzO/AH9DP0Q/Zb9+f0w/k/+ev6p/gT/Vv/E/xMA7f86AFYAvgDdAAABSwHbABQBDgE7AZgBfwG6AVcBJwEyATsBmgFWAR0B+wDXAO8AaQGCAVUBvADjAH0BmQHQAYYBUAFIAb8B8gHkAUIBFgFbATwBSwHxAJYACgC2//f/QgCt/xD/Pv44/rr+bf+U/8H+U/6Z/sP/OABPAA4A2v8+ACUB6gHSAXcBBQFoAVwCxAJ8AgcCqwH7AVoChgIMAn8BjQE+AWABCQGaAKIAkwCHACQAJwBTAD4AigBjAG4AlACRAMsA5QATAfkA6QDnAOoACwFLAfQA0AC1AJMAjgBwAF8AzP/F/xUAbwAoANj/4/9IAL0A2QDwANgA6QB2AU8CpAKMAisCPQJGApUCDAP7An4CDALyAdkBpwFsAdkAjgAkAKL/u/9x/zb/Ov8o/73+aP5w/nn+1P4X/yT/rv6x/k7/d/+b/3L/S/9v/5n/7P/U//z/0f+R/5T/fP+E/0z/Qf9m/0j/Zv+t/0T/Zv+d/xgArADJAOoA5ABhASgCqQLzAgUDzgLgAj4DrgMhBMkDUgMSA+ICygKQAiIC1QE7AasAoQAXAMb/gf8X/3f+JP7s/fz9+v2Z/XX9Q/1v/Vz9bf1t/Wf9kv3L/dz90/34/QH++v3R/f796P2Y/VL9av2w/Zr95/3v/aL9mv3w/aj+Rv9y/7z/3v8+AM8AZgHzAR4CKgJtAtwCEANoA4QDegNNA0IDGgPfAtgCpgKGAg4CxQFlASMBBQGJADEA2P+q/3v/WP8r/wz/2P7U/rL+nv6o/tf+xf5//o3+p/69/pr+sP57/oP+VP4H/vP9vP22/dr9xf2J/WL9Rv1h/dz9h/7E/uv+8P4z//T/pgABAf4AGwFbAdMBIwJqAnkCdAJjAqACpwKCAmoCIQJVAi0C3QGTASQB2QCeAI0AcgDs/6H/S/9K/4v/dP98/2X/Lv8R/zf/b/+P/1T/OP8//1P/aP9u/1L/4f7Y/tX+xP6+/vD+Cv+i/pb+pP7r/kP/gP+G/6T/4/8kAN4AbQGJAXoBrAH+ATUC1ALdAooCugLwAisDJgP3AqACYwJbAnUCUALwAT4BEQEVAdgAmABJACcAs/91/4P/t/8AAM3/i/8Y/zT/0f/5/+D/Zv/w/g7/Vv+I/2f/B/93/j/+pf4E/yL/2P6B/mn+mv7e/kr/k/95/1L/g/8IAFoAuQD/ACYBcQGJAe0BOQKeAqcCXwJIAlMCrAJvAkQCNQL0AWgBXQGSAV0B0ABoAAQA/f/y/+z/Xf8u/2//fP+X/yj/IP8Y/27/j/+w/0f/5/7i/uj+QP9h/wT/tv50/j/+zf7X/pT+DP74/T/+ff5w/qP+wv6y/h7/Lf+P/+3+NP8hAG4AwgB+ALsArADkADwBqAAjAfAAFQGkAZIBHgGqAF0A/v9pAIgAoAA6AKj/pP/W//P/uP/C/5L/Zf/I/xAAvP/v/7b/gP9v/5v/iP9B//7+Gv92/+3+Mf86/4f+0v3z/SP+QP5h/on+GP6+/Qr+Nv70/sz+9/7q/pf+//5U/1f/SP9o/5j/9v+rAKQA4f8PACIAhQBPAUYBhQHGACcASAD0AGQBHwENAUABTgHQAOcAzwD7AKQB/gEwAZ4ADQHuADABXwFsAUoA0AEqAfv/nwAZASAAAAEwAi4AUQCP/Uj/+gGSATUA5wCE/xv/0/9aAUUAq/+e///+wwCiADoAr//E/rj/IwFuAToBmwCn/9X+LwBpAZYBAQFeADMARQAGAW0BpwG5AFMA6ABYAWkBOwFwAM//4v9FAAoB8wAJACcAav/M/woAGAB3/63/mv9M/08A4v73/qj+a/8j/0kAxgCq/jz/Wv67/uH/lAD2/wX/R/7H/k0ALP+X//D/Cf8S/y//XP/M/rT+T/9z/v399P6Y/hL/NP8E/4f/cf+w/xL/mwBxAEUA3P+t//z/HwGfAYoAjQBb/6v/QgG/AFQA7/9k/s7/zP9JAL0Agf+5/k//jP/n/xgAqv6R/+r+RgD+/xkBLwAK/4b/TP7e/8v/AQCm/0r//f4//03/ZP/Q/xYA8v5o/5H/JhN2C8DzAvDH83L+rAfMDdIH4v3K99n4kvzW/2MDMQW6ATX95vvU+wX9hQFDAoYBLAH4/2T9lf0hBGQExgFuAyIByf0e/uX/ygBt/8f/fQAjAAH/n/+c/u/8PP4YAGYAywKmBq//gfjB+Tv9mwA6AaIFtRLICsj4VfV5+H8BMQzzCwQD/vpT+oD8Rv/3AC0BWAB5AbgB2vdU+FIBXAGXAKf/Bv4G/gD/8v4d/b78I/7/ACsCTAFv/yb/Nf9a/zcBgP/pAHQAhQC4ADQAIAF2/sr/KgFpApEAEQazD2UJxADj/3v6Mf1pC+AW9BF+CMX+tffj+TICSg2oDNgGtgL3+zb44fgX/eP+NQBQAKT+DPyv91z49/iE+3b/4wBtABX/Hv5x/AH8T/64/2//vAEyA7z/iP6j/03+O/4HAnIEQwDTAAUAPP2d/pUB+gGAAOQBhwHGAKf+6v/uAFEBZQSRA+sBKAGy/TT+LwHGAb8C9gLSAF//7f2t/R0AkQAsAJgCXwGn/TD9Af0J/if+W/82ACL//vxA/Er9h/ty/X/+jP6j/s792/ti+9/7c/vv/dz+T/7q/Bj8tPwL/cT8fv6Y/7/+uf1L/ZH9C/1m/KT+CgCt/5b/jf/p/kr+nP97AO4B8wHOAZYBFgKXAZMBpQJnAygEhQQyBdEErARNA4kErwPYBOAEvAQjAykBCQFK/8r9t/u8/CL7z/mY+Bj3EvPh8Wjy7fKX8qnw+/EU8knw8vD08kzz0fSD9Xj4Nvhg+kr9fADb/7n+QgNHBLgEfAX9CQQLCglbCd8NZw7dC7YPNRMnFLETAxT8EvwQCA6wDsoQlw9kDRMN5wrsB9gF9ANyAYP+Avz5++X5BPTG7rPqaeXp4M7dUd592BnN98es0kjzTSKILYcDmusY+64C4PqKFMcwpyQjD0AZohiW8xHede6RCKkHTQi7FN8Q5fKK5Or1EP139nIFvSFCH7kLoAp9EfQE2v/fFQEmAByeEq0YQBIrAC796AWfBVAF1A7BENH+VfHs9Uz4z+927OXyqe0K41PfJNiVwtGu2btl670eNSi/Du360Pzb/asC6hpZK3In/yO/LcQfB/eF2X/fXPEa+FcBhgrRBB7x3+b35fHm8etGAbIasCOdHLwSnAo8BJ8IdRJVGYwakx5kHV4PBf3+8//0IPrSBhMP4wv1Aob+H/4iAMwAOf5O/Wb7Q/iS7cfgRtX0z4XJIcWVvz2/OdinEuxCwzS1Cu77pgYNCaET5CsiNQ8jPRpNGqsAptMZwdPU2Omm9ML++AVS/orzVPLf8oDxMfsVFLMn1SwaKDccggnA/88CIwk6COgG+QuWChUBTPhc9FnuFPFp+yQJFAyaC+gNsxDcDkkILwOT/TT8OfbK8EzpW+JN2F3RpMFYtKTHugNKNVoxIRmNDFIMXAeiC68Q8xH9EUQg3yOMDLHkOM6d0Cnbkeql/LII3gliDzoR7AZA99L3KAbFFt0jAygLIYYRKwQt+Urz4/Ff+YQCFAcnB5wFLgC29xH3jfojAKEGUg8IFGISCw2ZCIgEovoc9CbyG/XG8d3sxejJ3TjPULxyu9jhtiPgQM0rXRJmE3AUEQmgBGMHTQjPCJUY7xev9w7PqcWx1WLkZu4y/j4TrB7yHTMV6Qhd/uwCpRI0IH8heRowEYkFY/je613lHuob+vMH0QrYBwAEJQFrAJQCFASkBaQIQhEiGMMVGwxUAuX8vvdH83Pviuwl7avtcufe1x3FebaOyDIBhzYGOhQcFRZZHLESygF1At8GQASqCf8Vwgtq5LDHXcvG2uXkn/ItCPYbMCPhI+wbMwuz/kkFZxa1Hl0bNRPvDW8D1fH84Qjh9erk9ocC8An3Co8FgwMUBZsE9/+tAqgMcBYSGLARqgjYALH7uvV+75vrsuvE65rrBeRd1xHCTLbD0ggVpz4FMR0Zlxt4HVAKiPsP/w4ErAImDrcTFgCP2PDENM8E4CjrEPkvEj0k5CiDJCEYuQWd+9kFDBczHDcUFA1nBmj7tOr+3VrgC+yH/HcJQg/ZC+IGdgaGBq0GagNtBcUL5BLJEtMMRQUe/+76T/U58arrbet760voC91M0R2+0MHb9C80rEEpIaQR9Br6F4f+8PY9/rUBkwPaDlULAup3xgLGQNyi6IXxewV4IEQqKiSiHWUV7gZG/3MK9hhYFrYIFANRAaX3OOb5387m0PPR/4UJ1Q94Dp4Lcg2ACxgFwADhA20KRw6IDdoLzwVR/sL5J/LJ6hbns+ps65blhtlW0P7DLNWADVI8EDf6GHkZmCIdEvzyX/H2/Pr8Rfw1BSUD3uHdyc3R1+Mf5zbyXBLALCgt7SOBIaUXTwPr+nQHOhAvDD8GXwZxAF3uSOHU4jTsf/ZaAisOORTfFOkSJxAHB/n8VPxuBfcK9QpaCuYLJQzYAtv1uenE5bbloOgV6P3nnN+t1LDKD99jHd9EsDSDFWcYEyKCDXzxOPBJ+i/6OP/YBkP3d9V+ykDcT+u07cr9EyKRN9wvXyBKGYAQ7QH4/HkH3A2BCBgCUgL6/CzqDNwY4lH1xQGeCWgRLxalFTMSTxASCFD9uvgo/4gIJw3uCfAFWQMO/nX1Q+o5457jKehF6Cflh93Bz1PZCw0IOyY2Nhm/EwIjYRRr9nDvC/l0/M/6KAIb+rbfYM+W2broyO1p/FYZmi/VLzAnyR/tEzUCV/qlAIYI2wdDA6QC9//r84LlT+MR7uj6vQY1Eu8aLRpsFiAROgij/P33+Ps+BJ0J4wlACEUG9gGw+ozwqecm5c/mkOpv6Hbkvdbzz6v0hitkPesjIhGqHDodmwL17oDzvfrp/AgDpf8J6TvROtI05Q7wDfecDl4qeTNcLJMiORdyBrD5PvzrBacJggYmCOwDw/ba52nkSOxK+LEELhAfGZwZ3hX7DxgHYP1B+O758P4dBowLQg2ICQMElvzg8lroI+Zk6GLpVOYv4kbbd80v44MbkT/vMEYVARbKG0gIF+9+7xL6sv2FA8QDOfBf08XLn90I7b/0DAilJUA2jS/mHzMSrwdR/9L8WwOnCfELYQjNAkb6X+4w5CPnmfbrBK8MRxEIFcATsw7RCHUDbv3g+aD9ZwUjCnwINwYqA1b/Qvdk7RfnfOQN5pPjYd3f0qzKlebZH/c7tSqMFEoYhh3MBqzty+2n/FAEJgktAV3pA9SYzl3YROQr8fIIYCVjM+8uPCFHEs0FRf7l/aADrgsqDXsJXQL5+mfux+NC5s3y1QGCCYwQYBZyFuYPUQcVAT78fvtD/VADXQcCCM0G2wQg/5T0Kuvo5YbmAOMN4Ujdo9Z1z6Doth4dN7kqQxTPFVAVqf9V7VjwJwCXBkQJ2f1k7L3aYdND1WbfnvHmCnkiui2uLKEfWxPvCP4AdP0NACAJ1w94DRwDj/ku8WLpgOqH8wAAAgrgEa8WPxYDEGYHVgCD+1j6Q/31AtIIKAmGB3EDq/3h9LrrJ+S04IHjN+Tj4cvSGtFQ+Ksp+jQQHxUQtRUiEcv7EO+c9GH/TQiQCdD5cuO91fPVkN4F6h77MhMPJlQrmiUeG/cMiAQnAHcCOAf1C5AOdArRACf1lOzT5gHvM/5bCuEOHxJ4FpgSOwk9/jL6JvoT/ioEdwkuC2MHoAMF/wj3uOuI5k7pcOrw6DLj/d110MjVEgQtLzIydxqzE3wYOAoM8Tzp1fddBXMKsgQQ8vfcrNXw2/XlYvALBR4dPys0KX8dCRCWBxMG1gXVBdQHKQtmDAAHJfyS8pXtlu/G+IoENQyNDzoTVxJsC9oD+v22/Hr/IAQoBjQGCQfDBzoE9/d57znqeOvF6wvqUOUx4PbbpNEs5CcWqjcQLHESCw7lE1AEEu747Iz9RgyCDWcAXOdk1wbY4+K97R365w2kI3UsAiQDE7oGWASNBjAJ8QjICTkMuQxgBvD6FfPT74H1cgAIB0gKgQz3DTkN0weNAdr+jv8DBGMGSAaqBooGzAX3/D70ue2V7qbsmest6T/iAt5B2LLWZvCyH20vJB9qDSIU/hUP/TTpgfC2BbQLCwiD98PmNdzm3QflKu/R/ZoQUyIeJrodzBEJCC8GnghzCikKvAvkDv4M6gQE++v0bvPG+LYBBwiJCcMLPw5DC4MFdv5p/oIC8gdgCg8K5weKBRYDE/qv8FzrNe0K70/ucOdi4Bzendgw2kr9NCTTJt8SPQuSGR4RMfXw6CX6DAlSCKj/DfIT52LjZeXq6ETv1P7cE+gf2h5+FqQOHAmQCaoLPwrqCKMMBxN2DS8AvPd1+BT6nf2ZAgEHNwvKDcYN9Aaj/aD8qQKmCQIMYQq4CLcL7Qia/zrzRe9A71jwhPC067nm+97L4tbXUtfu/S0n4idxEl0OmRjMCwPwe+vX+10GEAQRARD2s+m84bjjpejM8FABPxTXHdkb8BbkDvUH2gfGCV4KwAsSEqQTrwoM//D6Pfii+QwA8AYhClwJFAp2BowA3fzS/Y8DfgysEBYNFgopBT4CLPnj8zTxT/Ad8Invw+vW5YngY9yu2DjV8P6oJbskjA9UC8QUJwhN8AHrefrqBFkIBQVv9tDlMuFN5uzq5e2h/bQSPB0TGjYQkQgABToHCgq4DLkPAROaFfMOxQM++dH2OvqzAEgFZQe9B28JvAcdAvb+kf7LBN8KlA0iCn0EFgVZBLf/UPKU7rfvCfJ+8KHpIObA3MbcHtdv2Kz3WRzRG+gKNwmiEAMJDfNU8aT9dAfIBnYEJfff5/TjEejv6g3tWvr3DaMYaRbFDSUI5QUtCYcNXhAyEgcV4xWJDwYGt/1r+bP6CgGeBuUFAwYlCAgJEgXrAN//6AMnCdELagvDBeUBtwD7/0b70PQZ8TbzMfE37fLkHd9f2vbbgNnY6OkMvhdpClUBGgv+C+T+kvf//scGwgdnBbr+VfLb6r7r3eu264jybwOLEDkTrQ4tCtoJTws8DcMMKg+sFF8bDBiFDYIDiAA3/9H8uf/CA8sGRwn7CPMEngHXAP4DCQeJB1YJ/wdxBtUCWv83/Dn6GfZH77Xskuyw6knm9+Lu4V7b9tVX7wgN/g8YAwoDuQ0tC7f+Uvcq+nUB9wqBDHgBmvKZ7pHwH+8G7lf1GQXIEQ0TVwv6A38EwgpZDQMMCA4RFr4dTRvGEfQFsv4p/14C3QFOAVwEuwmBC74HRQTz/7EA3wPIBPoD3wIiBVgHeAT7+sj06O/x71nxze5/7djqFOrB5A/a9Nlr8uIFhgTm/CwBBAt6CjEC8Psb+yoDiQwxCln/K/WN9d724fPe8Z32eQHDCt4MZQjpBbgI9wt+Cl8HIwkTEEEWChbjEZELLgceB0QFFQE0/4EErApuCZQFYgJ9/zAAegFAAVwB5QNXCqAKbARD/cr6gvl/99v2m/UW9sL0x/CP6PDiFuqI9qb71vcJ9rz6l/4O+xD1qPTQ+68E7gUAAo/8Cv3G/Xn7+/hI+SAAJgjhC6MIWwU7BtkJrwmgBpsHIw2MEb4SvxGzDi8LrQk5CbYH1gYjCRENwA0MC9QI9QYoBNsCswMwBLAEFQaDBn4EbQGF/uD7Cvdx9On1Nfe39AnvFOkq5sfo8+th8CHzpvPM9nz5BPd981z0LvoK/6sAVgKBAfYBmAGD/uP7Bfxu//oC7ARABqEIAQq/C3gLfAkhCUQL6Q/hET4RURGCEvMSDxKsDuEL/AvnDfcOpg2ZDAIMzAsECQwEhQEMAvICqgD//T3+s/+N/MjzK+w16QDouOel59bnkemR6rDqb+lb6Rjt3/Ki9eL1gPUd+PL8K/77/NP7+Pxi/1cA4f96AD4B7QGLAkQDLgQcBfMIFA1MDxcQ7hBrEp4REBHuEHQQxRHoFJwVqxKXDgwL0wkpCYEIlgeRBgkICwtNCh0G/QFE/zj/Sf4B/Q78vfpz98nwJ+mR5EXjoeKf417o1u1U70Lug+0B7i7vSfGr8jH0F/ff+Vf7lftK+3n6h/qZ+zr9VP+qAJsBXgOsBJ4EoASyBg0JbQqvDGIQxhIRErwQoxHjEQkRORPWFYMVDhPHEGcONAy7Cn4KxwmvCBAIywf6BlsEUANcA6cBVv5//K362PZY8pPuqepd5tHhut/+4Sjn+utv7kjvlO6O7snvSPGm8yX3J/rD+4f7v/rv+Bb3nfeG+mH9DP+WADIDVAXhBFIEXAZmCrwM2w4tEd4R4BAWD+wNXA8PEp4TuRREFdcUPBPRECoOSgzvC0ANug1iDOoLZQsACTkG5AP/AB7/9P3U/PX5EPZo8pXtC+rK51TmcOSj44fl8+cd6SnqrOym71XxgvIU9Zb24fYN+E75rfj+9q32w/dN+AD5J/sY/Y3+2f8JAuIDtQWwCBoMHw7RDi4PihB8EWIQxBCAEjIUHRRzE9wTKBT9ErQQHg8KD84Plg+dD4cP3Q/EDz0NtQkSBgMCJf/G/Bf68vev9B7yve+Z7A7pZ+cU6LDoy+f65fTm2+jz6prtT/Gf9Ff16fPK8+Hzt/JD8pXzdvWP9U/2yPdE+nb8Iv16/bb+RgE2BLsG2ghACy0OLRDfD4APAhBwEIcQ8BCnEYoSuBMjFFQT8RMdFQEVYBRYE4wS0hG/ERsQHA0DC0IJNwZpAuT/dP4t/c/6i/gB9vjxse7Y7NjrhuvJ66LsM+x/6lXq8+or68bsx++o8lzzG/Po8kTyKvF18WXzvvS79bn3IvrZ+0b90/5vADYBtgLjBakJLgxeDcANzA0yDtINKw6QDnAPRRApEUoS/BIZFI0UIhWWFb4VJRQMEl0QuA6EDbwLFAmBBg0FgQT0A4EB+v0j+6D4FPa987Xx+O+77YnsMevG6T/pBumf6Mro/emo6ufr+uzW7eTuR/DD8F7xLfP+86T0fPU1+Kn63PwUABgDGgRaBGEFqAaOB7YITQunDC8NTg5fD+4PfBDTEQsTyhK9EeMRpxIAE3sTcBMIEmcQsg6ODLcK5glxCWcIxwZABJ4Bk/4f/JH6T/m/95v2ZPUe8pPu8uv96RjoIOdo55Lpvuu/7Frtxe3t7eHtQu8C8QDzcPWH92/5h/rq+gn7wvwT/4IAKgIjBZ4HnwjjCdkL6gznDTYPXxD9EegTmhRtFP4T4RJMEfcPGg/qDWUNEg4LDm4MDQpMCGoGVQTvAnAC/gEDAMX9Zvyi+ln3b/SN8jnw6O1A7eXsFuwE7PnrRetW6nnqQuv860TsLu0I8B/zAPam+En7xf37/loADQFpAbkCyQQ6BucH8QniC2gM3wvGDKkOrxAXEoITERVzFXEVURVmFGMSpREaEfwPoQ7+DdUMKwk3BSMDuwGe/lj8OPxK+8X4fvbl9D/zK/BJ7Rvs8Ory6bvp6+mH6uvpzegk6Ynpqeld66ruNfKj9I/13vYq92L4Fvos/Cn9sv4OAR4C0QIbBOAFOgc6CQkM4g7+EJIS1BMWFIQTCBPJE2gUkRRwFDsU3RN6EhYRDBFzEP4Oig0QDHAKvQgrCL0HPQZjBGECWQDE/S779/ge96v0MPFb7QnqJugL5wrm5+Xk5lbnvuaN59jpaOsC7ArtLe5577vxi/Sw9pP4sPu1/Jz6TfnJ+e76aPvx/EwAZANBBQIICAoiC2oN6A+/EO0QDBNzFUcW/BVSFdIUXxRME88RbhCaEPoQ2g9jDikO5Q2JDBAMmgyFDKIKFgniB8EF+QIWAZL//Pwi+mT3M/TI8N7tauti6TLojufV5/DnLeiJ6XnqD+rQ6nbtNvAO8mb0Wva69arzrvLI8QHx1vKA95j7Cv0b/jYAdQJmAn0CxAWqCDILAw1oD+wQlhEvEm0SuxEIEbEQHg/7DbgNRA6gDh4PXQ9CDyQP4A7KDv4Nhw2QDjgPPw0ZCyYKlwdUA/7/hP5s/RP8YPsY+wD6Cvhz9QDzSvHS7xnvxO4+7hzsWetP623qmemD6nbr4Oqa69vuufGg8vD0g/f695P2aveS+Qf51Pi7+z3/TgC3AnwFQgY3Bh0H9QdpCOUJGwzUDSsOlw+PEVsRhA8bDk0OOA7hDb0OBBFxEiMSXRG4DyANrgoVCvkKVwveCgILOAsSCewFIQTdAr4AFP9b/p79I/ww+6n5jfYF9JLyPfF479rtQu057BvqRujX59PofupP7NbuPfE38kzxR/HH88z2qfge+qn7K/29/HD6tvlS+kD7hvvg/RYCOgT7BCYGewYGBUoGagoTDRMOrhAPE3oSpxAxEB8PigwcDA4OuA8VELsQOxCZDkYNaQxlC2ALywyfDaANvAwRC9cIygZcBRUE1ALAAQUAgP6Y/E76hfht9zz2e/RR8tLwgO9j7XTqfOdB5s7mzOjr65vvBPHi8HbxFfGK78HwJvV2+O/5wfvl+8r52fdW9un2i/iz+9z+3wC2AncDtAK7ATQCfwQqCEMMTg+XEbASRRH2Dx0PCw0zDf8PSBI3EboP7g87Dl4LbAqUC+cMxQ3dDqEPaw4fDckLugrWCW8JDwnJBzoGSQMD/1T73fnT+MD5IPpG+MH2FPUZ8tjuHu2L7evsmeq46f3pU+lU6JXrGfDe8LjvQvCE8urzXvJ587r3bPkl+tb6DPsa+pn5zfqh++X8agAUBAQFpAMEBC8FGQb1Bn4JswxwDuMPUhFBEdoQvQ8aD70Png9eEJgQ7A/KDxoPjAxYCicLmwviCrMLRw3BDegM3gvRCl8J6AeTBo8FPQWkAxkAmfxp+pb4mvfq9Xz0ePTu80vybe8P7bHrh+qr50jm0+UY5gHqQPLZ9v3yAPBs8q/0O/JL8zz5t/2G/WD9rvxW93nyX/Ok9235Hvx/AtkGngXIA2gDoQNvBdQKlhD5ESQSyxNsFJoRCA6IDYIPmxHeE14Tiw/uDEUNrA0sCwoKSQwpD2APYw7eDI4JwAg6CYIJPQjdBu4G1AWhAVP8qflD+AD4Z/jt99r1yvML88zx1+6I7Kjsvu207TXq4OUJ4nTi8ur/+Un+zPSP8Fv2aPe68zf27fwp/7MAiQVHA8/19O159IX5Vfkc/qMFxgabAwgDbgK0/n4Bng2DFZITBRI+FJMUNxBjDOYLjQ3wENQVUhefD+wH7gcVCk4I6AbjB48Ljw1DDDMJjQV+AxUGbgtNCzUIpwePCPMEfv4C+9v5D/lq+hP8N/fN8GPvgfAB7fjp3+tA7sntD+u06DfiZNxV7EMLTw8e+C7rw/Xq+yn4X/n7/of/bwNhDDEES+oD30LuBvyq/00Fagn6BHoCnAR4AI34Uf5qE9EedxeeDZMLZgoPCQMJmwYLBiEO7xh2FkwIvP/EAg0JHAzwDIEMiwzoDvwP3wrbAycFZQtqDyoOhQj1A4ECXgEP/xX8bvrL/Fz/Bf5v9uvwofND9ATy5+5o8GPtXest58zlLdqz1qr6pR2JDmvoGejj/Az8yO/69p4AeQGIChMT8Pdk1fTYL/W5/1v+qAamCv8CH/91Adf4mfQBCn4jdyL6E+ENCAv6BkQGuQfIA08IzRUSGvQIp/le+88DVgwcEQgRpQxyELMW/RBtBO4DDw0aE70Txg53BvQAMgEIARf62fUo/N4ByP8i+fPxvO558F3z6PHq71PsxutN5X7fwNbyziHn7RQEHcD29uG78KT5K+6g82MG+gsECYARLwGN103L5ekUARwBbggsEsEJSfnF+Zf7gvaKBO4key5nGu0LNwl5BeECPQlkDXANaBMLGhkOpfqx+YMGZg8FEgEV6BIoEX8TSBOHCbwEcw/PGPsWOw9vCNgB8P1q/9n/GvtB+ET8yf6a9hTsa+oJ7Dfth+2K77HpQeJW3J/dhdOpz4j0WB/1FhbtsuZ6+Z/6EO6H+HQJggq4C9MOS/NPyjzOuvOzBfoCAwk3EFQH6vqO+5v9KP9EE3gs+io6FLgIDwjbBUYENgrpDkcPWBONEysGNvgw/GMLrBYfGTMWhhAgDgMQ6w9KDFMMjBLrFqITkwpiAqf/uQBgAswAvf0z/Q38xvfc8DXta+6Z8W/yPO6A6rDkOuEH3NjZlNH00mb56iQjHBHukds07Z36KfgDAfoMBAxsBuoFde/kzMfOI/HMCoENBAuICE7/mfbE+vkDnQjcF9YrMCmLEQsACgAaBeYIFA6iEhERGg11Cc0BL/na+jAMJBrhGJMPDQuqC+0MHw08DWoQlBMcGOsUyAmK/9791gHVA0kDsgDr/OP4Rffy827vpe8880X0pu8x64zjmNxd2KzbANdp1lb/7ieGFy/juNYN9R8Gof5p/ZUKkAobCF4CF+ofzEvSsPZ4DocMVwQdA/n86PiZ/DYDfgrgGaIm2R9zDf0AEQL5BFsIagy2DosOGA36Ca//A/mf/ssNihe4F9oPugjgCG0MMA+iDq8PTBJPFbQSdAvTAaf+OQLBBMYCAf+e/EH5pPXt8RPyofEm81fy+O/l56nix92x2yfcotdb1p76IiiZHjjrj9AD7hUEXf/d/SUPqA/LAEX5xeqR1tnTaPJUDg8U4QjlARP8cviR/cAGugy8FmYljiLkDgb/+/8EBo0JvQ4ZFA8RZgiqBdAClvzR/VUMbRkJFlIMOgmmCgYLPQsLDxgTtRO7FFgSAgoN/6n9iwLNBAIDIAAK/Hj1iPMw8ljyz/Ce83fyN/CV6dziutoP1xvbTNhH2e/+cCzpFrfgF9FL9KoG3P3tApIV9Q6V+9348ewr1RfW+/m8FmATOwJL/A36ZPiS/p8IBxAmG4Yidxl+CQ4AfQNdCBAMsBGPFBcOrQd6Be4A1vyaAi4RqBiDFFULBQfkBcIJcQ+REMsR5xCkEtIPFwcU/rP+4wR2BzMFPQDP+7/1//IG9Tv1mfPC9Dj2du9r5hPg691W3UbZ0d3I1r7uExs1I2zyw9LZ5boA5ABhAqcU4xB2+wz1p/Xh3MHV+u+jDlcULghR/3f5qPT9+NEHKQ4CFVIg5hynC53/+gO/CTkK3Q+bGMgTqgcgBfwDmv5eACwQbxr3Es4JpgmQC4AIwgq9EM0TBBKzEuESHQuy/7z+qQR5BnIF3QS2AP/2uPOT9uT1pvFN85f08PAk6VPmVd9n2RDXDeAZ3HXglAobJo8FJtVW2bH7tANVAA4PpRQT/7Pztvwr6crRf+T1DcYZFwov/1/7nvQA9hUF0Q2pD80YEB/lEewBhAAPB94L9RHSGKQWwguNBoAHfwV2BIwMbxWTFUsRGQrxBnYFzAmTEOMSYRKPEM4ODAqiBHcBegL8BEIFUAPw/tr4R/Tx8/j0SPQI8wLz/u9K6enkw+Ci3IPXONzv3F7g0gGFHJYHQNuw3ID5r///+kQM/BnFAfDycvrt8iDZ3+ZbCjsXvAfr/2oA5/V88v0AEhHHEL8Xsx0lE+ABXQDUCuMNUxA9GFobcw8kCI0HUQTeAskNbhnyF3UOHwcaBv4FyQq8DfIO2Q/GEWsPKgcMAeYATAOQBFkFVQSF/g/2ovOT9I/z8O+y8sX0te6650nlquL62dvZrt9L4ATbMvorHD4MZ97s2MT3IgFU/PkGbRR0AK/1SQFP+8Pdi+JhBmYTRgjIAvsDTPdB8/kCfw1xCogR6B09FegDngHCCWQL6A4gFysX4g/nCuMKxQeZBtAL/BENFZgSUAxbBQUFtAkUDCYODBA7EN0LKAfXBMYBGgD0AnEF0wIK/uv4KfWo8z/zvvLK8tvyaO8m6XPjbeLu3r7d19923gnbXfGBErwMlOjK3VX0cgEd/DQFmA2iAPL3tQQ6/3njoeKIAjgRjQRIAnoEpvfN8QEB5Ap8BlANdRvYFF8BDQJ5Df4LeAqIE1AXFhDkDk8RCgtKAngJ+hIQEwcRNw8JCd4CfAZSCrAKgQykEFENNweUBaUDhABjAlMGRARJ/lj7jvtE98ny2fLs80XyhvDT68vm++LF4X/hAuNX4X3czeumCRoNLfG25DP0N/7r97z+OgqcBIn9sAa8AUfpO+jXA3QOdwBg//EFYP4V9Sj9AgWhAZwGFhRCE3cFXgR7CwwLpgmjEFIVoREKESsS5wz0BmkJGA0VDYUNRQ9aDF0HTQd/CJsHtAiTDU4NAgmsBloF6QLRAOsDAQVbAu4AZv5d+fb1OPdc+WX4r/TQ8RfvLeyc6cXlu+QS537lc+L38oAEyv2C7JXuQPp29sPxw/yFB/0ChgLIBO/5yeyO9TME7QHf+/ICmwac/Df4X/3P/hf+nwYUDrMKBQaOCCIJNAWFB9YOGhGXEMoSpBH1DMwJEgv+CXUJxgy9DTwL5QmwCmAJowg/CkQMvQo3Cl4LkgnfBkgIdwnvBoAEcwNDAqP+af0F/0r9jPl9+PP29fKx72Dufu3v6rzorOgM6Dnp8O5c8PDsd+yA8P/wYu8E8jX43PkK+ar7Nvus9zX3JPx+/df8iv5zAWcAaP1x/m3/BP9IABIF4AY1BvoGpwiwCJUIogvGDoQR0RMaFgYW/RT1E3ET9RLkEjITpBLMEm0S6RDFDl0Otw1rCzoKvgpgCugIhAh0B70FVQOKAYX/Gf54/er8Jvux92v0hfCH7YrqG+l55x/mD+Tl4Sbh3OLC5qTomuj46GjqQurL66rvHfS79p353Pu9+q73SfhY+7f9JAAXAyYETAKPAToCeAJdAu8FOAoyDNcMlQ02DkUOvg/oEoIVrhY2GU8a6BjLFpIVXhVEFX4VaxXLFIQT9BFJD4gMpwoNCUAIYwf8BkcFFgPpAYEAXP53/PL7Gfv1+Yj5EPmf9j3zCPB17l/seepf6a3nVuXX4mnh9uHP5MvoU+ow6cjpguo26yntVvJv91H66/z+/bn7PvmG+3f/HALeAxYG1wZzBZUDSgRUBcUFrwi3DCYOew1BDe0NGw95D2URkxO+FGkVyhX+FQkV0xPZEwQVyhRxE8gStRGhD/4MPguFCWUHMwV4BGED7gAe/zz+tf1W/Lj7LfsO+tr4g/h3+AH3FPQP8pzwuO4/7J3q6ej35kHl6+Nb5Vvpdutp6lLqa+vw7N7tp/GS9oj5A/xi/pH+j/ts+tz8xAC8AuEDogWUBYwDXgOMBHkFowZ1CRcNyA2bDHgM2A38DhcQnBFnE/gT/xMaFJATcBJmEXMSwhP9EmgRkBATD5EMdQqVCUkIOgbKBCwEUAIx/4X9OP0L/SX8efsm+y/6wPhm+AT4NvYX9N/y4fHD7zPt2Oof6b3m3+Un5q/oBezx7CDri+uY7SvuafD89Dz66vvp/C/+Yv0s+4/7bv+bAm4DAwQABZcE4QO+A3MF4QYUCG4K0QzfDOYLGAwRDrIPVxBiEZASGBO9EtkS0hJvEskRwxJlE4sSmBD1Dq4NvguBCWEIsAdoBaQD1wFfAPL9sft/+9z7DvtF+sn51Ph998L2evbk9J7yivGf8Hzu6uvy6X7oYOYN5eDmDes07QLswesu7Snuhe8E9Dr52PsQ/S3/Jv+C/Mv7D//AAnMEJQX/Be0FbgSOBGMGyQftCHALMQ5pDv8MzQxVDhcQHhGhEuoTORP1EbMRRRINEp4RmhIwE68RPw/BDQYM/wnYCCsI0AbzA7sB9v/W/TD7ufkB+rH5vPjf9wL3vvUD9cL0q/Sy80PyT/G678DtMevn6aTozeeH6D/r3+3Z7Znsbu1N7zLwNfMu+Mf7+/z5/UD/e/5g/er+XQKCBDUFwgVFBuMFtgXrBssIUAp3CzgN1g30DCsMcg3EDyoRLhIFEwUT+xFDERURRhGIEXESyRLwEc4PBg0PC2oJoQcVBiUF3gPLAVb/Qv1c+6/5NPlY+QT57/fX9p72svU79Sb1a/Rs8+fxJfDa7c/rC+tC6n3oGOib6t/tje5g7lPv0fDx8bb0//io+439KQDWAfoAf//r//sB1wMbBYIG6QY3BhEGtgawB1wIwwksDG8NzAzgDEEOpQ/dENAR0hLZEg0SORGOEJ0PMg+9DwMQCQ+cDI8KtwgAB5gFwwQnBLkCMgEbAHv+cvyE++76bPph+VT45/en90z3sPa/9eH0mPPf8RPwQe7+7M7rF+rj6RPsZO4R71DvkfA18k3znfXL+OD6Tvya/gkAxf+s/mX/PwHuAbUCwgMTBLUDLARbBUQGpwauB70JyQqBCs8KpgxcDnUPLRALERgRWRAqEEMQ3g8cDx4P6Q6HDWILnAlGCM4GeAV7BMADgAICAaj/0f7m/R39XPxU+/z5d/io98D2PvYJ9kb1xfOn8bvvfO5G7ZPrr+oE7FXuC+/u7sPvhfCZ8fbzfPcY+mL7Yv1g/sX+8/47ANEBjAJAA20DhQIsAnAD0wQMBsoG4gcfCbUJXQp5CwsNfw7WDwURnRFCERcRMxHeEI8QcBCiD3kOpAwLCyIJAwchBsIFoATgAkgB8/8r/2n+S/4q/qD97fyW+0j6NvnD95z2fPUp9N7yAfLw8CDvvO2w7NXrouoC6nXqFewC7qHvV/Gb8+315PcF+hD89/3W//IBkQMTBEYEyATDBAUEVQQYBfcEWAROBcAGNwdbCEsKgQuMCzYMKA5WDz8Puw/0EIsRIhE4EI0PGQ5KDCgLvAoxChgJ4wdxBj8E5gGJAMb/8P4B/kf9lPx/+7D6d/oF+rz4EPeA9Sb0AvO48cXwePBO8Gjvdu3161brxuq46z3uj/Dn8ary8/PV9SX4RfrJ/Cn/FwHLAv8DvwNlA+EEdQaUBmgG1QYOB+YGUgfkCF8KjAsKDfgNNA6qDpgPnhBDEU0RgRF2EYcQEg92DeULkAqACY4IbAfMBSQE0gKzAbcAiv+X/r393Pyd+y/6XPmP+O/3D/eV9czzIPJ78Ubx5PB88K3vRO6i7MbrE+xs7F3ti+/v8a7zJfUb9+f40vlr+6L9ov8kAXYCggNbBEUFSwYfBxcHeAcVCIYIwggHCQYKQAv/C7IMPg1BDVANhQ0JDigOWA6gDrUOKQ4/DfgLnwqsCd8IHwhDB24GsgSJAsAAff8h/pj8Qfsk+j35nfgL+Fj3P/ZO9UH03PIB8k/xu/Ar8LTv8u7Q7SrtQO157f7t/u5n8E/yV/Q39vz3p/mp+7r9BwDPAdICCwRCBRAGpwaTB9QIywmJCgYLDgs3CycMTw3ZDeQN4A0LDjYOtw1tDVYNhg2UDQMN8AvCClQJ9QdEB7YG6gVXBRcFUQTUAm0BRgD9/mT97fs1+mL4CvdB9jv1DvR182rzrfJO8aDwC/CR71Dvc+/P7xTvxu2b7bnuGPAq8UjzOfYK+CT57voL/Y3+1v/HAdMD/gMPBbYGqQdPCHQJJwvuC64LBAwhDYMN2Q2qDh0Pow4DDuINqg0WDXAMJAwfDMsL5Qr5CdwItweyBrYFwwSuA8ECwwGnACn/n/3Q/BP8T/tM+mn5tPjF97z21vX69BT0cfPR8knyxPEY8dPwB/ED8VXwD/BO8LLwqPEM85b0ePZu+O35ZPsD/b7+eADYAewCswMQBX4GVwfkB8oIhwnOCU8KRwumC/MLkgziDMIMDwxaC+IKtArVCsoKXQrdCf8IJQg6B2QG2gVcBe8ERwSrA/kCzwFeAA3/JP6Y/cz8Evwf+xf6/vgi+L/3Wvf09l/2LPbY9Ub1r/RF9Ef0WfRF9Az07vPz81n0V/WZ9qH3HfmH+kj7+vvV/Ln9uv4qAAgCIgMcBFUFygUeBtgGrgdvCO4IxAljCnUKnQpjCsoJawlmCXcJNgnJCGcI7AdfB6AG9wV3BToFCQWtBDAEmQPaAh4CRgFyAN7/g/9C/6D+C/5P/Xv85Pu7+5X7DPtr+gb6mPk3+d34tPid+GL4TfgO+Kj3Ffez9tT2DPcc98P3Wfhn+Kn4Lfl5+aP53fpK/Gn9Nv7p/m7/p/+cALwBdwKAA9cE3gVNBp4G9wYrBz4HuQdUCF8IXwhgCDYIkQexBnMGcgZDBgAGxQV2BQAFqwRuBDMELAQCBOkDtAPnAkECqQE1AbUAOgDT/4H/9f4G/mX9+Pz2/NH8ivyK/CD8dvsm+wH7l/r8+aL5c/kM+Zf4T/jd98v3EviH+Oz4F/mO+fT5cvpl+/r7nvxQ/dr9kf4u/9z/PADlAMQBgwLnAgED3AOKBCkFlQWjBdwFcAa3BkYGKwaeBvIGoAagBqoGUQa2BUoFGgXnBCYFBgVgBMcDhAOkAlUCEgLiAfoBIgEPAdYAgf92/vL9rv31/ZL9rf1E/aT8ePx8/S38Lvuu/Nr7G/uK+3z7/vo2+2/6cPod+xj70/oW+jX7Rfwh+8n60fsq/I/7Kvyr/EL8v/zN/dn+YP5y/jQADgGjADEBswIsAx4DDQP4A0MEWgNRA2AFsARQA+8EJAXkA14EVAVXBHEEOAQxA64DlwTjA98DMQNZAu0CUwNDBBoEUAN0A0IDeAP5AsEA3wIVAmP/nQBAAVr/sf4oAYb+2/2S/0r/DvwX/J4AtPzS+gr/PP8C/Hr8Rf3F++b87fvo++n9lPv/+tH+//pc+Pr++vyp+tH++Pxz+i3+N/6p/jj+xvziAPH+uP6wA0cBBv7LAVcAYP9jBMQCkADtA8kBOwG4A9IBDwIIB7wEuwAgBfMDGQRMA6EChAXOA94CPwT3ApEBpQNDA6gB4QQXAF0CIARh/asBqwPP/uMB3AEI/+QC6f+VAA0AKv9iACcAGv8jAJX/sPwCALz8Nvw/AQ38RfsmARf7Pvpf/A79qPyG+i79fP38+xb73P3Z+/X+dfy1+wgDG/xa/hgBVv+u/af/xgCj/0ACvP6qAuQEFwBcAtkA+wFcAr4EVwSHA5MEHgBjBooBaQFkBsMAjANCBWf/4QGPBAn/5wH+AwYA3AE0AKT/BwFa/QACIAA2/f8DEP+f/CcBKv8O/d8Be/8r/loCJP1+/M/+rv1S++L+ygGg/ZP8rv3O/ab7x/7A/4X89v2i/8v+gPz1/k7+c/56AMT+nf2w/kT/QABl/6b+5AE3AOwAGgGg/pAA9wAxApoE2gG0AmcDagJXAkECigNKBLgDIQTPA4H/qAL/Atj/EwNLA0ACVAAJAUoABP/a/9//s/9h/3EAWP9x/zT+4/38/lf9wwCb/wT+NAER/Ib9Iv9g/Cv//P74/dr85P2W/Jr9kv7//D3/m/4Q/Sz+3/2+/ob+Ev/p/4H+Mf/6/uf/u/6TASICn/9YAbcBTQJnALoA4QRuA8MC6ANcA6gDXAO1A8gEkAWABuIDCgTJBCAEtgMuAwQEbAMZA/MBigH/AM8Af/+3AKj/vf2aAIL/eP6J/Vb9rv4d/af9rv2F/Hn9hfz5+3X8lvwm/AL9g/z5+w797/uz/Cj8CP1B/R78Q/0+/an8sP1g/oP8tf3r/dz9P/78/db+zv4H/6r+UP4//5b/uf/3/ywAQgDQ/xoAsgB4AD4BQQF1AToCdQFRAZ0B3QFMATYCXwKwAa0BCwImAaMAUQF9AOgA1gBsAL4AnAAIABQAUgDk/+X/UAADAC0AOQD+/2oA+v8pAJkAXQCMANMAEgHnAMsALgF6AYkBdQF8AfYBzwGGARMCAwLlAQgCaAL+AaMBIALHAawBqAGlAZQBegF6ARMB7gDEAEkAjgCfAGoAcwBUADMA1//7/8r/rf/S/5//iv90/1r/EP8d/9j+oP7h/qL+Kv4m/nL+P/4f/hz+O/5c/i7+J/5i/or+Qf6//sX+ff7a/jD/Lv8t/0f/vf/u/53//P/h////aABeAEcAkACvANQACwHMADYBQQHaACIBlAGCAUIBZgFiAWIBXwEiAQsBLwHqAMMAygCHAG0AawBOAPX/sv+R/wsA7f9s/+b/kf9f/3f/ev9b/3f/V/9k/5v/O/9S/4b/cP+J/6r/af+B/5X/hP+R/5z/nv+3/7P/rf/R/9n/of/c/wQA0f8VALD/wf/w/8X/xv/n/+D/z//Y/wUA1f8fAOT/5v8xALj/WwAcAA4AVgBPAD8AKgB8AEEAMABhAHEAYABQAFgAXwBbAD4AVQBiADkAQABAAEUAgQBDAHAAZwA5AFsAYABeAGoAaABhAKcAYQB7AH0AewCdAJwAogCQAJoAkQCTAEsAYQBqAEUAQgBFAD4ADAARAPf/6f/T/7D/lP+z/4D/W/9t/xn/H/9B/xn/9P4J/+T+yf7+/u/+8v7p/s7+Cv/0/hX/Cv8I/y3/Q/9R/1T/gP9p/5//wP/D/8//2P/s/wkAFAAqAFEAYAB0AEoAfACwALoAtwDNAP4A1QDQAOIA1QAKAf4AEAEdAfYA6gDQANoAvgDeAKIAigChAF4AeQBLAD8ATQAkAA8A1f/R/6T/g/+P/1v/Q/9b/yL/AP/1/tD+5P7J/tL+0f6+/q/+uf6l/pf+rP69/uz+0/77/gz///4c/07/R/9L/5P/iP+t/9z/rP/u/wgA9/82ACQAVwBxAGkAeAB8AIUAqACaAJ4AvgDLAOEAyQDcAMoAzwDsAPMA2ADsAOUAxQDRAI0AoACTAHkAlgB2AEAALQAsAAQACgDf/97/6f+0/7b/mf9p/23/Yv9s/0f/Wv9R/zT/Qv8r/zj/N/9G/0X/S/9P/1j/OP9V/3T/af+C/5v/sv+u/8v/zP/H/9L/8P8FAAcAGAAbAC4AIAAvAEsAXABaAFkAaQBbAGoAegCGAIcAigCIAI4AqACZAJwArAC+AKcAnACUAIEAcQBtAGwAUwBoAE0AOQAoADEAIQAVAP3//v/2/97//f/h/83/uv/D/9f/tP+R/6X/pv+R/5T/nf+U/4n/o/9//4T/m/92/33/kf+p/5b/if+g/5r/rf+s/4f/rv+2/6H/rv+x/8n/xv/U/9T/3P/g/9P/2P/I/9n/5/8CAA4A//8BABAABQANACIAJwA7AEEASwA0ACUANAAsAC8ARAA9ADQANAA5ACcARABEADwAUwBRAEsASAA9ADAATABHAEkAQQBFAEwAGwAUACMACgAOAA8A9v/3//b/2//R/8//uv+8/7z/pP+W/5v/iv+J/4X/a/9w/3z/Xv9g/2X/Yv9e/1L/av9S/0f/Wf9Z/0f/Vv9m/1H/bP+F/4j/eP+i/67/qv/A/7//9f/n/+r/EQAIACUAIQAjAEAATABUAFgAZQBuAHkAgQCMAJIAjgCrALMArQC3ALUAzgDAALQAwgC+AK0ArQCcAH4AcgB0AFgARQBFABgAFAD1/+7/6P+8/6v/l/+J/3b/av9g/0r/Rf84/yf/Ff8L/xn/B/8P/yT/EP8C/wj/A/8O/xf/Fv8t/zX/PP9P/2D/af9p/47/ov+Y/8n/wP/E/+P/AAAhAB4ARgBYAHMAiQCQAKYAvADRAOMA/AD9AA4BMQEiASYBKAE6AT4BKAE/ASUBGQEkAQoB7ADoAN0AvgCsAJwAcwBZAFcAMwAdAPn/5P/I/7H/pP94/2L/Uv8+/xz/Dv/x/uz+3/7F/s/+uf7F/rb+vv6//rb+vf60/s7+1P7p/v/+Bf8e/zv/Vv9c/3v/kf+v/8v/1f/q/wQAJwBLAHAAiACuAL0A4ADxAOsABwH/ABIBKQEqASwBNQFCAUsBSQE6ATQBKgE1AScBBQH0ANsAygDHALEAoQCEAG8AWgBCADIABwD0/87/uv+a/27/Tf8e/xb///7j/sb+wP6Z/p7+kv5m/nr+df6D/oX+lP6a/qb+vv7P/uD+/P4R/zj/Uv9T/3//mP+1/+X/+v8bADUAUQB7AIUAngC/AOAA/gAEAR4BLgFBATsBWAFYAUsBVwFNAVIBSAFLAU4BQgExARwBAwH3AO8AywDGALYAlgB9AF4ARwAqABUA/f/X/8P/of+S/37/cP9b/0n/MP8g/wv/6v7f/sr+yv6o/qn+sv6x/rL+s/66/rL+3P7J/s7+8P77/gL/Gv8+/0j/VP+F/5v/p//M//T/BgAhAEoATQBaAGkAogCtAKoA0gDmAPIA7wAHASIBJwEnAScBGQEWASEBDQEbAQ8BAgEEAfYA6gDOAL8AtACdAJYAjgB4AF8AUQA1ACIAFQAKAPb/5v/c/9P/xP+1/6v/jv98/3//Zf9R/1L/Vf9F/zv/R/9K/zn/Pv86/yz/HP8e/xT/D/8e/yz/OP9G/2T/av9u/4D/nv+d/6L/yP/d/+f/6v8kACQAOABSAFUAYQB1AHoAegCJAJYAkgCnAKEAmgCvAL0AwgC7ALcAxgCtAKkApgCyAJAAiQCYAHoAYgBdAHMAXgA2AFQASQAsAB0AEgD8/wAA3//s/8X/yP+o/7P/vf+J/53/nP+M/3b/i/97/3r/bP9t/0//Rv9s/1r/O/9Z/2b/Wf9W/3n/j/9v/4z/l/+d/6r/tP+s/8L/0P/L/7j/tP/O/83/0f+h/9D/3f/t/yEAKQAWACYAXwCdAKgAiACJAGMAlAD5AOQAlQKOBeMAAPpcAS4H4v5I+uwC6wMX/cf/6wSDAWj/gAK8AYb/hwCBAN/+tf7+/5f/6f5xACIBt/8gAPsAWQBF/1oAIgDM/r7//v85/3n/PwBS/2L/twC5AOT+NQClAP7+hAAwAJ//N/9QAFD//f7L/2D/tP8IAI8Apv84AF8Atv+7/0cAo/7C/nwAhP+j/skAsQAZ/+IAbgBOAeX+UwDcAEz/qf+N/3//df8bAan/KQDM/joAvf/9/isAlgAzAMj9JwCSAdT/ov6OAAr/RgFp/Wf+xQK0/pf9fAFfAj79kABUA9T+2fxMA3IAD/3C/xQB/v4o/VoDj/6M/XEBtQIMAEf9RQGRAbj9Df1QAPX/ufwp/3sCsf+K/x4BlgC8/mMAuADB/Nf/igEs/4T+WAKKAUEBAwHTAboAuP9wAXj+e/89/27/7f4MALoApwHSATP/1wFWA37/dP3gAHAAC/3z/pgAjv9hABIAJQIuASP/FgDf/+sA7P9g/YEB+QEV/sT+GQK1Aav++v27AX0Bbf5KAGkARwH3/ob/ewBaAegA1vzsARMDHP4z/q4CHwKg/WX+xAM3AMv95wBeAB8Bmv8B/1ECKAHU/gQAFgImAMX9pwBh/zT9hP85AKr9JgD0AXj9xv65AUEBLP4n/j0A8Pww/vP+Qfz1AZn/qf7tArADZAF8/3kEtgAC+2r/sP8R/mf9ZgAmAf39qwBSA6cBMgDmAfcCAQCW/YMBfgBD/aYApAFxAXoB1QK/Ab8AHAI3ATAAYgBf/+L9y//XAcH+OP84As8BvwBdAD4COP+r/k8BDf/L/fH+lf9S/VsAeACn/nD/SgBW/6D+Xf/g/oP+ov6//tz9Xv5FAHr+l/1n/yH+B/4R/6z+Xf1a/2gB6v6//28BfgG5/0gBHgIW/9P/EAFiAPz9hv9yAWwA5P7PAJICdAAfAakBEwLd/5IA0AH/AMH+1/+2ApAAIv9KAGcD9f9w/vIAnAItABf+tQHqAc7/Jf+CAZABYf2D/YkB1v4r+hb9KgGl/Qn6Lv6X/yX9VfyX/qz/OP1S/D39pf7D/af9ov0F/ycA9v7W/7oBfgKz/7wAzQIqABf/rQH1AsYAlACBAnQE4QKJAfACiQTBApgBsQL4AhQC9wABAxcDTgJaAUkC9AIyAaoATgG0AL4AbQBT/5IBEwHV/n7+FgB1/f76tvr1+oz5+/fh+FT4SfcP9+L4tfeV9pn4EvhT+B35g/lZ++77+vxG/ssAXQGAAfQDtQO2BNkCmwQ4BcsEjQaCBU0H5gd2BxEI8QjrCLAHYge0B+kGzwQRBfwFkwWyBA0FdgaDBTYERgQTBIACawJLAfMAxwDw/hz/1P5j/pz80vvw+qb5vPbf9HX0x/G28Nvude8p78vuLfAt8vP0G/R898j8F/99/hgCxgV8A2QC7gXzB5oEiAMPBkkGkwSbBNMI+QnqCLcJvQy3DMEJggkoCnIJkAZxBVEGVQXkA+8EgAbmBjgGlAZmCFQHRQaEBFoD/QKbAOH+n/6A/UH8rvu0+4f6VPgt92H1p/Rh8IzsjuoT7DvqG+VA5xzsLu/R7TL0cPu8/Hz+OgOWByMG4wTxBUsGQgWYAmUFcgXxA6IFhQj4CTwJiwsADLILwAscDO0H4AZIB6sGEQUHBIkGAAauB98IBAoeCyUKcAo9CiEJyQZ9BoUGygP8AaICmgLLAPn/SACZ/5H9BvtJ+F329/O+7qrpMOiw57DkzuMq5wfpxek38N73avkl+hYCVAcpBOYCRgYKBw4CrAHpAyEDzwAFBGIIoAhgCI0LShCODVoMuQzODAkKkgfuBggGowSMBO0GDwfRB10J+AoOC+oKFgz2C0YKkwhrB2IGBwT+AokBTgHSAG8AWQAIAP3+K/xo+sf3JfRg7/XqXOai48HgE+Gu4jzkqeZ16/PzzfgZ+1f+OAbPCPgFVQSvBnEG2QEZAlADeAE5AE8F6wllCJ8H6wwpEasN1gkHC0AMLwiiBOsExgOBATUCCga0BUgFHwg4DEUMhgtzDZUNPww4CUgI2gY3BDMCVwMrBO0BggCWAiAEKgJQ/7/8gPmR82vva+tB57XizuDv367h3uU26SvthfIT+pf+yAB+AzQHFwdqA/UCeASIAsv9X/7cAQMCkgCEBLQJ6gjwB7sLFA1dCT4GHgh6ByoCQwH6ApYCLwDZAhYHZAfwBmgK8Q1ODCgMPg0wDdAJJwliCswHXAWGBRQItga9BHUEwgR9A5AAqf0v+aT0VfDV63jm9OLn4lfiWeFd4x3pQO1p7631S/3mABAApwK+BbsF4wLsAc8BJADN/88AEAIOAAsClAYCCbUGSwbLCaUKOgiHBX8FMAREAqwB2QI1AjkBDgNoBu8HRQfwCIsLNAzpCgMLRAsBClIJJQrOCXgHMgfzCUULzQiMBsYGggYMA0r+tvoD99TyUu6K6r7m8eO941Hlz+i16szqwu4X+JD9VPsq+woD7Ad4AlD+dAHEAhn9dPrZ/un/IvtU+/kBiQRiANn/EQZkCJUEsQLDBUQGfAM0A7gFCgYqAwYE3AevCRgIGgjaClIM3wpjCrMM9g0aDIUJRwnNCSkJPAhnBykHRgYMBeMDrQKXAJr9Dfyu+Wb13/AJ7yjuu+k45bnl1OmJ68zqo+0I8x72vPdJ/IkAqP9l/qwBcwNq/4n7d/1KAC79HvpB/EH/qf7C/Q0BlAOcAqQCbgbpCO4GlgVSB3sIGQd7BU4FTQYQBxQIPghRCNMICQpsC7cLzAuxC1gMbQxdC4QJVwgHCDoIcAgVBzoF5AN3Ay8C6v5p+/b48ffz9GfwyO2j7Efq0OYo6J/sVe1m6/PshfML9472x/cn+zL8B/pK+oL9+fyJ+VX40frU+235zfkE/q8ASgCh/8kBdQS0BH4FSAcDClMJbwdqBywJEwphCLQIgAsRDUELxwq3Db0PCw9ODmEPXA9DDdALHwwHDHsJsQePCLoJ2AgDB4sGywWsAyYA6P3/+3f4e/Tp8KfuOOtT6H/o1OpF6tvngehy7bfxCvE18Vn1Mfk6+MT2SfoO/oX8dPlg+hD8bfpP+Ib6MP5h/kH95v58AooDRAJUAwQHEgn2B/sHLgpYCzgK/wlnDAkOSg3PDLoOFRCfDsgMBA3EDdMMzwq0CV8JWggaB6wG6QaDBgAG6QXPBbQEBwN0AZwAjP9E/V76G/jY9efyXPCq7pvtUuzd6/7ro+zz7azuNfAq8hr0o/TL9sH5jPrx+Sf6kfuk+6n7Fvwf/N36+fm9+jf8Lv3x/VT/CQFUAVEC1gMnBpsHfwj+CTsLSwxcDFEMYQx3DBgMPwxODM0LVwviC5ULCAolCbMJAwp8CXwJ0AnhCfAIIQjTB1IHcgZKBXoELAN5ACT+If3E+wD5Z/bn9PjyRvDZ7gHv1u0W7LjrXO2S7mvvcvEs83j0TPQg9VD2DPir+S36nPk8+bz5RPqi+or7jfwC/LD7lP0dAIsAOgCtATYEXwUeBUYGkQi/CeUJWgo9C+kKsQlHCY8Kagu0CioKtwqlC5ELGgt/ClAKWgq8CqkK+gmoCRwJmwgJCJwHxQYCBukFsAUjBOIBawC1/yj+Yvsk+Xf3CfVe8oLwou+e7k7uTfDu8bXxi/F/87r18vUW9lv4A/qZ+bj43fnw+qj51vjR+WX6u/gf97f4hfsM/EH7dPzZ/sn/w//qAFUDsgQ6BREG/QatB64HMwiACSYK+AkmCvoKUAv0CrkK/QoTC+gKwAoAC1IL7gqfCmkKBgqUCUAJXwmECQsJjgdWBsgFEwWEA2ECDgKYAMD9Vvu7+cb3DvUO8+jyn/J48SLwKfDn8PbwNvG48oD03fQX9ej1dfbW9Z71UPbq9n72BvYw9lT21fbN91X5cvor+6b7Nv0p/7cATgEXAjsD9AMEBCcFIQfpCK4JhArIC7gMow36DdAO7g76DoAOGQ7lDVYN4AyADJwLYgrbCUwJDQnqCHgJswnxCFQIIwhiB84FvAT3A4wC7P9K/b/6F/hh9T7zNPLp8Orul+yL6zfstuwk7cnutPCm8crxyfKA9Gr1H/aj9xf5VPkn+RH5IPle+cn5a/p2+sX6HPyx/fL+MgAhAWMCCQMkA98DJQU9BqoGaQgHCuQKVwuUDDkOcg4EDhcOXQ/fD4sPzg63DoEObA2YDC0MpAtrCp0JaglFCZAIkQc6B9YG1QU0BMACsAF6AM7+bvyC+tf4xfY69H7ySPFI79Hs3etR7e3uDe+u7gzwJfH98CDxAvPc9Oz0pvXw94j5OflI+Yz6ufud+3X7Gvyk/Hf9gP5s/0n/bf/+/44ApADxAEsCfwPGBLIGBQmCCnMLnAwjDiwPeQ+uD+kPTxDuD3kPpw5kDaEMVAy2C2AKhwk1CfgIegjtBxUHBwYJBUAEaAMQArQANwCr/4f+Mv3L+y36IPg19sz0VPKg7yXuLe8Q8A3v1+5I8BzxcPBL8ajztPS59CT27vd/+BX4Wvhy+S36Hfop+s76pfuD/JD9vP6D/wQAoAAlAWsB4QGwAmwDtgT8BW4HjAivCQ4LfQxnDbcNkQ5SD5UPVQ/4DlcOaw1hDKILDQt6ClgKmAobCyMLvAoYCkAJdwhBB8cFUATxAr8BrABl/wf+kvww+5T5sfcN9pz0EvMo8a3ukO2U7lnvhu4S7k/vYvDz71HwzvIp9JL0xfW395f4rviH+e/6zfuw+8n7iPxg/YD+df/S/0cAAgFxAdoBLQIRAz8ESAWIBuoHTwlYCoYLrgzzDQEPtg+oEEQRnREpEVMQhw+tDnkNawx1CyML9wrJCvYKfQrECXkIUgcxBroEDwN7Afz/d/7g/E37wvkr+Fb2vfQ7843x6e/T7U3s4+zB7uHuaO787pHwcvBo8BPyz/N49PH0wvb09474kvno+uH7NvxU/Jr8jP2i/tL/fABHAeQBnwIWA0cDzQPWBAQGFwczCG8JnwrvCyYNJQ4mDz0Q1BAEEecQUhCXD6cOCg7yDMMLpgriCZQJSwn5CIwINwiSB3sGigXKBJsDbQJiAR0Av/4w/eP7YfrN+GH3uPUl9HLy4vAh7wLtPOuS61vtJO547cLt0+/i8NbwOPJK9Er12/VL9xD5vfmB+hj8QP2v/Z/9df6g/64AgQGjArADRQTvBKEFsgWdBWUGbQdFCPsIDQpTC4cMww2yDrsPihDSEBMR7xAVEB4PSA5uDVsMIAtJCqQJaAnsCHYIBgisB8YGbQV1BHgDAgJEAPf+n/3O+yv61fi296z2i/WC9BTzVvGD77rteeyK7YvvTvC0743wkvLO8tzyZfQu9qP22fZU+J/5evpx+6n8t/3H/T792/3p/nn/MgBAAUMCwQLsAkwD0ANTBLUEnAVaBssGZgeUCOkJKgsHDOIMvw3qDQoOpw0SDbwMewyIDAUMCAtiChEKnAkACcQIxwhFCI8HvwYVBkkFggQ9BJIDVwIEAUgATP/6/eX88vux+h75U/eF9YnzafFx79zt2u0O72bw1u9I74vwkPGH8cfxe/Nz9OHz6PTI9vT3dPiA+Rb7+Pv++3v8mP2N/j3/xv/RAKgB/AFkAi4DsgMSBAoFKAZVB2sIzgl5CwQNzQ19DjoPaw9JD8MORw7TDXINrA3BDb8NdA0PDeMMfAzSCxkLmgqfCXUIZAdvBl0F9wOwArUBOACb/j79FPzy+pf5oPhL91r1O/Or8c3vre0p7GfsQ+4Z7+7tA+5K72vvVe+s8MXy+vLV8gP1rPYp9w34z/mm+1/84Pw1/nz/IADiACUCgQMWBFwENQWwBacF8gWlBrMHfQh0Cb8KEAxFDTcORg8nEM8Q9RDqELIQ7A+JD4UPBw9mDrUNGw11DKsLOQtcClMJUQg5BysG6QSrA4UCRAHi/3b+7PyI+xD6jPgO99H1g/TX8gfxZe/B7crrHep36WbqOOy97LjsnO3u7lPvG/Ap8mn0IPXc9Rr4ivko+jr73Pyb/kb/5v84AY8CeAP1AxkFYgbEBj8HzgcjCFwIfwj7CAYKGAsKDEQNYQ5hDygQkRAqEW4RJhEUEZgQ7g+CD7YO7A0QDQ0MLgvUCdUIHAimBj8F3QOjAgABYv9A/vv83vuc+lT5Vvj39or1YfSh82/yk/AG77ftN+wK6yXq/eoj7bXuze7z7gPw7fAq8e3yDPXk9WH2l/cr+WP6LPuc/Nj+1f9gAHIBfgJAA8cDuQSnBUYGdwa7BiQHNQeaBzoILAltCn8LmgxyDRsO2w6FDxgQdhBvED8QsQ8OD5oOFg5hDa8M1AvWCsIJhwjNB1QHMAZcBYoEVwM4AsMA0f/C/nD9WPwM++T5h/gD98f13PRv893x0u8d7tXseeuo6hTrguxx7tjuZe5g7+nwavGD8kr0x/XX9Sj2APin+Zj6tvup/UX/JwCIAJEB9QJ/Ax8ERQVABl4GRQaGBtwGYwf0B+kIRgppC3gMTw0cDv0O/g8KEbgRtRFvEfMQkBBLEPIPmg+zDs0NzwyXC18KbwkKCR8ICge0BWYEagPQAZ4AwP9G/vf8fPv2+S34NPaO9Fbz8PGW8P3uLu2M6/7pyeg+6EToRekz6yrtXu2g7SvvCfEx8nrzuvXc9h33Avih+W37sPwH/vn/rQFNAq8CtQPnBDMFsQXHBn4HnweYBwQI8QjKCZYKmQvVDKkNPw4PD+IPiBBwEW8S9xIaE8sSPRK8EXgROBFFEAEP9w0GDakLEgr1CMwHZQanBPQCoQFBABv/pv3F/A380/pU+a33qPWw8/nxgfA/783tdOzo6hXp+ecV54HmaOaO5/Tpduy27cPt1+4I8k70bfXs9jH4OPmp+ez6BP2U/sn/PQEuA8oExgSdBI8FZQahBgcHbwexBx4IcAgNCTMKvgsrDToOUw9rEHMQqhDnEAoRHxKGEg8SvRHkETsSUhIbEqcRmxAcD10NEwvQCCwHDQYgBYwDwwFDAL3+cf3L+/36Tvpp+aD4bffP9fjzX/LD8FvvoO3966Lqk+lC6NXmAuas5YDlj+Xk5tvp8uz27obu5e8v9JT39vgw+VL6kft3/GT9Qv7a/9IBQgPvBCkGsQbPBjMHlgh2CQwJjAh9CCsJDQoVChgK1AsrDqYP+g9jEBgRZBGyER0SFRJkEncS4hG6EakRRRG2EDYQSQ+tDXQLRwnvBiYFCgRvAnQAs/4J/YH7+vlz+FT3vPdi+C34bPaE9PbyxPG38NTvU+4q7NLqUekt6EDnWeaI5dLkoOT65J/mbuys9e/4BfZs9vb+PAQ9AFj6Afs0AbUDPP+0/GUAqwTnBv8GDwhzCQ4K+wnBCV0JHwhBBrYF8waUBykGhgW0CdAPpBKeEhQU8hfVGQgYBhbHFQQWlRRSEqsQbg/WDV8LGQkFBz4EaQK5AKL+Tvwt+0f7B/xj+6f6SvpE+u76I/ps+HL22fRz85zynvHr7mLrP+rc6QDoeeUz4vreR96o3+ThR+XC7hT6sP7qAE4JohQEFu4NtQdSC0kOBQVJ93f0nPs2/t/4dvV2+wMEPAamAnsCLAfFCNgEtQFTAiED2wJzBO8JfA/8EQ0U4xhZHV4cThhaFpUW2xMQDwAL3QhRCAEG9wRhBLIClwCh///+Kv7U+9T5MfvC/I/8xfuy/FH/YQHdAKb++f3y/Ov53/Qr8arutuyC6aTmTOVK5Bfl7eNH4vDgj+TR7sf71P8BARIJ7BYNHDsS1Ad0B5MKDwC27wXpCu/v8lLvq+7d+M8FzQmyCHIKZBDBESgNMQi/B4cHAwWNAr8DSgazCFgL2g0MDxwOIw8REFMODgraCa4MHw43C5YKzg0KDz0M4gafBX0FIgMM//H9ev9cAGr/owBbA0YDKQOyAcwAP/4t+sb3Zfe19enyvPFV8WXwg+xo6r3ovecL4ojfLd3w4xLym/xs/94F/xUDIQ4cAArDBVkHFP+M6VzcvOAy6aznoeYb8p8CKw4BEHESvBXiFvgSug7xCRIFhwK8BL4EHQLxAekHoA5xC7AF9QcpD74OMwnjB5EQZhYAFRsS6xOOFnISAAy4BVgDYgCJ/cX9RvzD/dUBJAZZBysFkwTTBSgE7/zV9qr1cPab81Lvje4L8aPxTOsP6eTnhOey4TPdtdvU3Rvrl/oxBK4GrRByIZwjnQ+D/xr+6/3M6dXTntX35ffsgupA8UwFCBa2F0oV9BahGYEVmRDhCw8HYgXvBbYILgXy/uT+pAc6B8IADf9IB+UObg1DC2gSbBzCG1EXLBS2FBEQzAao/+z6UPe996D7A/66/2ADIwuwDkEJzQJzA1IEsvy+8gbwQ/Vu9kDx8u0G8nz0fu+S6J/mkueV5KHfttsG2nrlw/vhBisHzAqDHvkpJhS492n1Nv3i7YPS9cvd46Xx0ux279gDqBY1GSgV3RSMGFIUaQ8GDUUJRQTeBIIJxAfo/pb6RwR5CN3/L/qFBNkRhBHGDJ0SXR40HjgVWRATEF8K6QBE+0L6C/hp9nH8AAOfBAwD4AdBDiwL3QLH/zUCxf+X9f3x1/Z3+kn2IvGK9P/2svED6p/pGerN6Mbhg+AL3t3hTvYkCIoJnwd2F0YqLx6I+5/v3/lX86bW5Mxi37HyEPLp8nwEHxTPFdoSJxSqE2AOywqKDTUMbwX1AwAMpgxxAAr5bQDmB5T+BvaH/+cPWBNpDZIR5Rz2Hi4Wtg/QDuYJewCm/IL9ffts+d78pAVcBtgCgQXBC2ALggNCAKkDgQOr+172m/k5/Cb4svJZ9NX31PJe7VjsaO656nPlg+Fk4E3eAO07A4wILAd5El0nMSMgAz3vovjd9W7aS8wM3RzzVPQ18vgAOhQ7FfUOpA2lDgQLCgn7CzMNAAqaCfIQLRAyBKj5nPsjAWD8z/U9/cIN5BOmEZgTPx3tHpwTZwt6DOkJX//r+lP+/QAe/c/9vQQJCHgEKQPJByAJ7wPg/88CNgNb/ev4l/o/+yP47PWF9oT3GvRI8m/x8u8K6xfn1ONk4a/ZGuDw+7wNkQkwCEYfHjAkFIjrU+vq+ZXnh8a1yyDu0/ww8+L5AxTHHW0RcQnVDGgKFQMIBA4MGQ22CIILMhRVD7H9KPj2ALgB1/bL9RIICRefE3oRrhstIU4XOgsoCSQIoAAL+un9SQITAAgAQgaOCaQE//8gA2oGFwFd/Pz//ARKAeP64fuO/lj50fBJ8D7znvGX7CzuA/Mj8kbswubV5Xngxd5p8kUIPQjUBhYYkStNG0T3HO5t+BXs5s5Dzj7pPPiW9sn9MxGjGrwSpw0hDiQIrQKwBUgLggv9CcAN+RIODbEA1vtY/jj/c/rF+QUGfRJjFBoUvxdLGxEWQQu1BTcEKwDi+0f9OgLyAs4BSwRHCCEFeP9rAA0E/wJf/hX/AgXuBA3+APui/Fn7TvTy7wL0IPcc9LHx4fSf9nfwBenx45Xintv15Ur/vwsBCTsSTidhJq8ECet98k/yeNngy6Xhp/pA/s79YQovF0YVDwy+BpQEKwBEBDcMiQ1EC2APLxZcEkkC6Pb1+cj8xPg09mEBdxFCGNwUixVdGzwY5ApyAbACswLX+9v5GwInBnkBewH0BqYG+f9r/XACwAPU/a7+JgTDBKL/Xvyu/Jv6FPb18pf0S/UJ9Q70GvUg8znwDetB5vviO9zk5UIB3A+MCqEOfyWCLN0Hlucx7tr3Rt4iyGXcjP2JAAP43QQSGMEUowZVBXoHKAJkABQKFRHuDG0JXBFTE5IDKvRg95MAgP3w9Wv+pBGoGGYTLhHFF6IW0ApHAUQBlgED/h783QEPBjoDkAHABbwGDwBq/PT/8AOx/038fgHXBf8ABvuI/J3+bPnt8YH0xvlG95Typ/W++Tr2B+7F53nlyN6R2BDp9Af0DG8GbRXgL4sm0Ppq57718/Lg0+zMbOld/8z7HP4eDqAVXgwiCEwLXwOT+54FbhPWD/8GLg0wGEkNWvd58vv8fP019cv3Ywg5FVgViRMwFjsX6A+2BW7/HwCw/cL75f/ZBM0DTwC0A0YHTwFB+Yr83QNFAaj7i/94Bk4FoP0A+6r8Uvq39OzybveT+VD5iflc+8H4+/Ko6rfkhd+S2Cbl3wB2DNIGkhNyLNUqLwLs6Tr13vX42Z3Phegc/+n+5vykCYMQ7AraBW0FMP7S+iwHfxPgD4QJDxLEGTIOyfm58135qvjd9Bb5xgRGESwWaxUeFWoUehC7Bqv/t/8AAOL9U/8lBEYG9QOFA+QEuALU/bf9kQCwADL/4AEyBcsDXQCX/mX8Fvh69a/1zPfy94z5fPsV/RD8BPbx7fTn5+KO3KTXYOyYC5ARyAedF+c0AyXE8eXfq/eC9TzSZcxQ9JcKXP6C+yQNvRJsBigBDQTSAMr9NgzCGBcREQlCEnUY/QUd8E7xJv3v+U7zrPt6DcgW5BNFEW0UwRNRCYIASQBEAtb9gfuAArMHugMV/gYCOQYHAJL4cfxbA3MCev6HAdYG+QNy/Z/6L/sR95TyWfQX+b358vks/dv+FPuZ80TrL+TE4DvYgt/c/BQTYA5nDj8nNzJ4DBLjAuly+Bzj4sp84GQBQgPP+3oHNRCCB8kCLgkyBKD69gaRGpcXXgjeC3kYRQ3Q80Xuo/iG+h/0ffiHBzoREhLPEewSExHHCxcGTQIxAe8BgwHFAckEWAWOAQT+FwDgAPf7S/rp/5IErAFSACIFtAQx/rb69vp1+KP02/bF+4r9n/zp/v//uPyk9Qjvwei34a/cNdqV7W0Lkxe+ELIYqi8KKUv7yN9T74H2RdxS0PvwLAwxBNb6pgiREEAE4PwVBEQG+gPvDOkZJhcfDCoMmw7/AvXxN++D9YD6E/0GAVgHwhBYFykURwu3CVAN5wZ9/Xn+QwZ3BKL/5QKtBjQDRv1U//wAGf1i+x3/9QIYAxcCtwSBBWsBsfyy+pr6ZPel9Sj5Uv5H/h79m/8RAbP66u4R5j7jb91B1hToRw6OGy4OwBVYNMouIPgm20LxH/op2V3NXPFCDCMCevo1CNgLWwEBAAoG7gL/AxgWgCCBEo0HUxFWEvD6keiX74z6rfiP95cB4g10EnUTSBJoDOII4gnhBuL/HQAmBfQFYgOEA0wDpf/u/Jj/rP6v+Rz99QRFBlEBDAHFBs8EBft+9k/5sflP9tD1w/v8AKn/Nf2v/n39y/Uf6t7hCOIe3e/bNfcaGVQaTA8jIUc2kxc54k3eu/YP65DPZd7aBasOkwBiAjULYwTY+8X+Rv+J/cEMQiGxHf4LOwxiFUgH+OwA6LD0WPm299T/RQutD1sTWhVlDrUH8QiGCYcCh/47BA8IwALH/qQBZgC4+4T78f/O/o37WP+lBOMC+f6dAYwEWAAU+j/5PfrQ+Ln3uPh7+6D/Bv/P+wb71vs29Bfo2+F/4avazNeO+l0jyB9ODQghJzmpFxTbGNXA9hPvndG34YgKzQ8rANsAAwZ1/aP3QwFNBV8DZBRUKfEhpAwnCs8Nx/1x6GDm5PEr+T7+Egb9CmcO5RNeEz8I5wIBCSULdgOmANkGAQi5ATv+DQCJ/UT6pfs8/xX/nv6PAsgFvwSuALkAiABd/P/3KPZL94n3Pvis+4z+u/4AANX/Jfs78zvpkeJ53ivU4tlVBOElKhuoDKkm/Do9C9DNNNktAMTuLc2c5s4UoRKZ+iH6NQPD/HL6xAIzBbQIIx+DLVAalwQ2BsEHGPNe4ojqOvw6BMgG7Qx0EAEQTA69CAgA8wFyCiILYwXvBi8NdwnY//z8uv0T/E/5F/vO/zwBYAH9A+AFWwJI/y8AVf/Y+v73C/mI+vv5v/pe/cn84Pzn/n0A+vs99JXrBOSX2qvNIONrFYInKBAIEaQ3rTNO61nCNueSAWjjYtS1/OgeDBBt9g73Jvp+9m76igAGBiQVyyy2LY4S/f9HA1D9g+e24IrygQRWCA0LQg+MDvQJCAjTA9f/IwarECoRDwp2CY0MVAae+vf1rPgn/Wz+tf/pAysHbQiLBkkBXv6G/6P+P/zs+rD7U/ta+l/8HP1Z+cf3E/1JACH9vPXR7qHkKuCg0wHYlgOsK7Yh0Ak6I/U90wp7wYDOVwNw+pbUU+hfHHQbj/Ti6gH5sPfY9bwB1A4ZGBInnTHyHqH8lvNL/RL0luOZ7EwFlg7EB0wEzQhACMYEAAW4BSoLSBY8F8MKhgNLBAMB+vYt8lv7CQR6AzwDcgUJBEEACf0x/SL+av++AcACcgEf/Rr5xPgs+8n8Y/tF/5YDdgEw/K74rvLn59/af9d02QPjFAwPMtAmNhE/HC4rsAAjxLvODQL+Aq3jb/TaGYsQBu3u4hDxQPaO9gkIAhu5Ip8oGykWFfT4EPAz9P3wW+63+EwIAA7CCh0Flv6J/BYDgAhiBlELVxwFIB4OVAHkAboA5PVB8db8hwWMBHUEFwTt/xb9SPzC/PX9CgHqBfkEFwDA/Sz98vnC90H79v24/Yj84P0M+5/xE+WW26DYhdO+47EaszayHVgN0CVWLmvvPLxE3voPOPwF4REA/xyE/svafuHa8gbyv/eoFMQmgSQvJq0l5g509HDwp/mj+Bn1tf+YDPsJVP9p+Rr2IvkaA6cLsQuWEzwiGyAYCsP74//f/m/0TPM2AcsJlAdFBLcBi/1O+MT3ovwNACcCSwYMB9MBkPtv+fv5Eft9/psEBwV0A0AAcv3T8MbijNl92o3cPda39lEuIDcbDCEGTykXIPzX0buM9cwYWv087PwKFRC77sbZ+ONB6pHuEAa+JDgsAiIrIhMbFgDD6/fuMPiT/LUCuAzIDnkFFv1s9s7umfMABkgO8w7kGIwk0hleARn2b/wZ+gbzoPs/BpkIPQflBUAAT/U58yH8pv99/fkA8AauBH39LfqI+gD7DfuV/qUAif5Z/SD4IfBo5bDeh9xN2UXdBgdXNXMr7Q09Es4oJQkgzRnPu/0XBy/yDv07Ddv/beKb4kXuzOqp81QT6iruKUQkoBxiEM77c+6P79H1JwBgCiIL1gPVAO76ofIY8Cz8ng4nFW8VNB0eH7oOAP4e9nT3t/jZ+ksCoQcZBqUEngEW+FnxPvXt/moFjgbKBzgI4QIw+2r22fQM+OH/+gOCAYD+zPsy9QXqDt6O3APemN+D4bALGz10M6MH1gTyIoINBc5zxn/+LxUl/uf2fwdv/T/gEd0u7jnwC/hlFocyUTGHHG4Sagz++2jrYezv+PgGsg35DOwEHfyv+Db0I/FH+pkO8hnHGaUa0hw7EPv6kPKD+FP6YPqqAbQHgwanAT7/hvkV8Rr0EgJ2CvQIDAdIBpwCz/un9FLy2vZ3/+UDfgBy+2H4yPFV6TniyN0L3kLdH+9VJrFAOh8T/nEQniRV95jEndgtDXoNNfu0/A/+s+jD3ffr/POo8H0F1i0mNrUg0BC5CkYC8PVH74v0XQHLDxQWfQcf8/Dxn/U98uX22wjtGJ0bExzYG2ITWPsB8l37iQFF/lL/gAgMC8MDNfcB807zefYnAOEKZQwDCIgHTwRs+MXtvfCB+/0B+QFNBHEAyvgJ8QvrSuYt4R3h8tg847oT4DYCI2wH8xGxH5/6htWZ4sb9XgBKAYAMtwBd5Onc6uuk683q8AGxIy0wpyeeHYwPj/439+j2IvW9+9MLgRRCCqT69vP/7Yvs2/kLDSMV8haxHSYeSw5/+5P2Gfv0/k4ClwdWCDMDwf9T+Ujx8O+b99gDbQvyDAINiAYA/RD52Pdz9N70Mv5rCM4F2/lU8qLureq15oflceJ05IfjHgJbNHw13QfI8qMSdhrU6k/Tm/UTEpoGd/lT+O7paNeB4Zv3N/i6+kobJzZhK9ESpgdbBI382/qI/9X+MQL6DjgPhvRe5W7wJv6x/3MEnRdRH58YxRJTEFkBdfQS+mIERAbHAWMCxwT//6f4VfcA9yD3zwH7C98NUQdkAooABfwO9jb1ofhh/HgEVgOd9k3s3e/q8BXsbOSg4/vqi+en/FEsXTQfDAXysgMwE7vzCd1o8+cPKQwtACv5FOTX0Qrfu/ozA1YBlRRcMFkrZg9F/+H/GwDeAxEKxwgvBGAHqQdD9HzjEOpV/RMIIQ1CFuQZExLmDW4MAwNV+zADgg0iCnECRgFQAMX4uvNI9/T4pPonA8wKxQaQAXgD4AMs/FX4MP5IAQv/p/5d/sX0ZO6M8DrxzeqV5ybnUOql4YTseB+tOT4be/mGAD0Qt/yk5CPxewauCFkF3f8o58HOs9j683T/WAHKEnErVyx1F7QEdf2Q/UwGyw/BDc4FuwVzBTb3YeV65Sf3nAMPDswXxRjtEcQMMA1CB70A7gCKCwsOugYkAB77ufVz8iD1q/iB+yMDoApFC/4FTwCp/Xv7v/0mAZYBxf69/lD7jPFX6uHlAOtB8NLyM+0h5s7ct/3XKRcoWAgR/MkOgg7V9arogPhgAGH+EgLO+RnhVtcl5mX5VgCLBqoa0yV9HvgRNgiyANwDuAyRDaAGnQH+A7z+ePDc63X0jwDcCv4TWBQiDV4IxApTDJQHxgXVCPYKaQjrADD9qPfQ9jL4kfuO/q7+pwN5B7gFkv1s+kX9XgL7A4j/Ufx9+Kz4LfnU8dTpAOvr8aHzHe3R5CbgbfWdIYwn7AmS+BEC8gso/Q7xFfd5AYwBTgUU+GXf0Nh36cf8mAMDB3APAByxGkIS/AY3AMQH1BEbEVIGzgEjAPr8k/UL9BD6rADpCVEQsA3MCHwKZA1pC68I4AhKCn4IDQRy/3z6vPgZ/rkAAv7u+8f+EAGv/3D/CAEGAigDygO+AXz7OPqZ+zD7gfaD7ijvdu8l9N7tZeo95kviQPfSGGAhEA7A/hMAvwbc/FP1lvZL/tsDXgfZ/MTiotqD5wv6fgI4B5EMtxJqFRoSwAnsAvMJ3RQNE6QFuf/AAL//CvtC+or+XgP6CU0OYwg//3gBFgteDxkP3AsgCQoFDgBa/TH7cvxDAl8FvAHX/N/7+/s0/eL+QQFcApkB0gK3/yb7nfdX+lr5Mfhm9bzxAvDc77ftlen05tXk7P39FNoXpwvm/6P/Zv4H/vn8iv0s/bECowT/9mnlbuF47ev8yQP4BoMImgrPDtMMjwRVA9YM8hQxE4cJ2gIoAYH/6/+BAWH/OQFzB1AKWwXE/0kBLwqWDGgLngotBzkE4wP8Aoz/n/8yApMD+wBh+3j5P/tL/tX/NgGaAHQAu/+R/Mb5wffK+iD+WPn888/0LvCP7q/sWOjM5H3wQgFeELwSOQh/AlH6Mfpi/Ar/8P+QASUHsgLH9Yvnreb/7JH6xQatCCIKfQmJCY8IUgYhCJYMVxBIEU8PfQbH/iEBnQQxBIoBUAMQB1gEMQJ0AR0DdgdEDWgOcwmCBRoEuQTDAWb+b/4qAVsE4AOF/rr4oPpA/4UAOP/0/H78Hf4AAA//w/n29WL2Q/m49ury5+7V7evujOvr8Tj+TgaEBpn+o/ygAAUBxAJGAc8D9ANjBTkAk/IA68zscfrs/+L/tf7tAGQFfQczCKcESgdZDhMT1A1FBAEDZAghC5AJpAZJAeoAjgMZBaUDNwKABrILPwzRBYoCIwILBL0FmgScA2MBtAKaA9UA+vyh/NgAiAGqAL/8x/3D/lT9If3++xH7QPVf9Tn19vQN8DvwPPAE8aTxcfWJ/ecADQC8AOECxQAEAW0ANAEhAWQDXwOg/tb4yPPI9Cv1Yvmt/Gj/tQC1BBoJ+gbiBZkEvQhGClEL4Qy8DNkLMwnzBugD7ALyAzcHDgg6ByQFTwLTAt4E/wV8BXwEYgQhBK4Bmf8MAcsDCwRoAm4AqABEATr++vou+8b+agGSAaL+9vk692vzT/Ld8g3zNvTp9CPzi/Ay71TwRPYF//kH3AvTB20B0/qv+UD9sgDTAfAA+QIUAmz9P/Qw7rrxLvrgA24LkgneA5P/7gGqBf0D0wbwDA0RnQ27BxEE/APrBqMJxApeCT8HaQURA3EA9gATBPAGRQg7B2sDh/9U/cD9AwEfBd4H+gYvA7r/m/0l+yn6RPxDAG0D6gFJ+/Hxc+xW7+X1W/kG+Gn0q/B374Tyz/WO+Tf+bwSKB4ACJPvs9rn4uP1BBH0I2QZ1Aaf8NPqT9qf1H/hE/cUBnQOnAtX/p/+bAfoEuQelCugNCA//DA4JhQU/A18EUQezCkILOwkJB+wDHQLRAdkCbwRQBvsGlgVPA6kAjP4Q/9UCpAW0BEABGf7f+0j7bPtj/Ar+3/zE+gH3QvP+8TH0Gfe09yD36/Wi9Vb1KfdJ+DH5+/k5/K8A8AIQAs79bvpz+xUAIwQ1A27/U/ws/f3/5v/0/K/5zfqiAIAGrQdrBLoApwBxBCwKAg3eCwQM5wx/DbAJEQVCA0EE3gb7BlIGMAY+B/YH9gUZA7gBHwMrBTMFgQOWAxwDaQEl/xj8qvo0+iH7b/y7/fn7j/mX9w/1qvQj9P30j/aa+Mb54Phy91f3mvhU+mf7V/uk++X7LftZ+er4iPzdAioGAQQmAF7+A/13+8r82f89AjYDDAIAAGr/NwD/AtoGfgpyC/MJjga1A0YDVwV1CZkLZAy3C2EJlwUiAvcA+QKc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type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "execution_count": 96,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from IPython.display import Audio\n",
        "Audio(speech.cpu().numpy(), rate=16000)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RPLVad8w3CvV"
      },
      "source": [
        "That all looks and sounds good! We can now process the entire dataset. This will take between 5 and 10 minutes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "3184693687d6429db567c62c054877c2",
            "33a2e6b82c704143bf0b0d500eda76a1",
            "2423d8e2f5dd42d5b0eaf62430911cb9",
            "aa95751339ed442081186fd94754b8c8",
            "13ff1315017141edb46583a187ebc796",
            "7e4ce813908746ce870dc66c44b9c988",
            "ee0d3c1b05bc415bb912ba94ad2e0210",
            "b993bea9adff41729dd5b1ff301b5051",
            "47312fec820c45debe53743175dcd243",
            "2407758596494ec18d56128f7c7294b7",
            "6be66a57f48c4fd681ef02b1d4f2d8d2",
            ""
          ]
        },
        "id": "ekis02SC2hBN",
        "outputId": "e17b2a60-6511-48bc-ced8-bb1dd736659f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/16441 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "dataset1 = dataset1.map(\n",
        "    prepare_dataset, remove_columns=dataset1.column_names,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7z_QGJ1u3LTa"
      },
      "source": [
        "Some of the examples in the dataset are apparently longer than the maximum input length the model can handle (600 tokens), so we should remove those from the dataset. In fact, to allow for larger batch sizes we'll remove anything over 200 tokens."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "440fff20db6744c199f53fbb06750ba2",
            "297273ce85a243f28e969af890ef6cfc",
            "0299b8451d634f57a38459c7d582a689",
            "6040e836e33c4630a596ec59c96689d3",
            "a931430feb5e4a568cf01264f3a7606c",
            "f83a3e82f31e4d0a860319abf509232d",
            "4c2ad28d006f4ac8a802effca32c2805",
            "a6e8b71cbc7c40f0a0ec8022d549fac0",
            "66199abcddbd4e258ab0604a73302fe9",
            "0d80c877ac82461598451a95fd6175eb",
            "81af4708f2f647ffa80aba5e74e45ffe",
            ""
          ]
        },
        "id": "Sy4kcOco3Ks3",
        "outputId": "46e9134e-1375-434a-e7bf-27d07aa45dac"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Filter:   0%|          | 0/16441 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def is_not_too_long(input_ids):\n",
        "    input_length = len(input_ids)\n",
        "    return input_length < 200\n",
        "\n",
        "dataset1 = dataset1.filter(is_not_too_long, input_columns=[\"input_ids\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KRaX-WqeHiDy"
      },
      "source": [
        "How many examples are left?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xi6DLMeEHlZ6",
        "outputId": "5ad6ff73-7b3b-4805-f625-d0f5edbca34b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "16441"
            ]
          },
          "execution_count": 99,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(dataset1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KAcyUJ6IHVug"
      },
      "source": [
        "## Train/test split"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UnDAXO5KTTE-"
      },
      "source": [
        "Create a basic train/test split. For our purposes, it's OK if the same speaker is part of both sets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IUuFNOjZ_viW"
      },
      "outputs": [],
      "source": [
        "dataset1 = dataset1.train_test_split(test_size=0.1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lZmUOJ956k8P"
      },
      "source": [
        "What does the dataset look like now?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xUBq8Y8X6htL",
