diff --git a/nbs/3B. Semantic to acoustic token modeling (enc-sum).ipynb b/nbs/3B. Semantic to acoustic token modeling (enc-sum).ipynb
deleted file mode 100644
index f1a24fddfd4519762d36e51053eb83a1c0a9aa1c..0000000000000000000000000000000000000000
--- a/nbs/3B. Semantic to acoustic token modeling (enc-sum).ipynb
+++ /dev/null
@@ -1,2320 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0a853249",
- "metadata": {},
- "outputs": [],
- "source": [
- "import io\n",
- "import time\n",
- "import torch\n",
- "import torchaudio\n",
- "import random\n",
- "\n",
- "from encodec.model import EncodecModel"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7ffec6c3",
- "metadata": {},
- "outputs": [],
- "source": [
- "import torch.nn as nn\n",
- "import torch.nn.functional as F"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "13462aa4",
- "metadata": {},
- "outputs": [],
- "source": [
- "from pathlib import Path\n",
- "import json\n",
- "from fastprogress import progress_bar, master_bar\n",
- "import fastprogress\n",
- "import numpy as np\n",
- "import pylab as plt\n",
- "import pandas as pd"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d7b796d8",
- "metadata": {},
- "outputs": [],
- "source": [
- "from IPython.display import Audio, HTML, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d72390bf",
- "metadata": {},
- "outputs": [],
- "source": [
- "datadir = Path('/mnt/')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b02bc209",
- "metadata": {},
- "source": [
- "# Dataset"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ec46329b",
- "metadata": {},
- "source": [
- "## Create a dataset index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5dd87020",
- "metadata": {},
- "outputs": [],
- "source": [
- "fnames = []\n",
- "speakers = []\n",
- "grps = []\n",
- "for grp in ['small', 'medium', 'large']:\n",
- " for name in (Path('/scrach/')/grp).rglob('*.flac'):\n",
- " fnames.append(str(name))\n",
- " speakers.append(name.parents[1].name)\n",
- " grps.append(grp)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e361412e",
- "metadata": {},
- "outputs": [],
- "source": [
- "data = pd.DataFrame(dict(afile=fnames, speaker=speakers, grp=grps))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "66b06532",
- "metadata": {},
- "outputs": [],
- "source": [
- "atoks = {x.name:x for x in list(Path(datadir).rglob('*.encodec'))}\n",
- "stoks = {x.name:x for x in list(Path(datadir).rglob('*.stoks'))}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "755e7192",
- "metadata": {},
- "outputs": [],
- "source": [
- "data['atoks'] = data.apply(lambda x: str(atoks[Path(x['afile']).with_suffix('.encodec').name]), axis=1)\n",
- "data['stoks'] = data.apply(lambda x: str(stoks[Path(x['afile']).with_suffix('.stoks').name]), axis=1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "773097a9",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'/mnt/acoustic-small/254/accomplished_facts_sandburg_add_64kb.encodec'"
- ]
- },
- "execution_count": null,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data.iloc[0]['atoks']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5925dbd2",
- "metadata": {},
- "outputs": [],
- "source": [
- "spks = data.groupby('speaker').count()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e2e68ac4",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>afile</th>\n",
- " <th>grp</th>\n",
- " <th>atoks</th>\n",
- " <th>stoks</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>speaker</th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>994</th>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2306</th>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4115</th>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2311</th>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4113</th>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>681</th>\n",
- " <td>374</td>\n",
- " <td>374</td>\n",
- " <td>374</td>\n",
- " <td>374</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>204</th>\n",
- " <td>1439</td>\n",
- " <td>1439</td>\n",
- " <td>1439</td>\n",
- " <td>1439</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1401</th>\n",
- " <td>2665</td>\n",
- " <td>2665</td>\n",
- " <td>2665</td>\n",
- " <td>2665</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3157</th>\n",
- " <td>2719</td>\n",
- " <td>2719</td>\n",
- " <td>2719</td>\n",
- " <td>2719</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6454</th>\n",
- " <td>3411</td>\n",
- " <td>3411</td>\n",
- " <td>3411</td>\n",
- " <td>3411</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>1743 rows × 4 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " afile grp atoks stoks\n",
- "speaker \n",
- "994 1 1 1 1\n",
- "2306 1 1 1 1\n",
- "4115 1 1 1 1\n",
- "2311 1 1 1 1\n",
- "4113 1 1 1 1\n",
- "... ... ... ... ...\n",
- "681 374 374 374 374\n",
- "204 1439 1439 1439 1439\n",
- "1401 2665 2665 2665 2665\n",
- "3157 2719 2719 2719 2719\n",
- "6454 3411 3411 3411 3411\n",
- "\n",
- "[1743 rows x 4 columns]"
- ]
- },
- "execution_count": null,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "spks.sort_values('afile')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "197b869d",
- "metadata": {},
- "outputs": [],
- "source": [
- "data.to_feather('token-dataset.feather')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9f77fad9",
- "metadata": {},
- "source": [
- "## Load the dataset index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "646a7bfc",
- "metadata": {},
- "outputs": [],
- "source": [
- "data = pd.read_feather('token-dataset.feather')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a5edd423",
- "metadata": {},
- "outputs": [],
- "source": [
- "import torch.nn.functional as F\n",
- "\n",
- "class SADataset(torch.utils.data.Dataset):\n",
- " def __init__(self, data, unique=False):\n",
- " self.data = data\n",
- " self.unique = unique\n",
- " self.samples = [(i,j) for i,name in enumerate(data['stoks']) for j in range(torch.load(name).shape[0])]\n",
- " \n",
- " def __len__(self):\n",
- " return len(self.samples)\n",
- " \n",
- " def S_tokens(self):\n",
- " return len(self)*1500\n",
- " \n",
- " def hours(self):\n",
- " return len(self)*30/3600\n",
- " \n",
- " def __repr__(self):\n",
- " return f\"Dataset: {len(self)} samples, {self.S_tokens()} Stokens, {self.hours():.1f} hours)\"\n",
- " \n",
- " def __getitem__(self, idx):\n",
- " i,j = self.samples[idx]\n",
- " row = self.data.iloc[i]\n",
- " jA = j * 2250\n",
- " Stoks = torch.load(row['stoks'], map_location='cpu')[j]\n",
- " Atoks = torch.load(row['atoks'], map_location='cpu')[0,:,jA:jA+2250].T.reshape(-1)\n",
- " if self.unique:\n",
- " x = torch.unique_consecutive(Stoks)\n",
- " Stoks = F.pad(x, (0, Stoks.shape[0] - x.shape[0]), value=1024)\n",
- " return Stoks, F.pad(Atoks, (0, 4500 - len(Atoks)), value=1024)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "abbee262",
- "metadata": {},
- "outputs": [],
- "source": [
- "data6454 = data[data['speaker'] == '6454']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ef8736c2",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Dataset: 710 samples, 1065000 Stokens, 5.9 hours)"
- ]
- },
- "execution_count": null,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "val_data, train_data = data6454[:12], data6454[12:]\n",
- "val_ds = SADataset(val_data, unique=False)\n",
- "val_ds"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d6c82c52",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Dataset: 161014 samples, 241521000 Stokens, 1341.8 hours)"
- ]
- },
- "execution_count": null,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_ds = SADataset(train_data, unique=False)\n",
- "train_ds"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0f5e4ad4",
- "metadata": {},
- "source": [
- "# Modeling"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "038dafd8",
- "metadata": {},
- "outputs": [],
- "source": [
- "import IPython\n",
- "\n",
- "class SimpleVisual:\n",
- " def __init__ (self, model, total_steps):\n",
- " self.model = model\n",
- " self.total_steps = total_steps\n",
- " \n",
- " gs = plt.GridSpec(2, 1, height_ratios=[3,1])\n",
- " graph_fig = plt.figure(figsize=(10,6))\n",
- " self.graph_fig = graph_fig\n",
- " self.loss_p = graph_fig.add_subplot(gs[0])\n",
- " self.lr_p = graph_fig.add_subplot(gs[1], sharex=self.loss_p)\n",
- " self.lr_p.tick_params('x', labelbottom=False)\n",
- " self.graph_out = None\n",
- " \n",
- " self.its = []\n",
- " self.train_losses = []\n",
- " self.val_losses = []\n",
- " self.lr_history = []\n",
- " \n",
- " def show(self):\n",
- " self.graph_out = display(self.graph_fig, display_id=True, clear=True)\n",
- " \n",
- " def hide(self):\n",
- " if self.graph_out is not None:\n",
- " self.graph_out.update(IPython.display.HTML(''))\n",
- " \n",
- " def plot(self):\n",
- " loss_p, lr_p = self.loss_p, self.lr_p\n",
- " loss_p.clear()\n",
- " loss_p.plot(self.its, self.train_losses)\n",
- " loss_p.plot(self.its, self.val_losses)\n",
- " loss_p.set_xlim(0, self.total_steps)\n",
- " loss_p.set_yscale('log')\n",
- " lr_p.clear()\n",
- " lrs = np.array(self.lr_history)\n",
- " lr_p.plot(self.its, lrs)\n",
- " self.graph_out.update(self.graph_fig)\n",
- " \n",
- " def add_data(self, it, lr, train_loss, val_los):\n",
- " self.its.append(it)\n",
- " self.train_losses.append(train_loss)\n",
- " self.val_losses.append(val_los)\n",
- " self.lr_history.append(lr)\n",
- " self.plot()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fb5d87d7",
- "metadata": {},
- "outputs": [],
- "source": [
- "# training code\n",
- "import torch.optim as optim\n",
- "import torch.nn.functional as F\n",
- "from torch.utils.data.dataloader import DataLoader\n",
- "import random\n",
- "import IPython\n",
- "\n",
- "def train(model_name, model, train, val, half=False, bs=16, lr=1e-4, visual_class = SimpleVisual,\n",
