{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "64ead92c-98e1-4f99-8419-8d5d84b75de0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n", "Token is valid (permission: write).\n", "Your token has been saved to /home/austin/.cache/huggingface/token\n", "Login successful\n", "Total folders: 473\n", "Selected folders (0% - 25%): 118\n", "Folders: 00013899, 00163dc9, 010b4e02, 012e4f22, 01a5575c, 02153faa, 0253acb6, 02d30f40, 034aea85, 03d42201, 047b2cc9, 04dfddf9, 05a45f91, 05b1a5fa, 066d5771, 06d63234, 074a35a1, 07afb6cf, 07ec6abd, 082dc264, 0850f695, 0a4a9528, 0a9523ac, 0ab8878d, 0ac15611, 0b8ae160, 0c109d26, 0ccb413a, 0d70cf5c, 0da07cfa, 0deadde0, 0e1e679f, 0ee82b61, 0f6fbea8, 10fe64fb, 1105cfcb, 11858a03, 11b1eb07, 13383861, 13478d0f, 14ac96ed, 14e8c9ac, 16fefdd2, 1707f3b6, 17184d5e, 17989c6c, 18460462, 18563891, 1967ee53, 1a32de6a, 1a5a3db8, 1b74d271, 1ba0d17b, 1cc3c6c0, 1ceb61c1, 1dbea640, 1ed99743, 1fb0665c, 20b5dff7, 20e4e850, 210577c7, 21fd006c, 2244c7e7, 224918d3, 224a42d8, 2293080b, 22d1fa2f, 239e7db6, 23e5ef98, 24592c0b, 24c980be, 24ceb09f, 2547b60d, 25714f7a, 26c430e0, 26ddd15d, 282cfa8c, 28d7d9ec, 297efce1, 29835f87, 2990a149, 2af831b5, 2b20ee07, 2bd06fbc, 2c3bea98, 2ca35c83, 2cd8d40e, 2cf01874, 2e045702, 2e3dbf01, 2f2ae696, 2fbfe282, 31ebd5d6, 332b9006, 3371a8ac, 338ab306, 33e59069, 3410d0ed, 342976be, 35d789d2, 361eb7a2, 36d0de98, 36ea135b, 37c014a1, 37ed21cc, 3951ab83, 39b99040, 39bbe2d2, 39d90db6, 3ae04663, 3c58f1c4, 3c8eb6b7, 3d0f6fe6, 3d505acf, 3d60427a, 3d995663, 3e02a4dc, 3e1b4af6\n" ] } ], "source": [ "from huggingface_hub import HfApi\n", "import math\n", "!huggingface-cli login --token hf_lxxxx\n", "def get_folder_subset(repo_id, start_percent, end_percent, repo_type=\"dataset\"):\n", " api = HfApi()\n", "\n", " # List the contents of the repository\n", " repo_contents = api.list_repo_files(repo_id, repo_type=repo_type)\n", "\n", " # Filter for files inside the \"data\" directory\n", " data_contents = [file for file in repo_contents if file.startswith(\"data/\")]\n", "\n", " # Get unique folders inside \"data\"\n", " folders_in_data = sorted(set(file.split('/')[1] for file in data_contents if file.count('/') > 1))\n", " \n", " total_folders = len(folders_in_data)\n", " start_index = math.floor(total_folders * start_percent / 100)\n", " end_index = math.floor(total_folders * end_percent / 100)\n", "\n", " selected_folders = folders_in_data[start_index:end_index]\n", "\n", " print(f\"Total folders: {total_folders}\")\n", " print(f\"Selected folders ({start_percent}% - {end_percent}%): {len(selected_folders)}\")\n", " print(\"Folders:\", ', '.join(selected_folders))\n", "\n", " # Return files from selected folders\n", " selected_files = [file for file in data_contents if file.split('/')[1] in selected_folders]\n", " return selected_files\n", "\n", "# Replace with the actual repo_id\n", "repo_id = \"litagin/moe-speech\"\n", "\n", "# Example usage:\n", "# First 25%\n", "first_quarter = get_folder_subset(repo_id, 0, 25)\n", "\n", "# # Second 25%\n", "# second_quarter = get_folder_subset(repo_id, 25, 50)\n", "\n", "# # Third 25%\n", "# third_quarter = get_folder_subset(repo_id, 50, 75)\n", "\n", "# # Last 25%\n", "# last_quarter = get_folder_subset(repo_id, 75, 100)\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "c0756271-98dd-4223-be75-ba0100abb024", "metadata": {}, "outputs": [], "source": [ "import io\n", "import sys\n", "import time\n", "import threading\n", "from IPython import get_ipython\n", "\n", "def start_logging(log_file_path='cell_output.log', interval=5):\n", " # Create a custom output stream\n", " class LogStream(io.StringIO):\n", " def __init__(self, filename):\n", " super().__init__()\n", " self.filename = filename\n", " \n", " def write(self, text):\n", " super().write(text)\n", " with open(self.filename, 'a') as f:\n", " f.write(text)\n", "\n", " # Create the log stream\n", " log_stream = LogStream(log_file_path)\n", "\n", " # Redirect stdout and stderr to the log stream\n", " sys.stdout = log_stream\n", " sys.stderr = log_stream\n", "\n", " # Function to save the current output\n", " def save_output():\n", " while True:\n", " time.sleep(interval)\n", " log_stream.flush()\n", "\n", " # Start the logging in a separate thread\n", " logging_thread = threading.Thread(target=save_output, daemon=True)\n", " logging_thread.start()\n", "\n", " print(f\"Logging started. Output will be saved to {log_file_path} every {interval} seconds.\")\n", "\n", "# Start logging\n", "start_logging()" ] }, { "cell_type": "code", "execution_count": null, "id": "9e3856a9-3c25-405f-b9a1-36b8c314d8f7", "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "from huggingface_hub import hf_hub_download\n", "from tqdm import tqdm\n", "import os\n", "import time\n", "\n", "def download_dataset(repo_id, local_dir, file_list, num_retries=10, retry_interval=15):\n", " # Check if the files are already downloaded\n", " downloaded_files = []\n", " for root, _, files in os.walk(local_dir):\n", " for file in files:\n", " downloaded_files.append(os.path.relpath(os.path.join(root, file), local_dir))\n", "\n", " # Download files\n", " for file_path in tqdm(file_list):\n", " local_file_path = os.path.join(local_dir, file_path)\n", " os.makedirs(os.path.dirname(local_file_path), exist_ok=True)\n", "\n", " # Check if the file is already downloaded\n", " if file_path in downloaded_files:\n", " print(f\"Skipping already downloaded file: {file_path}\")\n", " continue\n", "\n", " # Retry download\n", " for retry_count in range(num_retries):\n", " try:\n", " # Download the file\n", " hf_hub_download(\n", " repo_id=repo_id,\n", " filename=file_path,\n", " repo_type=\"dataset\",\n", " local_dir=local_dir,\n", " local_dir_use_symlinks=False\n", " )\n", " print(f\"Successfully downloaded: {file_path}\")\n", " break # Exit the loop if download is successful\n", " except Exception as e:\n", " print(f\"Error downloading {file_path}. Retrying in {retry_interval} seconds... ({retry_count+1}/{num_retries})\")\n", " if retry_count == num_retries - 1:\n", " print(f\"Failed to download {file_path} after {num_retries} attempts.\")\n", " else:\n", " time.sleep(retry_interval)\n", "\n", "# Usage\n", "repo_id = \"litagin/moe-speech\"\n", "local_dir = \"/home/austin/disk2/llmvcs/tt/stylekan/Data/\"\n", "\n", "\n", "download_dataset(repo_id, local_dir, first_quarter)" ] }, { "cell_type": "code", "execution_count": 18, "id": "466f33bc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total paths checked: 525530\n", "Valid paths: 525530\n", "Invalid paths: 0\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "\n", "# Load the dataset\n", "file_path = \"/home/austin/disk2/llmvcs/tt/stylekan/Data/train_List_updated.csv\"\n", "df = pd.read_csv(file_path, sep='|', header=None)\n", "\n", "# Assuming the first column contains the paths\n", "audio_paths = df.iloc[:, 0]\n", "\n", "# Check if each path exists\n", "valid_paths = []\n", "invalid_paths = []\n", "\n", "for path in audio_paths:\n", " if os.path.exists(path):\n", " valid_paths.append(path)\n", " else:\n", " invalid_paths.append(path)\n", "\n", "# Print the results\n", "print(f\"Total paths checked: {len(audio_paths)}\")\n", "print(f\"Valid paths: {len(valid_paths)}\")\n", "print(f\"Invalid paths: {len(invalid_paths)}\")\n", "\n", "# Optionally, print the invalid paths for further inspection\n", "if invalid_paths:\n", " print(\"\\nInvalid paths:\")\n", " for invalid_path in invalid_paths:\n", " print(invalid_path)" ] }, { "cell_type": "code", "execution_count": 1, "id": "27129da6", "metadata": {}, "outputs": [], "source": [ "from pytorch_lightning.loggers import LightningLoggerBase" ] }, { "cell_type": "code", "execution_count": 7, "id": "75be34ff", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Audio\n", "Audio(\"/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_sp/data/3ec57102/wav/3ec57102_137.wav\")" ] }, { "cell_type": "code", "execution_count": 11, "id": "5d00a787", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(118592, 2)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "# Load the dataset\n", "file_path = \"/home/austin/disk1/stts-zs_cleaning/data/filename.csv\"\n", "df = pd.read_csv(file_path, sep='|', header=None)\n", "# df = df.sample(frac=1)\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 9, "id": "eead0525", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 101/101 [00:00<00:00, 16215.92it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "All files have been moved successfully.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "import os\n", "import shutil\n", "from tqdm import tqdm\n", "# Define the source and destination directories\n", "source_dir = \"/home/austin/disk1/stts-zs_cleaning/data/reconstructer_set/valid/noisy\"\n", "destination_dir = \"/home/austin/disk1/stts-zs_cleaning/MP-SENet/VoiceBank+DEMAND/wavs_noisy\"\n", "\n", "# Ensure the destination directory exists, if not, create it\n", "if not os.path.exists(destination_dir):\n", " os.makedirs(destination_dir)\n", "\n", "# Get a list of all files in the source directory\n", "files = os.listdir(source_dir)\n", "\n", "# Move each file from the source directory to the destination directory\n", "for file_name in tqdm(files):\n", " # Construct full file path\n", " source_file = os.path.join(source_dir, file_name)\n", " destination_file = os.path.join(destination_dir, file_name)\n", " \n", " # Move the file\n", " shutil.move(source_file, destination_file)\n", "\n", "\n", "print(\"All files have been moved successfully.\")" ] }, { "cell_type": "code", "execution_count": 7, "id": "9bacea33", "metadata": {}, "outputs": [], "source": [ "df.to_csv(file_path, index=False, header=False, sep=\"|\")" ] }, { "cell_type": "code", "execution_count": 15, "id": "79ad8c55", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Updated dataset saved to: (525530, 3)\n" ] } ], "source": [ "import pandas as pd\n", "\n", "replacements = {\n", " '/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/fumika': '/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/imas_split/fumika',\n", "\n", "}\n", "\n", "df[0] = df[0].replace(replacements, regex=True)\n", "\n", "\n", "output_file_path = \"/home/austin/disk2/llmvcs/tt/stylekan/Data/train_List_updated.csv\"\n", "df.to_csv(output_file_path, sep='|', header=False, index=False)\n", "\n", "print(df.shape)" ] }, { "cell_type": "code", "execution_count": 11, "id": "425aa5fd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "val -> (131, 3)\n" ] }, { "data": { "text/plain": [ "(52323, 3)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_val = df.sample(frac=0.0025)\n", "print('val -> ', df_val.shape)\n", "\n", "df_train = df[~df.index.isin(df_val.index)]\n", "df_train.shape" ] }, { "cell_type": "code", "execution_count": 10, "id": "be7eb479", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(92971, 4)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top_10_values = df_[2].value_counts().nlargest(35).index\n", "\n", "\n", "df_ = df_[df_[2].isin(top_10_values)]\n", "\n", "\n", "df_.shape" ] }, { "cell_type": "code", "execution_count": 16, "id": "a0caec58", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(52454, 3)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "# Load the dataset\n", "file_path = \"/home/austin/disk2/llmvcs/tt/stylekan/Data/metadata_cleanest/FT_imas_remapped.csv\"\n", "df = pd.read_csv(file_path, sep='|', header=None)\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 20, "id": "b196c9be", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(59297, 4)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "# path_prefix_1 = \"/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/imas_split/shiki/shiki_fine\"\n", "path_prefix_2 = \"/home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/tsujido/vo/001/\"\n", "path_prefix_3 = \"/home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/saori\"\n", "path_prefix_6 = \"/home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/sakura_moyu/01/\"\n", "path_prefix_4 = \"/home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/imas_split\"\n", "path_prefix_5 = \"/home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/tsujido/vo/011/\"\n", "\n", "# Filter the DataFrame to keep only the rows with the specified path prefixes\n", "df = df[df[0].str.startswith(path_prefix_2) | df[0].str.startswith(path_prefix_3) | df[0].str.startswith(path_prefix_4) | df[0].str.startswith(path_prefix_5) | df[0].str.startswith(path_prefix_6)]\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 48, "id": "f47b9043", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 1 \\\n", "0 /home/austin/disk2/llmvcs/tt/stylekan/Data/moe... \n", "1 /home/austin/disk2/llmvcs/tt/stylekan/Data/moe... \n", "2 /home/austin/disk2/llmvcs/tt/stylekan/Data/moe... \n", "3 /home/austin/disk2/llmvcs/tt/stylekan/Data/moe... \n", "4 /home/austin/disk2/llmvcs/tt/stylekan/Data/moe... \n", "\n", " 2 3 \n", "0 jakei ga...kiɽabijaka na sekai ga, ɯɽajamaɕiː ... 