        "outputId": "f68db5f2-3adc-4625-c45e-456abdf52923"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_ids', 'labels', 'speaker_embeddings'],\n",
              "        num_rows: 14796\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['input_ids', 'labels', 'speaker_embeddings'],\n",
              "        num_rows: 1645\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 101,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LEbtkD095H4u",
        "outputId": "93d072d3-7753-4de4-f167-0ea1c714f31a"
      },
      "outputs": [
        {
          "data": {
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              "  -0.03625825047492981]}"
            ]
          },
          "execution_count": 102,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset1[\"train\"][0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zLvJ4HRb3aCp"
      },
      "source": [
        "## Collator to make batches\n",
        "\n",
        "We need to define a custom collator to combine multiple examples into a batch. This will pad shorter sequences with padding tokens. For the spectrogram labels, the padded portions are replaced with the special value -100. This special value tells the model to ignore that part of the spectrogram when calculating the spectrogram loss.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rZOX8bTe3Kq5"
      },
      "outputs": [],
      "source": [
        "from dataclasses import dataclass\n",
        "from typing import Any, Dict, List, Union\n",
        "\n",
        "@dataclass\n",
        "class TTSDataCollatorWithPadding:\n",
        "    processor: Any\n",
        "\n",
        "    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
        "\n",
        "        input_ids = [{\"input_ids\": feature[\"input_ids\"]} for feature in features]\n",
        "        label_features = [{\"input_values\": feature[\"labels\"]} for feature in features]\n",
        "        speaker_features = [feature[\"speaker_embeddings\"] for feature in features]\n",
        "\n",
        "        # collate the inputs and targets into a batch\n",
        "        batch = processor.pad(\n",
        "            input_ids=input_ids,\n",
        "            labels=label_features,\n",
        "            return_tensors=\"pt\",\n",
        "        )\n",
        "\n",
        "        # replace padding with -100 to ignore loss correctly\n",
        "        batch[\"labels\"] = batch[\"labels\"].masked_fill(\n",
        "            batch.decoder_attention_mask.unsqueeze(-1).ne(1), -100\n",
        "        )\n",
        "\n",
        "        # not used during fine-tuning\n",
        "        del batch[\"decoder_attention_mask\"]\n",
        "\n",
        "        # round down target lengths to multiple of reduction factor\n",
        "        if model.config.reduction_factor > 1:\n",
        "            target_lengths = torch.tensor([\n",
        "                len(feature[\"input_values\"]) for feature in label_features\n",
        "            ])\n",
        "            target_lengths = target_lengths.new([\n",
        "                length - length % model.config.reduction_factor for length in target_lengths\n",
        "            ])\n",
        "            max_length = max(target_lengths)\n",
        "            batch[\"labels\"] = batch[\"labels\"][:, :max_length]\n",
        "\n",
        "        # also add in the speaker embeddings\n",
        "        batch[\"speaker_embeddings\"] = torch.tensor(speaker_features)\n",
        "\n",
        "        return batch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffNanldmrlbD"
      },
      "source": [
        "In SpeechT5, the input to the decoder part of the model is reduced by a factor 2. In other words, it throws away every other timestep from the target sequence. The decoder then predicts a sequence that is twice as long. Since the original target sequence length may be odd, the data collator makes sure to round the maximum length of the batch down to be a multiple of 2."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CJKY4DVO4khr"
      },
      "outputs": [],
      "source": [
        "data_collator = TTSDataCollatorWithPadding(processor=processor)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "McZfpBzL4mRK"
      },
      "source": [
        "Let's test the data collator."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "78-XVnD04ouJ"
      },
      "outputs": [],
      "source": [
        "features = [\n",
        "    dataset1[\"train\"][0],\n",
        "    dataset1[\"train\"][1],\n",
        "    dataset1[\"train\"][20],\n",
        "]\n",
        "\n",
        "batch = data_collator(features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vuWoBSpW4pY9",
        "outputId": "3641da2a-a5d6-4544-8d24-bf40ff37df8d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'input_ids': torch.Size([3, 20]),\n",
              " 'attention_mask': torch.Size([3, 20]),\n",
              " 'labels': torch.Size([3, 110, 80]),\n",
              " 'speaker_embeddings': torch.Size([3, 512])}"
            ]
          },
          "execution_count": 106,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "{k:v.shape for k,v in batch.items()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YbDPdb0V5H4w"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sVMpz-tpP-vw"
      },
      "source": [
        "Looks good!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_iOqGsqq4uJv"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uQiZlXN9ICmc"
      },
      "source": [
        "It's always a good idea to upload model checkpoints directly to the [Hugging Face Hub](https://huggingface.co/) while training. To allow this, first log in to the Hub by entering your Hub authentication token:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Et5pSY56QEFA"
      },
      "source": [
        "The `use_cache=True` option is incompatible with gradient checkpointing. Disable it for training, otherwise it keeps complaining about it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DmjX8QsH4vUW"
      },
      "outputs": [],
      "source": [
        "\n",
        "model.config.use_cache = False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QRWLpbljIagm"
      },
      "source": [
        "Define the training arguments.\n",
        "\n",
        "We won't be computing any evaluation metrics during the training process. Instead, we'll only look at the loss. The lower the loss, the better the model.\n",
        "\n",
        "Note: If you do not want to upload the model checkpoints to the Hub, set `push_to_hub=False`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2fXg9cfJ7JQT"
      },
      "outputs": [],
      "source": [
        "from transformers import Seq2SeqTrainingArguments\n",
        "\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=\"./checkpoints\",     # change to a repo name of your choice\n",
        "    auto_find_batch_size = False ,\n",
        "    per_device_train_batch_size=2,\n",
        "    gradient_accumulation_steps=2,\n",
        "    learning_rate=1e-4,\n",
        "    warmup_steps=500,\n",
        "    max_steps=30000,\n",
        "    gradient_checkpointing=True,\n",
        "    fp16=True,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    per_device_eval_batch_size=4,\n",
        "    save_steps=1000,\n",
        "    eval_steps=1000,\n",
        "    logging_steps=25,\n",
        "    #report_to=[\"tensorboard\"],\n",
        "    load_best_model_at_end=True,\n",
        "    greater_is_better=False,\n",
        "    label_names=[\"labels\"],\n",
        "    #push_to_hub=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ksyDvPosI1Vg"
      },
      "source": [
        "Create the trainer object using the model, dataset, and data collator."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ekIPeQuD7RwA"
      },
      "outputs": [],
      "source": [
        "from transformers import Seq2SeqTrainer\n",
        "\n",
        "trainer = Seq2SeqTrainer(\n",
        "    args=training_args,\n",
        "    model=model,\n",
        "    train_dataset=dataset1[\"train\"],\n",
        "    eval_dataset=dataset1[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=processor.tokenizer,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l9OkZG-ZJAh5"
      },
      "source": [
        "And with that, we're ready to start training!\n",
        "\n",