- " weight_decay=0.1, pct_start=0.3, warmup=5000, warmup_mul=1e-2, epochs=10,\n",
- " run_valid_every_iters=100, table_row_every_iters=1000,\n",
- " device=\"cuda\"):\n",
- " try:\n",
- " scheduler = None\n",
- " visual = visual_class(model, epochs*len(train))\n",
- " all_params = set(model.parameters())\n",
- " wd_params = set()\n",
- " for m in model.modules():\n",
- " if isinstance(m, (nn.Linear, nn.Conv1d)):\n",
- " wd_params.add(m.weight)\n",
- " if m.bias is not None:\n",
- " wd_params.add(m.bias)\n",
- " no_wd_params = all_params - wd_params\n",
- "\n",
- " optimizer = torch.optim.AdamW(lr=lr * warmup_mul, betas=(0.9, 0.95), #fused=True,\n",
- " params=[\n",
- " {\"params\": list(wd_params), \"weight_decay\": weight_decay},\n",
- " {\"params\": list(no_wd_params), \"weight_decay\": 0.0},\n",
- " ]\n",
- " )\n",
- " scaler = torch.cuda.amp.GradScaler(enabled=half)\n",
- "\n",
- " Path(f'/scrach/{model_name}-checkpoints').mkdir(exist_ok=True)\n",
- " \n",
- " train_loader = DataLoader(train, batch_size=bs, num_workers=8, drop_last=False, shuffle=True)\n",
- " val_loader = DataLoader(val, batch_size=bs, num_workers=8, drop_last=False)\n",
- " chkpt_every_iters = 5000\n",
- "\n",
- " it = 0\n",
- " start_t = time.time()\n",
- " next_val_it = it + 50\n",
- " next_chkpt_it = chkpt_every_iters\n",
- " next_table_it = table_row_every_iters\n",
- " \n",
- " val_loss = torch.nan\n",
- " avg_train_loss = torch.nan\n",
- " \n",
- " visual.show()\n",
- "\n",
- " mb = master_bar(range(epochs))\n",
- " mb.write([\"samples\", \"train\", \"val\", \"time\"], table=True)\n",
- " running_loss = [0]\n",
- " for epoch in mb:\n",
- " bar = progress_bar(train_loader, parent=mb)\n",
- " for xs,ys in bar:\n",
- " # zero the parameter gradients\n",
- " optimizer.zero_grad(set_to_none=True)\n",
- "\n",
- " with torch.autocast(device_type=device, dtype=torch.float16 if half else torch.float32, enabled=device!='cpu'):\n",
- " ps, loss = model(xs.to(device), ys.to(device))\n",
- "\n",
- " scaler.scale(loss).backward()\n",
- " scaler.step(optimizer)\n",
- " scaler.update()\n",
- "\n",
- " if it > warmup:\n",
- " if scheduler is None:\n",
- " scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=lr, pct_start=pct_start, steps_per_epoch=len(train_loader), epochs=epochs)\n",
- " else:\n",
- " scheduler.step()\n",
- " lr = scheduler.get_last_lr() \n",
- "\n",
- " running_loss.append(loss.item())\n",
- " running_loss = running_loss[-5:]\n",
- " avg_train_loss = sum(running_loss)/len(running_loss)\n",
- "\n",
- " if it >= next_chkpt_it:\n",
- " next_chkpt_it += chkpt_every_iters\n",
- " torch.save(model.state_dict(), f'/scrach/{model_name}-checkpoints/{it}.pt')\n",
- " \n",
- " if it >= next_val_it:\n",
- " next_val_it += run_valid_every_iters\n",
- " model.eval()\n",
- " with torch.no_grad():\n",
- " val_loss = 0\n",
- " for xs,ys in val_loader:\n",
- " with torch.autocast(device_type=device, dtype=torch.float16 if half else torch.float32, enabled=device!='cpu'):\n",
- " ps, loss = model(xs.to(device), ys.to(device))\n",
- " val_loss += loss\n",
- " N = len(val_loader)\n",
- " val_loss = val_loss.item() / N\n",
- " model.train()\n",
- " visual.add_data(it, lr, avg_train_loss, val_loss)\n",
- " \n",
- " if it >= next_table_it:\n",
- " elapsed_t = time.time() - start_t\n",
- " next_table_it += table_row_every_iters\n",
- " mb.write([it, f\"{avg_train_loss:.5f}\", f\"{val_loss:.5f}\", fastprogress.core.format_time(elapsed_t)], table=True)\n",
- "\n",
- " it += bs\n",
- " bar.comment = f\"#{epoch+1}/{epochs} loss: {avg_train_loss:.3f} / {val_loss:.3f}\"\n",
- " except KeyboardInterrupt:\n",
- " mb.write(f\"interrupted\")\n",
- " mb.show()\n",
- " pass\n",
- " finally:\n",
- " visual.hide()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "89b5e60b",
- "metadata": {},
- "source": [
- "## Add resampled encoder features at the middle decoder layer (replacement for cross-attention)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "66ea4203",
- "metadata": {},
- "outputs": [],
- "source": [
- "from torch import Tensor, nn\n",
- "from typing import Dict, Iterable, Optional\n",
- "\n",
- "import whisper\n",
- "from torch import nn\n",
- "from vector_quantize_pytorch import ResidualVQ\n",
- "\n",
- "class LayerNorm(nn.LayerNorm):\n",
- " def forward(self, x):\n",
- " return super().forward(x.float()).type(x.dtype)\n",
- " \n",
- "class Linear(nn.Linear):\n",
- " def forward(self, x: Tensor) -> Tensor:\n",
- " return F.linear(\n",
- " x,\n",
- " self.weight.to(x.dtype),\n",
- " None if self.bias is None else self.bias.to(x.dtype),\n",
- " )\n",
- "\n",
- "def sinusoids(length, channels, max_timescale=10000):\n",
- " \"\"\"Returns sinusoids for positional embedding\"\"\"\n",
- " assert channels % 2 == 0\n",
- " log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)\n",
- " inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))\n",
- " scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]\n",
- " return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)\n",
- "\n",
- "def init_transformer(m):\n",
- " if isinstance(m, (nn.Linear, nn.Embedding)):\n",
- " torch.nn.init.trunc_normal_(m.weight, std=.02)\n",
- " if isinstance(m, nn.Linear) and m.bias is not None:\n",
- " torch.nn.init.constant_(m.bias, 0)\n",
- " elif isinstance(m, nn.LayerNorm):\n",
- " torch.nn.init.constant_(m.bias, 0)\n",
- " torch.nn.init.constant_(m.weight, 1.0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a243b559",
- "metadata": {},
- "outputs": [],
- "source": [
- "class MultiHeadAttention(nn.Module):\n",
- " def __init__(self, n_state: int, n_head: int):\n",
- " super().__init__()\n",
- " self.n_head = n_head\n",
- " self.query = Linear(n_state, n_state)\n",
- " self.key = Linear(n_state, n_state, bias=False)\n",
- " self.value = Linear(n_state, n_state)\n",
- " self.out = Linear(n_state, n_state)\n",
- "\n",
- " def forward(\n",
- " self,\n",
- " x: Tensor,\n",
- " xa: Optional[Tensor] = None,\n",
- " causal = False,\n",
- " kv_cache: Optional[dict] = None,\n",
- " ):\n",
- " q = self.query(x)\n",
- "\n",
- " if kv_cache is None or xa is None or self.key not in kv_cache:\n",
- " # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;\n",
- " # otherwise, perform key/value projections for self- or cross-attention as usual.\n",
- " k = self.key(x if xa is None else xa)\n",
- " v = self.value(x if xa is None else xa)\n",
- " else:\n",
- " # for cross-attention, calculate keys and values once and reuse in subsequent calls.\n",
- " k = kv_cache[self.key]\n",
- " v = kv_cache[self.value]\n",
- "\n",
- " # watch out, the returned qk is not valid\n",
- " wv, qk = self.qkv_attention(q, k, v, causal)\n",
- " \n",
- " return self.out(wv), qk\n",
- "\n",
- " def qkv_attention(\n",
- " self, q: Tensor, k: Tensor, v: Tensor, causal = False\n",
- " ):\n",
- " n_batch, n_ctx, n_state = q.shape\n",
- " q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)\n",
- " k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)\n",
- " v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)\n",
- "\n",
- " wv = F.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0, is_causal=causal)\n",
- "\n",
- " # we've returned q@k which we don't have now, but it's not used so just let's keep two\n",
- " # return values\n",
- " return wv.permute(0, 2, 1, 3).flatten(start_dim=2), None\n",
- "\n",
- "class ResidualAttentionBlock(nn.Module):\n",
- " def __init__(self, n_state: int, n_head: int, cross_attention: bool = False):\n",
- " super().__init__()\n",
- "\n",
- " self.attn = MultiHeadAttention(n_state, n_head)\n",
- " self.attn_ln = LayerNorm(n_state)\n",
- "\n",
- " self.cross_attn = (\n",
- " MultiHeadAttention(n_state, n_head) if cross_attention else None\n",
- " )\n",
- " self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None\n",
- "\n",
- " n_mlp = n_state * 4\n",
- " self.mlp = nn.Sequential(\n",
- " Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state)\n",
- " )\n",
- " self.mlp_ln = LayerNorm(n_state)\n",
- " \n",
- " def forward(\n",
- " self,\n",
- " x: Tensor,\n",
- " xa: Optional[Tensor] = None,\n",
- " causal = False,\n",
- " kv_cache: Optional[dict] = None,\n",
- " ):\n",
- " x = x + self.attn(self.attn_ln(x), causal=causal, kv_cache=kv_cache)[0]\n",
- " if self.cross_attn:\n",
- " x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)[0]\n",
- " x = x + self.mlp(self.mlp_ln(x))\n",
- " return x"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "159774b6",
- "metadata": {},
- "outputs": [],
- "source": [
- "# encoder model, accepts 1500 semantic tokens\n",
- "class SEncoder(nn.Module):\n",
- " def __init__(self, sin_embs, depth=6, length=1500, width=384, S_codes=1024, n_head=6, unique_Stoks=False):\n",
- " super(SEncoder, self).__init__()\n",
- " \n",
- " # embed semantic tokens\n",
- " if unique_Stoks:\n",
- " S_codes += 1 # for padding\n",
- " self.embedding = nn.Embedding(S_codes, width)\n",
- " self.register_buffer(\"positional_embedding\", sin_embs)\n",
- "\n",
- " self.layers = nn.Sequential(*[\n",
- " ResidualAttentionBlock(width, n_head) for _ in range(depth)\n",
- " ])\n",
- "\n",
- " self.ln_post = LayerNorm(width)\n",
- " \n",
- " def forward(self, Stoks):\n",
- " xin = self.embedding(Stoks)\n",
- " \n",
- " assert xin.shape[1:] == self.positional_embedding.shape, \"incorrect semantic token shape\"\n",
- " xin = (xin + self.positional_embedding).to(xin.dtype)\n",
- "\n",
- " return self.ln_post(self.layers(xin))\n",
- "\n",
- "# AR decoder, accepts and outputs interleaved acoustic tokens (1024 is a start of sequence token)\n",
- "class ADecoder(nn.Module):\n",
- " def __init__(self, sin_embs, depth=6, length=4500, width=384, A_codes=1024, n_head=6):\n",
- " super(ADecoder, self).__init__()\n",
- " \n",
- " # embed semantic tokens\n",
- " self.embedding = nn.Embedding(A_codes+1, width)\n",