490 \n", "1 bɯdʑi ka naː—? doɯ ka daː—? 480 \n", "2 maːmaː, aʔtakakaʔta ka naː. samɯi dake dʑa nai... 498 \n", "3 ima wa ɕiʔsoɯ sɯrɯ kibɯɴ dʑa nai ka naː. basɯ ... 480 \n", "4 toʔte kɯɽerɯ? pɯɽodʲɯɯsaːsaɴ. 482 " ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_train = pd.read_csv(\"/home/austin/disk2/llmvcs/tt/stylekan/Data/metadata_cleanest/FT_imas_valid.csv\", header=None, sep=\"|\")\n", "df_train = df_train.drop(0, axis=1)\n", "df_train.head()" ] }, { "cell_type": "code", "execution_count": 13, "id": "88efa8df", "metadata": {}, "outputs": [], "source": [ "df_train.to_csv(\"/home/austin/disk2/llmvcs/tt/stylekan/Data/metadata_cleanest/FT_imas.csv\", sep=\"|\", header=False, index=False)" ] }, { "cell_type": "code", "execution_count": 25, "id": "b2c07de8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(17, 3)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import librosa\n", "import os\n", "\n", "# Define the path to the CSV file\n", "csv_path = '/home/austin/disk2/llmvcs/tt/stylekan/Data/metadata_cleanest/FT_imas_valid.csv'\n", "\n", "# Load the CSV file into a DataFrame\n", "df = pd.read_csv(csv_path, sep='|', header=None)\n", "\n", "# Function to get the duration of an audio file in seconds\n", "def get_audio_duration(file_path):\n", " try:\n", " y, sr = librosa.load(file_path, sr=None)\n", " duration = librosa.get_duration(y=y, sr=sr)\n", " return duration\n", " except Exception as e:\n", " print(f\"Error processing {file_path}: {e}\")\n", " return None\n", "\n", "# Filter out rows where the audio duration is longer than 18 seconds\n", "filtered_df = df[df.apply(lambda row: get_audio_duration(row[0]) > 10 if pd.notna(row[0]) else False, axis=1)]\n", "\n", "# Save the filtered DataFrame back to a CSV file\n", "filtered_df.to_csv('/home/austin/disk2/llmvcs/tt/stylekan/Data/metadata_cleanest/FT_imas_valid_more_than_10sec.csv', sep='|', header=False, index=False)\n", "\n", "filtered_df.shape" ] }, { "cell_type": "code", "execution_count": 2, "id": "c419205d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(59297, 4)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load the dataset\n", "import pandas as pd\n", "file_path = \"/home/austin/disk1/stts-zs_cleaning/data/puckysamples_subset.csv\"\n", "df = pd.read_csv(file_path, sep='|', header=None)\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 76, "id": "64758651", "metadata": {}, "outputs": [], "source": [ "import librosa\n", "import torch\n", "import numpy as np\n", "import torchaudio\n", "sample_paths = ['/home/austin/disk1/stts-zs_cleaning/audio-diffusion-pytorch/samps/01008270.wav',\n", " '/home/austin/disk1/stts-zs_cleaning/audio-diffusion-pytorch/samps/syuukovoice_200918_3_01.wav',\n", " # '/home/austin/disk1/stts-zs_cleaning/audio-diffusion-pytorch/samps/nande.wav',\n", " '/home/austin/disk1/stts-zs_cleaning/audio-diffusion-pytorch/samps/01-DDDP2.wav']\n", "\n", "def valid(paths):\n", "\n", " dire = \"/home/austin/disk1/stts-zs_cleaning/audio-diffusion-pytorch/samps/24khz_fucky/\"\n", " for path in paths:\n", " name = path.split('/')[-1][:-4]\n", " y, s = librosa.load(path, sr=48000)\n", " \n", " augment = Compose([\n", " # BitCrush(p=1., min_bit_depth=3,max_bit_depth=4),\n", " Mp3Compression(p=1, min_bitrate=24, max_bitrate=24)\n", " ])\n", "\n", " \n", "\n", " \n", " # downsampled_audio = y\n", " downsampled_audio = librosa.resample(y, orig_sr=48000, target_sr=24000)\n", " s= 24000\n", " downsampled_audio = augment(samples=downsampled_audio, sample_rate=s)\n", " # downsampled_audio = librosa.resample(downsampled_audio, orig_sr=16000, target_sr=48000)\n", " \n", "\n", " downsampled_audio = torch.tensor(downsampled_audio).to('cpu')\n", " \n", " # noise = torch.randn_like(downsampled_audio)\n", " # if np.random.rand() < 1.:\n", " # downsampled_audio += noise * 0.1\n", " # else:\n", " # downsampled_audio = downsampled_audio\n", " \n", " # downsampled_audio = downsampled_audio[:2**18]\n", " downsampled_audio = downsampled_audio.unsqueeze(0).unsqueeze(0)\n", "\n", "\n", " # sample = model.sample(downsampled_audio, num_steps=50)\n", "\n", " torchaudio.save(f'{dire}_{name}_original.wav', downsampled_audio[0].cpu(), 24000)\n", " \n", "valid(sample_paths)" ] }, { "cell_type": "code", "execution_count": 114, "id": "c031f40a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 114, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from audiomentations import *\n", "import numpy as np\n", "import librosa\n", "from IPython.display import Audio as ad\n", "\n", "# Load the audio file\n", "audio, sr = librosa.load(\"/home/austin/disk1/stts-zs_cleaning/audio-diffusion-pytorch/samps/syuukovoice_200918_3_01.wav\", sr=48000)\n", "\n", "audio = librosa.resample(audio, orig_sr=sr, target_sr=24000)\n", "sr = 24000\n", "# Define the augmentation pipeline\n", "augment = Compose([\n", " # BitCrush(p=1., min_bit_depth=3,max_bit_depth=4),\n", " Mp3Compression(p=1, min_bitrate=16, max_bitrate=24)\n", "])\n", "\n", "# Augment/transform/perturb the audio data\n", "augmented_samples = augment(samples=audio, sample_rate=sr)\n", "\n", "# Play the augmented audio (assuming `ad` is a function to play audio)\n", "ad(augmented_samples, rate=sr)" ] }, { "cell_type": "code", "execution_count": 2, "id": "921300af", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data preparation complete!\n" ] } ], "source": [ "import os\n", "import pandas as pd\n", "import shutil\n", "# Define the input and output directories\n", "input_file = '/home/austin/disk1/stts-zs_cleaning/data/puckysamples_subset.csv'\n", "output_dir = '/home/austin/disk1/stts-zs_cleaning/data/reconstructer_set'\n", "\n", "# Create the necessary subdirectories\n", "train_clean_dir = os.path.join(output_dir, 'train', 'clean')\n", "train_noisy_dir = os.path.join(output_dir, 'train', 'noisy')\n", "valid_clean_dir = os.path.join(output_dir, 'valid', 'clean')\n", "valid_noisy_dir = os.path.join(output_dir, 'valid', 'noisy')\n", "\n", "os.makedirs(train_clean_dir, exist_ok=True)\n", "os.makedirs(train_noisy_dir, exist_ok=True)\n", "os.makedirs(valid_clean_dir, exist_ok=True)\n", "os.makedirs(valid_noisy_dir, exist_ok=True)\n", "\n", "# Load the input data using Pandas\n", "data = pd.read_csv(input_file, sep='|', header=None)\n", "\n", "# Split the data into training and validation sets\n", "train_size = int(len(data) * 0.998)\n", "data_train = data.iloc[:train_size]\n", "data_valid = data.iloc[train_size:]\n", "\n", "# Copy the files to the appropriate directories\n", "for _, row in data_train.iterrows():\n", " clean_path, noisy_path = row[0], row[1]\n", " \n", " # Copy the clean audio file\n", " clean_filename = os.path.basename(clean_path)\n", " clean_output_path = os.path.join(train_clean_dir, clean_filename)\n", " shutil.copy(clean_path, clean_output_path)\n", " \n", " # Copy the noisy audio file\n", " noisy_filename = os.path.basename(noisy_path)\n", " noisy_output_path = os.path.join(train_noisy_dir, noisy_filename)\n", " shutil.copy(noisy_path, noisy_output_path)\n", "\n", "for _, row in data_valid.iterrows():\n", " clean_path, noisy_path = row[0], row[1]\n", " \n", " # Copy the clean audio file\n", " clean_filename = os.path.basename(clean_path)\n", " clean_output_path = os.path.join(valid_clean_dir, clean_filename)\n", " shutil.copy(clean_path, clean_output_path)\n", " \n", " # Copy the noisy audio file\n", " noisy_filename = os.path.basename(noisy_path)\n", " noisy_output_path = os.path.join(valid_noisy_dir, noisy_filename)\n", " shutil.copy(noisy_path, noisy_output_path)\n", "\n", "print('Data preparation complete!')" ] }, { "cell_type": "code", "execution_count": 2, "id": "96a6f111", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 212/212 [00:00<00:00, 488030.98it/s]\n", "100%|██████████| 212/212 [00:00<00:00, 434388.10it/s]\n", "100%|██████████| 212/212 [00:00<00:00, 871757.30it/s]\n", "100%|██████████| 212/212 [00:00<00:00, 805427.94it/s]\n", "100%|██████████| 212/212 [00:00<00:00, 691440.47it/s]\n", "100%|██████████| 212/212 [00:00<00:00, 870051.32it/s]\n", "100%|██████████| 212/212 [00:00<00:00, 51838.89it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Copied 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/home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/sakura_moyu/03/03000410.wav to /home/austin/disk2/llmvcs/tt/deepfilter/valid_speech/03000410.wav\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 212/212 [00:00<00:00, 95994.00it/s]\n", "100%|██████████| 212/212 [00:00<00:00, 52739.77it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Copied /home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/sakura_moyu/07/07008670.wav to /home/austin/disk2/llmvcs/tt/deepfilter/valid_speech/07008670.wav\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 212/212 [00:00<00:00, 2983867.28it/s]\n", "100%|██████████| 212/212 [00:00<00:00, 3087473.78it/s]\n", "100%|██████████| 212/212 [00:00<00:00, 3130959.32it/s]\n", "100%|██████████| 212/212 [00:00<00:00, 3198533.99it/s]\n", " 0%| | 0/212 [00:00\n", " \n", " Your browser does not support the audio element.\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Download and open some audio file. You use your audio files here\n", "audio_path = \"/home/austin/disk2/llmvcs/samples/recon/nagi_voice_301290_6_06.wav\"\n", "audio, _ = load_audio(audio_path, sr=df_state.sr())\n", "\n", "import librosa\n", "audios = librosa.resample(audio.cpu().numpy(), orig_sr= 48000,target_sr= 24000)\n", "audios = librosa.resample(audios, orig_sr= 24000,target_sr= 48000)\n", "print(\"24\")\n", "Audio(audios, rate=48000)\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "7d9a35a1", "metadata": {}, "outputs": [ { "data": { "image/png": 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sZ591AK6qatdfpPF4/Hyb1NLS8jJhe6HtufeJg/+JEycYDAY729M0fdZj7r77bj7/+c8zGo34b//tv/GOd7yDz3zmM9dtEH7eA/Dv/d7vsbW1xT/8h/8QgDNnzpAkCUtLS7v2O3DgAGfOnHnW8zzyyCP823/7b59vM1paWl6GuCBwV0i02P58O6rhakiShFe96lUAvOENb+Bzn/scv/Irv8Kv//qvv7AGPwvPewD+4Ac/yFvf+lYOHz78ghrw8MMP89BDD+28H4/HF00GGU0v24nRCelYoBfB+N5ANtJ4tTAHPagq4BKgF81xlwV8Eo+VVVw1dwnoMpqj0W0BXixM3sU2BMiaaLolF1fuw0JyrxkQ3REhmswuCfggyFVDIi25apAEev2SyTghiIDoOCqlyc/HFW2Api+wnRiNYfOAmihkAy4PqDK2VTaecIkZF1JHv1fQSRoCoIWPD9pCtlGVggD0dUWqLJ1OxTTJqAcCWQmy2u98/41ElR6VCmwukSbgk4BL44p5jAIJBAN6IpAWmjxc7GcRomtCieiecNGMrvuCbDPsRMIAuI6hWhK4VJJuiUuiXARBAUHgsoCwAp5WsqbTqyhmCSGJCRm+I0mUo2NqrJfxvtQCaWOCR1ByT6MgRID5IUHTVfhEkG7GPlF1/L5qEJMgpocMtguqjO4UVccEIrHolyYHZJwdNl0VXV69EBXKt7Xt5SVuHB/7FxWwCtCBbr8k0ZbJNI/3rwJZSJrhxd+O8LEGYZAB0hC7UwecA9tVSBt2nsu95upSkV+4+8N7f1U+4+fL8xqAn3zySf7wD/+Q3/3d393ZdvDgQeq6Zmtra9cs+OzZsxw8ePBZz5Wm6XOaBC0tLS1PxweJv0KUg79G//PDDz/MW9/6Vo4dO8ZkMuHDH/4wn/70p/nkJz/5Qpr6nDyvAfhDH/oQ+/fv5wd+4Ad2tr3hDW/AGMOnPvUpHnjgAQC+9KUvcfz48eu6itjS0vLy43rMgM+dO8dP/MRPcPr0aYbDIa973ev45Cc/yfd93/e9kKY+J9c8AHvv+dCHPsQ73vEOtL54+HA45J3vfCcPPfQQKysrDAYD3vWud3Hfffc9vwgIgACiETEZQkA1XAR3L6IPmo7ALpIZTBF2EhuCleTDkiZJd1Z9ZQNmFrAdgUsEwgeSUTSnfBIjHpLJwkwTF00s4QWyifoNIsTvqAce2Yjovlis0gMk0uKDQBKofOyboAIiczDVMSlEC1wav7PpB/RMkFYsohQCddejCgk+Rls8Xfp/sDZjtTtnmBRo6Smdpmo0oYxRDyJA2WhSZXFBkBrLdK2i7CuSUbK4tnDdVqefDVV5hI9mqawDtueRqxX2fIYqBD7xBC0IyLiSngSC8IhGIguFKiHbcmy+RuGTQLohUQvLUJW7fSqyAWGh7gmkiy4nM3E0fYWeQ5VH8/3p9POSYp4gcrdwLwWWsgLrJUf6W5zMV5kdMiQTgXQBVTR7WnfMJZJqKSw0M0JM2gHMJLpp/PbPTWxHRcSIHV3E6IbpHQ5VyPjMNXH4cYmg6YVFNE4gP6nxw/ice7OIIpGgC0FYOFT8kYq13oxEOpSMz4nXIKyI7raOJ1mPrgWXAiaACKiuxRhHnWjCiSgbi78+/i4PV/QBX+s3f/CDH3ze7Xm+XPMA/Id/+IccP36cf/SP/tEzPnvve9+LlJIHHniAqqp4y1vewq/92q/tSUNbWlpatrm6KIhbX2nhmgfg7//+79+1KHQpWZbx6KOP8uijj77ghrW0tLQ8G1cnxvMSHIBvNIIYfdA57/A6uhy2TbFk4hFO7mhABBkjFwjQyyvW0x62A/WqQ48VS18NSAvOxKQEb+K+wgEebBbz/10aTTyXQrYeNQS2IyeCYscEdFl0jYhKYpRjqAvmLiGVlsprelmF3SdRyjNTGfrJbMd0bAbRlVKteYSX2IFDOIEI4mL0hY45/dvuBQDnJUY65jaqGHR0TSdpLpZNd1EHVQuHRKKkJ9iYv++yGMx/M1T66qHeWXWv+5KQOFyhUU2MahBBYLZipEFQgAwgA8HFlXZv4nnsskUUCjONroad39hC20I2nvyCJ92KYh4ui26foAS68MgmuneCjO6aSylrg68VlBJVSNxSzOZYy6ak0iESH11FSoDdezeOqjx6Ht0m3gtcHqN3grqYjLGdfJKdD6STgHAxkccn0YVl99VgZTT9hYxJS32305/NUOF1IBlJmr6nWhLoOTRDB4mHRpKlln5S0dMVZyb9xf0LuL6Ldn3qCFJFOdY0IIwnWIHWDiEuuiy8ETvRGHtNExT6iokYt74c2i0/ALe0tLQ8natbhGtnwC0tLS17jr+KihhX+vxW4JYegPVUEmQ0nXURNSFcFqtLiEWuf9MRqBrqQYwuCBpk5uinFef7llCYGFUgYf0bBdIKqv0OOZfR3ZB59Gxhj267IxaWi7SC2W0C8+WArAJeCKquICiPN+B6HlRAJA4fBJtNBxsUTVD0VEXHNBTGxCgFL2gGHrehYnLAIj9flTExAOOh0ogAeiYQfrEibSS4i9WtQgAtfYy2EIHSGea1YTvPx2UBJWMUxkCXhCCipVxLglhU27gJStWq8pRLGm