        "Training will take several hours depending on your GPU or the one allocated to the Google Colab. Depending on your GPU, it is possible that you will encounter a CUDA \"out-of-memory\" error when you start training. In this case, you can reduce the `per_device_train_batch_size` incrementally by factors of 2 and increase `gradient_accumulation_steps` by 2x to compensate."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "87U167Vf7V5Q",
        "outputId": "870f6e63-0055-4bd1-bbae-97bac8c7cc55"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/root/miniconda3/envs/tts/lib/python3.9/site-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='30000' max='30000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [30000/30000 2:51:45, Epoch 8/9]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.516700</td>\n",
              "      <td>0.469474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.521100</td>\n",
              "      <td>0.440654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.482900</td>\n",
              "      <td>0.448762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.458700</td>\n",
              "      <td>0.431671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.435900</td>\n",
              "      <td>0.408881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>0.455000</td>\n",
              "      <td>0.416171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7000</td>\n",
              "      <td>0.428400</td>\n",
              "      <td>0.412858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8000</td>\n",
              "      <td>0.427800</td>\n",
              "      <td>0.399520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9000</td>\n",
              "      <td>0.411600</td>\n",
              "      <td>0.395977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10000</td>\n",
              "      <td>0.410500</td>\n",
              "      <td>0.386496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11000</td>\n",
              "      <td>0.414700</td>\n",
              "      <td>0.382391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12000</td>\n",
              "      <td>0.407600</td>\n",
              "      <td>0.385941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13000</td>\n",
              "      <td>0.390900</td>\n",
              "      <td>0.375529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14000</td>\n",
              "      <td>0.391000</td>\n",
              "      <td>0.381122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15000</td>\n",
              "      <td>0.384300</td>\n",
              "      <td>0.369355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16000</td>\n",
              "      <td>0.377600</td>\n",
              "      <td>0.366594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17000</td>\n",
              "      <td>0.404400</td>\n",
              "      <td>0.372042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18000</td>\n",
              "      <td>0.373500</td>\n",
              "      <td>0.362107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19000</td>\n",
              "      <td>0.375300</td>\n",
              "      <td>0.361264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20000</td>\n",
              "      <td>0.364900</td>\n",
              "      <td>0.351932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21000</td>\n",
              "      <td>0.370600</td>\n",
              "      <td>0.348038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22000</td>\n",
              "      <td>0.360000</td>\n",
              "      <td>0.347656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23000</td>\n",
              "      <td>0.349000</td>\n",
              "      <td>0.347609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24000</td>\n",
              "      <td>0.354700</td>\n",
              "      <td>0.344748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25000</td>\n",
              "      <td>0.352000</td>\n",
              "      <td>0.341828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26000</td>\n",
              "      <td>0.344300</td>\n",
              "      <td>0.340620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27000</td>\n",
              "      <td>0.336900</td>\n",
              "      <td>0.340282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28000</td>\n",
              "      <td>0.334400</td>\n",
              "      <td>0.337066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29000</td>\n",
              "      <td>0.348500</td>\n",
              "      <td>0.334811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30000</td>\n",
              "      <td>0.344400</td>\n",
              "      <td>0.333708</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=30000, training_loss=0.4036881135304769, metrics={'train_runtime': 10305.7831, 'train_samples_per_second': 11.644, 'train_steps_per_second': 2.911, 'total_flos': 1767417190567776.0, 'train_loss': 0.4036881135304769, 'epoch': 8.11})"
            ]
          },
          "execution_count": 303,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ooEluBcXs5hJ"
      },
      "source": [
        "The training results can now be uploaded to the Hub. To do so, execute the `push_to_hub` command:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ojrUgMeEs_LP"
      },
      "source": [
        "You can now share this model with anyone using the link on the Hub."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5K1AQd8B7b-p"
      },
      "source": [
        "Inference"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TrqUYy3-Qr1w"
      },
      "source": [
        "After training finishes, let's use the model to synthesize some speech!\n",
        "\n",
        "I'm loading the model from the Hugging Face Hub, as the Colab notebook was terminated before training finished (which is why it's a good idea to use `push_to_hub=True` when training)."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "dErEw0QsiAjS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5318181-4653-4550-88f8-01fcdce6351c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cBOWFCudi3-M"
      },
      "outputs": [],
      "source": [
        "from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LA1P79Tl5H43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
            "823dc8ef16c14d58ac7d6f9e5988ff09",
            "9b047c4ec59340fa957bd135deb2a690",
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            "90510c20f1f84a4abcc5bba8e983bfeb",
            "bbbb30a8c1374700b3ce325eaeb4885c",
            "483ad72e13a2425d835939d02a0efd18",
            "4e128896c4f443ff9a2bbc731f365692",
            "a6f3d8453a7e4bcabb10559fe5fa66d2",
            "5d57b760e51345adb18f78762acc9e7c",
            "c92cb7a7008e4fd3929f286b0c31ef8e",
            "0510b1158ab746b486657ce95f159734",
            "19a0cb8a33364145abe3af49e2144e95",
            "29868e9ccd784dfa98cdf12b88208830",
            "ea9bb919ef724c78b11ef30c6f37429b",
            "3a66f1039f8b43de98a751706850a8fe",
            "9be5506c8be64552960c5b8759f617f6",
            "9889bec521724101922153e1e7a86d9e",
            "22e1a123ff784ebeba66124aaa227f83",
            "f68cf0125c87438d89ce309f15100d72",
            "062280031e484030a5bb357c801020b8",
            "d39c0156f66f4f368c3d07e516900ad5",
            "69a2d3f554a14221a87a51342550d243",
            "b701bf3aac87424cadc14d26be3fe502",
            "72fc7478365a4d589bc68af371c43d19",
            "e63507e56aaf4601ab4d78180f04f9ef",
            "9467b8892b4e4e4b9811c6fa5aa8ae02",
            "05f56db8bbc64e84a9fe051e47e5e6fe",
            "8cb0ae31eeba461b8c848bac3c57b0c1",
            "4a29423fd2284aa8a64a9b5c640832d9",
            "5331dbd9ab8c456a83e8ab5d007ba979",
            "6863b627711d484487e927db8e021cdb",
            "1426e7745a504b2584a752fa1877a6bc",
            "491078ebbeeb456a9b24487f45a6828e",
            "3107628f9e844848bbb2137bc44c9913",
            "1eaccc1458cc452a832ff566a7702d3e",
            "6a42adfaedb948fbaff8e2f577563cc5",
            "16dc99f5141f4756b7db883fee55c2e4",
            "d6c3bc41feaa4fa6ae0b4489eb8cf68e",
            "75d10dd6036541f69d483f53e765f38a",
            "511637b7cd3e410d8ec29eee6cc940a0",
            "72f74625994c4f6b9d813381d695f871",
            "d966321ee8c643a78742a3d87b88783f",
            "87a6a4c5a54441709d3cd8e4c249b64b",
            "e6c3fd5dedcf4fba95587789b92182cb",
            "2ebc19985de740d09e5d866f407b4e3f",
            "3b61184a46fc4b628a9dca86fc1fa20b",