- " self.register_buffer(\"positional_embedding\", sin_embs)\n",
- " \n",
- " # before adding the encoder features\n",
- " self.layers = nn.ModuleList([\n",
- " ResidualAttentionBlock(width, n_head) for _ in range(depth)\n",
- " ])\n",
- "\n",
- " # after adding the encoder features\n",
- " self.layers2 = nn.ModuleList([\n",
- " ResidualAttentionBlock(width, n_head) for _ in range(depth)\n",
- " ])\n",
- "\n",
- " self.ln_post = LayerNorm(width)\n",
- " \n",
- " def forward(self, Atoks, xenc):\n",
- " sot = self.embedding(torch.tensor([1024]).cuda()).repeat(Atoks.shape[0],1,1)\n",
- " if Atoks.shape[-1] > 0:\n",
- " if Atoks.shape[-1] > 4499:\n",
- " Atoks = Atoks[:,:-1]\n",
- " Aembs = self.embedding(Atoks)\n",
- " Aembs = torch.cat([sot, Aembs], dim=-2)\n",
- " else:\n",
- " Aembs = sot\n",
- "\n",
- " xin = (Aembs + self.positional_embedding[:Aembs.shape[1]]).to(xenc.dtype)\n",
- " \n",
- " x = xin\n",
- "\n",
- " for l in self.layers: x = l(x, causal=True)\n",
- " \n",
- " x += xenc.repeat_interleave(3, dim=-2)[:,:Aembs.shape[1]]\n",
- "\n",
- " for l in self.layers2: x = l(x, causal=True)\n",
- " \n",
- " x = self.ln_post(x)\n",
- " \n",
- " logits = (x @ self.embedding.weight.to(x.dtype).T).float()\n",
- " return logits\n",
- "\n",
- "class SAARTransformer(nn.Module):\n",
- " def __init__(self, width=384, depth=2, n_head=6, unique_Stoks=False):\n",
- " super(SAARTransformer, self).__init__()\n",
- "\n",
- " # generate positional embeddings and subsample for the encoder, so they stay compatible\n",
- " pos_embs = sinusoids(4500, width)\n",
- " \n",
- " self.encoder = SEncoder(pos_embs[::3], width=width, n_head=n_head, depth=depth, unique_Stoks=unique_Stoks)\n",
- " self.decoder = ADecoder(pos_embs, width=width, n_head=n_head, depth=depth)\n",
- " \n",
- " self.apply(init_transformer)\n",
- "\n",
- " def forward(self, Stoks, Atoks, loss=True):\n",
- " xenc = self.encoder(Stoks.to(torch.long))\n",
- " logits = self.decoder(Atoks, xenc)\n",
- " if loss is not None:\n",
- " loss = F.cross_entropy(logits.reshape(-1,logits.shape[-1]), Atoks.view(-1))\n",
- " return logits, loss"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ae4b75a0",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Automatic pdb calling has been turned OFF\n"
- ]
- }
- ],
- "source": [
- "%pdb"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0db70760",
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- " }\n",
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- " <thead>\n",
- " <tr style=\"text-align: left;\">\n",
- " <th>samples</th>\n",
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- " <th>val</th>\n",
- " <th>time</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
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",
- "text/plain": [
- "<Figure size 1000x600 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "model = SAARTransformer(depth=2).cuda()\n",
- "with torch.backends.cuda.sdp_kernel(enable_mem_efficient=False):\n",
- " train(\"saar-encsum\", model, train_ds, val_ds, half=True, bs=8, lr=2e-3, epochs=10, warmup=0,\n",
- " table_row_every_iters=40000, run_valid_every_iters=8000)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1bb14cfa",
- "metadata": {},
- "outputs": [],
- "source": [
- "torch.save(model.state_dict(), 'saar-1000h-encsum-10e.pth')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c3ec4fb8",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [],
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- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
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- "<style>\n",
- " /* Turns off some styling */\n",
- " progress {\n",
- " /* gets rid of default border in Firefox and Opera. */\n",
- " border: none;\n",
- " /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
- " background-size: auto;\n",
- " }\n",
- " progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
- " background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
- " }\n",
- " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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- " }\n",
- "</style>\n"
- ],
- "text/plain": [
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- "metadata": {},
- "output_type": "display_data"
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- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: left;\">\n",
- " <th>samples</th>\n",
- " <th>train</th>\n",
- " <th>val</th>\n",
- " <th>time</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <td>40000</td>\n",
- " <td>5.63437</td>\n",
- " <td>5.96061</td>\n",
- " <td>05:56</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>80000</td>\n",
- " <td>4.96111</td>\n",
- " <td>4.77276</td>\n",
- " <td>11:52</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>120000</td>\n",
- " <td>4.46836</td>\n",
- " <td>4.35054</td>\n",
- " <td>17:47</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>160000</td>\n",
- " <td>3.99235</td>\n",
- " <td>3.99778</td>\n",
- " <td>23:39</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>200000</td>\n",
- " <td>3.91253</td>\n",
- " <td>3.73423</td>\n",
- " <td>29:30</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>240000</td>\n",
- " <td>3.61962</td>\n",
- " <td>3.48045</td>\n",
- " <td>35:21</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>280000</td>\n",
- " <td>3.53960</td>\n",
- " <td>3.30636</td>\n",
- " <td>41:13</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>320000</td>\n",
- " <td>3.40125</td>\n",
- " <td>3.18816</td>\n",
- " <td>47:10</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>360000</td>\n",
- " <td>3.33214</td>\n",
- " <td>3.08719</td>\n",
- " <td>53:01</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>400000</td>\n",
- " <td>3.22842</td>\n",
- " <td>3.02770</td>\n",
- " <td>58:52</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>440000</td>\n",
- " <td>3.20582</td>\n",
- " <td>2.98393</td>\n",
- " <td>1:04:50</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>480000</td>\n",
- " <td>3.17362</td>\n",
- " <td>2.95143</td>\n",
- " <td>1:10:46</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>520000</td>\n",
- " <td>3.12916</td>\n",
- " <td>2.92572</td>\n",
- " <td>1:16:44</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>560000</td>\n",
- " <td>3.23334</td>\n",
- " <td>2.89763</td>\n",
- " <td>1:22:38</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>600000</td>\n",
- " <td>3.10370</td>\n",
- " <td>2.88667</td>\n",
- " <td>1:28:33</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>640000</td>\n",
- " <td>3.13410</td>\n",
- " <td>2.87610</td>\n",
- " <td>1:34:27</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>680000</td>\n",
- " <td>3.10298</td>\n",
- " <td>2.86994</td>\n",
- " <td>1:40:18</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>720000</td>\n",
- " <td>3.08475</td>\n",
- " <td>2.84739</td>\n",
- " <td>1:46:13</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>760000</td>\n",
- " <td>3.04511</td>\n",
- " <td>2.84010</td>\n",
- " <td>1:52:07</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>800000</td>\n",
- " <td>3.04836</td>\n",
- " <td>2.83309</td>\n",
- " <td>1:58:06</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>840000</td>\n",
- " <td>2.96002</td>\n",
- " <td>2.82011</td>\n",
- " <td>2:04:00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>880000</td>\n",
- " <td>3.06576</td>\n",
- " <td>2.80926</td>\n",
- " <td>2:09:53</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>920000</td>\n",
- " <td>3.04002</td>\n",
- " <td>2.80321</td>\n",
- " <td>2:15:46</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>960000</td>\n",
- " <td>3.04942</td>\n",
- " <td>2.80005</td>\n",
- " <td>2:21:40</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1000000</td>\n",
- " <td>3.00471</td>\n",
- " <td>2.79158</td>\n",
- " <td>2:27:33</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1040000</td>\n",
- " <td>2.99747</td>\n",
- " <td>2.77878</td>\n",
- " <td>2:33:27</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1080000</td>\n",
- " <td>3.01967</td>\n",
- " <td>2.76833</td>\n",
- " <td>2:39:25</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1120000</td>\n",
- " <td>3.04421</td>\n",
- " <td>2.75896</td>\n",
- " <td>2:45:21</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1160000</td>\n",
- " <td>2.95754</td>\n",
- " <td>2.75151</td>\n",
- " <td>2:51:20</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1200000</td>\n",
- " <td>3.03571</td>\n",
- " <td>2.74071</td>\n",
- " <td>2:57:20</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1240000</td>\n",
- " <td>2.95168</td>\n",
- " <td>2.73593</td>\n",
- " <td>3:03:20</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1280000</td>\n",
- " <td>2.85460</td>\n",
- " <td>2.72531</td>\n",
- " <td>3:09:18</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1320000</td>\n",
- " <td>2.97630</td>\n",
- " <td>2.71872</td>\n",
- " <td>3:15:14</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1360000</td>\n",
- " <td>2.94897</td>\n",
- " <td>2.71092</td>\n",
- " <td>3:21:12</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1400000</td>\n",
- " <td>2.93373</td>\n",
- " <td>2.70161</td>\n",
- " <td>3:27:08</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1440000</td>\n",
- " <td>2.84430</td>\n",
- " <td>2.69850</td>\n",
- " <td>3:33:05</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1480000</td>\n",
- " <td>2.86902</td>\n",
- " <td>2.69156</td>\n",
- " <td>3:38:59</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1520000</td>\n",
- " <td>2.90178</td>\n",
- " <td>2.68827</td>\n",
- " <td>3:44:53</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1560000</td>\n",
- " <td>2.88388</td>\n",
- " <td>2.68601</td>\n",
- " <td>3:50:47</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1600000</td>\n",
- " <td>3.00206</td>\n",
- " <td>2.68482</td>\n",
- " <td>3:56:36</td>\n",
- " </tr>\n",
- " </tbody>\n",
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",
- "text/plain": [
- "<Figure size 1000x600 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "model = SAARTransformer(depth=2).cuda()\n",
- "with torch.backends.cuda.sdp_kernel(enable_mem_efficient=False):\n",
- " train(\"saar-encsum\", model, train_ds, val_ds, half=True, bs=8, lr=5e-4, epochs=10, warmup=0,\n",
- " table_row_every_iters=40000, run_valid_every_iters=8000)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "984bb213",
- "metadata": {},
- "outputs": [],
- "source": [
- "torch.save(model.state_dict(), 'saar-1000h-encsum-10e-5e-4-ce2.685.pth')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "27358725",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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- " <tr>\n",
- " <td>360000</td>\n",
- " <td>3.46209</td>\n",
- " <td>3.33173</td>\n",
- " <td>52:48</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>400000</td>\n",
- " <td>3.52250</td>\n",
- " <td>3.24571</td>\n",
- " <td>58:41</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>440000</td>\n",
- " <td>3.45666</td>\n",
- " <td>3.16816</td>\n",
- " <td>1:04:38</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>480000</td>\n",
- " <td>3.34137</td>\n",
- " <td>3.09860</td>\n",
- " <td>1:10:32</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>520000</td>\n",
- " <td>3.27845</td>\n",
- " <td>3.05158</td>\n",
- " <td>1:16:30</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>560000</td>\n",
- " <td>3.19562</td>\n",
- " <td>3.00454</td>\n",
- " <td>1:22:25</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>600000</td>\n",
- " <td>3.19548</td>\n",
- " <td>2.97702</td>\n",
- " <td>1:28:21</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>640000</td>\n",
- " <td>3.22728</td>\n",
- " <td>2.95390</td>\n",
- " <td>1:34:17</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>680000</td>\n",
- " <td>3.05132</td>\n",
- " <td>2.93575</td>\n",
- " <td>1:40:09</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>720000</td>\n",
- " <td>3.17548</td>\n",
- " <td>2.91963</td>\n",
- " <td>1:46:07</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>760000</td>\n",
- " <td>3.14583</td>\n",
- " <td>2.90713</td>\n",
- " <td>1:51:57</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>800000</td>\n",
- " <td>3.07782</td>\n",
- " <td>2.89860</td>\n",
- " <td>1:57:56</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>840000</td>\n",
- " <td>3.12980</td>\n",
- " <td>2.88934</td>\n",
- " <td>2:03:51</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>880000</td>\n",
- " <td>3.00311</td>\n",
- " <td>2.88597</td>\n",
- " <td>2:09:50</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>920000</td>\n",
- " <td>3.06421</td>\n",
- " <td>2.87300</td>\n",
- " <td>2:15:43</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>960000</td>\n",
- " <td>2.99549</td>\n",
- " <td>2.87411</td>\n",
- " <td>2:21:36</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1000000</td>\n",
- " <td>3.08708</td>\n",
- " <td>2.86715</td>\n",
- " <td>2:27:31</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1040000</td>\n",
- " <td>3.11569</td>\n",
- " <td>2.85489</td>\n",
- " <td>2:33:26</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1080000</td>\n",
- " <td>3.10673</td>\n",
- " <td>2.85690</td>\n",
- " <td>2:39:21</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1120000</td>\n",
- " <td>3.02194</td>\n",
- " <td>2.84081</td>\n",
- " <td>2:45:10</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1160000</td>\n",
- " <td>3.00532</td>\n",
- " <td>2.84338</td>\n",
- " <td>2:51:06</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1200000</td>\n",
- " <td>3.05387</td>\n",
- " <td>2.83231</td>\n",
- " <td>2:56:59</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1240000</td>\n",
- " <td>2.95787</td>\n",
- " <td>2.82819</td>\n",
- " <td>3:02:52</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1280000</td>\n",
- " <td>3.09160</td>\n",
- " <td>2.82575</td>\n",
- " <td>3:08:45</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1320000</td>\n",
- " <td>3.06746</td>\n",
- " <td>2.82263</td>\n",
- " <td>3:14:37</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1360000</td>\n",
- " <td>2.96440</td>\n",
- " <td>2.81626</td>\n",
- " <td>3:20:35</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1400000</td>\n",
- " <td>3.07043</td>\n",
- " <td>2.81295</td>\n",
- " <td>3:26:31</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1440000</td>\n",
- " <td>3.02470</td>\n",
- " <td>2.80366</td>\n",
- " <td>3:32:25</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1480000</td>\n",
- " <td>3.00143</td>\n",
- " <td>2.80674</td>\n",
- " <td>3:38:23</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1520000</td>\n",
- " <td>3.01396</td>\n",
- " <td>2.79875</td>\n",
- " <td>3:44:23</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1560000</td>\n",
- " <td>3.08450</td>\n",
- " <td>2.80055</td>\n",
- " <td>3:50:16</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1600000</td>\n",
- " <td>3.01390</td>\n",
- " <td>2.79089</td>\n",
- " <td>3:56:11</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1640000</td>\n",
- " <td>2.99889</td>\n",
- " <td>2.78968</td>\n",
- " <td>4:02:07</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1680000</td>\n",
- " <td>2.98531</td>\n",
- " <td>2.78523</td>\n",
- " <td>4:07:54</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1720000</td>\n",
- " <td>3.02506</td>\n",
- " <td>2.78334</td>\n",
- " <td>4:13:46</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table><p>\n",
- "\n",
- " <div>\n",
- " <progress value='14011' class='' max='20127' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
- " 69.61% [14011/20127 16:24<07:09 #11/20 loss: 3.051 / 2.783]\n",
- " </div>\n",
- " "
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "model = SAARTransformer(depth=2).cuda()\n",
- "with torch.backends.cuda.sdp_kernel(enable_mem_efficient=False):\n",
- " train(\"saar-encsum\", model, train_ds, val_ds, half=True, bs=8, lr=5e-4, epochs=20, warmup=0,\n",
- " table_row_every_iters=40000, run_valid_every_iters=8000)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e7036705",
- "metadata": {},
- "outputs": [],
- "source": [
- "torch.save(model.state_dict(), 'saar-1000h-encsum-20e-5e-4-ce2.655.pth')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "843d6703",
- "metadata": {},
- "source": [
- "# Sample from the model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "46afee25",
- "metadata": {},
- "outputs": [],
- "source": [
- "model = SAARTransformer(depth=2).cuda()\n",
- "model.load_state_dict(torch.load('saar-1000h-encsum-20e-5e-4-ce2.655.pth'))\n",
- "model.eval().cuda();"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1a9d792c",
- "metadata": {},
- "outputs": [],
- "source": [
- "from encodec.model import EncodecModel\n",
- "Amodel = EncodecModel.encodec_model_24khz()\n",
- "Amodel.set_target_bandwidth(1.5)\n",
- "Amodel.cuda().eval();"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c353c186",
- "metadata": {},
- "outputs": [],
- "source": [
- "def save_wav(name, Atoks):\n",
- " with torch.no_grad():\n",
- " audio = Amodel.decode([(Atoks.reshape(-1,2).T.unsqueeze(0), torch.tensor(1).cuda())])[0]\n",
- " torchaudio.save(name, audio.cpu(), 24000)\n",
- " display(HTML(f'<a href=\"{name}\" target=\"_blank\">Listen to sample {name}</a>'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "da227a3b",
- "metadata": {},
- "outputs": [],
- "source": [
- "dl = DataLoader(val_ds, batch_size=16)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f9f8ae84",
- "metadata": {},
- "outputs": [],
- "source": [
- "bx, by = [x.cuda() for x in next(iter(dl))]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "145d3f4b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(torch.Size([16, 1500]), torch.Size([16, 4500]))"
- ]
- },
- "execution_count": null,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "bx.shape, by.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ca57316b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tensor(2.7411, device='cuda:0')"
- ]
- },
- "execution_count": null,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "with torch.no_grad():\n",
- " logits, loss = model(bx, by)\n",
- "loss"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "eedd0e79",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<a href=\"test-teacher.wav\" target=\"_blank\">Listen to sample</a>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# teacher forcing output (every output token sees the 100% correct context)\n",
- "save_wav(\"test-teacher.wav\", logits.argmax(-1)[0])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9bfe3b2f",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<a href=\"ref.wav\" target=\"_blank\">Listen to sample ref.wav</a>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# the ground truth compressed speech (the best we can hope for)\n",
- "save_wav('ref.wav', by[3:4].cuda())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3de7dc9b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "<style>\n",
- " /* Turns off some styling */\n",
- " progress {\n",
- " /* gets rid of default border in Firefox and Opera. */\n",
- " border: none;\n",
- " /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
- " background-size: auto;\n",
- " }\n",
- " progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
- " background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
- " }\n",
- " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
- " background: #F44336;\n",
- " }\n",
- "</style>\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "\n",
- " <div>\n",