8EZhZIzutFwktc4Tdbkmbo0Wsl7nQOXtDbN6OuNbZRFFW2KBfSEFJPcSBKTiIETW5YFRJCLOBZrEnqnqD/lCXZskyORZGE7WiaoAPBx0SXSxEikHRrmnm+SNqIP+KerimcARliwsjCDbBdiHKv0PMGlyYxWSQJILcjH+L3bFdoEU3sinI5Vs2wHUG1fPGeCuNj22RMalHdhuAFvlI7z7Y3Mdmn6QnqIehhjXcC30ici9+37eYCcD1Hd/+MqjRxPxOf3yBAaE/erxl0SpyXaO2oulmMUErMnvbRNv4qZsAvyUW4lpaWlpvN1SVitAPwC8LmgWA8TU8zeoXGpXEW4FIQCpquJJ3ENEwzvSjKnnfqHfOjGTiSTUW97GDNI2SgtzSnLA3BS7ASi4HFjCfIEBdguh7RCEQjmByTJCNBMg40A0HIPMErZCnxaYxf3ZdPMcJhg2KoCzoyilCPxh18oTG9GrccaC7kuDTOolzfkz+l4myrkegDc5xVNEsSsWlizHIud820vJdIAlo6MmWpvWK5UyAW+/jFpMUIT+U1uWkQMiDnku01i5sxA7a5pOkLmu7FBU6f+5gSHBblzFdq0qxh2ksw/ZphXlJoTVkbKpXiEwHao7uOxmiEM8ha4PJLa5ItxMyNYL5PI11833Qk3XOepgc+D7gVy3ScMLykjQf7E4q85JyOFkdVJmjhmS5mglKFKOSeRTF0l5s9Xc/0RpFdiKnpqon/BhXTgLdT3326vXgcF+mCgOKgx/ccaI8cGYIMuE6CdI5iX1yZ3p79phtx1lytxPhh4eK9CEEgJCACttJ0dE3t1M7vSM0lTaNitnHiaPbV2CzBJwG5iBW2TiFEoGl0NFYChIVK317T1oRraWlpuUm0M+CWlpaWm4TjyjNc95yf3hrc0gOwPjDHloagFV4LVBlF1+uBwA4Ds4MSaaHuQzBRZFvNBcoLnJf0ludMz/bwJppQTkIwnslmB5xAFCqW3p5KfBIVumQjkZUALwgqkIwl9VJMQTbTuPgnc4sXgBPRzmokiXSkCyU0gPWmB4BvJHKiaISBWi6UzwAJeiypVgJmKjBLJc08Qcwuuja2zfNL402VWqQ9K4dHkCmLke5ickwArRxaOsyisF1o5E68rM0l2fzGP5reCGweTepyTeB6FoxHrRv8Qnjc1RKfiR0FtKLRJNoxL1OEFTTdAJVCdWtsneDVwo1zTkB4WjpyHU3rOpMIt1jA3RQ7biYhA09fJNfSkypLN6uZFil5p6Kja3LV4INk0J8zOgTzCznpaO/7SFiP7QCL9HlhFip9i8fMm8WinAC7FnbioH3qQQZUslDAlSAbR/CBpgdSenyREIynWgl4E2Ot6zSgikV6to+uBZ8LhAhslh1SHVe6RZrGxVLAlRqRWaTxuCR+dyev6WUVVRMbV1eadCIwMwfN9XFBtDPglpaWlpvEdrTRc+9zE3RXr5F2AG5paXnR0aYi3wBspWM6qmJHtazpCVwazSiXCppBrBu3LTIufEyJVNIzG+XIUkaT0y1M22YR86sXdd+GFp8o1GxRbjsP0PexZtY8xs6qUiAWgu3ZhmeynqJcVIcKKqpmlU7H+F8ZJboqrykag5hqfMejOhavFc1A4XoeNZWoSuDyKL/mnYpukUUMZrIlkU1csd4dBSHo6JpUWSSBvik5X/QulqV3YKRnqAvkIuhTZg5vZSxFHp5pet8owqL2nmyANN6wbCNec9ODShmazCLnCptrSmMIQTDsFmyEHqoSqJmkMimiEbiuR09jGXqhY/lz2cTnxS0iIXwCsopKd003RsmoqSJUEl3sbl9lNRtFh6I2lPOENG8onaZvoPIKJQNZp6bpZ8gmoEfFnvoZg5ZUKx7hBN6E6B4rxU4ExLbLU7joClPFojZhI8EJ/FwRMo8eK0RpQQqGX3WcPpohSwGFXsQwR/daSD3FYcjOKJT2CBFQ2pOkDcO0QIpAZixyMKBZsWi3+I3M47ChFvcxT6KbYfvfqXbRZaJFfC7Ly0jPvUDaRIyWlpaWm0SbitzS0tJyk2hnwDcAk1mCF9RrFrkoCy4b0HNBo6BeDriOJ+iFqd1rqDqGpaThtu6Irzf7o1oZ0U2A8YhSxQgHvxBfdwL6FtsDfTZBEnA9F026viMUscaaEILpsUW6s4yr9slYYDsxOD+RMR258ppj6QWMcBzpbzE+mlLXsZu9CHidEDoO129wI0NIFiLxlaK7OqfsJLipRjqNbBbqZZdEQQgR3R1aOqQIjJqc2iu0vVgHL9WWnioZ2Q6pspjEUolYPw8h8OmNnxkUK5Jq1SN8LPcuE0ewEptBugnJVlQuC8c7mAqqgcR7iRCB2ipszxNETJChiu0XjViUpQdhNKGpmR02UfVtCrazEO7vRQUzl0Z3VdAx7dxdphLWeJahlCfMNbUMzJqUYVKiRFgkGSh6J4kpzWZv6535ROLzgKjAZx6kpE4CshE7dQptd1FHb/FIqArMWOBTget41ESRnxWg4jPuUkHQC/WySSwiYDuBoEFtxmcsKHBW4hYuBgDrJZmyJNoishRRKqwO4ASyYxECyuUEOdFsdTox2UcGvBc084S0WdSaa+ye9tFOX71c9YBbWlpabjbXUhX5VqYdgFtaWl50tC6IG0CSWGaTDACvA2YS1dCq1ajvUO23UTUsXaxFL2rIdZMaIx3YKLAubRQ51xtmZzU5VIAIiLlGEE136WJyRO1NFO020VT1BqSPCRPbehTCxf9vr1hr6chVQxMUE59hpEULjwBso/F28TAkASpJyALSC0JYRHCIQFkkJGkDaUM16yFsDM4Pdb3TJ02t8UFivSI30e2gpSfMZsCiPUFQeUNflTReUc+TGGHBQoVN3/gHU1WBZFMSTFTi8rWCSiIc1EsXS6zrWWybUIHqTIcyySAIVCmi6yDxyJEm2VzUhBOQjC+aul6JHfeCbBYRAwFUHWsCbocS2J4nGe/WxJg2CUlimZ7uIRpJvr8m1Zauqlmvu1gncWc6UUPBB0RRs5cIH6JbZVE+PsiA2PahsdBs0IHQCPCL6yoBBFUaj9UFNH1wnQTBQrXNBHzPUXU8g7/W0SWjA6IQdM5GfY6wkca6iVOJvdcxaTJsaCgbjTt3EuGOgPYEKVDa0etUjI+lBBnodipmRUI1SwiNRMyja6buSTr2+rggwlUkYoQXQRjaNbfw5MmT/IN/8A9YXV0lz3O+8Ru/kT/90z/d+TyEwM/+7M9y6NAh8jzn/vvv58tf/vKeNrqlpeXlzbYYz5VetzrXNABvbm7yHd/xHRhj+P3f/33+6q/+iv/wH/4Dy8vLO/v8+3//7/nVX/1VPvCBD/DHf/zHdLtd3vKWt1Beh1jAlpaWlyfWRyvwuV+3/gz4mlwQ/+7f/TuOHj3Khz70oZ1td955587/Qwi8733v41//63/ND/3QDwHwn//zf+bAgQP83u/9Hn//7//9a2rcfJaiE4vbSBBOkK0Hiv0CVcpoXtYSPRf4wqCquCJupgL52oALAjWskdLjGoVvFNI4hAp4K6MWgBOEWqHGiqBDrB0mIdmMIu/VSjSJdXlRqL1zWjA9Ft/bTohylJ3ofshkg3eCE+UKB5MxpdOM17uxRleI7gE9E+h1je2oKCo/jXoU4XyC8FAfCyjl8bmHmUK6gFwa4tYvxE4RASk8RzubGOHIZEPhDOcXEpOuE8XaS28YmgItYr0uVExeESFKM+bXdCdeOIMna6RLcAmUqwK/btBzQTDR1HZpTLQRjpggIQNiqSaUGpFY5EaKqgRBaKQV+DSgyxihsl2eHiCZeoJSBBdlL1UVy6N5A7YjSCbQDBeumqe5YiZlSqIt/cMTtIyJCQeyCZXX9HWFEAE9FaQjH11Zem+jIJJTY5LN/VEXoxToeXRHiIUQe1DgtYrl6BcSCz6B4oBHVYJsK+qWyCYgXEx0ESEKpgcvEZljflDF30qQO7KgZgLpRnyvCtBZTPTJVENmLGr/PnzHQx2v13uJ8zJKgXY8LgiarQw1kzGxSMYkJ1UH8NdH+tQj8FeY4V7p81uBa/oT8T/+x//gW7/1W/mRH/kR9u/fz7d8y7fwm7/5mzufP/HEE5w5c4b7779/Z9twOORNb3oTn/3sZy97zqqqGI/Hu14tLS0tz8V2FMSVXrc61zQAf+1rX+P9738/d911F5/85Cf5qZ/6Kf7ZP/tn/PZv/zYAZ86cAeDAgQO7jjtw4MDOZ0/nkUceYTgc7ryOHj36fK6jpaXlZcS2GtqVXrc61+SC8N7zrd/6rfzyL/8yAN/yLd/CX/7lX/KBD3yAd7zjHc+rAQ8//DAPPfTQzvvxeLwzCPtCkyxZmmGDdYZqOZagDzKWKu+ckphJDLIPMpY3dwmYRUSCAJxVmNTijYvVJEQA7WM4gAw7OQ5iIbcXJDuRD6oQZBcW5ei1iKadJ5pZihgpoQMydSyZqL0wdSlnygE9VVF7jUg8oZag4ndt6yFEEziakqqG8mAsSx9mBtFtSFcLKpnhErkrESN4Sc9U+CBQMpqZRvgdUy90HKvZjCPJBkY4uqYiG1ZUhcFXUb5TFze+IoZ0AVVHTQ49h2o10PQDcuE6clnALVmslZivaIITJN2G2guYmEUtu4Us4+ESOzW4RKGLRYWUJGpB2EzE50NuJ17EW52O4v1qejGiJj5Hu9uoRKynd6A3YikpuFB2WUlmdGRN5XWs+OBjtML1sG7L25eo9rmdCJx6LT6Xah5rGuLj13oTn0lZRVeMzzwiSHwSNR6cBDUpccHH/bQniBArevQ8PhGLRA9IN3XUWOkvaiPmgJdkqqGra/pJDDURi2dHWIG4oJkcVBgLspRMTvXRE4UdONLVAtto6lGGS54pE7pXeK4iDO2l5oI4dOgQr3nNa3Ztu/feezl+/DgABw8eBODs2bO79jl79uzOZ08nTVMGg8GuV0tLS8tzERY+4Od6hZfaAPwd3/EdfOlLX9q17W/+5m+4/fbbgbggd/DgQT71qU/tfD4ej/njP/5j7rvvvj1obktLS8vFRIwrvW51rskF8e53v5tv//Zv55d/+Zf5e3/v7/Enf/In/MZv/Aa/8Ru/AUS9hJ/5mZ/hF3/xF7nrrru48847+Tf/5t9w+PBhfviHf/iaGyczS5Y01EZHCUUJTS8QDlQUPcP+/x1XZcvVRYLENMoQ9kxFT1Vo41DKs9abcWarT5rWCBGwVlFMUoSIUn7brgCfBEIZkzeCj3n341dAfl6QbAV0FaKJ23NI4/CVQqYOZaIOxNwnbNRdfBAY6ejpik6votQ+VsY4n2AP1NQuQVaCZhgLf6abAjVs8FagT6eEkaJatuAF5Yoiu6SwYZo1jOucOzsXmC/qqp8uBohOA7NZrKghAmUwNEFTe43WjtKmsZBkcnMSMaplQ7kUC4NKB36tgVlMqDAzmB8E021oCoPtRLO5nieILYOeCIZfiQkGzUDg/aI6Q88hnMKlYsdNU/cFth9wTYym0LP4nk1BMg/UQiCtoOnbKPV4CbVdhC51Ykl2u/BRrJkJTVBo5QhbF11Rl7qG9oJiVSNXSlypUalDiIBrJLYro4zqNqmDUhFMTE5RU0m2LqmHAZ/GNvksQWiDSwRSBHRu0doxbSRsJpiVEltpXKKjnknP49PoEhpqSyIdibQkyuFMRuhZnBNkJw3CQ/LXKYMnPNWSYHZYU+2ziI5lbTAjAKdGhnJVEyt97j0vy4oYb3zjG/nYxz7Gww8/zC/8wi9w55138r73vY8f//Ef39nnX/7Lf8lsNuOf/JN/wtbWFt/5nd/JJz7xCbIs2/PGt7S0vDx52aYi/+AP/iA/+IM/+KyfCyH4hV/4BX7hF37hBTWspaWl5dmwQSKuMMO1L4IZ8C3dwuAFo1GHcCFFNtF8lo0gBAHGR10DBWYSyM8FOmcd3sRjpQgYYylLw5mtPtVWxvR8l8l6l3KeQBAEJxDDmtCz+P01fmiplzz1IFAvx6KHwkO6Ed0PhFgUUWUWoUKMcHASpQJShB3XQ7IoiJmrBuckrtQwNaQXJHJkCAqqAw43cLGAogWlHcr4KLcYBHKkkUU02S/VggghPlgjmyNF/F7n5c5qs7CC0hmmLmPiMhqn0NKDFcha4BXo4sbXytJzj6oX1SoSohylCpT7HcX+gF222FpBLXF5IO/W6NTGIpqLacLSV5qYvDGLG/QkJiXEC4+znXS0uE+pp17xzG9z2E5AhHg/66VY9URkLq7Sc/F45yQhCI5vLvM35/dRWk1Hxr4f2Q65sVTLgWI1VpbA7W0/Cg+uipVRQgBbaEKtEGUs1CqsiIVaG4lY6EHokSI/KzHjGEmCgJD6nV/27JAgy2uqwjCfZQQv8F2Ha6L7znajnComJhS5ZUs3aUhVLDCrhYMQYgHbjiU7H6N4goB0yxIENEOPHDSknQYtYyIQcpFAkpg97aNtrocP+JFHHuGNb3wj/X6f/fv388M//MPPWPPaa27pAbilpaXlclyPAfgzn/kMDz74II899hh/8Ad/QNM0fP/3fz+zhdDV9eCWVkPrDQu8l8waie0I7EAgK4k+mV6MwxTbC2hxJkmA2mluSzcxyuHGCY6LdbDQAak9abeirgzaOBoRZ6C1M3EBTsU4y5imKTBFQJceZwT5hcDWehrLyk8l3gTKRR2383WfmU1IpGPuElJlsV/rkc1iSqlsICw12ImOMZ1zFWM+F38Gk7ShWQvYShMaCR6SiUCmKa6K+badtOHu/lkOpiNuT9Y52yyx1c85ofoA+OWGgSnZb8asN30OdMZsljkidTRLcSXzCsVkrwtBxvhjaaEeRGU2UodZbqhHaZyBzTTCCnwSkNIjZEAdmuMaxchlIDR6DmakCMRFU+Gj0l3YXqgUMX7bZTF1FxVn3bKO74UjWgJbSUyV3WlgoN+pmBZpFBUvNXVW0yyq7xoRFffqQw3VuYR8HUK2t7O7wVcmXHhqgAhQr0gwHryIqn4LBT7ZCIKISnB6Lkg3A8IGxq8En8d6csmgiip6IQYOG+UYDuc0TjGddwFIssWi3KpGlpLlfRPK2mAXM+MVMyOVFi09xfoGIdmPAKa3x3voDSAEm9/sUP0GqRyr/RmpsszquDh8PZ+z6+ED/sQnPrHr/W/91m+xf/9+Hn/8cb7ru77rmtt4NdzSA3BLS0vL5QhcOdFi+8/r0+UN0jQlTS9TDuVpjEYjAFZWVp5PE6+K1gXR0tLyouNaXBBHjx7dJXfwyCOPXPn83vMzP/MzfMd3fAff8A3fcN2u45aeAYcgyJOGme0gGoks4t8LPROkG9EEhWjeukRQ92OpcSk8S2qOUT6a+l1LWJTSRkQVtKZReC+o5gamBk9cBBHbdcYtiCAWAuFRKU01cYHHjBUuj3XhUNFE3Ki7TGzKhbLLvnzK3Cc0XkU1Kh+Fs4uDnlComPIp43Y9E5hJIOuWJNoylll8eGQg1JL0QkO4JN400Zb9yYS+LCm9YeRyrL/E1isVuYrmuBIeIzzOC6TxeCvIttzFa7yBSBtwidhZJHWNRAiop8nCzJaQecxJg09jWrCUgU5WUVtNkacU++L9V/NthTqBrKFzzhOauFjWPV1TDVNsJ6p7sRA3TyeeahhroqUbgno5ukIupbIKY+KqnrMKJT1GuB1x+6LRC6H0mFotN6fs6TKcj8UDbGeR6uwEwouLymcmxEU4AcLHRWmXCtwQmiWLHtTYicE5ic8N+EDnTFQGrK2Oqese1FgjlgNFkews7GnlydOakAgO90ak0mIWdQdDXSMWRQ9cx+NEdL8FARiP0jFmWYrAZpkzmmboLYWeh4uuoT3mWlwQJ06c2JVhezWz3wcffJC//Mu/5H/9r//1whp6BW7pAbilpaXlclzLAHytEgc//dM/zf/8n/+TP/qjP+LIkSMvqJ1Xoh2AW1paXnRcj0W4EALvete7+NjHPsanP/3pXVrn14tbegCernexSyVypjBTgRkvFMtUFHuONb7AGwEiUA2j0takyfhKeYB5bdATha/lxZpis2g6ORlXw6MIdTyvrOPqbroFdqFYnoy2007BZpKmI+Lqro8xtQDCSgoXBdBPTJYY1xn70wlSROFu2SxUuRSYTUUyFjvujvx8oOnAxlaXvFOTmoYipIRZXJ3WW1NwF30GRW34zPpd3NYZUXnFsXyTL24cZIW40KAniplNOF0vRYF4BNYp3MQgnaDuRbdIciNu4CUIFxYRJrE+XKhjBIhoZFQvQ4D29J6C0atgqVOQ9KYo6TmxtQRE079ejq4fMYvp4SLEeFRETEdOzkzI9ie46UIZbx5dH9VQ0HQFqoB6OUa66PnuH+h0nJPmDeV6jqgl+f4NlIhOho6sWMpLNpoh+YWF48Hs7c9Hnd8iGQ1xOeCIv86FELtsortFBOIzVbOzyhQkqEKi1hx6xdHUmiAEUgp0Eai8pJgn+JlGb2qkhWIzByfIziuagef8uQHUEtmxTAfRRG+8wgdBqCpCqdh/dJOzkxXQAcaS6WGNHAXUsMQ2itMXhpjEolSgzgIuvX5LTO4qEjHcNSZiPPjgg3z4wx/mv//3/06/39+R0B0Oh+T59Slh0C7CtbS0vOi4HnHA73//+xmNRnz3d383hw4d2nn9zu/8znW6ilt8BtzS0tJyOUJYZMReYZ9rO+eN18m+pQdgvaEpXY5yAuFi0LnLxY7p1XTjCnFQMe0xv+DxSjBvDD4ItPRk5wUixGD9pi+QdiH+nbDjWvBJrPWlatBzkHUgqeMKsy7BGQiZ3EmYyM4LbB6F1L2GSsa/toUzdExD4xXnqj61U/gkUC9H8Ww9FQS9qME18rEGnYBkGuB8yiwz2NViYWcS3SaZRviLa+1HhiP2pVPu7Z5GirhK301qxCIVV7iYhn13Fj8/V/cJgMgcoZFIB0HeeJES4QPShpjDCuAEahZdQwSwvYDaVLG+m4wr6rMmwQXBbJyRTqIYv8uiAHm9Csn56E4p9iUc/f/l+Pmc6tCAyTGJmQbyC4EgoFoSUYFNs1NjzUyeafwpfTGdm+U6pnADPVUy9yl9UyFKhTMC2fi970clSbcCxQEu2qYhiqcHFVPJXRpdMF7H+naD45Yzb1K4JYv0AikD4qkMWY8JC5H+yZk+ohaYuaR7QlCtgFnXIGD4Vc/oVZJkMyUYqBaRJkNd0JE1x4sV1GCAqCWzKiFZKQleEDY188Mxuck2iqbUiLnGDwVCxnv0dLW5veRlK8bT0tLScrO5HjPgm0E7ALe0tLzoCFcxA24H4BdI0AE9UvS/DtVytMyDhKYbNRKEDegiIBoggK4CXsfy4heaLmVtEGZRmtuCywNWRKHuZBzVpFSzUG0KMfLB96KpGkxMnhBekK8HVO2j1oSA2WEVA+W3X15wajZELZSgnJeU1lBYg6wWylAhJhBIFyMCpkckNodktFB4W67Ie7H0OUD/8JiRH1Kt5aTW7vTJUjJnJZmxpmPUg0fSNTW2LAFohlGFbUVN8cj4/04BwMRJbKYx8xvv6xKNR9XRTeSS2PfCRfdDUIu+aQRbrw64jsdIx/myy2yakTyZ0n0qqpmV31ix1CuYzVPcNEfYGB0QLokUqZYD9RK4TKJnAV0GzDSQ+Zh84VJB0w8U+3f/QI/s20SKwHljyYxlXzZl4jLuzU7hEBzIx8i1iuntOfmmJjm5t2vYYTanXBV0ToMdKbyOrhm5FSMXvI7RG7IWJCNIxoGtV+hYo7CRUeEvRP0S4QIBSLccyXlNvd9ilxpmISXdEDT9+NzPDklcFl01ApCFYFxF7e7bk/N8qrkHMYzZM7NRzv79IxonGckcl4GqBOHJDvlM4E2gJsF3LGZDocvr95wFrqyHf+Of8mvnlh6AW1paWi6HRyCuoAXxYijK2Q7ALS0tLzpaH/ANwGuQHmwuUBU0vVh+3kxBz2K+vNeLxIiFveFSQV1rLlRdQhDUax7ZRDm/oAIu9wQlo3vBAVJgKrCdxTkE+EUpc9+9GKy/rQMhPNhOQNULGUQBZiZ2XAel1UgR6OgaLR22Gwipp9F+R0g7P62Y31WDE1T7BL0nNKtrE8azjKaIPhPvJMF4ZO13VrMBvj5ehQE04Q5uSzc5Uw2ZNQmm2ARimfDj02X8qqQjK1b0jMPdEZvzHCEDZh5Q5Y0XZLddjU2jtGe26RBOouexJH2Qsd/tq+fYuSY9mTC6I6OYp/iJoenFGyN8lOy8bTjiq/Xajkxjfj7sJKukZyaIsEpQ8flp+oJ0I5BMPWbiaLoJuoQiCzsaC9sMk5LSabKFHkTpND4I9ukxpTfUXtPpltQyw6vr8OPet8L0dhfdKlZgRoJ0PSYX+WQhu2ri8y4tTI8Jqn1xfz1WhIHAnssgC1FEXQrm+zXN0RqTWoQM1Pskwhu8DkhilE/TQD0MoAKiERSNwQjHhutxajpgrdxCVgIvNVvTnDSxJFuSdGshrm+h2BfwKeBATDTpliDbsHCdIm6cF1FD5Er73OJckxPr53/+5xFC7Hrdc889O5+XZcmDDz7I6uoqvV6PBx544Bkl6ltaWlpeKNsz4Cu9bnWueRXhta99LadPn955XaoW9O53v5uPf/zjfPSjH+Uzn/kMp06d4u1vf/ueNrilpaXlpTIAX7MLQmvNwYMHn7F9NBrxwQ9+kA9/+MO8+c1vBuBDH/oQ9957L4899hjf9m3fdtnzVVVFtaj2AE8TTx42YDzTThJrmjWxCkA9DGTnJNlGIKgoceh1/ExVAWMc1iuclbhli6tkzF+3ApE7ghV4I9A2rsD7NEYp6DnYEM0qIcClAduJppQ3knog48ruwvXgNdTLniDhtu6Ik7MhRnpcEPRNycymhK7FdBt63ZKiSii3MuolicosvlZQaeqlQC5CrIk20chaYDUIHXA5pCtLuPULO91SOoPCs9l00dLHKIhOB1dVqFpQOc052+eOpGLDdtmqc6yTiC0DIfbZjcZ2FE0XbC7RHUnoNtgiJga4HKqVQJo1UWjbGCZFhlQeMocXUC1rsvVAsZHzNVZxX+1hxjECwsz8Tvnz+e1DhBWoEljcy/nBmITgtaBaFsxvi/XP1PruihY2SA51xpwcDakqzdYkZ/mOgj8r7qAJitPzAU2jUYWISSVqb6Mg3DCnd2xM02ik9JRFEt1i62mUjfREF1oSkKXE9+1OuXqbehgnkHjq/R5hPd4HXAo6a+h1KhqnsJmiGSgYNITzCfl6YH5IEHo21qLTgmmRsmG7HK9W2Bp3WF7/CsK9CrlUUReGapyS6eg6CgrKIbgMXM8hq1jJxWuQtd+lY7KX+BATrK60z63ONT9BX/7ylzl8+DCveMUr+PEf/3GOHz8OwOOPP07TNNx///07+95zzz0cO3aMz372s896vkceeWSXWPLRo0efx2W0tLS8nAjh6l63Otc0AL/pTW/it37rt/jEJz7B+9//fp544gn+9t/+20wmE86cOUOSJCwtLe065sCBAzuqQpfj4YcfZjQa7bxOnDjxvC6kpaXl5UMcYK/kgrjZrbwy1+SCeOtb37rz/9e97nW86U1v4vbbb+e//tf/+rzl2p6rPlPeL+nnFec2Vne2+QQQgfKQQxeKeilgpgKXxsB04WG5U7CazsjyOpYaV55gJaJWMWphHoPPq+4i99/FZAk1kwQV8CYgK0nntMRr8IlE2IBNo6RhkODTgAgCnwaCCVF6EuglFeMqwwdJV1fkSyUrvTkBWO3O+crmQezAxbY0Ej2X1KuOojYEJ8GLaMrNFTQCm8sd8xpASU9X1xxMR8xdSkdVJHINu20OB2i8ZO5TmqCYupRpneL99rUJrlGlb88o12LihbQwWJkxNRnVLKc8ViNTR10rghc0K47b+lMapzjdDPGlilKUQ4GcKprNPsEEqtW4aq+Lixc0utNQ31ESSkV2ypBuQrUSTeXgwXYhmIBIHUHtdkFM6xQtPMO8ZK4MIQhWkhlLas49yWn+unOIJy+s0PTjL1uU9Z72j2gc1iq0dmTGkiUNtdVUxmFrjdQeGaL0qs8UFAo1k7iBi/3asdE95gSumyCUoh4IDi5PaLykkzR4Lyi8wKQWJxKEC+iZpDE+VncRAaU8fzPdTyJddIuFgF2yLPfnFFUUMq0Sj55lFPsDvuNRU4nIHfRiaElTZswOG5bE9XnYXiphaC+od5aWlnj1q1/NV77yFQ4ePEhd12xtbe3a5+zZs5f1Gbe0tLQ8X8JVvm51XtAAPJ1O+epXv8qhQ4d4wxvegDGGT33qUzuff+lLX+L48ePcd999L7ihLS0tLdu8LKMg/sW/+Be87W1v4/bbb+fUqVP83M/9HEopfuzHfozhcMg73/lOHnroIVZWVhgMBrzrXe/ivvvue9YIiCtRTFOKWUrnKYXtBppBoOlbRCWhZwEVq0yMA6ll58+eC4LKx4D6SSPBSkQtCWksSmh7nqBiNINwi4BuEXBpDEYH8B2PzRXJKEop6sJh5gqXXJTAbHoesVyjlad0hkQ6CmuobAzgXzIlTa0pGo1RnnljELVEWIFXAXxM5iDAIC+xVlGWakeyUZYCPXP4rdFOn0yrlPOmxxkzpKcrGq/54plDvEKfi8c0sDHp8tVyPxOfsVF3cEEgpSckHq8Upr4JcwMRXQ+EqNVwR3/KbJ5S7XcgwCQWYxxzq5CFZFqlrHZmmMRSZRIRNE0XxIEKO4qug+SCWiQlBIJtLn6P8bhaYfOALgSdU4Hhl2cUB3KEBawg1PIZU6TNec5KNqNramqnKBvN6XLIHdk6J+1ylBvNKjYHnXiA39uEFnXqAkYvkyyKXM7mGdUsgVoiUo+ba4QO0bfpBLKKEqkicyjt6XQqGhvLtASjEcTInkmZstKds5zOObfVozcoUCKwNUwolw22E1hbnWCdJNGO3DRo4VlJZiizuMYg2N+bsqVznI/zNq+zWORWBXwakIkjTRukDMwGCcWaYnidoiCuaor7IpgCX9MA/NRTT/FjP/ZjXLhwgX379vGd3/mdPPbYY+zbtw+A9773vUgpeeCBB6iqire85S382q/92nVpeEtLy8uX4AX+Cplu4UWQCXdNA/BHPvKR5/w8yzIeffRRHn300RfUqG3SJzKqfS4uhOmojmY2ND4J+JFBzwN6HoXRdRmwWSwdvzHpArA1yTFnkpjuqqFZcqi5jH+ty/hXPF2XMZVZbJe5j8LXiDibFAGSzbjY0jlbUy9ppNXUPY/vulgeHMlm1aFxis15nCEsmTkAdmLY8l2U8pjEEnRA1AKMByvxqSc9p5kfThAiIHIbHy4ZkI0mOzO/OLsDDvXHZKrhFfl55j5h7lJ6eQU63ko9FzguCmq/cfAkAH/tDiD2AaJ/UwTZk5ElPyfjPaoFSniWB3PWC81gZYZ1Ei09YZQgPax2ZpwcDQlBYPIGm6foWVxIQwdwYud6ZXMx5ijb9Iw2L8bN+sW9LfdlCL9t9QDGx9nwJRSlYb3osTnPqSqN95K/2dyHkW5R4y9woDdltNzFJQkh3dvKeqHf5c7ljWhFOc1UpuT9imKcgQwIHWN6CSKWnt8uEJA3JMaSGstklMd6gnVBcA4zC4zKhLI2bGUZQsBsmu3MDosDUcWsYxowUDlF4xSl01Re42z8EllKTo0H1LVGKU85TRgUUDuBGVb0bytY6RQMk4L1osesl2NzhTCaUO39LPilsgh3S2tBtLS0tFyWbSGWK+1zi9MOwC0tLS86ribR4iUXB3yjCTLGedp+wHY9wXiapQCpR8wVQUu8iotk830SMwtIC8ZY9uUzzl4YEvJFDa1sYd7Pkx0hddGIGEc8iwLh26mVqoi1x4SL8cXFwSwqiAVwiYw1xUYSXwiaYSx3b71kXKZUdezSJ2arbJRdRCXxaLwA35WoqcT1PFJ7VN7gRx3MDGZFwrBXUi+O9zLgjSKkCqENoVm4QXTNwWxMXxbcZjY5Zwd0k5owngDQdAOpsTwxX2OeJqTS0tM1/bSibHRcRLSX7e7rivABVcVafHoWr0P3PLVVrPVmDJOCJ0fLMRZbwFo2Y6vMSbTFOkVpAtkG1IlDZpa61NQBzJaiXBUgFXhHNRSIQUXwAisNwkkKJUgmKor327jASqmeEQOktSfXDSc2V6PynQp0k5rGK46lFzhZLJFIh0ks0iWIorr8xT5P7GqPFXOB1XTGzEX3hvUSenPWx10G3RIpAolyjIqM1FimRYoQAeclVROfHREEclriQlxAkzLgnMB7ST1J4qKzF6D9jkrgqMjidwHWKo71N9msO7BY8AwL18W+lQlb0xwx13TOeaZ3wP6lKUoEKqspF7HVoY71B0WSEKq97af4Bbz8FuFaWlpabgVaH3BLS0vLzeRFMMO9Erf0AGxfVTDsFWxtdhkuzREioJXHKMfGpEtzoo+q46q2qqOroRoIumnNvmzKwdURp+wS3gtMFu1uuxZNeV9LcAKROUorQQaoo2qaHSyKuEnQm5oNrcjXJd0zDmkDZrpQfwoC20gCnlmdUDVxhVgIsEGR6YbQt6jUMezPkQI2T6dkqwVNo2hGKUkRU4NtrZlXhuAWxbmCwOeeZpCQGr3jgjgzG7A/nTD3KWftEB8kqbKQRNNPz6OpWTjD3Cc8VS5xruizMeswm2R0mhAjA24wqrCYwhCkwOawWcVYWq0czktqr5nOM/RMUK06tuqcVwwvsFXnjKuMuY732TuJ1g1LSzOmSYZYC1Rf66F6Xdx4jEvFTnn5JlM0QwhzyeyQJD8fsL0AqYuRBOnuX3BdayqnEdNFBwm4MOtwtLfJ14p9bNU5hTWU0+geCOnuVOYXijl+nq9srcWago2maRS20QgR8F6waWORAakc3ksmNifMNSJzhABZryZYiWxElPMTEmkDTaM4sDLGeYlIPGGuQQVUx2KmCfWSYFbEa/JeorXjfNljOZ2jZ9EtEToOKcPOMx5UQFUxOmJz0iFJLEoElIy/he1qQKHe23TtbdoZcEtLS8vNovUBt7S0tNwk2jC060/wglmREqyktgohQKuaxkXFqEAU3FYGXBYD8qtlwVpSsWTmrORzzmc96lFKUyfxhmiPmGlIPGomYazxaRS5JgC1QPZjOqV3AteVhJHC5mKRRBAo9odY6814SDwImFcJSnk6ScPGuENPV6Sp5fxaTApJtGOQVKwvDzjQn3H63BIkPno6FnkWdWUwqaUuDaFQMfjeBkS/B/P5Tr9s1F2yherUkppj1KsIi0QM6aAsDbVXDHXBU/MlpnWKCwJfqZj2W/hoot7AOJ2mZ6j6MtZ1mwamVUqiLUWV0DjFvDGEEEulM2xYSWfkqqH2isKa2E82llov59FcrrfSmIYrISyuRdbgrMQ3CjlVCB+3BclOQo/JG1yj8MluF8KgV9B4CQpwUTUNYKvu4INkfd4l0zZGEAQQbm9Tkf3WiI3xGmlqqUoTlfycIDgFTuAbAYnHKRmF2K1AeEEoFaJjESKwsn/Mpupjl3JE8HgjSBLLtExR0nNg34iz54esrkyZlQnZZoxkEIAxjqqSO9EgPghs/+I1+loxVwlZ2oAMBCnwScAVBq09adKQ6wYtPRdMH5cA/jo9Y+0MuKWlpeUm0c6AW1paWm4ObSLGDcBPDbXQ6C3NvJHIXkNdaWwTV6n1UhRgRyzqwhmB7QZy3bDVdDiUj/jL8jBmXe8kXwSpwIO+oHGdgKwEckvhOgE9jcfbSmK7DlFI9FSy/Dcxl73pSGwO9nDNcHmGlB6jPNWihpeSgfWtHp1OhQ2SrvDUVjPMy50g9+5yQT+pOKsCrlAIF+uaJVlDCCKWo6/i9YlKoue7g9gvzDrMG8N9SylLao4UPta+aqJLIixEvhLpmNqUUZ1jlENLz/L+CS5ZiVoIN+HprJZjzT5ZC1JtmZRpNLMX2gZNYVAahAwMdMXYptReszXPUWUsxw7xuZjPDGZTEaQmPyt2ao/1TjtG4wTZCMxYokpIxpCOPHVf4LKA9IvvfFoUhJKB0TwHK1CVwEnPfJZR9TUYaJzkcG/Gcb0CQhHM3v58hFK4RlGLGI0QrIyCMgvdEhEEwUpwUQtDFTFpJRhP1qnJ05rJLCPvl4BGaMPolXD3vnU6uub/nLpt1/dJGagGEp86fKXJsmYRxROY1CmQRjcdkJzVcNeMQbekbDQ4wfSIImjH0vKMPGlYzedIYlKIUDFxSVwSwbOntC6IlpaWlpvES8QFcZOK07S0tLQ8f0S4ute18kd/9Ee87W1v4/Dhwwgh+L3f+709b/ul3NIDsN5SiFJF8XQgWIltYi21rFPjTaBZ8jT9EMvEL7TMOzqaPCfnSzA1+DSKrQdxMeLAJyGKWQcwc6JIegII0BOB3tCYiUTPBLoMUUdgYfaEUmG9pLYa5yX9rOLOpQ3KRiNFwHuJD4KT8yHDvCQAuWnwCMoi4dR4EL9YB8o7KmwHlPK8cv86/V7BYN80rjLnHtsxCHnxNk0vdJgWKSerZZ6qV/hKeRApwo4cpdeQ5zUeQeUN37z8FPvyKd9z25c5PBgzP3hzasIFFV0I1bLAdgT9pCLVDq0dvbxiX39G1qvxOjAczhnoghPTZTaKKCjfDDzlcpzRqEGD6je4oyWu63EpiEVEQ9ONuh5AlKM07NShkzWoSqCURxuHKi+ZIUnFtEijbzHxeB0ISTTHK6dZMgVvvu3LZMqSdWuqgSTkeytHCURh9aym2y1JuzUqdZiVEjVsoN9A6tD9BhKP31fjlxvMsCJLGg71J7z+6FPohfkfnMN2A5lq+PLGGkIEpvMUAlgnUdJTrcZnf/++MYcHY1YGM7T0aOljTbiV+IOxvcDacIqSnleuXCBZKZkdDnSOTMmTho5pOD0ZYIOkcho/MTFJ6lnqPb5grlNNotlsxjd90zftmaTulWhdEC0tLS8+rpML4q1vfeuu4sPXm3YAbmlpefHh2bF0nnMfYDwe79r8XJXYbzS39AAcTCBkDlHG3PWkU5Mmlmqhyr9djjsoiU8CqhYEDRtVh1w1jKuMkDlCE5MtkBI79AgnsIlH1BKvRdQn6MS6VtvfSyyOQV5qpocUnXWPbAI2F5A6ZuMMgCpx5CsNh/MRo37OSTvEOcmozjnUGZGpaMJJEVhNZ5yd9FjtztHSk680nN0Y4DLoZzWv6F1gKSmwQfJ/g2A+TSnXUjJ9yW2ykroyfG7jdg7kE1JpGVcZfVksOo0YkJ/MuW/wFSYuwwjH1KVR58BDNVTkN/JGAvP9hmJ/WFSnCLyhM2LeJOzrTlnLopwhwPFhh2FeYqRjWqXMypjg4k2g6cHKcEY/rais5txmH+tEdA0tAv7n+yVydY7WjmrZEJxElwl+DMks4LKAUZ7aadzSRV1OmaX0OyUhCJqlCpYDS92SbtJw1/A8qWwY2wwbJNZKVBUQzd5Weggh0O1U3L12jtopaq95cnOZRFtyY9mYdjDakRqL7ckoQVlrpIwSpEtJwdmiz+zLS+itdbwUJGPJE1urlLVhuTePGiqVomo0RkcXhc88S1nBUlpwbtpjMs1xQbCvN9uZRYaVGiUCvbTkSGeLvwoHEA7m0yiHeWHcRYh4G8raoGaKdCMQ7HXSPr2GKIijR4/u2vxzP/dz/PzP//z1aNU184K8ge95z3sQQvAzP/MzO9vKsuTBBx9kdXWVXq/HAw88wNmzZ19oO1taWlousu2CuNILOHHiBKPRaOf18MMP3+TGX+R5D8Cf+9zn+PVf/3Ve97rX7dr+7ne/m49//ON89KMf5TOf+QynTp3i7W9/+wtuaEtLS8s21xIFMRgMdr1uFfcDPE8XxHQ65cd//Mf5zd/8TX7xF39xZ/toNOKDH/wgH/7wh3nzm98MwIc+9CHuvfdeHnvsscuWp6+qiuoSxfxL/TWqEPg0FtFUHYv3gqI02NLgEodoJMm6QtqoIWAm4LVAisBKMkOKNYQK+DS6HYIAVEAOanytFrkIklqH6HZIfZSkFCGWse84goDRqwM+UeTrnnTs0anDzgyikDSJ5ql6GSkClVNRqm/xl/eVnXUuNF1OFcOYV+8Vk9N9ymWDWkR2iBM5egZVo/nfp+9gtTvHSEeiLS6X1L1sd9JEAFfGjAQpPDOX4IMgdKNTIWhoGs24yThv+5yslhnbjAtVl6c2luifjpUpbrQWRLUssH0LuYNGspLMeOPanNNllNScW41Rjt6xMSEIRjZHqxglMR3lqEJiprA56tJZazi31UN8tUNvQzA47nZkD7ONwNRKaithakB76n4gM4J0yyG8pK5j4oy7ZPoRnONgb8KsSTjQmzCusugGcIoTsyVCWKawhtopmmmCmfs97z+hNfMy4a/OHyAEQVUahAzMRjkbpUINarwXTMY52jiaUYqoBcEEqsIwKTJevXYO13PY5Q7COVQB6+cGDFZmHOhMUCJwYisjBMF4vUs+EzQDODvp8+SFFapZQm9pTqodkypFjRZl7p1gVhv2dyZ8/sJt2HM5nU2Bm2fM7woEL1DG05i4v888IBHiOsXivkQSMZ7XDPjBBx/kB37gB7j//vt3bX/88cdpmmbX9nvuuYdjx47x2c9+9rLneuSRRxgOhzuvp/trWlpaWm4U0+mUz3/+83z+858H4IknnuDzn/88x48fvy7fd80z4I985CP82Z/9GZ/73Oee8dmZM2dIkoSlpaVd2w8cOMCZM2cue76HH36Yhx56aOf9eDxuB+GWlpbnRHDlRIvnM/f+0z/9U77ne75n5/322PSOd7yD3/qt33oeZ3xurmkAPnHiBP/8n/9z/uAP/oAsy/akAc8VEuLNIkEi9cgAzVaGLCVmKqhXHXomCTIgGxFlDIlFNc/PenxVrZEqy+GDm5zZGKC1oznZRW5p/DxKPepCIhuQtaDpe5hLZC2QDahSIBuFmQZsR1Atg9cSMw2EIKKLIlk4moLgzFYfa6NMZjVLmOYlf7xxB41XNC6aZZMmQxaS/FDDbJZRn+yiArHw5zzFW8l40kFpRz2PiQXL07CjcwCxP+h6jnS3ADiabzK3CTYdxj5LQpTv9Iqvl2v0VMXMpsxtQi+v8EkPUezJrbsm9DygJwpfR/2CL0/2c/fgLKfnA0ZFRuMUUgSmo5wQBMWSYZBUDNOSSVax8dWD+AT82YwTbpkwShieFnTPONJNiy9LAHpP1WyeTkFAuiEQTlGtBCZ3QtAKsT9evHcKfeHi4x+qitopUmWxXtJ4ybn1ATqxdJOa1WzGZhnbRiOxmUSUe6xxoDXNuZw6SUFHvYftaBwAN05wHkg9dsvEQcjGCipSBTppzdwm8ViAEPAGkm6NdZK/PneAPK0x/Yq61HSWC/zxuH9q4g/Ie4EABlksALqx+G4xNsh9MG1SaqcIOuqw2E6MbFldnZJoy2o+p7Ka414yP9yHPdbL2OE6xQF/93d/94606Y3gmlwQjz/+OOfOneP1r389Wmu01nzmM5/hV3/1V9Fac+DAAeq6Zmtra9dxZ8+e5eDBg3vZ7paWlpcz1ykT7kZzTX+evvd7v5cvfOELu7b95E/+JPfccw//6l/9K44ePYoxhk996lM88MADAHzpS1/i+PHj3HfffdfcOH+4xJea9JShqSRmHkuqew16rEg3Y1n5sCij5hPwOrB+ZkCqLVLEcvFSBKrNjGxTomoIQqALQMRzJeNAVcqYHTyLJdRtB9KNgMvAzATCQT0A2xEo7WC5QilPkli6ac3Z00vo3GKtQiVxIa6whq15TmqiWHbjY72u8UYXdcGACSRbcZbjG4mQAVdLpIwLfe5CGv/Q97twOvZJSDwmsVyoumSq4VheM64yuqNp7DMTVa6sl1iv+Jtif0xVBlbyOScOrtE5v7fxq1dDMgmoSiDrOCsprOHJ+QrjMsV5ifeC+TyFkaHQgZPzJWqvOL05IE0sQS3u+0xgSTFTge1C05X0vzLficnP//oM6m/fjvCxhhzEmXC5FijWojh5OU+Q2iOfFqJaWkPlFOubfezMoLoNSgVSZSmdoagNhwdjLuR9QBKSva0JJ/pd9EQCEuHA5QEzjRZDjHWO1xTU4qVD7I88UOuEjcWM77ajFwhygCCmEOdpw12r50mk4y/PH6TfLTnUn3B22mfUCTHGflGbz5hFoQMv6aYlPlnExnctzgu2ypx5laCXalSpsSsWox25adgqMr5p9RSV05yfddla84TyOpSkJ6aZiyskYlzp81uBaxqA+/0+3/AN37BrW7fbZXV1dWf7O9/5Th566CFWVlYYDAa8613v4r777rtsBERLS0vL8+IlEgWx5w6a9773vUgpeeCBB6iqire85S382q/92l5/TUtLy8uZdgCOfPrTn971PssyHn300T1RExIqmkdmBnohPq1LKNcCnZOC+eGAGUV3gtcLMXINesMw329ItGN9q4c4npPP4kKdKqMyFgHSLY9Lo0pXthFIxgGXRDMuv+BRtWdyWJNeiOf1GuohHFoeR2WpKmUpL5AE/MERG1s90qyOi3Ey1tWaqjTWppv1uGv5PKe7++ivzKi6Br7SxeaQbkFwEmksUngQcaGPQYPXyW5TN8Sy85JAV9dcqHtUTtGZbsY+85ClDR3dkKuatXTGkXSTr873caHq4jU4IxDaXB+h7Geh6QjEIm3YJ4FRlUV1uNrEFFYvY8y2DmR5TSItJ2ZLWKviwpAKJGOB7YGeC8xUkJ0P6MITLlGLw2jSzah8JnzAZVH9rXNG0PRAa0fejWaxHHd2tdEHQcc08XerPa5S9LslPgi0cBxd2uJod5PZwYQmPUDI9tYFQd3EBeXFwKE34rMdRHz+goguCG+ABnQRxfxVKbB9SZbXHO6NOV90CVqCVJiRYDLK+WJziCMrW7xqZZ3NqsNSUvC6207y377wHeAFuW6QIjCa5OxfnuwsRuLj72F5/4RhXnJ6c4B3EjtO0GVAFIrugZpUW16z7yxdFft2tTtntNpBJAnMZnvbT1yd3OTzkaO80dzSWhAtLS0tl+UlIsjeDsAtLS0vPloXxPVHPZHjh55kK1AvCXQZTWwzjnG5AXBZNMl0Cbj4ue16nJeM54ZwJkNZwMeS7WYSSCeBaih24itlHc+TTB3FqiaZeuquJEiJ7UB2IcZTksbl6JPrS9hSQ4B1MSDp1ty57wKTeUY5T1DaE4jC8Im2O6pfuWoIJopdTwsDS57slEJYEMrjSo1MHN4p3CzWsVP1056isEgLtQk9U+GVwHkJIprhqhSUlSFXDelimd8juFB1KaxBz2O6drDNDbqLEV2GRbx1FMKvGs3mpENTxkdQyBBLyVcS5yRfPH0I7yQCaGYJKgv4JKaTq1KQjALJLPaN65pd8ZRmEggazDTgC2j6AruQf0u1owyC2SSj+7RV8oPdcYx97XYYuxypAkVloA8H8wkuCHLVMK8NiQu4Xvq8gv2fjdA0BBmiq2bxLHu9KCLg4uNq8/he2kWc/EJ03oxiD/zlU4cJ51LupEZIQedMoPqGQCerOHFhidXBDCM9Xzx/kMHhEnekJMssqbb0kxJWYVYn3LV0ntPzASGPoSSdRcn5tcGMAJyuFdWyJuSWeZnw7Xd8gc2mgxQBSWBaJ/ipuW5qaK0LoqWlpeVm0c6AW1paWm4SV1PzrR2AXxheQeg4dCmxDcgm4FKB14uAdBOohtE2E7UgP6NimflUUNWaptJk67FOuy7AzAK6CngN2aanyWMqMgJUBUEKzNwjfNhxT6ga6oHYcXOYqaA5m2Gqi8Zn01N81e2j06koNzO8DkzzlE3d4VB3zMFswkbdYWJTMIHxNEdeMJixJJnEdoVCg/H4WqE2NHLnzgTEJcHsahqjAjaWcorGsJQVdEwDMrZHz2FWxNX5sc2Y2ZS/2LyNc9Mes3lKbxyQNz4PgyCjm0dVgqYXojpcHev7iUrhjUdvaoSDciujv2/KZLODmGvkQmgf4sq/KqMZXi4JzAyEu/hLcys9fLII1A8xOSdGvASqZcFomtGMUtRUkW7u/oUOTcnx6TJlbaJanvYsDeYMkpJc1kxdyucvHGHrySVuKwKysnv6GxdJgi7isyZcXENSdpFQslhz0sXFRAxZxd+EUoJqOVCd77DvyBYsTVFljtAa2xG4ueb2o5ss7ysYNRnrRQ8pPZ/863vp9Cqq0nBH9wK5ahjVObVVVE5zdtLfSWue1wYpAue2ehAE4kKCKgAvWBtOuVD3+PJ4Hwc6Y05Mlzl7donsjN6VRr+ntDPglpaWlpvDSyUT7pauitzS0tLyUuaWngGbqcCuCFwSIxWCuFg6PmhIL0hYl0gL9TCqM5kpNEOoS0MoFUHEsvN6ftEeiSvpgWaxMu7SmJQRZPzMa4FwAZvFJA1vLpp7shJ0TsoYFSGiietSgdtMKURMHBHas78/5bVLp5nZlFzV/J8zryJLGtSmRhyy2KHFjBO2S6YTQMwVIfUIL6Ah1rGDKJ6+3XYVy6WHILBespLOODvt07nk2oIXnC4GAGjhWZ/H0u62VpjpIjzyBio+wSIKYiGcr4uFyJcK0CzmAC4mTGTrAp8omiUNXqDHcsffJyugE90P3kSXkTfQDBO2C8T7zCBrQIJXsd+EC8gazDggsob0z3rYDiTT3VOkmU2o/UKo3wl011I2mtIZmqD44sYhTp5YpXNK4ZVDNG5vrdymWTybRLfaovCkbAJOCtCx/4SN15duhdgvTYADYIYVRjk2Jl3WNmZ4a0nGARrB/zl+hNXlKa9dPcPp2YCNE0vIfsP0fJe1wyPWqx6fP30b3azmtuGIuY1C/0LFPtra7MIyBC9xjSR0fEwa6Vh6puYTX74XbyVPmFWclYgNQ34mgL9O09DWBdHS0tJyc2jD0FpaWlpuJi+CAfZK3NIDcDKCZiM2UTaBdBwYDWPkQt2NkpGdUyImUYwFehq1GlzPkWYN3eUpo8kKuojl56WN9dCiAz8gXaDJF2W3pUD4gGoCsgqERWIDQZCfC9R9gdcCVQfMPJrBdV9QD0A2gnCgpNlKUcMaKQKHOiN6quJUMeQr4zWkDGTGsrnaoEVATjTeBIKKLhbRsTAyIAN2rYFGIAsVIz7sxZXkkIQoSygC3aTeKeeOXNTukpD1KzIVc/uliGXYlQxMGomuQozyuMG4NGpxBBUTKYBYly1zIEOUE3WCoOJ1VLMkuidCdEsEFe9ZMwDhRXTThMslqsRIl23ZRl1AtSQJC00RpKdeRBRUS4L+JYfelm8xtwkbSYcmtyz1C5azgo2iw6xJWB93QXtkA+mm3eUa2gv8ZEp2IdD0BNIttE0UeBNlWHELSc4iusekDTRdEfU/1gXT/QmnZiuIQuEHgeBj5EfnwAwpA/Mq4XOnjjHbzJGVRI0y0rmgWDM8OVkmSxqcF4yqjHmVUDcaxjGiRqeWTtKwVfcITqAmCtlAb1BwfGMZv5ESjKeZa4QTmJkgqOuYCty6IFpaWlpuDq0LoqWlpeVm0c6Arz/1YBFw3kTTy6sYMeASsIOoo9D0AQ8oUHoh1acCUgaODkZsiRWq5YAuBL2nojnnVdSBcEkM8K/7AlXFRAubSkQSg/xlE3PvzRQ656J0pTOxDdWSoF6KZqKeCoJ2eCvw5zNcz3Ku6KNE4Kubq9RWkycNiXIknZo0sRRVB1VEjYIgBZ1+RakDbqZJ+hX1KCWs1DSdFJqL+fTCCoKOpl2qLE+MV9HqoovCZSBF4MxswEo+p3aKqlncZh+TWLILNz4To+4JmmGgGTrUVLGsHT6Jbp7gZXQ/AOXaIhqlVPG+Cmj60DkT8DqWtleVAiHILoRFhZSLpq4+P0Efzqh7Ub40qHiPvI73bVakGAFN3+PS3VGY39w9TuU1HoEdStayKetlj8pqJmUatSkW7VGNRzR73I8h0AyizOZ2lI2s2fm/sEAao0VUFfBmIe9poFoLyC3NK77pJLluGB06Rq4U5Sqsdgu+bd/XGeiS//wXb0JtmBhNs5C3LL86oDyc4K0glIpRx5J1aqrC7FQwWRnMuXvpHKm2aOGZ3pZwvjkApcE1iiBjbUTZXWiMbGZRItZdnyiIdgbc0tLScrNYhOhdcZ9bnHYAbmlpedHxUpkBX1Mm3Pvf/35e97rXMRgMGAwG3Hffffz+7//+zudlWfLggw+yurpKr9fjgQce4OzZs8+7cT6JyQjeQLFfMDkm8ToWGtQjSTMIVCuecr+nuM1ipiEW27SScp6wnM7xPYeZxRXzpgvVUNB0oVySyCaacdEdcbHgYbkcIx58Er/LLRIyggBdhUVBxKgfoUqolz3VRk5IPCzV0EhOjob8zdY+pvOMsohpAucnPapZglaO1W86R7XP72gkVKUBEThwZJMksdGNoj3F/t0rycIC2jPIKs5Oe2zMc848sYpI43fIenEuIFMNkzpFK0/jFBiPzcSer95fDfmGJzu/KKF+ZM7dK+d4xeoFjq5t0e8VdPslQQfCgQpVCkS20F9c6B94Hd0n5A6XQtML2Dy6hGx+SaJKnlAuScJ2fkcW3TzeCOq+oCk1woLv+J19tnFBcDTbwIcYCbBe9jg9HjAt0vi5lQQnMFOwmUJc2Nrzfir2BXQZFlEfF10o2xERqgqEhe5HPYgVMarlKNO57551jnS30MIhfCA4h8sCh7pjnpit8vVilcGgABnIz8mogbKQeE2zmmBl7HcgNRalY7IFwLcd+DqvHzxJ19RRtpJ4XLOZ4W08l1i4LoJfJDCle949F3mJVEW+pgH4yJEjvOc97+Hxxx/nT//0T3nzm9/MD/3QD/HFL34RgHe/+918/OMf56Mf/Sif+cxnOHXqFG9/+9uvS8NbWlpexrxEBuBrckG87W1v2/X+l37pl3j/+9/PY489xpEjR/jgBz/Ihz/8Yd785jcD8KEPfYh7772Xxx577HlVRa6XPEEHmn6Mi5R1XBQjgO15OifVIjYXvJYEGahWom0iZODLW/tAB2wnioGrMs42IcalNr3FzLjHQgRboauAqsD3wEuBX6hBlUsSXQasFrBIWRYWfA4+85BG+apQK1CxplvjFM4q0qxmViZkScOs6iJFFCRPbpvhZj1kIxj051inWMoKAMRqQEtPqTq7U5ElEARaeu5eOc+Tk2UmaZdQxOOCBj81THopm6bD+qiHlIEQYuqvzQRNV+6k7t4ozNSTbkrC1zSz11oOZSN8EFwoOrGfnGTf0U2K2uDmOSFxuHFUR3MG9CL9XKcOu9ag1w3VSlxcTU9edPaJxlGuRpU0iM/Ldkl7l8fFPZfF+22mu3+hSgRGtkPlNGWjObexH1sp8n5F3WiCkwjtFyLocs8tieAcPglMj8QZvIoTTfx2Kv5iQTpoaHpx1utNjANGwKxK+L8bB5gUKUfPFoTgCSqwms740tYB/uLEEdxUo7ygHgbyswvrr+fpZjVq1TPZ7CB1oGo03gtIYx89NV/iWLrBrEk4U/VJlIvPogyoCwbX84Ra4jJ5ccEwgFCScB20/1+WLohLcc7xkY98hNlsxn333cfjjz9O0zTcf//9O/vcc889HDt2jM9+9rPPep6qqhiPx7teLS0tLc/JS2QGfM0D8Be+8AV6vR5pmvJP/+k/5WMf+xivec1rOHPmDEmSsLS0tGv/AwcOcObMmWc93yOPPMJwONx5HT169JovoqWl5eXF9gz4Sq9bnWsegO+++24+//nP88d//Mf81E/9FO94xzv4q7/6q+fdgIcffpjRaLTzOnHixDP2abrxVQ/joliQgIL5bY56Kb7XRYyHVKUAL1A6KlXtOzDCdkKspaag6ca0zm2zVISLuqEujeXTm67AZvE7gwrUfbC9aPa5NH4O0TRsugHhBSp1SLOICxWBle6cjmlYWZrSSRuaRuG9RE0lG19eYfN8H2clLgvUS561zpyV7pxX9C/w+n1Pcag/4chwFFNQL6mr5XMPKjCpUs4Wfc5v9kEGWKQXuzSgBjEd+kh3C+8kVWGwNoqfQ4x/vtFUS4pyTaDnkP91xms7J/nhfX/GvnzGbcMRvbximJVUpcFlAak8wkW1M5/Ee2s78E1Hn0KoQFBRWL9aEgsB/XhNbphTLweqpfi9cZEzxNqBCsRiwQgPZr77F1p6gw+CymrK2hAWlXdDEAy7RXQ/yPi8ycYTmr21rYXWMZ23F5/FehmaQXxOm350p8wPB2aH47WrKm4TDtyBiulGBx8E3kt8omKdwCBQInCsv0GnWyILhZnGBc1yf8BmEETg/JPLzL+8hJjonZHLJDaKwQP/99wBJi7j37/yv/HIPb/Lm/Z9HZcFRC1xPY8sJDhBU2u0cdh+XEhEXifF25fIDPiaw9CSJOFVr3oVAG94wxv43Oc+x6/8yq/woz/6o9R1zdbW1q5Z8NmzZzl48OCzni9NU9L0ei6XtrS0vOR4iWTCveA/T957qqriDW94A8YYPvWpT+189qUvfYnjx49z3333vdCvaWlpadnhpeKCuKYZ8MMPP8xb3/pWjh07xmQy4cMf/jCf/vSn+eQnP8lwOOSd73wnDz30ECsrKwwGA971rndx3333Pa8ICIDQt+BjnK1wAlktzMmOp39owvTEANnEVOHtGm4uDVFRrNac3+ox7JX4tYbSGESI6louFbgUqtWAKhaRDiKm+DZpdHXEGnHbqb+AjdvTTUjGMZrAAslI0ARJo5KoaCZCXFkOglw3nB310dqhVGC01cEUAtsL0EiarRSRBIIJ3Lt0ho6sGeoYzSAJaOn4innFM8w4mTjG8xjOkec19kIGapHWqyBNGw4NxozqDGclUnu8lYhC3bQaKF7FWOtyJZqmp+plDpgRPVNxcjJkkJW8fuUEfVPy+fVXsJRXbKkcD9ieQ1odTW6v6A4LZlNNMpL4BMplRSokBEe9lOKSgNICfHRbNP1Y6t3r6LpQBeiZpBrubqMSnvN1HykC3ktcpTCdGmMsSnr8xIDxi5psApYGsH5hz/pIGL0TteOTQLIVY90R0c21/WybLUnT99huVIYrc0+oFEdvX+eNa0+ipefx9PWYLEU42Kg7vK5/kn1Hp/wBd1PUS6SbsY5c0IAOLB2YUNuoRDcfZ6S9grzbcFZ0AehmNV+dr/HX2SFmPuV/n7uT4d8Itl4bEJmLHjAVXX1Ke5quIwiJ6OQwn+9ZH+301csxCuLcuXP8xE/8BHfffTff+73fy+c+9zk++clP8n3f930AvPe97+UHf/AHeeCBB/iu7/ouDh48yO/+7u9el4a3tLS8jLmOPuBHH32UO+64gyzLeNOb3sSf/Mmf7EmTL8c1zYA/+MEPPufnWZbx6KOP8uijj76gRrW0tLRckesww/2d3/kdHnroIT7wgQ/wpje9ife973285S1v4Utf+hL79+/f8++7tYtyNoLuSoE30e1guwHX9XQPzCiLhP7RMT4JjO52zA4H0q2AKqNYdLdf4hpFURvkBROTACQ0g8D8cKAeBMxI7AS4E0DWYZH+HLDdsPN99dBT7gt4EyhXomtCuhDLyS+SMlCBMNdgJchA4xTnpj3ytKaqNE2tkcbHFOZDczr7ZqhhTVCBZLnkjb0nMNKxrGd8a+drrCZTThVDggB39tzFPhEx0UIpz0o+jymjUwkLIXNVCppGkakGLT29QYEgmoXBePyipPkNv5U9QbUWzXefeU5VS9yVnuFAOuboYJPldM6d6XnOzvvRTeQlSEhGkvyUXiSgwKnpgF5WEToO2130vxTRfCe6l4KOkSU+JaY3q4DXIZrzLCJgFISnJVJsuQ4Xqg6psqz0Z6jUkWUNRjlqq+kdnCIzBwHMxEK6t+ksoW7A+JgSn3qq/TH6Rc8ksoJ0QyJrQbPikbXAJyG6Z1Zq5Exx91J8Tk7MlxE2piKbmeBC2eX/9dU38PiFY3TTOrrcAvS/HhMx0qWSYV5ycDghBEHarTk23GI1n+MGMQxCikAqHb/0hbfy//zz72d9s4/txuPFpsEsVRw9eoE0j5EhaqSQLorMXw+ulw/4P/7H/8g//sf/mJ/8yZ/kNa95DR/4wAfodDr8p//0n/b+IrjVB+CWlpaWy3ENLoinJ3pVVXXZU9Z1zeOPP74rmUxKyf333/+cyWQvhHYAbmlpedFxLTPgo0eP7kr2euSRRy57zvX1dZxzHDhwYNf2KyWTvRBuaTnK7r45qWmYS1BTuWM6Wyt5/bETnJoOmeohIfU0SWD0Sr1jlkoRkCow7BTUhzXlNMGsa/Rc4LJAMFGtSc/AdaIQtu1EvQjhiAH4OqDmMXBfzwTeBOQiiiLb9NR9QTIG1wGROhgbgg5QKjJt2Rh10drRzWvGkxxfadKJYD5KsY2kd2TMpNRRB0GN+RsOkgjLeTvgTDVAC48IEC6t4ebjyjfAvEmYlUnsl0WyhnDQFAYpAn1d0c8q5rMMV0WNCnGTAtSbrsCnDvJAdtJwfL7M/zd5DXd3zrDRdHl9/zh/Pj3GpExRpWS82YlKXAH0HGw3Jh1YJ7ljuMG59QEu07hkoQ626CObSRABn3nqQXQ7ieWaOjGQ+EWZd0i2xE7kzDabtst60aO0mvX1Psp4ummNdYpBVqJkwnyaIi00A41+/JlJQy8IITCZxd3m4z3eSvC5p+rFKI4YkRMQtcAdrghTTbKpqAW84htP8bXJKucmPabnutxdFOAD6SZ8/ewqAXhyoxOTlBaDk0tBFSCM46m/OIjrO/CCdLVgVGeUVsckH+DYYJPzZY8feMUXqbzmi1uHeGo5j822giS1nNkY4IMgeIG0IrpS6npv+2iba4gDPnHiBIPBYGfzrZR3cEsPwC0tLS2X41rC0Lblc6/E2toaSqlnSOheKZnshdC6IFpaWl58+Kt8XQNJkvCGN7xhVzKZ955PfepT1y2Z7JaeAc+nKU2qUDOJXbFRzDuzJEmsS3XyiwfonJYIJ6MORBUD5MtDjsk4p9OrOHthiKtjnrpPAs4TzdqFS8F2YsA+IeoNyIYd94NLA77nEIXE5aDmcrGCLFBV1IS4VNQ76BDN3FpyYdpBSo+1Cp9YghPIkV6YTgJRC2ZPDDGH5jSbGf9j8/WMbYoRjo6M5eafmi6RboLMUvwimF34aDpPL3TQ0iMEBBMQWbbTBqGilCXAvEqQykEWcDOzo3txowkqmqqiifoXksCKnvHdnS9zT3qKLdfhdD2km9ZMBo7usGRWS/xc0PTF4tpjTbdRFU1fn3lEFZMxCJdcmAREiHXfeh5mBlELzHlDveZ2JEmfLsjekdFcliIQKoWXgUmRMewUrGYzTm0OCVsXIx/kcLBzX/YCoRS2VggZUMYT1ircVCPnClkLXNcjlmryTs18kiK6Fj9VkDleNTjP/zrxCoppCl4grCcQpVZfefA8Xz2zj2xQUT/VxXU8ei6jxkk3QKXxWcBsapp9DXnaMK1SUm0ROvbrWjrjTNHnq9M1aq/52ol9dCeCei2gb59SHO/HunADi9gyCEDPAzJN8WW5Z32001fXKRHjoYce4h3veAff+q3fyt/6W3+L973vfcxmM37yJ3/y+TX0CtzSA3BLS0vLZblOWhA/+qM/yvnz5/nZn/1Zzpw5wzd/8zfziU984hkLc3tFOwC3tLS86BAhIMJzj7BX+vzZ+Omf/ml++qd/+nkde63c0gNwqBSd4ZxRL6fzdYMqYXqHJFuZ8hfnDoGA+SFPWKsJc03va3onyDw4gbWSbrfkG+88zZ8cP4abd7CdQLIZIypsN6DmMXmhHnqy8zLKFqZhoQ8BopJRR6BvMWfUTmWOakVc/CvsQT+V0hxskInDO4FYlF4LXlAUCWIzISw3cD6N0QhOkGwJyhWNLCVfm64ySErOLAQK1qsuW0XGwT+f7yRZAKTnFNWqh04gSxrqucKnl5jfIZrrF8outVH4IGgmKaZf4axAzwPp+Mb7IbyJrhK8QJWC9aLLR596PeVtGiMcXy/X6KmKs19bQzhQMroXVLFdsy/ExAonuXfpDLMm4aRdRpRylytBLO6HSD1MVLx/qxW+VoSpRJYyyjd2wzMqWpyr+wzSktOTPulSiW00daUZi4zlrKCpNSF3eK1QhYc829tOSlM6/Yq60jSjFNltUP0GtRTjVhPtCEEw28jRHYvbSLE9x6uOneNrkzUGnZIsaVg7MqfafxDzFw7bCdzWGfG2b/4LTtdLfLh4IyKAzTW6hHo54ApDun9OesSirWIyzUnShn2rU04WcYjYqnO09PzFqcNU6zl6rBbuOqimKdKDbCSuloSeRTgNCMLzHASvyEtEDe2WHoBbWlpaLsdLRYynHYBbWlpefLQz4OvP2qER88ogl2rCgYbKSVa7Jfu7U+4dnOH/ffJvgV24AhLP9K4GvaERXiBTR7WRU8nA4LaSfrdkdFDiJgaxLnFpQE+j7Sp8lLlExP8HCT4L0G8wmcUYR540XNB9ghX8/9t78yhLjvrO9xMRud391r50V69qqSVa+0YjsVgIxGKMgOcBBh8D44EDljxg+XkM7xj84AwWY88Z85jHwLPHSHhsDINnALOJwZIQCKSW1FJLai29L9XVXXvd/eYa8f7I6pIKgdbqBSk/5+TpvplZ92b+Mm7ciF/8ft+fNWen2hFxGk2hQkHQp1P3Q8dCNRVRWeG6EUIY+sttjs565Eo+3bMMlq2xqj7R/iLuPo+wV6faB0BkJGPeAvfNjKGEISrZWI69ZBMZkxZCdDRSGOJYIbTALKZXiiTVfTgw1cfL1x0k50S0vBidKES0WGHCOvkVMRIXEAbjasJNAZbUbCzPAjBqL3COO8Gn9r4F2ROgZ12a9RyYVLoz6o0RgcQog4gk82EeA+RKAd1AkbhWWk0ijklsoBRjIklSTpC5mMG+Bn5o06hV0TlNVBaorsCpL/+GujImSCzqzTxJx8IuplERSSLpxjaWHWPZMeCgHQndFV7dlwLHitFa4A61aM0WsBYsopzG2IYoFBhPU+jvYAx0FttFzorYPTXAdS/7CbaI2ecPcld5Fa5jIwPBQpijHucpKZ/NY5PsPjqURoL4aXFSjCBoO0hp6M7nUIWYghcSaYX003a5t9bPhuocl6wepzHk8cjhEYLEQxRiaNqokS591RZz9VS+MgKCqoOJ4l9xsy+MbASckZGRcarIRsAZGRkZp4ZsBHwSOC6BkNRtup6CRDDnW7xp7BEmgzL2vMTqCpzdHlEJ/H6NttNceaMF5eEmjbkCtzx2Dm4uQs+7CJEW9ozLGtkV6FwqVygSkcoV6nQaL32BThySAU0cKdrThXRVPkrdDmJRUyDx0mgK7Wpo2YhEkOQ0clGvIQotJufLmGJM4Du4+YgkTgtl5s5oEOwv49QkGkFORRRVQGQUrxzex72za+n2V8k9SQvC6gAizTsIYwvHiYmANMMkTUbxOxbFvg6d2EZJjePFxJHCOIbEEaekKKdYdBOpOZuE9Lr2N/sYchtUVJcDwSBrSwvM1IqIrsT0a4T/RJKMcTX2nEVkGx6eHqWSSwt42pWAsGQhPBd8n8QRmCCVshS+RCvFQL7NtCmmmhnKIGJBWDFYe5d/Q2fCEtPNIknbRjYVFENyXkSrlUY7JLEiiSRuCCpI0IM9cGwFRVrimI7vEM7kMV6CUw5QPV1ELNFa4nkRrh3R7roE8zlUKeKMkWn2zvTz6nV76bVa/KyxiQdmV5FrJJhE480Zpjolbj56OVHTpTrYxLJjgr4Eb0bh1ATxAMi6jR53UHmDVfXTCiBGpMkVgB9Z7Fvo54KBCUKtEBJkKBDCoKVhy6qjTHVKVEtd/MhCSkOSsxFSLMuRWTEMz5xUlHXAGRkZGScAY9Ltmc45zTl9O2CpaHddCrmATi7BzkUkUSq0fqTbw+2Pn4XMpyLbYUVgdQTlvYLGxnShKgoVnY6LsDUbV80wlGtSXddlV2OQvbtGUF2JiAXST9XQTC5BdyQ6r9OUYmWQhRilNLFWOLNpLG5cMARDSTpqDmRaBysRaVluCdrRWE0FvYZux0U3bfrHaszOlNA1Bz+Xvo930ME/02CvbcGjJQ7N9FCwQs4vjbPOmWV/MEiQKBJbIH5R+FuDVIYgVjhWTDth6RyRgAgU/cU2obbwrJigY2NimV4jqZh8GqR88hqo1Qa6Cowgd8RiarDEG9c9yohTZyLsITGSWpgjmcynJl1wUaQx2mLaIskZZAQqHxNryepiDVfF7D06gBuwFCtt+QaZjxHKoB2JZWv2z/UR7C7jBIIkEARDMc6cIigLSk+6xrkgTxQr8r0ddFUS+haBNFh2ki54+hbWtE3igAw1Omf/kjt9AfT3csGqCTpD6bN8eO/qNK3Y1ghL43cd2nN51q6doTo0TT302FyZwhjBa6uP8t2589lf72fyaA9n1gKQgs6IYEOhwUIrj87H1A9WEb2poH3QK7Ba6SxDxKnanFzVJY4VltTMt/PYzbTNjJSatCOHOw9vIAwszJyL04DACEbXzzLdKXH0SC8DI3U8O6bd9LAkS4ujK82LxQXxnMR4brzxRi699FJKpRKDg4Nce+217Nq1a9k5vu9z3XXX0dfXR7FY5B3veMdT1IUyMjIyXhDPQZD9dOY5dcB33HEH1113HXfffTc/+tGPiKKI17/+9bTb7aVz/vAP/5DvfOc7fOMb3+COO+7g6NGjvP3tb1/xC8/IyHjpIvSz2053npML4pZbbln2+uabb2ZwcJDt27fzqle9inq9zt/+7d/y1a9+lauuugqAm266ibPPPpu77777OZWnF7aF68Q4VoK0NH2VNp3AoeQF3L7rTDaOTbM3TDU6EylJPIG2BCpYNHy8qKA17zBVLtLjdihbXV5WPcbQBU0m2hWOzPSkqbtNG9FS2C2BXzJYNUW8WAsrjhRmwcHqCIxK41mtjsAIUGEqOh3n0kU9YxlkR+LNCvwxiZcLIRdiqQShDLInjdXVCy5WB8TjOfR5TUQMjpMwlGtQVR3yIl2Iq7dz9NX1simctkH5EgNYUtNse6nLxEtFpo+L1vuxhatiOpGN5cbggvYSnAc9rO7Jb5lCgztjIXTqxrlseIJV7gLn5w4BsCcY5p9mLkANdYmnc2lduKEQrTQmkdjjDkILyqUOfYV0gbHidtkwOsv4/rGlFdvOoKRQ8il7AZPzZaTSDJRaHHZKGNugPYMzt7j4lFt+jaFOxfEtK8Ff8NJy61pQzAccma+i3ASxNkLMFYiKFrl9cyQraCPj2YzlFtjX6qdoB7z1gh20YxdXxrQTh36nhStjIqN4rDHMO1fdx+PdEd428gDzcZE9tQGMESANwYCHB4S9CT1Ol95ih4bl0lpwUHaCvT9HXNREPRrhK/RgyMZVM5Rsn8emhumENp4T0VrsIWIjuaBvgp/4G4gjRQJoC3KFgERLpvf0o/oDbJWkNQjtBBmLZWn0K8qLJAztBekB1+t1AHp7ewHYvn07URQtq6m0efNm1qxZ8ytrKgVB8JSaTRkZGRlPx4kqynmyed4dsNaaj370o1xxxRVs2bIFgMnJSRzHoVqtLjv36Woq3XjjjcvqNY2NjT3fS8rIyHipcDwK4pm205znHQVx3XXXsXPnTu68884XdAEf//jHueGGG5ZeNxoNxsbGEJai69sUvYCxoQUO7R/EaihaIz7nrZsgNpLcIRunCVbXEFTFYrorJK4BW5PPB7AuoDFX4N659eworKJa6jJabKQ104pdAMKcha0S7I0Jo25AxemStyIakcfjR4cwXZm6NhJSdS7ALFouWqxBJyKRbotTI6k0QhjaLY/WbAFha3rKHRptD23Sv5cxhIeK5AI4e3CShTBPaCzu6WxkNioSx5IoJxDFAszOAWnssd0QdH2LWlhE2hp3VsFiyqeMQISCmfkyss9QdELanoPv2xgDsQedfgv3JDdOFYCRBhkLChOCI60qxzrnMdHbgy0SdjZG2TJyjMdnhrBmJf5AAk0LnU8QliYuGEoHBWFsUbJ9YqMIF90sSc6ATJ+LdsCWmsQI+qstRot1Hj6yivwxid00dIdkWs49B6Xp5TZoRw5KaTqzeay6RdxvSITCsRKKbovpepGeUofZvjyWn6x4WXrhR/RYHc6vTPDPh7fwsv5Jep0O5+SP4oiY0FjMRiXumNnEfDePNxSxY241m1ZP8fXxSwgiK7VPT4ewVMGNYoynObMwSdnqctuRM5FBmnosQ/BmJO01aURIVBR0I5tm4CKEoRs4xFqi7dRG8+08Y0PzfPplj1BL8twyt4Vt3kZyRjA13pPWSgwUUaIIIouo7eD4LKXIrzQvliiI59UBX3/99Xz3u9/lJz/5CatXr17aPzw8TBiG1Gq1ZaPgp6up5LruaVUkLyMj4/Tn2Syy/Toswj0nF4Qxhuuvv55vfvOb3Hbbbaxfv37Z8YsvvhjbtpfVVNq1axeHDx8+YTWVMjIyXoK8FF0Q1113HV/96lf59re/TalUWvLrVioVcrkclUqF3/u93+OGG26gt7eXcrnMH/zBH7B169bnFAEBIByH4Z4ma0oLXFI5SO+aFnrx9+KfJi/mkd2r6Zs0JG5aSt6bNajQUD8jrTlGKIkTSTnvs/6MefJWyKDXpB7luGdiDYHvYDTopo3QAtUbYNkJQWShCwIp2oSJQlmaSBm0JUiKi9f2pKVvkYqoIaM0PVkvRkpEszmigo1yEjZsmORYvUy9mSPq2KieEEZDgr1lChMCbYGf2Izk6izEBRaiPJN+GcvSyARMu7v0eUEPBAMxRAJVijFAWDEkiy4KkYBTl/hFi2bBRQB5JyKOFdG8R+IKZHTyG2ZpPKa+WRCHgsQVXFadYq03z0KcB+A3Bx/ifx69iPZUATtvUN1UaF02JSoUxDlD/eyYqkrYNTPIYLlFj9vBT2zi3ggsa9E+hrXlBlWny1S3xIDXwhhB0GvoDhm0bbAW3Uh+3/KUbGMEYWCDZYj7IkRHoS3JvJ1noNoiaDvMxAq7KzBSIBptVhJRb9JKXC4r7sdbF/HPE+fys9kKDw+PUrIDpjpFpmfLvGrTXl7ef4B/mrwYJTX/NHER4/sG+D9efi+XFvfTTHJ8fuTtaZKJMqx1ZnlFfg+vLj/OPw1ewt2H1oFIS9J70xYygLhmM7d/mKii0f0hlhujtb00RHPtmG0L6zhr5Bgl6ZNTESKWBL6N7Ch0Jaba16Li+ewdH0GEAqsNRp+YtvaSdEF88YtfBOA1r3nNsv033XQT73vf+wD4q7/6K6SUvOMd7yAIAq655hr+63/9rytysRkZGRnAiyYM7Tl1wM+mvIjneXzhC1/gC1/4wvO+qIyMjIyn4yU5Aj6pKIter8OqXA1bJGx2j1FL8kQoHnl8DKRh/gKNiFIdCIAkB848S6pX3bZLcLBEsMlidaXOKlHj3OIEl5x9kFbiMe73EmiLeLGoWDNykcIw3SlxrF3m2FQVE0m8plwm1q78VAXt+C+sVqDdNBFDddK6csbSKFtz4ZpxOrFDKeczOd2HXfUZqLYYK9VYqNTZa62mOC7ZNTnIpjOmmY9TQeuzi5P4AzbH7NKykutGGrAMbtVHKY0xgniwu5SIEBfAH4opDbRIEolrxQy7HR6srUYUYoyyKB2JTtpjPE5QVhg3RsSKsD/hrPwUvVaLXqvFgNXEFjFHahVUSxH1JCANdikVRA/mvHT13taMVWtIDDPdAqG2GMo12VsMEU4akRANR/S6bVZ7NRqRx08Pb0SqJNWRCCRhVSND8FdHmEkLpAKd+pS29Bwj1pLphRKFfEBtpoj00mP1Tg7l6FSo3YDfZ+MeXOEkAyFIkOwPBrFFwu+vv4PamjwTQQ+1OM+WylF6x9q4MuKR9ipmOwVGi3Ue2nYGW1/xONdWt+MbG2fRRyZkqvPwUGcND7GG1c48q70alqWJX9bG7CqgLYO2QJdi4sBGOwaTSBwnQUlNuJB+Nyquz5rCAn945zvJl326TY/eHZL6bxhiV/P6LY+gjWTGL1IYbBPsLtMZNUjHRvsrma6yiDZPyCU+3TmnOadvB5yRkZHxq3gpuiAyMjIyTgcEz8IFcVKu5IVx2nbAQknWFObxZMS2+npunzuLiVYFVyWs2zhFzop4/NGx1DWQCOK8wRprEwxaWIc9zEjIcE8TezRh//4hFo5UeNhZzdBojSuG9lO0AspWGl3QY7dRGGyRYIuYTtVlKiozXurhcLOHCdG79GsqbE1ka4wWmFhiIpm2hFiCm0BbYWyQ+Zjeaot9C33Um3nOHJlm0tLEocVco8BCK8+qnjre2iZ+p0x/pcW+1gAXVsdpGZdG7NGNbRIPxJPipFUoUmF4pVEqlSjURiCLBZKFkDhnwEuwpGak1MRWCbPdIq4X0ZkpEfQYwrJiZVMInpnWmEA4CaJm4dYtfjK3iVqQQxtBf65FM/J496bt7B4d5Of7N2LZMeWCT8EJ8asWjY5H/FiZPZUBLl41zlnlKfa1+jnWKRM1XLDTpiyk4dLyITra4eU9B/BUxPb7NhH3aihFFMp+Ki3ZcQFr2RR5PsxTdAImmn3Up3OQS9C+ojrUphs4SKlJIhujUndUuKoHeeDQitnIFHIE2uIfD15CGCuuXf8Q/VaLfrtFj51GXPja5l9mNvP44WHecM6j/PjQGZx3+V5+d+jn7PDX8vXxS+j1Olgdk7abSDLhV7njvnNQvQHrhuawrITBcov5C0Ka8wVER3HGuimOlKv0FztMz5XTKC4WayUCtkq4qvwo344vxO86GD9N3rDshLdvvYdm7LG30YcfW/QWOhy1SohIYJITFIyb6QFnZGRknBpeLIkYWQeckZHxa4cwBvEMI9xnOn46cFp3wFsKE0yEPVxaPsRPF85gbXkBiWEsv8DPpjZQ3SmJc4L8tKY9LGmVPcpDLZqrJPg2tkq4pO8wVw7soxW7zEcFHpsf4luPnY+pOYhKiOPFDFdTbYg+r42nIpQwNCOXZuhhAKEMxlepi0GASQQ6UBBLVFuSFNNVexKBNysI+gw6UFQ8n2bg8rbND3Ln1Aau2vI4D82OUm952PeWODBUZOBlM8x4JdaX5wHot5scDSrYQhMkFiJZnk+vfEAL/K5DLh+SxBJTe6pDQSzeTydOjxkD1mAXmnliT570ihjdVQn9/U0Ko3McnurlDQM72dbYwIxfZLZb5OyeSV5VfJwDnX4cN6LghcwcrTKfj+mptFnfN8+eTYqNA7M8ODWK32+xvjBHO3HZ7a/GLLogaFmUVJcjYQ/aCCq2T3XjPP6ivGLOjpmpFzGTHnHepNETflpevup0mQ/yyFyMXLAQgyHaCHrzXZpKk7MjrEHNgdbqtE6d/YLEBJ+CaHXot1v87vptHAl7+MaeC+ku5CgPtMg5EbVWjmA+x9j6Gd5x7gMc8ysEXZv3jfyMBMFf776S9p4qR4YCVk8lmCBAxILDrR6MrUnmXeZLOQRwZKaHSrnNwFCdmeky71m1DW91xAOdtdyuNxHFioIb4i9eW9n2OdeZZsP6KfzYojQasEuMclbfPHkZ8u0D5xLM58BNcPJRWmtRglw/RrJ734raCUhrBT7TCDcbAWdkZGSsPNkIOCMjI+NUkYWhnViMMdSTHPU4R4/V5l8PbUOiaeocP5g7l9lGAScniErQlhJtQ3GPTTOXw2iBdBISLXmsMczLew7Q77XYkJvhkvJBgjU2zcSjox26ic2kXybWklhLUNCOHZqhx8HZXuJQIeZtnKbEqVvEObD8VHPAWGlBDRKBKMTQSlfI0em+ku1z7cgOBqwGjdjjVZXdNGOXh7ujxDnwZiXtIK30kFMRvU6b+xrrcGQqLRlrmS4kPKmqQNBnMF5CpdRlsNjiQNhLXIohSVfyVSjAV/TnO0hh8BOLS/sPsc/r52Ctl5aVT0cGQoI5AQHyvwJRDokTSckJuHz9QS7KHWSdM8tkXCE0FlXVZk8wzL2TY/gdB7/jQCwQEx6zDZvOoM1ApcWG4ixnlabY1RxiolulZPt4o210KS0dTyJoJmk0wd7mAIdrVZJE0qnn6ER5ZD7GOuzBWh9xxFuKngAYcJocVRVWDdYY9/up5gPq9Txz7Tw9+S45K+KM0gx7SyNoW2Iv+Cs7y82l97DWmWGTO8ll5+2nluRpao9A2+RlSF4GeDKiluT57r4t/O552/BExJeOvobG0RKWBmlpopxMC2Lahg2lOfbbg8imQEmD1ob8/Tlqq10K6+sQS9raRUrNeKeHRAtsK2GhnSMupHe4KlejaSz+r/XfA6Cpc3xZXUkQW/zPA+cTLHhY5RBjBK4bEQoPaQRxX/HEhINlURAZGRkZp4YsFTkjIyPjVJGNgE8wScLP5s7gdQOP8oPpLby89wCJkcxFBX72+Bmcte4YZ7x7N1W7QytxmfZL3L1nA6ZjgTAYJRguNNj2+Ab2lAfIewFDxRbnVo+yxp2jqHwckU7123mXQNusduaoyg6eTLUS9q8aZEd7DQ8vjKKNINESJTVjxQXU4s/rXFDgkcMjIEC7CYmXug1kW5G3IupxnoN+Px8e+DH7ogEKVshwtcGRQpGwahD7KthtwXnFI3S0wy2HzqYn32W40KBgh3RdMPUn6uTJEHA0ltK4VgxGICyNyOeg0cBugR8JjjVL5KwIYwRSGByVUPYCakVNY62iqhRGnzwXBALKXkAj8GiGLnvCYQ4EA3QSBykMO+NVfPfRc8kXA9ACOWdT2FinVfTI5dL7mJjopez6/KuR+7i8tI9DYT/1OM+G/jkCbwhJGunxivweRu0Ffr//Dmra4YZd76RTy1EdbuLaMaNrj6R6H7PLCwFsdKfYYw2ysX+G1488xo76anbpQVodl9Fyg8cnhimuC/D6uvjVIiVrZaMgjKXYWtjDN+cvoWx1eXlxL2P2HAUZYIsE39h0tMvD/mr+ZepsfnPjTt7fcw+fOvoGHnhkPfkjFmElbZftEUlvtYKIFh0AkUA7ht5cB2MEe8eqeNOSVm8OVVd8adcr6bRdjBZ4+ZCeYoexnhq7FtLKpbZI+KvJ1/GbvQ9iixiJphF4uComilJfnFxMDrKkBk9jAolqBickGOF0iAP+zGc+w/e+9z127NiB4zjUarXn/B4r24IyMjIyTgbHxXieaTuBhGHIb//2b/PhD3/4eb/H6TsC1pqzylMUZMDvjv6cvz+2lV2Tg1y+5iAfufxfeE1+F9NJEU26gBAVFeeXj3D7zJnsHh+ip7fF+4Z+xpv6HuJb0xdyqN7DVKvI4YWXsbpaA6DqdsmpCEtoAq1ISoJ56dNrtfBEOgouqoCBXIvWolKaJRJcmRAbibX4E+t4MX7LQdg6HZFqAxKk0Ny9sB5LJJwxaNgReLRjh4t6xzlYGQJLY83a2C3oaIe9nUEGSy32HRkgGFQsNPM4RbGsLH1UTBuVpRKONcsUCz61+eLSAk66OixotzxGV9U52qkQaIvLKgd5RI1yqNCHsdQyhbWTgW7ZXHrOIR6ujbJr/wi1VXm+fP8VmK5KU6fdhA9fdAcKw/cmtzBdKfKWdTtZ785QVR3+5sgr2VUfoRW6/Ky+iQtKh7k0d4DHgxEelcN0XYUECvkAW2iuyk0ymQiGVcAH1/2Ef7BfzmihjiU09chjulZEF+NUDW2RelIgNpJz8kepJ3k2Fmc50qxS9AKiRFEuddg5NUJfqc30hiL9O1Z2eSnpKTBmNXBlxA+PbOYubz3rSvNszM9QUV2mojL72/08PjfIYLHFR/p/igLu2LcJq6HojMWIQszmkWn2DK9FD6XVyt/c+yCHz+jh4GwvG0pzJEawOzdCd9QgZx2Sasz5Q0f5+fazyI22GK40mayXcFSCCFL7tBKXSCv+z395F3iaN567k/GpHs5aPYVtxwS5GKXSNuWHNoQSFQg4ML6iNjrOcwlD+8VK6ytVBu1Tn/oUADfffPPzfo9sBJyRkfHrx3MoSTQ2Nras8vqNN954ii/+CU7fEXBGRkbGr8LwzJluiwPk8fFxyuXy0u7TqQjwadsBmzim12rzcHs1fzp4J2rk5yQjAl/bvC5/kAj4ZuNMJoMy3cSmanfps9uM5BvUBz0G8m2u9OpImlyRO8icdmlqL401baznYKuX3XMDtNrp1L1Y8GmEOfYv9NJueowM1Jmpp0XgosBaVD0DoTSmmy70oQVYhlxPqqpmOhbaMcgIdCGhaIWsz8/x2tIj7Iok/2vqIiyR8JbqDl5x1V6+Pn0p9+1bS6tgs7/bz7Zja1hdqWN7MXO1Io4b49TSdNnjbghhQM3bhP0KrSVCGKStMSqdzCQeGFsz2l+nFTu8afBhqqrNY91VrMnNQ6BwGmCSk7gAB3h9Xc7Nj/O68k7u69/AgNXgX19wD/NRgVbssD4/xyvzu5nTBX4gXkYl53PMr1CxOpRUl619BzizPE1sFLcd2MQdeiNv2bSTYbfO49NDDC96A7qBzd3dDdi5vfy3uSvpaofLSvuZaReYbhXZ2DvL9r1rsXMRxBKhnpgEdrRDyQp4uLOaWw+fSdEL0EYQx4qJrkcl3yWMLTZUZplUQwT9Hu4KpnTrnIWN4d/23clbe+7na3Mv58G5UX667wySpk1xqMWqSp0PbfopV+T24QnBlxYuRs85mGrM+17+My7MH6QgAz7w8L8l6slhijFXelOcc8bX6WywOBz3UEvy/KT3DPwFj0QJ8j1d1uTm2daRRKFFzkrdb93IRrVS+2gj03OaClqKOw5vRMeSvBUSRRavO/sxXlXZzX96/HXU5gvIrsRqCmSpiG6vbO08eG4uiHK5vKwDfjo+9rGP8R//43982nMee+wxNm/e/Owu9Bl4zi6In/zkJ7zlLW9hdHQUIQTf+ta3lh03xvDJT36SkZERcrkcV199NXv27FmRi83IyMgAFtc6nskF8dzf9o/+6I947LHHnnbbsGHDit3Gcx4Bt9ttzj//fP7Nv/k3vP3tb3/K8b/4i7/g85//PF/5yldYv349n/jEJ7jmmmt49NFH8TxvRS46IyPjJc4JigMeGBhgYGDgeV7Uc+c5d8BvfOMbeeMb3/hLjxlj+NznPsef/umf8ta3vhWAv/u7v2NoaIhvfetbvOtd73r2HxRFTARVZoPUDXBVbpIEg28M81px8/wr+Obj56NnXayWRMYQDCb0r67R6rooYXg0UlRlSElAyQpJTMAmu84r83vxjaKpPUKj8I0NpOmV/qBNZBQd7dIc8ZiLChxs97E6n9YYs2XCutwceRUQaYuS8ulohy/d+RvYdYURi+nJseCC4mHeVNhNQUj+fOYKurHNUM7nNTlNYmq8Ys23+U7fmXxh16uZ8YtYUnNwrpfecpsgsjBGoCIQOQ86nUUjQ1JJmD9aYXjNPC3fJWlZiEXha734RKUw5FTEoNVgzJrnJ/XNtBMHq6GwWyc/QH1t3zwaSZ9qc3VpJwpDuejT0B6RsSjJLjv8NRwO+th/rJ++nhY/3rOJ2+KzGBqqc2bPNBeVD9OrWpy3ZZxjUZUHamPMhEW6TRdk6oMIah4FGfCD1haKVsCBeh//z9GrsJQmjCWr8zUuv/QAA1aTT9/9lmVp3hNBFY2gm9h4dszUTIViuUtzpkh5oEXLd6nkuww4LZQPRonUPfQktboXQpyzeDAc5tXeNMOqw8DAbTT7bZpPslFJhgyrhLxQ3FQ/h7979DIqa+u8a8N23lbeQUkYIkBogZECIQ1NbcgLKKkIKeaZkQE3XXwz2/11fOPIxfixxaDTQKxtM1htEetU8D9nR1jt1K4awYhTR9sGYxvW9i6w2x/i3PJRXt//KNcW91CRDj8eHufOcANh3Sbs1ZhSASZXxDzL0TxzyYsTHOhz+PBh5ufnOXz4MEmSsGPHDgDOOOMMisXis3qPFfUBHzhwgMnJSa6++uqlfZVKhcsvv5y77rrrl3bAQRAQPKkB/2LISEZGRsYvcjqooX3yk5/kK1/5ytLrCy+8EIDbb7+d17zmNc/qPVY0DG1yMv2pGxoaWrZ/aGho6dgvcuONNy4LERkbG1vJS8rIyHgx8hzC0E4UN998M8aYp2zPtvOF0yAK4uMf/zg33HDD0utGo8HY2BgmMTy8MMqZlWn+V2sDm9y0A9dG8v8evYo9cwNw1ENYkOQNiQF7XlFr9JHkNRPzHn9ReCP75vt55ap9bMpN02u1WGUtUJVdbKHpVR0UBolBCUNkJAkiTTtenN/MJQU6FZe8SEfpjkgYUq2lVOTECKaSIn9dvJLIMrjHLBI3nQJ6IqSuFfdHfdx65Ezev/FujoQ91HWXuk7olRZvL+7h+z3nsrV3P1IYHhxfTVN62Cqh2cpRrIplyQJWR5BUYGB1DVumaZ/WgvWEIDksJoEYDrV6+Zm1iRGnTmQkP92/ERWCXuFq6s+GDaU5bJHw3+dfwUWFgyhhmIoqdHQqGO9rm/+193wAdKiYrxeoVDosHKkwPVOmHTh0YofNpSkuKRxgzJljxK5xJOzFK4aw+D6yrUiM4BxvAlvEnJcb57HqKIN2g1X2PAOqSUHEzCQFvGIA1hPGiI1iT22AC/omALDdGL/rIDqKnnyXj234AZ6IuL11NtGagO5el7xlrZgLInEFt9XPZjzsY4M7xSpVpyBiSqq57LyDscOecJgvPvZK4tDiTy78Dhd5R5hJcjwcV/FkiK5EyEhjIsU/1C/h8dYwmwrTXF7YR4LgSs9ni7OHzqjDA/U1XFN4lIsuO8iA7PCD1ha+cPTVlL2AxQr3izY9wqYLxulEDi+rHGOssMDbKvdzlq3wjeRoHLA2N8c99hp8TyNCAfIEpRroZ5GLrE9/RfYV7YCHh4cBmJqaYmRkZGn/1NQUF1xwwS/9m5XKSsnIyHgJcRr4gFeCFf15Wr9+PcPDw9x6661L+xqNBtu2bWPr1q0r+VEZGRkvYY77gJ9pO915ziPgVqvF3r17l14fOHCAHTt20Nvby5o1a/joRz/Kf/gP/4FNmzYthaGNjo5y7bXXPqfPOZ4oMOC0+Pyu36Dr2xgt6au2mN7fR3F1gzMvPsxILq3nNhfk2TkximVp9JECohoz2y1SO1bmO/PnQSKwiyFSGvrKbcZKNYa9BkUV0Gu1cWWEJyISJAqNLWIKMsQ3NgpNw3g4IllSSjvuomhrl/GoD6MFwk2QoU3iGow0NHWOj+77VxyY7Oc3Nz/MVYXH+bJ/BX9bexnfGL+Qd67ZzttLO8lbIRflDnJ5fh+fCX+T3YeHENJgtMDqGoT3xAzBKBC+pOCEdCMbP7RJ8hrjpZEcMgYczXSjiGvHRHqM6ebZ9BfbuG6E6eYBg1BqmcbEiWbAaRIZRT3K8al738JrNu3htofPRgQS42iELzGlmDPWTLO36dJfbVFxfdp9LlIa+otttj+2nj2DA0wMV9mUn6bfbjLi1AgDCxUsDncMVFWHWpKnrV2U0JzpHUufnYjoaJcZU+KBzjq0ftIUWQgirZhrFvhXZ27jyvJuHuis5ZH6CI8Go2wsz/Iqr4lG878bip7eFp0Rj74VnGInjiDQFp+7/yqkMgz2NthcnWZNbh5PRszHBcY7Pdw3Pkbs2whL844tD3COe4z5xONP972Ng4cGePP5D0OgsKfrCL+P7028jJlHBvhZ8UzsVyTMRQX65L2stQLO8SbY2xlkWEGfaiOBovJxvYjJ+TL2oodmolNleLDFJ9Z9hwSJNpJRq8mAFMwnMX/fOJ+/33sZZ/VP06znUE2F3RKI6AS1sZeqHOV9993Hb/zGbyy9Pu6/fe9738vNN9/Mv//3/552u80HP/hBarUaV155JbfccksWA5yRkbFy6GehyH6C1dBWgufcAb/mNa/BPM0vixCCT3/603z6059+QReWkZGR8St5qY6ATxpG0+e1WePO8erVe/nutovAwILSXHnxY1xUPswr87spyQgbQ4SgOWbzSDDKd0fOx5EJW6v72FZaz7FOhflODilgbrbE0ZrHUZVK9QlbI63FJIZIYmIJscAqRcQdi0JvF1sl1OYLCGWw7IRowQVPQyKQXkyl0sFEEhEohAarKxBGYYuE8bkquXzAGyoPU9Muhzq97GoOUbt7iM9PX0X9gjyxkSgMm+02bxt5gJuDrUwd7AUJIgYThktmkaHAakomZqtoIzDTHjISiDi9B+UDviSaLqE3tFg1WKNgh8Ra0lfscGS4iNNUy/QlThZ5GZBTEZVKh3rkpaXdcwlIg8jHvOOcHbwsP8G/5M6hE9v832u+Q7RB0tQe93fXcV9+LYeaPfx0/0Z+7J/FhjXTXNBzBB0qrHorrVSe1zzmr2I6LPHDQ2eTJJLXrX+cZuxRDz2kMOw4PIYQBteLUmnORcbbVeJIcYWrCZwpLvfG+R/WhbRCF1fFHEtCatrh0cYIYWylqa4rqKmhLUE9ylEo+TQX8hzbO8AxPYDVTkX+o6pGdgVGgbu6zW+d8TBbi3t5OFjFl8ev4NDjw9htyT3Ta8gfshCtDlZzgMmJHpQAEQl+Pr+BVuhyy8GzOWtgmt8duYupbpkfdlZhi4S5pMjO9ir8lgstCyuVOSFILPZEAwyoBp6I8GRCYgT3BhX+8tAb2DcxgJh3eDCwMZHEbgpkBHT9p73n58+zCTPLOuCMjIyMlScbAWdkZGScIvSzqEv/YvQBnzSMYbJdpjAYcHXlEbg8rUuVIHldZSdV2aEqQyRgCygIGFIJVXmQ6lAHjeQcZ4qNzjRt7S7pPcyvLzIbF/G1TTexiY1islsiNorpdpEgsugGNnGskA2LpCLpNDwsN0EnAq0lOBphaUxo4eYitgwc46czJYytMVJROGJorUlv43c230tFdUkQ/LfpV6ONYMHPoW2DnHH47zsvx3ZiHuldRWgm2eRO8uqRvfwgPJtWLU/iKsJzVqOmpgHIHzN0BwXhkTz2uhaBZdJ8/TCNzrA6BulLjGUIuzbvHLgHT0T8j7nLeLC7CiTEeZDlEvq4vsRJYCHKM1yqc93A7byjt8hMXObS6iGaiUcj9hh0mmwt7GFYtZC9mq8evZxeGVMQEinabLAf4qrCYxyOe/he7/lMdKrMdfMcaPch7QTjps8XZajHOSKj6HYcxKTLd+rnI90EqQwbhmZBGK46YxfHuhVi+UTOfidyEFIznXSIACWgojoIYdg5P8Inwrcw5xdohC6tqSJ9cwazgqv8QkNORbxubBfJmORot4Kf2DQCjyBRlJ2AHq/DKq/GoNNkyK4zHvbx/+26ku7hEpYvkIFgblcflZoBx8ZqC7RtIWOBiAUH53tJEkl0uMCOySLaSB6ZGOHPJt9CNJ2DSoTxFapupWtci33YxEKFP62/lTU9CxTtgPWFOQ60+6gHOSYWKpiuQkaCsO7i9fio0CEqGkwUrZh9lqET4BncPyez5uHz5PTtgDMyMjJ+FdkIOCMjI+MUkfmATzwT+/tprknlBc8vjKOETstza4f7gyH2dAc53O6laAcMuQ0uLR5g1F5AI6kleSaTAqFRlGSXPtFCCs1GexpPxEhh8BYT3Y9rQHS0TYjC1zYRisRINJK8CCjIgARJZFSqHyE00WKkg0PCsU6F/cf66a4FY9nICOpJjjeWHmIuKfCNucv46f6N5PMBWktkLJANAa0cdhP+qXoRxgjetfpe1rhzvGrVfnbmRxg/cxiv5lBatIm20wQQowx+y8FuylT+UqT7rS7IWBBXY+haeCJinVVna3kfx7oVGkMebSsH+dxJfZa7G4M0qx7rrBYDqsGkalNVnbSgqlFIoZmJy8zEZepJAU/F/F3tYobsOuucGUZVk1ErpldNs3HwNhIEDwer+FljE/tKPn5/ERew5i2KKiAyij+48Ha0kRwLK2zOHaOqOtgiprzOp1d1+O/zW3lEPVEpoRU4SGl43553cbRR5sLhIxStkEOPDWM8zbjsQ3QUphSTP2SRn41XtLKICg1T3RKb8tPkVcC5+SN4MkIt5tR6MsIWMb52qCV57mlu4H/vPhsx7iEsQ25KEJbBOAYZCqLhKub4N1xDUjCEgYXWktykJOgxPLRzHaopSWyD25QkTUmSN4gkdYmoxQCc4EgR7Wj27C6jPcO95fUQCbxen9C3UE2FdjUyHxN0bMSgxmoLhHWCupjjguzPdM5pzmndAWdkZGT8UrIRcEZGRsYpQmueUW3npaaGttLYvT63zGyhG9tU3O5SlYeHZkZYmC+iplziYoKIBcY2fDO8jNWbp2gFDkFkUc77tAOHgWKbPq+NJTRnFadY7cxTUl2qsoMjEqTQ6EU3RGLkkvtBCY1DQkEGeCJGYQiFXDr3uBpTSBrMb+JUFyAqGJy64MHGGBXVZUdrDf/yyNmItiKyE4KOTS4EFUBYhsSBo/MVwrrLF9qvZrjcpGCFBIlKpSWjJ37JVWBIXNA5jWhZRL0xIpSYXCrH6LQ0IpYIX2IPdblp6pVsLMyQlyHHmmW6TRcRyJPeOHcdGuYnPZv5ul/mFZV9zMdFjgQ9RFphy4RG5LFjchVhYCGkIezYPCRWYzqK4kiLvkKHVw/tYdBusMmZpCADqqrNxcWD3GePoZ30YcgY7llYx5rCPJAmf5ybH2fYqlOQQfoMUcwkBdqJu2yUVFsoUCj7HJrrITlY5Ocdl6RjUd6naG4CU4hxZxVxf4B2ISgpVtKR49YiHts/ymynQN6OOK93gj67TUn52IvtNDKK/d0Bdsyt5sjeQeyapLwPmusEYSl1TYlIEJUExpKIGJKixpmwQJIm72hIXHAXBDJSaMcgQoEMASNQoSDOpW3Y6qT2seuSsJr+TXtdgnfIIejVBNN5VFuifAiKGktpopaLNODNC3Sj+bT3/LzJRsAZGRkZp4isAz7xFPMB7dhh764RrKZCBRCVDHZD4gAyBLthYRQYCcUjhqnWMIln0BZ0cnmcaYtDuQoHLDD5hLuDMzGuxqv69JXaFOyQnBVhyQRLLqYkG7E02s6piF47LattiwSNoJM4REalJcuNwhIJUaIwXYVdV6iuIM4bHpkZZvvEGH7LwR13iAsaY0DMOeSmDX6fID+ZNpLOggca/D0V9paLqdCIEThNQX7yiXROp2kwtkAGaawvicAog15UQ7O6GiMlGEEUWNxzaC3326vJuSHzsyWcCQflC8wJSxH95VgzDgfafTx0dJSfH9jAUG+DY3sGELFAhoKkoLFaEiPBXhCEa2MMYDUULVmkZYr8w0wPykoY7a0zVlyg1+lQsboo+cRoXgaChw6tonhGwCO1EXrcDusLcxy2+pGLqzJHgyoH273Mdgr0PilUScw5JIWQKLBQCcRNGxEJWus1xjKoOZu4aBAiVbtToUHIdES5EjhTLUSnh7ndfcwnMC6HMX0h+VKAkppES8LAIp7zkF2J2xQUJtLrd+cEiQfCF4SVtP0LbdJFNEsjYrAbgm64OFuzIJbgNMDvS5tbXDCgQfkCJVPlvWRxZpGfNEQVCHoMqiNBgN0UKF/iNNKZnGpLIuXgzCn04ndyJRcpl5GFoWVkZGScGoxOMObpO/dnOn46kHXAGRkZv36YZzECzlwQL4x6I08QWdg1hVHgzQiMhNIhQ9AjwKSlwKw6yNhgtw35o5LOKOAaEkfg1ARqWhBUgTmJMKB8RZyzmSwXUF1BkjPpQp5lsNoSowxWR+DNGtqrIRiN8I44WG3IzaSuAwx484bmOkFUMAgDrg92S1A+qJnfLGkcqCIigduFJGcwEoKmi9sSiATsFjgNQ5wT5I5YdEdjjABnRi0ukKSLbuqhfUvrvYkjcGfTRRahIfEWYzZN2hyVn+DN2/i9YCIHuaZN0LWJ9pWwF9uj3QZdq5/UZ2m1BPftW4uJJNa8zbFQodrpoqXTEERaYjfSezIyVe4SUar85cykaa5MWhhlmHAKjKtRkAYzGKBDxdrFhUp3Afyu4v6jqwnGixyOBDvEJrSnEVG6cFo4IukMG5CGvmixWKwx6FJCeLCIHPVRgSBZTOnWngadTtutuiBYcPFigd1JMCs4zTWHJkD2LLY9QewBsx7GeEQyfdZ2V5DrgEjSlPI4n9pMOyy6HRZlciVIP8LqgKo94aYTCzbGXmwsAkrjGhlKjILukEGFAhWkseTKh8L0Yqx8MV2k046hMCGJClA8DIg0Nd5I8GYkUVeQmxaUjiQkjkhV91aoZt4ynk1NuJXyDZ1ATusOOCMjI+OXko2AMzIyMk4NRmvMM4yATTYCfmGocQ/fcxGOwalLrK4hLhjCUjoFcppgN9LVXqsLfq/EaqcrwBiB0IKwJ1ULEyaNu7WbhqgksDpgLa7miprACBDmeJnV1EXgNA3mmMBuODhNg/LTqZ5TNwS9AhkbrJbAWRB0R9L0T5EAxuDNgb8qQcXp3E876Uq0d8hB24b2qCA3Y0hcgdMytJXAaijiaoKMFXEeBh+IiAoKfkFRyvIhKhusrsDqpu4Q2Q5IABFrnJoh9gRxPnV5eOUAa1bg1gwyAm8hXibyfjJw6hA0bXJTCncB2mMxMnDRi9Nh5Qviglma+jrzCmMZoqJBGEF1jyGoCoJeAdqQmxfpMz6aRuJanTSqIzenacSS8ECJ3KzAmzP4/QKRKBIH4mLaPvKT6d8TPMkOIp1ex+sjIs9gNQWJJ5AJJI7BuAYZQ/GAhTdrKOyrkazgl1w4DiSkkSERuF1DVBQgQbXAmgYjUheY0KkbwljpQE/GoG1IPPCmJcUjGrXQxpsr0RpL3WxxPm2vxXFBWEldFlqlLougkor/J64BIRAx5KYN7nyq9ibjNOpI+Wm7EhrcuqE9IlFB+j5h2aTRES2DSAzVhxbQ/glwP8CLZgS8olWRn8wXvvAF1q1bh+d5XH755dxzzz0n6qMyMjJeamjz7LbTnBPSAX/961/nhhtu4M/+7M+4//77Of/887nmmmuYnp4+ER+XkZHxUsOYdJHtabfTvwM+IS6I//yf/zMf+MAHeP/73w/Al770Jb73ve/x5S9/mY997GPP+n1kBHHJIAZDfMfBHwaKEd2RGHO4kE6/AZEIQhv8AUN5H6lbQaf1xqJiRCSApo2RhsIhK40ecFOXhNUGFaWuABWk0+H2akHP45rETleE8zMJTiPB77EQXej2CuyGIajIdNrnplMwu5VO0yCNXpDdNOg92tDFaIH2Fc6CJOozaZB7QxBWgZn0b622wBcKpybQNqiuJiwq9JNWkVWUJmgoP115FnrxM6fnFm1h8BY0nWGF1RFoJ8GxY2Q7nXGXxkO8fTPEJ7lx2q00yD84s4vfcLBlmszgzgu6w5qknKSuiJbC6ki0bYhLGpGkym5GLq7kC3DnBZYP3UFDdVcagWLPdtCA09CotiIuJkSBIipB8bDBbRi6/ZLuqoQ4n6qBWT6YTnfpGmXTIiyno5KoL0YEEndGEfakURDEqYupfDCh+tODaSTJCtrRxDHKT6NwmmtBuxrlp+nE8WqNjNJnLkPQbhr9Ujwkaa9edIXVF10RFvTefpBkdh538wBJThLnNEiDDm2cpmFhS+o66d9p6AxJ/FURKAORxD5kIaPUnRHnFRaQm9ZEeUlcTJ+b0BBU0u9fVEjdIVFvgjOTRixZ7QQjJSY6Ma4uow3mGaoiP13x4NOFFR8Bh2HI9u3bufrqq5/4ECm5+uqrueuuu55yfhAENBqNZVtGRkbG02GS5FltpzsrPgKenZ0lSRKGhoaW7R8aGuLxxx9/yvk33ngjn/rUp56yPyYi8X10V4Mbpv8qQEXoJMb4iiQQmJjFhS9IfEMSgvbT17obIZRORXO6CUYakmBxBKyBAEQIRIZECAjN4vsI4kijjUAbiCONjBPiKC3TkoTHNXkFSZDGV2rfpNcTQRwlJKFE+yatatwJMEYsXnOC7mq0L0gCQSIhCRf/VjzxPlpDHAfEUUJsoqWRVhz5JKEkCRZjPjWYAGIdkpgIE/vEESRBOvrWHZ9EBZjQx8SGOA6JdZC+50kkCdNnKdwQ09Xojg+Lz1D7Gm2nI2DhK5IgtZ22NEILtEhIQiu1lw9y8bmlz9sggDgJ0CYijny0r9AqQftq8bMhjkz6TLoxIpIkgUQEEJvUbgDa99PY306A7kpEIEl8hfYX44AlJIFMn4kO0SZEr6AdpQnRvg/hogtTa0SQjoC10hCnI2BCgTbpCPi4rZIgbYuatBLP8fYQRz66KzEsjoD9hCQy6O7x9pR+J3T3iRFwEliYCGRoltpfEvrpZ1np56JTuyYqfSZCg+7GaF+l9o5D1OIzeTIx6esXOjqNTfCMcb7HP+u0xqwwExMTBjA///nPl+3/4z/+Y3PZZZc95Xzf9029Xl/aHn300ePLm9mWbdn2It3Gx8efV//S7XbN8PDws/6c4eFh0+12n9dnnQxWfATc39+PUoqpqall+6emphgeHn7K+a7r4rru0utiscijjz7KOeecw/j4OOVy+Sl/k/GraTQajI2NZbZ7nmT2e2E8k/2MMTSbTUZHR5/X+3uex4EDBwifZRil4zh4nve8PutksOIdsOM4XHzxxdx6661ce+21QDqVuvXWW7n++uuf8e+llKxatQqAcrmcfQmeJ5ntXhiZ/V4YT2e/SqXygt7b87zTulN9LpyQKIgbbriB9773vVxyySVcdtllfO5zn6Pdbi9FRWRkZGRknKAO+J3vfCczMzN88pOfZHJykgsuuIBbbrnlKQtzGRkZGS9lTlgq8vXXX/+sXA6/DNd1+bM/+7NlvuGMZ0dmuxdGZr8XRma/54Yw5tcgWjkjIyPjRcgJ04LIyMjIyHh6sg44IyMj4xSRdcAZGRkZp4isA87IyMg4RWQdcEZGRsYp4rTrgDMh96dy4403cumll1IqlRgcHOTaa69l165dy87xfZ/rrruOvr4+isUi73jHO56SDn748GHe/OY3k8/nGRwc5I//+I+J4/hk3sppwWc/+1mEEHz0ox9d2pfZ7+mZmJjgd37nd+jr6yOXy3Huuedy3333LR03xvDJT36SkZERcrkcV199NXv27Fn2HvPz87znPe+hXC5TrVb5vd/7PVqt1sm+ldOLUytFsZyvfe1rxnEc8+Uvf9k88sgj5gMf+ICpVqtmamrqVF/aKeWaa64xN910k9m5c6fZsWOHedOb3mTWrFljWq3W0jkf+tCHzNjYmLn11lvNfffdZ17+8pebV7ziFUvH4zg2W7ZsMVdffbV54IEHzPe//33T399vPv7xj5+KWzpl3HPPPWbdunXmvPPOMx/5yEeW9mf2+9XMz8+btWvXmve9731m27ZtZv/+/eaHP/yh2bt379I5n/3sZ02lUjHf+ta3zIMPPmh+67d+y6xfv36ZEM4b3vAGc/7555u7777b/PSnPzVnnHGGefe7330qbum04bTqgC+77DJz3XXXLb1OksSMjo6aG2+88RRe1enH9PS0Acwdd9xhjDGmVqsZ27bNN77xjaVzHnvsMQOYu+66yxhjzPe//30jpTSTk5NL53zxi1805XLZBEFwcm/gFNFsNs2mTZvMj370I/PqV796qQPO7Pf0/Mmf/Im58sorf+VxrbUZHh42f/mXf7m0r1arGdd1zT/+4z8aY8ySyuG99967dM4PfvADI4QwExMTJ+7iT3NOGxfEcxVyfylTr9cB6O3tBWD79u1EUbTMdps3b2bNmjVLtrvrrrs499xzl6WDX3PNNTQaDR555JGTePWnjuuuu443v/nNy+wEmf2eiX/+53/mkksu4bd/+7cZHBzkwgsv5G/+5m+Wjh84cIDJycll9qtUKlx++eXL7FetVrnkkkuWzrn66quRUrJt27aTdzOnGadNB/x0Qu6Tk5On6KpOP7TWfPSjH+WKK65gy5YtAExOTuI4DtVqddm5T7bd5OTkL7Xt8WMvdr72ta9x//33c+ONNz7lWGa/p2f//v188YtfZNOmTfzwhz/kwx/+MP/u3/07vvKVrwBP3P/TfXcnJycZHBxcdtyyLHp7e1/09ns6Tuuy9BlP5brrrmPnzp3ceeedp/pSfm0YHx/nIx/5CD/60Y9eNDKGJxOtNZdccgl//ud/DsCFF17Izp07+dKXvsR73/veU3x1v96cNiPg5yrk/lLk+uuv57vf/S633347q1evXto/PDxMGIbUarVl5z/ZdsPDw7/UtsePvZjZvn0709PTXHTRRViWhWVZ3HHHHXz+85/HsiyGhoYy+z0NIyMjnHPOOcv2nX322Rw+fBh44v6f7rs7PDz8lKrocRwzPz//orff03HadMBPFnI/znEh961bt57CKzv1GGO4/vrr+eY3v8ltt93G+vXrlx2/+OKLsW17me127drF4cOHl2y3detWHn744WVfgh/96EeUy+WnfLlebLz2ta/l4YcfZseOHUvbJZdcwnve856l/2f2+9VcccUVTwl73L17N2vXrgVg/fr1DA8PL7Nfo9Fg27Zty+xXq9XYvn370jm33XYbWmsuv/zyk3AXpymnehXwyXzta18zruuam2++2Tz66KPmgx/8oKlWq8tWnl+KfPjDHzaVSsX8+Mc/NseOHVvaOp3O0jkf+tCHzJo1a8xtt91m7rvvPrN161azdevWpePHw6he//rXmx07dphbbrnFDAwMvCTCqH4ZT46CMCaz39Nxzz33GMuyzGc+8xmzZ88e8w//8A8mn8+bv//7v18657Of/aypVqvm29/+tnnooYfMW9/61l8ahnbhhReabdu2mTvvvNNs2rQpC0M71Rfwi/yX//JfzJo1a4zjOOayyy4zd99996m+pFMOv6Lg4E033bR0TrfbNb//+79venp6TD6fN29729vMsWPHlr3PwYMHzRv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" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import