            "d382a92b0c7b419d87b005ca70519fad",
            "5087e2baa20e493c9006025f0b7cd93f",
            "8dde211b3bbb406792243d8e2c659853",
            "c3cd225ff8c846deb8fff95446e57d60",
            "9e3550129bc744cba1b0758caeab006c",
            "10b9e4a185f6433793ed5ec723421fed",
            "f45e4d1dffe74d178a50e5903206bcc1",
            "47b2fa4099534f0fb90035e097b4a2da",
            "9d9b1aae27544abd973b93d752f1f4d5"
          ]
        },
        "outputId": "da74ea40-e62e-49b6-c834-f79b36483c33"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)rocessor_config.json:   0%|          | 0.00/433 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "823dc8ef16c14d58ac7d6f9e5988ff09"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading spm_char.model:   0%|          | 0.00/238k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "19a0cb8a33364145abe3af49e2144e95"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/40.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b701bf3aac87424cadc14d26be3fe502"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/234 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3107628f9e844848bbb2137bc44c9913"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/232 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2ebc19985de740d09e5d866f407b4e3f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech\n",
        "\n",
        "processor = SpeechT5Processor.from_pretrained(\"microsoft/speecht5_tts\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import SpeechT5HifiGan\n",
        "vocoder = SpeechT5HifiGan.from_pretrained(\"microsoft/speecht5_hifigan\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "dbf79356a8354e91809ff899b8774c5c",
            "b76259b466df41299f9768300cd9138c",
            "8f8db16f95724089a645204ff94c8121",
            "3d6c6b225a034100884409cc535e36af",
            "b7ce4b7084fd43308cfd8b75ee341903",
            "5c2e57d026474af28189c19a0e0eb1cf",
            "2331ca9e6e174fd499e3e4eb444efa54",
            "1cccd95f5afc40ba943112621a641599",
            "3d97400bcf7242eaab8eba2a6bef8f33",
            "5dc6a133489448088d7fbcb01b8705c8",
            "bb1ebc925634417692c59021145c7f89",
            "b3a5939de5054dbca56cc94563eebc09",
            "5cb8dfb99a4541969ca16218df811e12",
            "7fab718a32334d3f900e2c9cbe98861e",
            "f013ce8a701c44c8b2a0cbe5976d4910",
            "4ef92d7fb05747d69e693bc62b030de9",
            "c3eda7397c414596aebd209f7c7a4fb4",
            "1939c9d6f4994e2484351a1242a04dbe",
            "de39b5f08fce45da88071481e2d28bf1",
            "2de543a0790a4da28709b86a5b0917a1",
            "50f3d63cf98a447ba0a1fe22247d1eaf",
            "be51182567fe4f059e9ec32e58081b61"
          ]
        },
        "id": "IE--yntg-t83",
        "outputId": "81394a07-1b79-4be2-a665-0315ce32a14f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/636 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dbf79356a8354e91809ff899b8774c5c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/50.7M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3a5939de5054dbca56cc94563eebc09"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bos-fW6CHijQ"
      },
      "outputs": [],
      "source": [
        "model1 = SpeechT5ForTextToSpeech.from_pretrained(\"/content/drive/MyDrive/tts/30k/checkpoint-29000\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vAVy2weV5H42"
      },
      "outputs": [],
      "source": [
        "speaker_embeddings1 = np.load(\"/content/drive/MyDrive/tts/20k/speaker_embeddings_kk.npy\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "wPVyS7f3r9WQ"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AePsNMjKHhBH"
      },
      "source": [
        "First we need to obtain a speaker embedding. We can simply grab one from the test set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_eWg0lSK5H43",
        "outputId": "2e1abdb2-0f19-4f4d-edd9-46c687f8f253"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 512)"
            ]
          },
          "metadata": {},
          "execution_count": 728
        }
      ],
      "source": [
        "speaker_embeddings1.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "08GkZiwq7dE7"
      },
      "outputs": [],
      "source": [
        "example = dataset1['test'][150]\n",
        "speaker_embeddings = torch.tensor(example[\"speaker_embeddings\"]).unsqueeze(0)\n",
        "speaker_embeddings.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lSbfTaTm5H43"
      },
      "outputs": [],
      "source": [
        "tokenizer = processor.tokenizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dUg-8t6xQ-k1"
      },
      "source": [
        "Define some input text and tokenize it."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "# Convert speaker_embeddings to a PyTorch tensor\n",
        "speaker_embeddings_tensor = torch.from_numpy(speaker_embeddings1)\n",
        "\n",
        "# Normalize the speaker_embeddings_tensor\n",
        "normalized_speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings_tensor)\n"
      ],
      "metadata": {
        "id": "AGgQteYe-LWK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T6uROO_97nnJ"
      },
      "outputs": [],
      "source": [
        "text = \"hi, aetihaasik\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LpuE7d9WOoE0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0e5af013-acaf-445a-9be4-3c41be3c6095"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'hi, aetihaasik</s>'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 749
        }
      ],
      "source": [
        "\n",
        "tokenizer.decode(tokenizer(text)[\"input_ids\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JCY1Hr987oox"
      },
      "outputs": [],
      "source": [
        "inputs = processor(text=text, return_tensors=\"pt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rf9feXjrlzJi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91dbf4cf-f109-40fb-dc61-21842eab9629"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': tensor([[ 4, 11, 10, 23,  4,  7,  5,  6, 10, 11,  7,  7, 12, 10, 28,  2]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n"
          ]
        }
      ],
      "source": [
        "print(inputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M5ShECAkRCUv"
      },
      "source": [
        "Tell the model to generate a spectrogram from the input text."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZvA6z5w47q7L"
      },
      "outputs": [],
      "source": [
        "spectrogram = model1.generate_speech(inputs[\"input_ids\"], normalized_speaker_embeddings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IVN17Mt67sTe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "outputId": "1262fc0e-efa4-47b6-ac8e-38a7378d75bd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure()\n",
        "plt.imshow(spectrogram.T)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "McG3EnpYRJVj"
      },
      "source": [
        "Finally, use the vocoder to turn the spectrogram into sound."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MpogdOe_7uoe"
      },
      "outputs": [],
      "source": [
        "with torch.no_grad():\n",
        "    speech = vocoder(spectrogram)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5H_FaVuW7wXt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 61
        },
        "outputId": "0cbf69a8-91ab-4661-b67c-81c97a44978f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
              "\n",
              "                <audio  controls=\"controls\" >\n",
              "                    <source 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type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ]
          },
          "metadata": {},
          "execution_count": 755
        }
      ],
      "source": [
        "\n",
        "from IPython.display import Audio\n",
        "Audio(speech.numpy(), rate=16000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PV0nUOmO7xvv"
      },
      "outputs": [],
      "source": [
        "import soundfile as sf\n",
        "sf.write(\"aetihaasik.wav\", speech.numpy(), samplerate=16000)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " Thank you"
      ],
      "metadata": {
        "id": "noX3daxSc0BB"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ykEAiKJBcgCg"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
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    },
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