- " <progress value='4500' class='' max='4500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
- " 100.00% [4500/4500 00:36<00:00]\n",
- " </div>\n",
- " "
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<a href=\"test-gen-T0.6.wav\" target=\"_blank\">Listen to sample test-gen-T0.6.wav</a>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "\n",
- "<style>\n",
- " /* Turns off some styling */\n",
- " progress {\n",
- " /* gets rid of default border in Firefox and Opera. */\n",
- " border: none;\n",
- " /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
- " background-size: auto;\n",
- " }\n",
- " progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
- " background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
- " }\n",
- " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
- " background: #F44336;\n",
- " }\n",
- "</style>\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "\n",
- " <div>\n",
- " <progress value='4500' class='' max='4500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
- " 100.00% [4500/4500 00:35<00:00]\n",
- " </div>\n",
- " "
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<a href=\"test-gen-T0.7.wav\" target=\"_blank\">Listen to sample test-gen-T0.7.wav</a>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "\n",
- "<style>\n",
- " /* Turns off some styling */\n",
- " progress {\n",
- " /* gets rid of default border in Firefox and Opera. */\n",
- " border: none;\n",
- " /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
- " background-size: auto;\n",
- " }\n",
- " progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
- " background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
- " }\n",
- " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
- " background: #F44336;\n",
- " }\n",
- "</style>\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "\n",
- " <div>\n",
- " <progress value='4500' class='' max='4500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
- " 100.00% [4500/4500 00:36<00:00]\n",
- " </div>\n",
- " "
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<a href=\"test-gen-T0.8.wav\" target=\"_blank\">Listen to sample test-gen-T0.8.wav</a>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "\n",
- "<style>\n",
- " /* Turns off some styling */\n",
- " progress {\n",
- " /* gets rid of default border in Firefox and Opera. */\n",
- " border: none;\n",
- " /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
- " background-size: auto;\n",
- " }\n",
- " progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
- " background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
- " }\n",
- " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
- " background: #F44336;\n",
- " }\n",
- "</style>\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "\n",
- " <div>\n",
- " <progress value='4500' class='' max='4500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
- " 100.00% [4500/4500 00:35<00:00]\n",
- " </div>\n",
- " "
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<a href=\"test-gen-T0.9.wav\" target=\"_blank\">Listen to sample test-gen-T0.9.wav</a>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# generate output using sampling, one token at a time\n",
- "for T in [\"0.6\", \"0.7\", \"0.8\", \"0.9\", \"1.0\"]:\n",
- " toks = []\n",
- " for i in progress_bar(range(4500)):\n",
- " p, loss = model(bx[3:4], torch.tensor([toks]).cuda(), loss=None)\n",
- " last_p = p[0,-1]\n",
- " toks.append(torch.multinomial((last_p / float(T)).softmax(-1), 1).item())\n",
- " save_wav(f'test-gen-T{T}.wav', torch.tensor(toks).cuda())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "51e4868c",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tensor(0.0072)"
- ]
- },
- "execution_count": null,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# it sounds reasonable but almost all tokens are \"wrong\"\n",
- "(torch.tensor(toks) == by.cpu()).float().mean()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "python3",
- "language": "python",
- "name": "python3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/nbs/5. Text to semantic token modeling.ipynb b/nbs/5. Text to semantic token modeling.ipynb
deleted file mode 100644
index 73f785ad31f4308f8fcaa6e640e3a2348c25c637..0000000000000000000000000000000000000000
--- a/nbs/5. Text to semantic token modeling.ipynb
+++ /dev/null
@@ -1,1656 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7c4adca2",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The autoreload extension is already loaded. To reload it, use:\n",
- " %reload_ext autoreload\n"
- ]
- }
- ],
- "source": [
- "#| default_exp t2s\n",
- "%load_ext autoreload\n",
- "%autoreload 2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0a853249",
- "metadata": {},
- "outputs": [],
- "source": [
- "#| exporti\n",
- "import torch\n",
- "import torch.nn as nn\n",
- "from torch.profiler import record_function"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "13462aa4",
- "metadata": {},
- "outputs": [],
- "source": [
- "#| exporti\n",
- "from pathlib import Path\n",
- "import pylab as plt\n",
- "import pandas as pd"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2b289594",
- "metadata": {},
- "outputs": [],
- "source": [
- "#| exporti\n",
- "import whisper\n",
- "from spear_tts_pytorch.train import *\n",
- "from spear_tts_pytorch.modules import *"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d72390bf",
- "metadata": {},
- "outputs": [],
- "source": [
- "datadir = Path('/mnt/stoks-txts/')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b02bc209",
- "metadata": {},
- "source": [
- "# Dataset"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ec46329b",
- "metadata": {},
- "source": [
- "## Load the data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5dde13ad",
- "metadata": {},
- "outputs": [],
- "source": [
- "data = pd.DataFrame(dict(stoks=[str(x) for x in Path(datadir).rglob('*.stoks')]))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c568210f",
- "metadata": {},
- "outputs": [],
- "source": [
- "data['text'] = data['stoks'].apply(lambda x: Path(x).with_suffix('.txt').read_text())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5d80ca7d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>stoks</th>\n",
- " <th>text</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>/mnt/stoks-txts/medium/911report_32_64kb-2.stoks</td>\n",
- " <td>Selection and Selection for 9-11. Twelve of t...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>/mnt/stoks-txts/medium/thousand_nights_vol03_1...</td>\n",
- " <td>smite these people's necks, their troops will...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>/mnt/stoks-txts/medium/21stcenturypolicing_03_...</td>\n",
- " <td>95% are law-abiding. This becomes a self-rein...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>/mnt/stoks-txts/medium/rewardsandfairies_11_ki...</td>\n",
- " <td>That's foolishness, he says. Who cares where ...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>/mnt/stoks-txts/medium/factorygirlsdanger_scot...</td>\n",
- " <td>into her old position and upon terms and cond...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>140654</th>\n",
- " <td>/mnt/stoks-txts/small/floridasketch_03_torrey_...</td>\n",
- " <td>And, as he sprinted up and down the sand in h...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>140655</th>\n",
- " <td>/mnt/stoks-txts/small/shirley_47_bronte_64kb-8...</td>\n",
- " <td>had been two hours before.\" This change, acco...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>140656</th>\n",
- " <td>/mnt/stoks-txts/small/goldenbowl_4-26_james_64...</td>\n",
- " <td>carefully thinking of it and watching it. But...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>140657</th>\n",
- " <td>/mnt/stoks-txts/small/millonthefloss_19_eliot_...</td>\n",
- " <td>this time of his trouble, they never became c...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>140658</th>\n",
- " <td>/mnt/stoks-txts/small/oldtestament_004_worlden...</td>\n",
- " <td>one, and every black one among the sheep, and...</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>140659 rows × 2 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " stoks \\\n",
- "0 /mnt/stoks-txts/medium/911report_32_64kb-2.stoks \n",
- "1 /mnt/stoks-txts/medium/thousand_nights_vol03_1... \n",
- "2 /mnt/stoks-txts/medium/21stcenturypolicing_03_... \n",
- "3 /mnt/stoks-txts/medium/rewardsandfairies_11_ki... \n",
- "4 /mnt/stoks-txts/medium/factorygirlsdanger_scot... \n",
- "... ... \n",
- "140654 /mnt/stoks-txts/small/floridasketch_03_torrey_... \n",
- "140655 /mnt/stoks-txts/small/shirley_47_bronte_64kb-8... \n",
- "140656 /mnt/stoks-txts/small/goldenbowl_4-26_james_64... \n",
- "140657 /mnt/stoks-txts/small/millonthefloss_19_eliot_... \n",
- "140658 /mnt/stoks-txts/small/oldtestament_004_worlden... \n",
- "\n",
- " text \n",
- "0 Selection and Selection for 9-11. Twelve of t... \n",
- "1 smite these people's necks, their troops will... \n",
- "2 95% are law-abiding. This becomes a self-rein... \n",
- "3 That's foolishness, he says. Who cares where ... \n",
- "4 into her old position and upon terms and cond... \n",
- "... ... \n",
- "140654 And, as he sprinted up and down the sand in h... \n",
- "140655 had been two hours before.\" This change, acco... \n",
- "140656 carefully thinking of it and watching it. But... \n",
- "140657 this time of his trouble, they never became c... \n",
- "140658 one, and every black one among the sheep, and... \n",
- "\n",
- "[140659 rows x 2 columns]"
- ]
- },
- "execution_count": null,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a7870820",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1015.8705555555556 hours of (auto)transcribed speech\n"
- ]
- }
- ],
- "source": [
- "print(f\"{len(data) * 26 / 3600} hours of (auto)transcribed speech\") # average sample length is 26s"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a52d5d27",