matplotlib\n", "\n", "import matplotlib.pylab as plt\n", "import torchaudio\n", "\n", "def plot_spectrogram(spectrogram):\n", " fig, ax = plt.subplots(figsize=(4, 3))\n", " im = ax.imshow(spectrogram, aspect=\"auto\", origin=\"lower\",\n", " interpolation='none')\n", " plt.colorbar(im, ax=ax)\n", " fig.canvas.draw()\n", " plt.close()\n", "\n", " return fig\n", "\n", "to_mel = torchaudio.transforms.MelSpectrogram(\n", " sample_rate=48000,\n", " n_mels=80, n_fft=2048, win_length=2048, hop_length=512)\n", "mean, std = -4, 4\n", "\n", "def preprocess(wave):\n", " wave_tensor = torch.from_numpy(wave).float()\n", " mel_tensor = to_mel(wave_tensor)\n", " mel_tensor = (torch.log(1e-5 + mel_tensor.unsqueeze(0)) - mean) / std\n", " return mel_tensor\n", "\n", "audio = preprocess(audio.cpu().numpy())\n", "\n", "audio = audio.squeeze(0)\n", "\n", "plot_spectrogram(audio.squeeze(0).cpu().numpy())" ] }, { "cell_type": "code", "execution_count": 125, "id": "5f38c36f", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "execution_count": 125, "metadata": {}, "output_type": "execute_result" } ], "source": [ "en = preprocess(audios)\n", "plot_spectrogram(en.squeeze(0).squeeze(0).cpu().numpy())" ] }, { "cell_type": "code", "execution_count": 10, "id": "d6de5261", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "en = preprocess(enhanced.cpu().numpy())\n", "plot_spectrogram(en.squeeze(0).squeeze(0).cpu().numpy())" ] }, { "cell_type": "code", "execution_count": 5, "id": "e9d633bd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "48_rec\n" ] }, { "ename": "NameError", "evalue": "name 'Audio' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[5], line 8\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# Save for listening\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m48_rec\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 8\u001b[0m \u001b[43mAudio\u001b[49m(enhanced, rate\u001b[38;5;241m=\u001b[39mdf_state\u001b[38;5;241m.\u001b[39msr())\n", "\u001b[0;31mNameError\u001b[0m: name 'Audio' is not defined" ] } ], "source": [ "# Download and open some audio file. You use your audio files here\n", "audio_path = \"/home/austin/disk1/stts-zs_cleaning/audio-diffusion-pytorch/ckpts_subset_diff_upsample/test_generated_sound_102500_nagi_voice_301290_6_06.wav\"\n", "audio, _ = load_audio(audio_path, sr=df_state.sr())\n", "# Denoise the audio\n", "enhanced = enhance(model, df_state, audio)\n", "# Save for listening\n", "print(\"48_rec\")\n", "Audio(enhanced, rate=df_state.sr())" ] }, { "cell_type": "code", "execution_count": 51, "id": "c98e5075", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Audio(audio, rate=df_state.sr())" ] }, { "cell_type": "markdown", "id": "1d8d9c80", "metadata": {}, "source": [ "# rest" ] }, { "cell_type": "code", "execution_count": 17, "id": "7387c5cb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import h5py\n", "import numpy as np\n", "from IPython.display import Audio\n", "\n", "# Load the HDF5 file\n", "hdf5_file = \"/home/austin/disk2/llmvcs/tt/deepfilter/deepfilternet/DeepFilterNet/VALID_SET_NOISE.hdf5\"\n", "with h5py.File(hdf5_file, 'r') as f:\n", " # Navigate to the 'noise' group\n", " noise_group = f['noise']\n", " \n", " # Assuming you want to play the first dataset in the 'noise' group\n", " first_dataset_name = list(noise_group.keys())[-1]\n", " first_dataset = noise_group[first_dataset_name]\n", " \n", " # Extract the audio data\n", " audio_data = first_dataset[0] # Extract the first sample\n", " sample_rate = 48000 # Assuming the sample rate is 48 kHz\n", "\n", "# Play the audio in the Jupyter Notebook\n", "Audio(audio_data, rate=sample_rate)" ] }, { "cell_type": "code", "execution_count": 8, "id": "c7b0acad", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "13953it [00:16, 825.93it/s]\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[8], line 50\u001b[0m\n\u001b[1;32m 47\u001b[0m clean_output_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(clean_output_dir, clean_filename)\n\u001b[1;32m 48\u001b[0m noisy_output_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(noisy_output_dir, noisy_filename)\n\u001b[0;32m---> 50\u001b[0m \u001b[43mshutil\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclean_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclean_output_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 51\u001b[0m shutil\u001b[38;5;241m.\u001b[39mcopy(noisy_path, noisy_output_path)\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mData preparation complete!\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", "File \u001b[0;32m~/disk2/micromamba/envs/sina/lib/python3.11/shutil.py:431\u001b[0m, in \u001b[0;36mcopy\u001b[0;34m(src, dst, follow_symlinks)\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(dst):\n\u001b[1;32m 430\u001b[0m dst \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(dst, os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mbasename(src))\n\u001b[0;32m--> 431\u001b[0m \u001b[43mcopyfile\u001b[49m\u001b[43m(\u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfollow_symlinks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_symlinks\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 432\u001b[0m copymode(src, dst, follow_symlinks\u001b[38;5;241m=\u001b[39mfollow_symlinks)\n\u001b[1;32m 433\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dst\n", "File \u001b[0;32m~/disk2/micromamba/envs/sina/lib/python3.11/shutil.py:269\u001b[0m, in \u001b[0;36mcopyfile\u001b[0;34m(src, dst, follow_symlinks)\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m _USE_CP_SENDFILE:\n\u001b[1;32m 268\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 269\u001b[0m \u001b[43m_fastcopy_sendfile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfsrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfdst\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 270\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dst\n\u001b[1;32m 271\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _GiveupOnFastCopy:\n", "File \u001b[0;32m~/disk2/micromamba/envs/sina/lib/python3.11/shutil.py:144\u001b[0m, in \u001b[0;36m_fastcopy_sendfile\u001b[0;34m(fsrc, fdst)\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 144\u001b[0m sent \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39msendfile(outfd, infd, offset, blocksize)\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 146\u001b[0m \u001b[38;5;66;03m# ...in oder to have a more informative exception.\u001b[39;00m\n\u001b[1;32m 147\u001b[0m err\u001b[38;5;241m.\u001b[39mfilename \u001b[38;5;241m=\u001b[39m fsrc\u001b[38;5;241m.\u001b[39mname\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "import os\n", "import shutil\n", "import pandas as pd\n", "from collections import defaultdict\n", "from tqdm import tqdm\n", "# Define the input and output directories\n", "input_file = '/home/austin/disk1/stts-zs_cleaning/data/puckysamples_subset.csv'\n", "output_dir = '/home/austin/disk1/stts-zs_cleaning/data/reconstructer_set'\n", "\n", "# Create the necessary subdirectories\n", "train_clean_dir = os.path.join(output_dir, 'train', 'clean')\n", "train_noisy_dir = os.path.join(output_dir, 'train', 'noisy')\n", "valid_clean_dir = os.path.join(output_dir, 'valid', 'clean')\n", "valid_noisy_dir = os.path.join(output_dir, 'valid', 'noisy')\n", "\n", "os.makedirs(train_clean_dir, exist_ok=True)\n", "os.makedirs(train_noisy_dir, exist_ok=True)\n", "os.makedirs(valid_clean_dir, exist_ok=True)\n", "os.makedirs(valid_noisy_dir, exist_ok=True)\n", "\n", "# Load the input data using Pandas\n", "data = pd.read_csv(input_file, sep='|', header=None)\n", "\n", "# Keep track of the unique filenames\n", "unique_filenames = defaultdict(int)\n", "\n", "# Iterate over the data and rename the files\n", "for _, row in tqdm(data.iterrows()):\n", " clean_path, noisy_path = row[0], row[1]\n", " \n", " # Construct the new filenames\n", " unique_id = unique_filenames['filename']\n", " unique_filenames['filename'] += 1\n", " \n", " clean_filename = f\"filename_{unique_id}_clean.wav\"\n", " noisy_filename = f\"filename_{unique_id}_noisy.wav\"\n", " \n", " # Determine the output directories based on the split\n", " if unique_id < int(len(unique_filenames) * 0.998):\n", " clean_output_dir = train_clean_dir\n", " noisy_output_dir = train_noisy_dir\n", " else:\n", " clean_output_dir = valid_clean_dir\n", " noisy_output_dir = valid_noisy_dir\n", " \n", " # Copy the files to the appropriate directories\n", " clean_output_path = os.path.join(clean_output_dir, clean_filename)\n", " noisy_output_path = os.path.join(noisy_output_dir, noisy_filename)\n", " \n", " shutil.copy(clean_path, clean_output_path)\n", " shutil.copy(noisy_path, noisy_output_path)\n", "\n", "print('Data preparation complete!')" ] }, { "cell_type": "code", "execution_count": 118, "id": "b34622e5", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "df = pd.read_csv(\"/home/austin/disk1/stts-zs_cleaning/data/puckysamples_subset.csv\", sep=\"|\", header=None)" ] }, { "cell_type": "code", "execution_count": 1, "id": "ff09b991", "metadata": {}, "outputs": [], "source": [ "import shutil\n", "shutil.rmtree(\"/home/austin/disk1/stts-zs_cleaning/audiotools/data\")\n", "shutil.rmtree(\"/home/austin/disk1/stts-zs_cleaning/data/reconstructer_set/valid\")" ] }, { "cell_type": "code", "execution_count": 51, "id": "d43210ea", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from audiomentations import *\n", "import numpy as np\n", "import librosa\n", "from IPython.display import Audio as ad\n", "\n", "# Load the audio file\n", "audio, sr = librosa.load(\"/home/austin/disk1/stts-zs_cleaning/audio-diffusion-pytorch/samps/syuukovoice_200918_3_01.wav\", sr=48000)\n", "\n", "audio = librosa.resample(audio, orig_sr=sr, target_sr=24000)\n", "sr = 24000\n", "# Define the augmentation pipeline\n", "augment = Compose([\n", " # BitCrush(p=1., min_bit_depth=3,max_bit_depth=4),\n", " Mp3Compression(p=1, min_bitrate=32, max_bitrate=32)\n", "])\n", "\n", "# Augment/transform/perturb the audio data\n", "augmented_samples = augment(samples=audio, sample_rate=sr)\n", "\n", "# Play the augmented audio (assuming `ad` is a function to play audio)\n", "ad(augmented_samples, rate=sr)" ] }, { "cell_type": "code", "execution_count": 3, "id": "447313b7", "metadata": {}, "outputs": [], "source": [ "import os\n", "import shutil\n", "from pathlib import Path\n", "\n", "def copy_audio_files(df, destination_base):\n", " # Create the destination base directory if it doesn't exist\n", " Path(destination_base).mkdir(parents=True, exist_ok=True)\n", " \n", " # Get the column name of the first column (assuming it contains the paths)\n", " path_column = df.columns[0]\n", " \n", " # Process each file path\n", " for file_path in df[path_column]:\n", " # Convert string path to Path object\n", " src_path = Path(file_path)\n", " \n", " # Find the index of 'moe_soshy' in the parts\n", " parts = src_path.parts\n", " try:\n", " start_idx = parts.index('moe_soshy')\n", " except ValueError:\n", " print(f\"Warning: 'moe_soshy' not found in path: {file_path}\")\n", " continue\n", " \n", " # Get the relative path starting from 'moe_soshy'\n", " relative_path = os.path.join(*parts[start_idx:])\n", " \n", " # Create the destination path\n", " dest_path = Path(destination_base) / relative_path\n", " \n", " # Create parent directories if they don't exist\n", " dest_path.parent.mkdir(parents=True, exist_ok=True)\n", " \n", " # Copy the file\n", " try:\n", " shutil.copy2(src_path, dest_path)\n", " # print(f\"Copied: {dest_path}\")\n", " except Exception as e:\n", " print(f\"Error copying {src_path}: {e}\")\n", "\n", "# Use the function\n", "destination_path = \"/home/austin/disk1/stts-zs_cleaning/audio-diffusion-pytorch/data_noise/kasif/\"\n", "copy_audio_files(df, destination_path)" ] }, { "cell_type": "code", "execution_count": 80, "id": "b5c82870", "metadata": {}, "outputs": [], "source": [ "from collections import defaultdict\n", "\n", "# df2 = df2.drop(3,axis=1)\n", "speaker_to_id = defaultdict(lambda: len(speaker_to_id))\n", "\n", "df3[3] = df3[2].apply(lambda x: int(speaker_to_id[x])) \n" ] }, { "cell_type": "code", "execution_count": 10, "id": "c8f23509-1bee-4434-ad3e-7f8ad7fa1ddd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(524711, 4)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "df = pd.read_csv(\"/home/austin/disk1/stts-zs_cleaning/data/train_List_updated_plus_reID_48khz.csv\", sep='|', header=None)\n", "# df = df.sample(df.shape[0])\n", "# df.to_csv(\"/home/austin/disk2/llmvcs/tt/stylekan/Data/mg_valid.csv\",sep=\"|\",header=False, index=False)\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 11, "id": "c74fd449", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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2/home/austin/disk1/stts-zs_cleaning/data/moe_4...biːzɯ no bɯbɯɴ ga, kaiteɴ sɯrɯ ɕikɯmi ni naʔte...4cb40d9c2
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\n", "" ], "text/plain": [ " file_path \\\n", "0 /home/austin/disk1/stts-zs_cleaning/data/moe_4... \n", "1 /home/austin/disk1/stts-zs_cleaning/data/moe_4... \n", "2 /home/austin/disk1/stts-zs_cleaning/data/moe_4... \n", "3 /home/austin/disk1/stts-zs_cleaning/data/moe_4... \n", "4 /home/austin/disk1/stts-zs_cleaning/data/moe_s... \n", "\n", " column2 column3 idsss \n", "0 a, sonna koto mo aʔta. 338ab306 0 \n", "1 soto niːte mo ɕikatanai desɯɕi, itsɯ mo ie de,... 282cfa8c 1 \n", "2 biːzɯ no bɯbɯɴ ga, kaiteɴ sɯrɯ ɕikɯmi ni naʔte... 4cb40d9c 2 \n", "3 gohaɴ o okawaɽi ɕitɕaʔte mo, desɯ ka? 00013899 3 \n", "4 sonna , modʑi toːɽi akɯɕɯmi na koɽekɯɕoɴ ni te... horie_yui 4 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# import pandas as pd\n", "# import librosa\n", "# from tqdm import tqdm\n", "\n", "# name =\"/home/austin/disk2/llmvcs/tt/stylekan/Data/train_List_updated.csv\"\n", "# df = pd.read_csv(name, sep=\"|\", header=None)\n", "# # df = df.drop_duplicates(subset=['filename'])\n", "# # df = df.dropna()\n", "# # Assuming df is your dataframe\n", "# def filter_short_audio(df):\n", "# def is_long_enough(file_path):\n", "# duration = librosa.get_duration(filename=file_path)\n", "# return duration >= 1.5\n", "\n", "# tqdm.pandas(desc=\"Processing audio files\")\n", "# return df[df[0].progress_apply(is_long_enough)]\n", "\n", "# # Example usage\n", "# # df = pd.read_csv('your_dataframe.csv')\n", "# filtered_df = filter_short_audio(df)\n", "# print(\"filetered:\\n\", filtered_df.shape)\n", "# print(\"OG:\\n\", df.shape)\n", "# df = filtered_df\n", "\n", "import pandas as pd\n", "import librosa\n", "from datasets import Dataset\n", "from tqdm.auto import tqdm\n", "import multiprocessing as mp\n", "\n", "def is_long_enough(file_path):\n", " try:\n", " duration = librosa.get_duration(filename=file_path)\n", " return duration >= 1.5\n", " except Exception as e:\n", " print(f\"Error processing {file_path}: {str(e)}\")\n", " return False\n", "\n", "def filter_short_audio(examples):\n", " return {\n", " 'is_long_enough': [is_long_enough(path) for path in examples['file_path']]\n", " }\n", "\n", "# Load the CSV file\n", "name = \"/home/austin/disk1/stts-zs_cleaning/data/train_List_updated_plus_reID_48khz.csv\"\n", "df = pd.read_csv(name, sep=\"|\", header=None, names=['file_path', 'column2', 'column3','idsss'])\n", "\n", "print(\"Original DataFrame shape:\", df.shape)\n", "\n", "# Convert DataFrame to Hugging Face Dataset\n", "dataset = Dataset.from_pandas(df)\n", "\n", "# Set up multiprocessing\n", "num_cores = 64 # Use all available cores\n", "\n", "# Apply the filtering function to the dataset\n", "filtered_dataset = dataset.map(\n", " filter_short_audio,\n", " batched=True,\n", " num_proc=num_cores,\n", " desc=\"Processing audio files\",\n", ")\n", "\n", "# Filter out short audio files\n", "filtered_dataset = filtered_dataset.filter(lambda example: example['is_long_enough'])\n", "\n", "# Convert back to pandas DataFrame and drop the 'is_long_enough' column\n", "df = filtered_dataset.remove_columns(['is_long_enough']).to_pandas()\n", "\n", "print(\"Filtered DataFrame shape:\", df.shape)\n", "\n", "# Display the first few rows of the filtered dataframe\n", "df.head()\n", "\n", "# Optional: Save the filtered dataframe to a new CSV file\n", "# filtered_df.to_csv('filtered_audio_list.csv', index=False, sep='|')" ] }, { "cell_type": "code", "execution_count": 18, "id": "8278f0d2", "metadata": {}, "outputs": [], "source": [ "df.to_csv(\"/home/austin/disk1/stts-zs_cleaning/data/PREPROCESSED_train_List_updated_plus_reID_48khz.csv\",sep=\"|\",header=False, index=False)" ] }, { "cell_type": "code", "execution_count": 23, "id": "636c1476", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(59297, 1)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "import pandas as pd\n", "from datasets import Dataset\n", "# Initialize an empty list to store the full paths of the .wav files\n", "wav_files = []\n", "\n", "for root, dirs, files in os.walk(\"/home/austin/disk1/stts-zs_cleaning/audio-diffusion-pytorch/data_noise/kasif/subset\"):\n", " for file in files:\n", " # Check if the file is a .wav file\n", " if file.endswith(\".wav\"):\n", " # Construct the full path and add it to the list\n", " full_path = os.path.join(root, file)\n", " wav_files.append(full_path)\n", "\n", "# Create a pandas DataFrame from the list of full paths\n", "df2 = pd.DataFrame(wav_files, columns=[\"wav_file_path\"])\n", "df2.shape" ] }, { "cell_type": "code", "execution_count": 49, "id": "ef717036", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(524711, 3)" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "\n", "df = data.groupby(2).filter(lambda x: len(x) >= 120)\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 1, "id": "c1e8d798", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 211854/211854 [00:33<00:00, 6383.35it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "330.95 hours\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "import pandas as pd\n", "from pydub import AudioSegment\n", "from tqdm import tqdm\n", "# Load the CSV file\n", "df = pd.read_csv('/home/austin/disk2/llmvcs/tt/stylekan/Data/metadata_cleanest/train_48_pure.csv', sep=\"|\", header=None)\n", "\n", "# Initialize total duration\n", "total_duration = 0\n", "\n", "# Iterate over each audio file path in the first column\n", "for audio_path in tqdm(df.iloc[:, 0]):\n", " # Load the audio file\n", " audio = AudioSegment.from_file(audio_path)\n", " # Add the duration of the audio file to the total duration\n", " total_duration += len(audio)\n", "\n", "# Convert total duration from milliseconds to hours\n", "total_hours = total_duration / (1000 * 60 * 60)\n", "\n", "print(f\"{total_hours:.2f} hours\")" ] }, { "cell_type": "code", "execution_count": 98, "id": "4fc28c9f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(24, 3)" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import re\n", "\n", "df = pd.read_csv('/home/austin/disk2/llmvcs/tt/AuxiliaryASR/Data/train_list_subsection.csv', sep='|', header=None)\n", "\n", "def has_repeated_sequence(s, repeat_threshold=3, portion_threshold=0.5):\n", " # Tokenize the sentence into words\n", " words = s.split()\n", " length = len(words)\n", " \n", " # A dictionary to store sequences and their counts\n", " sequence_counts = {}\n", " \n", " # Check all possible sequences but limit the size to avoid complexity\n", " max_seq_length = min(5, length // 2) # For example, 5 as max length for subsequence to check\n", " \n", " for seq_length in range(1, max_seq_length + 1):\n", " for i in range(length - seq_length + 1):\n", " sequence = tuple(words[i:i + seq_length])\n", " if sequence not in sequence_counts:\n", " sequence_counts[sequence] = 0\n", " sequence_counts[sequence] += 1\n", " \n", " # Determine if any repeating sequence meets the criteria\n", " for sequence, count in sequence_counts.items():\n", " if count >= repeat_threshold and (count * len(sequence)) / length > portion_threshold:\n", " return True\n", " \n", " return False\n", "\n", "# Filter rows with a repeated pattern in the second column (index 1)\n", "pattern_rows = df[df[1].apply(has_repeated_sequence)]\n", "\n", "# Create a new DataFrame with the filtered rows\n", "df2 = pattern_rows.copy()\n", "\n", "# Optionally, reset the index of the new DataFrame\n", "df2.reset_index(drop=True, inplace=True)\n", "\n", "# Print the resulting DataFrame\n", "df2.shape" ] }, { "cell_type": "code", "execution_count": 118, "id": "e93fc370", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(525540, 3)" ] }, "execution_count": 118, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "# Define the paths to the CSV files\n", "hallucinate_path = '/home/austin/disk2/llmvcs/tt/cotlet/hallucinate.csv'\n", "train_list_path = '/home/austin/disk2/llmvcs/tt/stylekan/Data/train_List.csv'\n", "\n", "\n", "# Load the CSV files into DataFrames\n", "hallucinate_df = pd.read_csv(hallucinate_path, header=None, sep=\"|\")\n", "train_list_df = pd.read_csv(train_list_path, sep=\"|\", header=None)\n", "\n", "# Assuming the filenames are in the first column of both DataFrames\n", "corrupted_filenames = set(hallucinate_df[1])\n", "\n", "# Filter out the corrupted filenames from the main data\n", "cleaned_train_list_df = train_list_df[~train_list_df[1].isin(corrupted_filenames)]\n", "cleaned_train_list_df.reset_index(drop=True, inplace=True)\n", "# # Save the cleaned data to a new CSV file\n", "cleaned_train_list_df.to_csv(train_list_path, sep=\"|\", index=False, header=None)\n", "cleaned_train_list_df.shape" ] }, { "cell_type": "code", "execution_count": 2, "id": "2d640b17", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "File: /home/austin/disk2/llmvcs/tt/stylekan/Models/Style_Kanade/succ_epoch_2nd_00002.pth | Modified on: 2024-10-05 07:34:07.563425\n", "File: /home/austin/disk2/llmvcs/tt/stylekan/Models/Style_Kanade/epoch_2nd_00001.pth | Modified on: 2024-10-04 12:41:09.543483\n", "File: /home/austin/disk2/llmvcs/tt/stylekan/Models/Style_Kanade/2nd_phase_51745.pth | Modified on: 2024-10-14 17:39:48.346199\n", "File: /home/austin/disk2/llmvcs/tt/stylekan/Models/Style_Kanade/2nd_phase_53945.pth | Modified on: 2024-10-14 20:31:06.624946\n", "File: /home/austin/disk2/llmvcs/tt/stylekan/Models/Style_Kanade/epoch_2nd_00000.pth | Modified on: 2024-10-02 02:05:11.827047\n", "File: /home/austin/disk2/llmvcs/tt/stylekan/Models/Style_Kanade/succ_2nd_phase_60545.pth | Modified on: 2024-10-15 06:15:02.589877\n", "File: /home/austin/disk2/llmvcs/tt/stylekan/Models/Style_Kanade/2nd_phase_72645.pth | Modified on: 2024-10-14 12:05:57.093188\n", "File: /home/austin/disk2/llmvcs/tt/stylekan/Models/Style_Kanade/epoch_1st_00013.pth | Modified on: 2024-09-30 16:09:22.864072\n" ] } ], "source": [ "import os\n", "import datetime\n", "\n", "def get_modification_date(file_path):\n", "\n", " mod_time = os.path.getmtime(file_path)\n", " return datetime.datetime.fromtimestamp(mod_time)\n", "\n", "def list_files_with_modification_dates(directory):\n", "\n", " for root, dirs, files in os.walk(directory):\n", " for file_name in files:\n", " if file_name.endswith('.pth'):\n", " file_path = os.path.join(root, file_name)\n", " mod_date = get_modification_date(file_path)\n", " print(f\"File: {file_path} | Modified on: {mod_date}\")\n", "\n", "if __name__ == \"__main__\":\n", " directory_path = \"/home/austin/disk2/llmvcs/tt/stylekan/Models/Style_Kanade\"\n", " list_files_with_modification_dates(directory_path)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 }