- "metadata": {},
- "outputs": [],
- "source": [
- "#| exporti\n",
- "def load_data(path):\n",
- " data = pd.DataFrame(dict(stoks=[str(x) for x in Path(path).rglob('*.stoks')]))\n",
- " data['text'] = data['stoks'].apply(lambda x: Path(x).with_suffix('.txt').read_text())\n",
- " return data"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9f77fad9",
- "metadata": {},
- "source": [
- "## Prepare the datasets"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9d23ab3e",
- "metadata": {},
- "outputs": [],
- "source": [
- "data = load_data(datadir)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a5edd423",
- "metadata": {},
- "outputs": [],
- "source": [
- "#| exporti\n",
- "import torch.nn.functional as F\n",
- "\n",
- "class SADataset(torch.utils.data.Dataset):\n",
- " def __init__(self, data, tokenizer):\n",
- " self.data = data\n",
- " self.tokenizer = tokenizer\n",
- " \n",
- " def __len__(self):\n",
- " return len(self.data)\n",
- " \n",
- " def __repr__(self):\n",
- " return f\"<Dataset: {len(self)} samples>\"\n",
- " \n",
- " def __getitem__(self, idx):\n",
- " row = self.data.iloc[idx]\n",
- " Stoks = torch.load(row['stoks'], map_location='cpu')[0,:,0]\n",
- " Ttoks = self.tokenizer.encode(row['text'])\n",
- " return F.pad(torch.tensor(Ttoks), (0, 200 - len(Ttoks)), value=self.tokenizer.eot).to(torch.long), F.pad(Stoks, (0, 1500 - len(Stoks)), value=1024).to(torch.long)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "dfb71569",
- "metadata": {},
- "outputs": [],
- "source": [
- "tokenizer = whisper.tokenizer.get_tokenizer(multilingual=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ef8736c2",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Dataset: 300 samples>"
- ]
- },
- "execution_count": null,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "val_data, train_data = data[:300], data[300:]\n",
- "val_ds = SADataset(val_data, tokenizer)\n",
- "val_ds"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d6c82c52",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Dataset: 140359 samples>"
- ]
- },
- "execution_count": null,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_ds = SADataset(train_data, tokenizer)\n",
- "train_ds"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b88131da",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(tensor([ 467, 390, 720, 35442, 337, 1419, 586, 572, 30705, 281,\n",
- " 6479, 720, 8913, 13, 1240, 40791, 439, 720, 43271, 337,\n",
- " 472, 46607, 7563, 11, 293, 11, 16005, 11, 3574, 1314,\n",
- " 13, 583, 750, 2729, 472, 574, 646, 490, 264, 4838,\n",
- " 11, 445, 490, 264, 1036, 935, 412, 597, 264, 2853,\n",
- " 727, 312, 1612, 11, 293, 11, 16124, 257, 25838, 295,\n",
- " 35172, 4877, 18864, 322, 264, 1823, 11, 750, 50257, 50257,\n",
- " 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257,\n",
- " 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257,\n",
- " 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257,\n",
- " 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257,\n",
- " 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257,\n",
- " 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257,\n",
- " 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257,\n",
- " 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257,\n",
- " 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257,\n",
- " 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257,\n",
- " 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257,\n",
- " 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257,\n",
- " 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257, 50257]),\n",
- " tensor([ 547, 995, 995, ..., 1024, 1024, 1024]))"
- ]
- },
- "execution_count": null,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_ds[0]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2d156fad",
- "metadata": {},
- "outputs": [],
- "source": [
- "#| export\n",
- "def load_datasets(path):\n",
- " tokenizer = whisper.tokenizer.get_tokenizer(multilingual=True)\n",
- " data = load_data(path)\n",
- " \n",
- " val_data, train_data = data[:300], data[300:]\n",
- "\n",
- " return SADataset(train_data, tokenizer), SADataset(val_data, tokenizer)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0f5e4ad4",
- "metadata": {},
- "source": [
- "# Modeling"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "159774b6",
- "metadata": {},
- "outputs": [],
- "source": [
- "#| export\n",
- "class TSARTransformer(nn.Module):\n",
- " def __init__(self, width=384, depth=6, n_head=6):\n",
- " super(TSARTransformer, self).__init__()\n",
- "\n",
- " self.encoder = Encoder(length=200, codes=50364, width=width, n_head=n_head, depth=depth)\n",
- " self.decoder = Decoder(length=1500, codes=1024, width=width, n_head=n_head, depth=depth)\n",
- "\n",
- " def forward(self, Ttoks, Stoks, loss=True):\n",
- " with record_function(\"encoder\"):\n",
- " xenc = self.encoder(Ttoks.to(torch.long))\n",
- " with record_function(\"decoder\"):\n",
- " logits = self.decoder(Stoks, xenc)\n",
- " if loss is not None:\n",
- " with record_function(\"loss\"):\n",
- " loss = F.cross_entropy(logits.reshape(-1,logits.shape[-1]), Stoks.view(-1))\n",
- " return logits, loss"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e98060d6",
- "metadata": {},
- "outputs": [],
- "source": [
- "#| export\n",
- "def make_model(size):\n",
- " if size == 'micro':\n",
- " return TSARTransformer(depth=3)\n",
- " elif size == 'tiny':\n",
- " return TSARTransformer(depth=4)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a4853d59",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "\n",
- "<style>\n",
- " /* Turns off some styling */\n",
- " progress {\n",
- " /* gets rid of default border in Firefox and Opera. */\n",
- " border: none;\n",
- " /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
- " background-size: auto;\n",
- " }\n",
- " progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
- " background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
- " }\n",
- " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
- " background: #F44336;\n",
- " }\n",
- "</style>\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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- "\n",
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- " 100.00% [1/1 13:36<00:00]\n",
- " </div>\n",
- " \n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: left;\">\n",
- " <th>samples</th>\n",
- " <th>train</th>\n",
- " <th>val</th>\n",
- " <th>time</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <td>20000</td>\n",
- " <td>2.93531</td>\n",
- " <td>3.08976</td>\n",
- " <td>02:06</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>40000</td>\n",
- " <td>2.40885</td>\n",
- " <td>2.46782</td>\n",
- " <td>03:47</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>60000</td>\n",
- " <td>2.24459</td>\n",
- " <td>2.34386</td>\n",
- " <td>05:41</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>80000</td>\n",
- " <td>2.25632</td>\n",
- " <td>2.26977</td>\n",
- " <td>07:34</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>100000</td>\n",
- " <td>2.12768</td>\n",
- " <td>2.20723</td>\n",
- " <td>09:35</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>120000</td>\n",
- " <td>2.07301</td>\n",
- " <td>2.15234</td>\n",
- " <td>11:30</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>140000</td>\n",
- " <td>2.11128</td>\n",
- " <td>2.12581</td>\n",
- " <td>13:33</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>140368</td>\n",
- " <td>2.09136</td>\n",
- " <td>2.12533</td>\n",
- " <td>13:36</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table><p>\n",
- "\n",
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- " 100.00% [8773/8773 13:36<00:00 #1/1 loss: 2.091 / 2.125]\n",
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",
- "text/plain": [
- "<Figure size 1000x600 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# make sure it works at all\n",
- "model = TSARTransformer(depth=3).cuda()\n",
- "train(\"/scrach/tsar-checkpoints\", model, train_ds, val_ds, half=True, bs=16, lr=4e-3, epochs=1,\n",
- " table_row_every_iters=20000, run_valid_every_iters=4000)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fd58189f",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "\n",
- "<style>\n",
- " /* Turns off some styling */\n",
- " progress {\n",
- " /* gets rid of default border in Firefox and Opera. */\n",
- " border: none;\n",
- " /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
- " background-size: auto;\n",
- " }\n",
- " progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
- " background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
- " }\n",
- " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
- " background: #F44336;\n",
- " }\n",
- "</style>\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: left;\">\n",
- " <th>samples</th>\n",
- " <th>train</th>\n",
- " <th>val</th>\n",
- " <th>time</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <td>80000</td>\n",
- " <td>2.56269</td>\n",
- " <td>2.83687</td>\n",
- " <td>22:59</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>160000</td>\n",
- " <td>2.26247</td>\n",
- " <td>2.39995</td>\n",
- " <td>41:30</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>240000</td>\n",
- " <td>2.16594</td>\n",
- " <td>2.27455</td>\n",
- " <td>47:38</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>320000</td>\n",
- " <td>1.96548</td>\n",
- " <td>2.00825</td>\n",
- " <td>54:08</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>400000</td>\n",
- " <td>1.86841</td>\n",
- " <td>1.88929</td>\n",
- " <td>1:01:07</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>480000</td>\n",
- " <td>1.82314</td>\n",
- " <td>1.85076</td>\n",
- " <td>1:07:27</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>560000</td>\n",
- " <td>1.83932</td>\n",
- " <td>1.81487</td>\n",
- " <td>1:13:35</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>640000</td>\n",
- " <td>1.80581</td>\n",
- " <td>1.79792</td>\n",
- " <td>1:19:55</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>720000</td>\n",
- " <td>1.74787</td>\n",
- " <td>1.78892</td>\n",
- " <td>1:26:10</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>800000</td>\n",
- " <td>1.79779</td>\n",
- " <td>1.78353</td>\n",
- " <td>1:32:32</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>880000</td>\n",
- " <td>1.78870</td>\n",
- " <td>1.78328</td>\n",
- " <td>1:38:44</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>960000</td>\n",
- " <td>1.74307</td>\n",
- " <td>1.77828</td>\n",
- " <td>1:44:58</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1040000</td>\n",
- " <td>1.66399</td>\n",
- " <td>1.76878</td>\n",
- " <td>1:51:11</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1120000</td>\n",
- " <td>1.78734</td>\n",
- " <td>1.76162</td>\n",
- " <td>1:57:22</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1200000</td>\n",
- " <td>1.74291</td>\n",
- " <td>1.75627</td>\n",
- " <td>2:03:33</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1280000</td>\n",
- " <td>1.77040</td>\n",
- " <td>1.74938</td>\n",
- " <td>2:09:42</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1360000</td>\n",
- " <td>1.73132</td>\n",
- " <td>1.74514</td>\n",
- " <td>2:16:01</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1440000</td>\n",
- " <td>1.75393</td>\n",
- " <td>1.74387</td>\n",
- " <td>2:22:16</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1520000</td>\n",
- " <td>1.66232</td>\n",
- " <td>1.73543</td>\n",
- " <td>2:28:28</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1600000</td>\n",
- " <td>1.69324</td>\n",
- " <td>1.73118</td>\n",
- " <td>2:34:47</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1680000</td>\n",
- " <td>1.68501</td>\n",
- " <td>1.72626</td>\n",
- " <td>2:41:03</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1760000</td>\n",
- " <td>1.70389</td>\n",
- " <td>1.71939</td>\n",
- " <td>2:47:19</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1840000</td>\n",
- " <td>1.68793</td>\n",
- " <td>1.71493</td>\n",
- " <td>2:53:36</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1920000</td>\n",
- " <td>1.63555</td>\n",
- " <td>1.70718</td>\n",
- " <td>2:59:48</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2000000</td>\n",
- " <td>1.63574</td>\n",
- " <td>1.70242</td>\n",
- " <td>3:05:54</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2080000</td>\n",
- " <td>1.65461</td>\n",
- " <td>1.69481</td>\n",
- " <td>3:12:09</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2160000</td>\n",
- " <td>1.64555</td>\n",
- " <td>1.68704</td>\n",
- " <td>3:18:25</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2240000</td>\n",
- " <td>1.62322</td>\n",
- " <td>1.68100</td>\n",
- " <td>3:24:48</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2320000</td>\n",
- " <td>1.66849</td>\n",
- " <td>1.67832</td>\n",
- " <td>3:31:07</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2400000</td>\n",
- " <td>1.68997</td>\n",
- " <td>1.66812</td>\n",
- " <td>3:37:22</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2480000</td>\n",
- " <td>1.60114</td>\n",
- " <td>1.66068</td>\n",
- " <td>3:43:26</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2560000</td>\n",
- " <td>1.59099</td>\n",
- " <td>1.65411</td>\n",
- " <td>3:49:36</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2640000</td>\n",
- " <td>1.56531</td>\n",
- " <td>1.64151</td>\n",
- " <td>3:55:43</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2720000</td>\n",
- " <td>1.54572</td>\n",
- " <td>1.63576</td>\n",
- " <td>4:01:49</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2800000</td>\n",
- " <td>1.58271</td>\n",
- " <td>1.62604</td>\n",
- " <td>4:07:57</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2880000</td>\n",
- " <td>1.56368</td>\n",
- " <td>1.61911</td>\n",
- " <td>4:14:27</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2960000</td>\n",
- " <td>1.52660</td>\n",
- " <td>1.60916</td>\n",
- " <td>4:20:53</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3040000</td>\n",
- " <td>1.52538</td>\n",
- " <td>1.60165</td>\n",
- " <td>4:27:04</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3120000</td>\n",
- " <td>1.51891</td>\n",
- " <td>1.59460</td>\n",
- " <td>4:33:15</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3200000</td>\n",
- " <td>1.52464</td>\n",
- " <td>1.58767</td>\n",
- " <td>4:39:53</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3280000</td>\n",
- " <td>1.46514</td>\n",
- " <td>1.58307</td>\n",
- " <td>4:46:08</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3360000</td>\n",
- " <td>1.48269</td>\n",
- " <td>1.57895</td>\n",
- " <td>4:52:27</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3440000</td>\n",
- " <td>1.51657</td>\n",
- " <td>1.57807</td>\n",
- " <td>4:59:13</td>\n",
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RERHpoHOaahgcHEz//v1b3RcYGEhkZORp94MVhqZPn05qairz58/H4XCQlZXFokWLmDRpEomJiW1Wv6qqqjhw4ID3dnZ2Nlu2bCEiIoKUlBQA5s6dy+23387w4cMZOXIkjz32GNXV1cyePftc3lK7TJsPuICmhk45n4iIiIiIfHWdc3ONc2Gz2XjooYcYN24cPj4+3vsHDRrEJ598QnR0dJvP27BhAxMnTvTenjt3LgC33347L730EgA333wzRUVFPPjgg+Tn5zN48GA++uij0xpunC/T1jzVUBUvERERERHpGMM0TbO7B/Fl0tzV8NU/fZeba/7D3qTr6fOdF7p7WCIiIiIi8gVrzgbl5eWEhIR06FzntY/XV4Kn4mVoqqGIiIiIiHSQglc7TLtnaqRLwUtERERERDpGwas9Dk/Fy6U1XiIiIiIi0jEKXu2x+wJgc6viJSIiIiIiHaPg1R6HFbwMTTUUEREREZEOUvBqh2GzNlBWxUtERERERDpKwasdhtOqeNndjd08EhERERERudApeLWjOXip4iUiIiIiIh2l4NUOm6O54qXgJSIiIiIiHaPg1Q7D4QeA3dRUQxERERER6RgFr3bYnNY+Xg5TFS8REREREekYBa922J1Wxcuh5hoiIiIiItJBCl7tsHuaazhQ8BIRERERkY5R8GqH3TPV0Kk1XiIiIiIi0kEKXu1wOP2tP3GB29XNoxERERERkQuZglc77L6+J2801XffQERERERE5IKn4NUOp6e5BgAuBS8RERERETl/Cl7tcHrWeAHQpJbyIiIiIiJy/hS82uF02qk3ndYNVbxERERERKQDFLza4Wu3UY/DuqGKl4iIiIiIdICCVzucdhsNqOIlIiIiIiIdp+DVDqfDRoOn4uVqVPASEREREZHzp+DVDh+HjQbPGq+m+tpuHo2IiIiIiFzIFLza4WO3UUYgAE1Vxd08GhERERERuZApeLXDaTfINaMAcJcd6ebRiIiIiIjIhUzBqx2GYZBvRFs3yo5172BEREREROSCpuB1BoVGDAC28pxuHomIiIiIiFzIFLzOoNBuBS97pYKXiIiIiIicPwWvMyiyWcHLoeAlIiIiIiIdoOB1BqXOWAAcdaXQUN3NoxERERERkQuVgteZ+IVRYfpb17XOS0REREREzpOC1xnEh/p5W8qrs6GIiIiIiJwvBa8ziA9rGby0l5eIiIiIiJwfBa8ziA/1Pxm8ylXxEhERERGR86PgdQaaaigiIiIiIp1BwesM4kL9yDWjrRuqeImIiIiIyHlS8DqDhBZTDU1VvERERERE5DwpeJ1BXKgfR01rE2Uqj0NFXvcOSERERERELkgKXmfg57RDYBRr3X0xMHn7mf/l32uPdvewRERERETkAqPg9TniQ/14oWk6AOMrF/D0JzswTbObRyUiIiIiIhcSBa/PER/qxyL3MI6Z0UQYVYypWUzOidruHpaIiIiIiFxALurgtWDBAvr06UNGRgbPPffceZ0jPtQfNzZeapoGwH2O19m+a2dnDlNERERERC5yF23wampqYu7cuSxZsoTNmzfzyCOPUFJScs7niQv1A+DfrknsdicTbZQzcMX3oaG6s4csIiIiIiIXqYs2eK1bt45+/fqRmJhIUFAQ06dPZ+HChed8noQwK3jV4se9tp9SYgaTVLcPnpkAx9Z18qhFRERERORidM7B68knn2TgwIGEhIQQEhLCmDFj+PDDDzt1UMuWLWPGjBkkJCRgGAbvvPNOm8fNmzePHj164Ofnx6hRo1i37mQQysvLIzEx0Xs7MTGR3Nzccx5LXIi/9/oVl47irsb7KDJDoXgfPD8VNrx4zucUEREREZGvlnMOXklJSTz88MNs3LiRDRs2MGnSJK699lp27mx73dPKlStpbGw87f5du3ZRUFDQ5nOqq6sZNGgQ8+bNa3cc8+fPZ+7cufz6179m06ZNDBo0iGnTplFYWHiub+mMmiteANP7x1MdM4wp9Y+QmzwDMGHBPbD67536miIiIiIicnE55+A1Y8YMrrzySjIyMujduzd/+MMfCAoKYs2aNacd63a7mTNnDrNmzcLlcnnv37t3L5MmTeLll19u8zWmT5/O73//e2bOnNnuOP7yl79w5513Mnv2bLKysnjqqacICAjghRdeACAhIaFVhSs3N5eEhIR2zzdv3jyysrIYMWJEq/sTw/wZ0SOccRlR9I4NYmTPCMoJ4pnIn8LYe6yDFv4SynPaPbeIiIiIiHy1dWiNl8vl4tVXX6W6upoxY8acfnKbjQ8++IDNmzdz22234Xa7OXjwIJMmTeK6667jgQceOK/XbWhoYOPGjUyZMqXVa02ZMoXVq1cDMHLkSHbs2EFubi5VVVV8+OGHTJs2rd1zzpkzh127drF+/fpW9zvsNl7/3iW88u1RGIbBuIxoAJbuK8ac8htIHgWmG/Z9dF7vRURERERELn7nFby2b99OUFAQvr6+fO973+Ptt98mKyurzWMTEhJYsmQJK1asYNasWUyaNIkpU6bw5JNPnvegi4uLcblcxMbGtro/NjaW/Px8ABwOB48++igTJ05k8ODB3HfffURGRp73aza7pFckPnYbR0tryC6uhj5XWg/s7dx1biIiIiIicvE4r+DVp08ftmzZwtq1a7n77ru5/fbb2bVrV7vHp6Sk8MorrzB//nwcDgfPP/88hmGc96DP1jXXXMO+ffs4cOAAd911V6ecM9DXwYie4QB8urfoZPDKXgb1lZ3yGiIiIiIicnE5r+Dl4+NDeno6w4YN449//CODBg3ib3/7W7vHFxQUcNdddzFjxgxqamq49957z3vAAFFRUdjt9tOacxQUFBAXF9ehc5+NiX1iAFi6txCiMiCiF7ga4OCSLn9tERERERG58HTKPl5ut5v6+vo2HysuLmby5MlkZmby1ltvsXjxYubPn8/9999/3q/n4+PDsGHDWLx4casxLF68uM21Zp1tQh9rndfaQ6XUNLqgz3QAqre9xz2vbmZHbnmXj0FERERERC4cjnN9ws9//nOmT59OSkoKlZWV/Pvf/+bTTz/l448/Pu1Yt9vN9OnTSU1N9U4zzMrKYtGiRUyaNInExMQ2q19VVVUcOHDAezs7O5stW7YQERFBSkoKAHPnzuX2229n+PDhjBw5kscee4zq6mpmz559rm/pnPWKDiIp3J+cE7Ws2F/M1L5XweoncOz7gE9qrsBht/F/Nw7q8nGIiIiIiMiF4ZyDV2FhIbfddhvHjx8nNDSUgQMH8vHHH3P55ZefdqzNZuOhhx5i3Lhx+Pj4eO8fNGgQn3zyCdHR0W2+xoYNG5g4caL39ty5cwG4/fbbeemllwC4+eabKSoq4sEHHyQ/P5/Bgwfz0UcfndZwoysYhsHlWbG8uPIwH+7IZ+pNYyCqD77Fe7nR/hkHK1O7fAwiIiIiInLhMEzTNLt7EF8mFRUVhIaGUl5eTkhISLvHbThcyg1PrSbI18GG/5mCc/OL2D+4j8PuWH4Q8QwL7pnwxQ1aREREREQ63dlmg7PRKWu8voqGpoQTF+JHVX0Ty/cXsy/2asrNAHrYCuhTtbq7hyciIiIiIl8iCl7nyWYzmD7A6qD4wfbjbM5v4DXXBAAurV+J261CooiIiIiIWBS8OuDqgfEALNpVwKd7C1nv7gNAhnGM8trG7hyaiIiIiIh8iSh4dcCQ5HAyYoKoqm9i4a4C9plJAKQbuZRU1nTz6ERERERE5MtCwasDbDaDR28ahMNmAHDUjKUeJ35GIxX5h7p5dCIiIiIi8mWh4NVBA5PC+OGkDACSI4M47kgGoCl/d3cOS0REREREvkTOeR8vOd2cib0IC3DSPzGUotd70KPqELbiPd09LBERERER+ZJQxasTOOw2br+kB8NSw6kITgfAv2x/N49KRERERES+LBS8OlltmDXtMLTqYDePREREREREviwUvDqZGd0XgOj6I+B2d/NoRERERETky0DBq5P5RadRbzrxNeuh7Eh3D0dERERERL4EFLw6WWRoIIdMa2NlCtXZUEREREREFLw6XVSgLzvcPQAwj6zq3sGIiIiIiMiXgoJXJ4sK9mGZeyAA5oFPunk0IiIiIiLyZaDg1ckCfBxscAzGbRrYinZDeW53D0lERERERLqZglcXcAZFsdXsZd04uLh7ByMiIiIiIt1OwasLRAb58JlnuiGabigiIiIi8pWn4NUFEkL9+dQ12Lpx8FNwNXXncEREREREpJspeHWBfokhbDPTqLSHQX05bH6lu4ckIiIiIiLdSMGrCwxOCsONjedt11t3LPkd1JZ165hERERERKT7KHh1gf5JoQA8UTmepojeUFMCn/2pm0clIiIiIiLdRcGrC4T4OUmLDqQJBzsH/sy6c+1TcHhF9w5MRERERES6hYJXFxmcFAbA0qYBMPgWMN3w5p1QU9q9AxMRERERkS+cglcXGeiZbrgtpxym/5mGsDSozOP4i7eC29XNoxMRERERkS+SglcXGZQcBsC2nDJMn0De6Pk7ak0f4otWwKIHu3dwIiIiIiLyhVLw6iKZ8SE4bAbFVQ3kltWytjaR+xu/Zz24+glY92z3DlBERERERL4wCl5dxM9pJyM2GIDdxys5WFTF++7R/LXR02L+g/th0z+6cYQiIiIiIvJFUfDqQpnxVvDamVfOwcJqAP7m+hq7Um+1Dnjvh/Dpw+B2d9cQRURERETkC6Dg1YWy4kMAWLKnkNrG5oYaBi8GfgfG/MC6+ekf4V83QMnB7hmkiIiIiIh0OQWvLpTpCV7bcspb3b8ttwKm/QGu/TvYfeHgYpg3Cpb/BUyzO4YqIiIiIiJdSMGrCzUHr2aDPZ0O9xdWsie/gh0xV8P3VlDXYxK4G2Hx/8KCe6mureNYaU03jFhERERERLqCglcXigj0IS7Ez3v7kl6RxIb44jbhiseWc80TK9hYE82fIn/PrxrvwI0BG18k/6+Xcdej/2BnXvkZzi4iIiIiIhcKBa8u1txgAyA9JohBSWHe224Tnliyn7c35/KKayp3N9xDvSOIXg17WeD4GUGv3QR7P+qGUYuIiIiISGdS8OpiLacb9ooO4oEr+vLtS3vy6I2DAFi6t4iymkYAPnaPYELNn/jQNQK7YZJatgb+czMs+YPWfomIiIiIXMAUvLpYy+CVFh1IekwQv7o6i+uHJTGxT7T3sQme68fd4dzdeC+X1f+Vf7iusB5c9md4ZSbkbPhCxy4iIiIiIp1DwauLDUkJw24zSI8JItjP2eqxO8elAWAz4A8zB5Ac4Q9AamQATSGpPNh4G/tG/wlsTji0FJ6bDK/eAqWHvvD3ISIiIiIi588wTc1ha6miooLQ0FDKy8sJCQn5/CechR255UQE+pAQ5t/qftM0eWnVYcIDfLhuSCLPLjvEHz7YzR9m9mfjkRO8tSmXawYlEFR9lO/wFmm5/wXTBXYfGP19GHcf+HXOGEVEREREpLXOzAYKXqfoiuB1tkzT5Hh5HQlh/ryxMYf7X9/qfSwqyIf1dyZiLPwlHFxi3RkYA5MfhMG3gE3FSxERERGRztSZ2UDf1r9EDMPwVsXG9Ips9VhxVQMHjWRKZ77KrgnPYkb0gupCeO8H8OwEOLKqG0YsIiIiIiJnQ8HrSyoxzJ+fTe/LDyelM7JHBACrD5Xyo1e3cOVHgfx7+Gsw9Q+4fYLh+FZ4cTq8fgeUHe3egYuIiIiIyGkUvL7Evje+F/dN7cOlGVEAvLHhGCsOFAPwyCeHeM3nWkZXPcJHfldgGjbY+TY8MQKW/J666gpueHIVc1/b0o3vQEREREREQMHrgjA6zZp2uDWn3HtfWU0jD7yxjUJ3CN8ru42dM/4LPcZBUx0sewTjiWGk5bzFO5uOUV7b2F1DFxERERERFLwuCIOSQ/F1nPxR3TIqxXs9wMcOwPP7g+D2/8JNr0BYKr61hfzZ+SxLfeZS9tFDUFP6hY9bREREREQsCl4XAF+HnWGp4QCE+jv51dVZPHh1Ft8dn8bL3xoJwPvbj3OiphGyroEfrOdpv29zwgwi1VZI6ta/wNOXQf727nwbIiIiIiJfWQpeF4jJmbEA3DQ8CT+nnW9d2pOfT89keGo4/RJCaGhy85M3trF8fxGFtSZ/LJvMJfWPM7fhexQ6E6H8GDw/DY6t7+Z3IiIiIiLy1aN9vE7Rnft4nYnLbbIuu5ThPcJx2lvn5Xe35PLjV7d4bw9MCmVbTjk+dhsNLjf9I9wsiH0GspdB4nD4zidgGF/wOxARERERubBoH6+vILvNYEyvyNNCF8C1gxN59a7RfH1EMgDbPE04rhuSAMCOUhtHxv+NRpsf5G6A/Qu/uIGLiIiIiIiC18VidFokD18/kJ9N7+u976qBCSR6NmS+7h8HeL7hcuuBJb8HFTpFRERERL4wju4egHSu716WhtNu43BxNZemRzEgMZTcslpO1DTyFFdzq+MTAvO3we7/Wo04RERERESky6nidZExDINvX9qT313XH7vNYEBSqPexSlsIzzVNt24sfQjcrm4apYiIiIjIV4uC10XuqgHxJEf485NpfRiXEcXzTdOpcwRD0W7Y8VZ3D09ERERE5CtBwesi1yMqkOUPTGLOxHSu6BdHBYG85pxpPfjpQ9BY170DFBERERH5ClDw+gqZkhWLzYCHT0ygKSAGSg/Bsj9397BERERERC56Cl5fIVFBvoxNj6IGP54NnmPdueIxyNvcreMSEREREbnYKXh9xfzyqkzsNoM/HcmgIOUqMF3wzhxoaujuoYmIiIiIXLQUvL5i+saF8K2xPQC4q/gmzIBIKNwJK/7SvQMTEREREbmIKXh9Bf14Sm987Da2ljopGfcH685lj0D+ju4dmIiIiIjIRUrB6ysoyNdB3/hgANYFjqex91XgbsL19t3gauzm0YmIiIiIXHwUvL6i+iVYGyvvyKvgcb+7KTMDsRdsg1WPd/PIREREREQuPgpeX1H9EkIAK3i9n+3ifxtvsx749GHI3dSNIxMRERERufgoeH1F9U+0Kl7rs0s5VFTN2+5LWWwOB1cD/OsGKN7fzSMUEREREbl4KHh9RfWNC8ZuM6htdHnuMfhR/d3URQ+CmhJ4ZSZUFXbrGEVERERELhYXdfBasGABffr0ISMjg+eee667h/Ol4ue0kxET1Oq+avxZPOwJiEyH8mPw6i3QVN9NIxQRERERuXhctMGrqamJuXPnsmTJEjZv3swjjzxCSUlJdw/rS6W5wYZ13VrztanYAd+YD36hkLOO2rd/DKbZXUMUEREREbkoXLTBa926dfTr14/ExESCgoKYPn06Cxcu7O5hfan0T7TCVqCPnVtHpwKwPbccotI5MunvuEwD/53/oWnjP7pzmCIiIiIiF7xzDl5//OMfGTFiBMHBwcTExHDdddexd+/eTh3UsmXLmDFjBgkJCRiGwTvvvNPmcfPmzaNHjx74+fkxatQo1q1b530sLy+PxMRE7+3ExERyc3M7dZwXuimZsYT6O7l5RAqDU8IA2JVXgdtt8n5NXx5tugkA44P74fjWbhypiIiIiMiF7ZyD12effcacOXNYs2YNixYtorGxkalTp1JdXd3m8StXrqSx8fRNeXft2kVBQUGbz6murmbQoEHMmzev3XHMnz+fuXPn8utf/5pNmzYxaNAgpk2bRmGhGkKcreSIALb+eioPzsgiPToIX4eNqvomDpdUs/pgCU+6ZrDENRi7uwHzXzdC6aHuHrKIiIiIyAXpnIPXRx99xB133EG/fv0YNGgQL730EkePHmXjxo2nHet2u5kzZw6zZs3C5XJ579+7dy+TJk3i5ZdfbvM1pk+fzu9//3tmzpzZ7jj+8pe/cOeddzJ79myysrJ46qmnCAgI4IUXXgAgISGhVYUrNzeXhISEds83b948srKyGDFixOd+Bhcjh93mXee1ZE8hGw6fwMTGA+YP2O1OxqgqoOKZq6k/keN9TmFFHXP+tYlVB4q7a9giIiIiIheEDq/xKi8vByAiIuL0k9tsfPDBB2zevJnbbrsNt9vNwYMHmTRpEtdddx0PPPDAeb1mQ0MDGzduZMqUKa1ea8qUKaxevRqAkSNHsmPHDnJzc6mqquLDDz9k2rRp7Z5zzpw57Nq1i/Xr15/XmC4G1w62pmY+9sl+ahtdhAc4mT15CLc1/JzD7lhC6nKpff4aqCkF4O+fHuT97cd5YumB7hy2iIiIiMiXXoeCl9vt5p577mHs2LH079+/zWMSEhJYsmQJK1asYNasWUyaNIkpU6bw5JNPnvfrFhcX43K5iI2NbXV/bGws+fn5ADgcDh599FEmTpzI4MGDue+++4iMjDzv1/wqmDk0kQAfO1X1TQCMTovke+N78fDtU3ip12Pkm+GEVR2Ef15PXUUxb22yql8Hi6q6c9giIiIiIl96HQpec+bMYceOHbz66qtnPC4lJYVXXnmF+fPn43A4eP755zEMoyMvfVauueYa9u3bx4EDB7jrrru6/PUudCF+TmYOOdmQZHRaJHabweTMWL49YwLfbPg5J8wgyNtEwzOXE1RnhdyCinoq6k5fxyciIiIiIpbzDl4/+MEPWLBgAUuXLiUpKemMxxYUFHDXXXcxY8YMampquPfee8/3ZQGIiorCbref1pyjoKCAuLi4Dp37q+7WMane66PTTlYIkyMC8EvI4usN/0ONXwwhVYf4yPen/ND+FoHUcrBQVS8RERERkfacc/AyTZMf/OAHvP322yxZsoSePXue8fji4mImT55MZmYmb731FosXL2b+/Pncf//95z1oHx8fhg0bxuLFi733ud1uFi9ezJgxY877vAJ940L4xZV9+fHkDHrHBrV67Ip+cew1U7ip6Xdsc/ckxKjlPucbLPO9B2PNPGiq76ZRi4iIiIh8uTnO9Qlz5szh3//+N++++y7BwcHeNVWhoaH4+/u3OtbtdjN9+nRSU1O90wyzsrJYtGgRkyZNIjExsc3qV1VVFQcOnGzYkJ2dzZYtW4iIiCAlJQWAuXPncvvttzN8+HBGjhzJY489RnV1NbNnzz7XtySnuOuyXm3ef0X/eP5v4T52VAVzLb/jr/0Pc+mxp4mqP0bkrkeg6L8w43FIGfUFj1hERERE5MvNME3TPKcntLM268UXX+SOO+447f5FixYxbtw4/Pz8Wt2/efNmoqOj25ym+OmnnzJx4sTT7r/99tt56aWXvLefeOIJHnnkEfLz8xk8eDCPP/44o0Z17Et/RUUFoaGhlJeXExIS0qFzXYxufX4tu49X8PvrBnBF/zj+seIAOz58iv/xfZ0Qd5l10ICbYOyPIao3OHy6dbwiIiIiIuerM7PBOQevi52C15mZponLbeKwW7NUVx4o5pbn1jIw0s17vT+Czf88ebDNAb2vgAk/ozioN4eKqhnRI/wLaawiIiIiItJRnZkNOryPl3y1GIbhDV0A6THWOrAdpTbqr3oc7voU0qdgOgPB3QR7FsBTl7L//83kV8/M5xdvb6fR5e6UsZTVNPDK6sPUNDR1yvlERERERLqKgpd0SEywL0G+DtwmHCmpgYQh1N38GteHvca0+odZaBuLicGY+pV87Pszxm6+n7889xJm7Ynzer1VB4vZmWdt2v3TN7fxq3d38uLKw534jkREREREOt85N9cQackwDHrFBLH1WBkHCqvIiAnigTe2selYOZDCXTVzuDJ2FleWvsLV9jVcbV/L1cfXwp/uxR0YQ75PKuEpWfgn9IfeUyG8R7uvVVBRx63Pr8NpN3j+9hEs3GVtJ7Atp+wLea8iIiIiIudLwUs6LCs+mK3HynhzYw4A723Nw2EzuCQ9imX7ivigIJwP+BGBU39OyPrHSSjfTLxRiq26kITqQjixHrYCH/4E4gZC1jXQaxLE9qfKZWdddgnje8ewN78Sl9taYzb7pfU0r07ck1/ZfW9eREREROQsKHhJh3370jRe35DD4j2FrDtcCsD3J/TixuHJXPbIUkwTAn3sjB41jvWx/RjzwjrifBuIbzpGTzOX/r6FzE4pxDi6CvK3WZclv8e0OXCbAaS5/MlPHEBgUBZjbYFsd6dR0RToff0jJTVU1zcR6Ku/ziIiIiLy5aRvqtJh6TFBzB7bg2eXZ1NZ10RimD93T0jH38fOpD4xLN5TyLT+cfj72BmbHkVMsC/5lZBPLzbTi7dqYcTllzIgvJE9n84nd83rDLXtJ9xdRQgVhNgq4HgBiXzCvzzd6Q+54zji24etrh7srwvl6M5gMgdfCjYtWxQRERGRLx8FL+kUP5qcwTtb8iiqrOfBGVn4+9gB+M01/UiOCODuCdamzHabwbWDE3h2eTYAPaMCyS6uZuneQgZMzuBtYxJPN/YATOIoJdioJcoo58rIfHq7DhBftYsUWxFptnzSGvOZyGfgA7z3OHyaBH2vgrQJENET/MMhOK5bPg8RERERkZa0j9cptI/X+TtSUs2x0louzYg643GHiqqY8f9WMC4jmvF9ovn5W9sZmhLGW98fyzVPrGBbTjnjMqJYe6iUcRlRLN5TSLCfg4hAH46U1PDarRmM9D0KuZvZvXU1lcU5DHIcxddde/qLJY+CkXdB/CCrcYfd2TVvXkREREQuOp2ZDVTxkk6TGhlIamTg5x6XFh3Exl9djtNuo7CyDoAtx8o4VlrDjlyrVfyfbxhIeIAPNsMg68GPqKxrorLO2q8rJSkFQntD+hT2hORw7/ytjI0P5F8Tqjix/SNKdi4l3lFJoLsSjq21LgC+oVbjjog0aKqDgEirIhYcb/0ZFAcOn675cERERETkK03BS7qFn9Oaihgf6k+f2GD2FlTy87e24zahR2QA8aH+3mPTogPZV1AFgL/TTmyIr/exvnHWbx62FdRj9r2KP+5M5rX66cT4+LLunv6w7hnYtxBKD0F9OWx+5cwDc/iD3Um9bziFrmDiwoNxOhxgGBCSBD0vA59AcDVAj0u9Uxkbmtz838K9jOoZweTM2HP6LAor6tiZV0FeeS1RQb70jQs+qwArIiIiIhcOBS/pdt++tCcPvLmNFQeKARjTK7LV433iQrzBKzUyAMMwvI/1ig7CYTOorGtiT34l/916HIDCynoqnJGETH4QJj8Ibjfrly1g5yevEGTUMWVAKmFUQGU+ZQVHCGooxkETNNVCUy2+9RUkA1SfMtit/z553bBB4jDwC+PEiQpmFh2nYn045shLMDDB3QQhCVaFLW4ghKWA3UlVfRO7j1cwPDWc/Io6pjz6GdUNrlYvc0mvSO6b2pthqREd/nyr6pt4YskBrh4YT//E0A6f70Lz+OL9LN5dwNO3Dicu1K+7hyMiIiJfUQpe0u1uHJ7EBzuO8+neIgBGp7UOXn3jgvnvVut6z6jWlSAfh41+CSFszSnn1ufXUdt4MsAcKKziWGkN727J4y83DWJ5Qx8eb7oDgMk1MTx/xwiKKusZ8YdPAJNrM/y4+5I4/ufNjVBdTKRRwdi0cG4bncL2Y6Xk7VtPfNlmYoL9iAsEjm+FnPUAxAKxNsA8Cmu3tvte3Q4/6l3+RLp8KQ30xzANXscF/g58fHwxmxpoamqg6GgoR58LIbJXAj0iA8HVZFXZTBf4hliVtrgBEJMJwQlQVw7lOWwodfL6thP8T79SgmtzwbDxyYEmPtvlYNXePrx3z2Soq4DGGgiKtSp5LVWXQH2FtR7u1Mc6aN7SA3y2r4gnbxlKZJDv5z+hE/xr7RH+smgfAM+vOMQvr8r6Ql5XRERE5FQKXtLtDMPgT9cPZNpjy6hrdHFJr9bNOfrEBnuvnxq8AP732v5845k1FFfVA2AzwG1awWve0gMcKanhzU257Mir8D5n8Z5CVh8sIa+suSGHwbv763l3/xEgCj9nDHWNbkqbwrmqxzCu/dcnuM1rgGuIdviy7t7JGCeyIXcTJyoq+N/391NGEHFGKd/u00hafAS1jSZBdfmYxXtxHd+Jw2zA1lRHJHVE2gDPS8fZABOo9w6FTLvn+mHP5SwN91w4cPK+64DrfMF1wqDp4QgcdSXWA35hmMFxVNU10OgycbprCa7Ltx6L7W9V6cqPgc0BfqGUV9VQUl5JQGAgEQEOfBrKwXSDww9isiBhCDh8obrICqW+wdZG2BFpbC1288jHewGDf6w8yL2jgqHsqFUFDEsGt9sKls7Oq0itOVTCg+/u9N6ev/4Ycy/v4+242ZJpmrhNq+umnM40TY6W1pAS0briLCIiImdPwUu+FGJD/Pjwx+OoaXARHdy6GtIn7mTw6tFG8BqcHMaT3xzKd17egJ/TzpTMGN7ZksfaQ6UcKakBYPXBErZ7GncMSgpla045j32yz/taGTFB7C+0pjNeOSCOm4Ync8eL6zlSUsOe/ErcJsSF+HGipoGiynoOFFaREZsGEWn8e+kB3nGfXNdlBKXQWObmrU05vDh7JA1Nbr77j3UEU0OwUcOYRCfRzgY2HSnBhpuBCUH8bGq6FTzsPmDYcVcV8MbyrRwtKMVph8zECEZnJBDi70ND9QnKjh8ksHQnPuXZON31uDGodEQQ0FiG03BR4JNCbO+R7CuopLwgm95GDqFGDTSHLsMGdWUYdWUEcwqbAwp2WJcWQj0Xytv4AWZ/1vYPds3frc8c2OIbSKkZTNLqYljd5D2kwIghylaB3d0IvadB+hTrgYpcKDkI1cXWZxPnCYNhyZRU1vHK0s0MHTiIy8ZeBg1V4HZBaDLYbNTWNfDX1xYyzMyld//BfJZn41hpLe9tzeXmESmthmiaJl9/Zg1bjpUxvnc0P5yUwYCk1lMy3W4T2xlC2ZsbczCBG4YlYZomOSdqSQr3v2hCytubc5n72lZ+Mq0Pcyamd/dwRERELkgKXvKl0bKhRkuJYf4E+Tqoqm8irY3gBTChTwwLfnQpDpuN1QeLeWdLHh9sP+59fPn+Iuqb3NgM+OvNg5n22DLWZpfiY7c2XH74+gEcLq4hLMDJpL4xlNc2AtZasa05ZQAMSAqltsHFigPFrDxQTEZsMKZp8vbmXACmZMbyye4CFu8uoLiqHrcJTyzZT4CPAzc2xg/qzeyxPRiUFEZ5bSNTH1tGcVU9980YA6es5bIBV2XdzLdeWs/a7FLIhrSqQD740Thue34d6w4PBq4HTEKopg5fGnDioIlA6ogPi+eda8cy609LKG5oYObgBFZs2UW6fwUvzb0JX/8g8vZv4Wf/Xk6DyyQxPIDDpfXEpg/mkesH8PD/PUyoWc6k0cOoqG3k0637qcdJQmQojfW1FFU1UGsL5u5JvekTBhzfQl3eDvJKq6g0/eg3dByO2mLIXkZ9RSG+rmrCDOsC4DYc2EITcJflEGsWQvMM0b0fWJe25KzzXo0E7gFY4bk0c/iDTwC+NWXMxwW+wAGoc4RQ7Wvi/sAH97pobP7hVkWusZbKuib6H0vDdKcRvKeIvYcO0i+hBFtsP+h7FX9fkcOB4yX84vI0osLDIDgWGmutyl5VITVNJn9+z48iwpmSGcPSXXn8+Y1lfHvKYL4zZeDJsbnd1vTNDoSx2gYXH+/MZ0pWLEG+X9x/vpfsKQTg1fVH+f6EXhdNoBQREfkiKXjJl57NZvCLKzPZllPGkJTwdo9r7nDY3KK+5Xqv+iY3AOkxQaRFB3Hd4ERe35hDg8tNWICTwcnhrRpZhAX4EOrvpLy2kcW7rS+dvWODCPBxsOJAMasOlnDH2J7sL6ziQGEVPnYbv7o6k092F1BYWe89z/rDJ7zX75va29utMDzQh3fnjKWosp5ByWFtvp9AXwev3jWaNYdK+dGrmzlUVM1NT69mW045Pg4biWH+xIf6cePwwbyw4jDbc8vJSopkW045NUVVrD5YQnFVAzHBvvzx+oFMOFTK6oowFh9uYHp/H+5bbrK6MZNLekXy9ct7c+NTq4kvNNlTbueVpkkAPLPWRkOTG+jFXZelMeeKvjS43Nz5jw0s31/MshU+fPaTCSxmAg+s2uY5Fl5MGcHEPjG8tSmHua9txZcGnpoeQllJIY+urycmMY23fnAZN/7lA3yKdxARl8q8rw+ELf+C4gNWRS4oBiLTT26CfXwLFO6BilyOltWTW+tLmi2PWKMMbA5Mw4bhaY5iA+pNJ+7AGPxr8vBrqsDPwJrSWVTS6nMOAX7lXN36w88D8jbB5lf4fvN9H7b5YyIAWOVjcNiMw+9JX66uymWmXyOsAHNtIEZwLJgmlB/DDIiiMGww4SGB+OCyploGxUBjHYSnWlMzD30KB5fS5AhgX10o79cOICEIZkVn8+IuJ38+lMqPJ/Tg3pEB1gD8I8Df83eose68p2su2JbHY5/s549fG8CIHq1/EdC8zcOx0lp25lV8JZu0iIiIdJSCl1wQZo1KYdaolM8/EEiPDmp128dh8waC5i+M3x2fxhubcjBNuCwjus21PamRAWzLKWfTUSs89Y612rw/8vFe1hwqweU2+XiHtSbq0owoUiMD6RUdyMEiq6rT8vplvaNPaxGfEOZPQljbVb5mhmEwplckD16dxQ//s5ltOdYX4P+5KpPbxvTwHje9fzxrDpUwqmckIx/6hMq6Jv6x+jAA4zKi8XPauXZwAk8vO8Ti3YX0ig5i9aESfBw2Hv7aQMICrY2lj5fXsfrgyWDS/LndPaEXD0zrg2EY+NnsPHvbcK58fDmHiqr53YJdvL/tOA1NbvyddmobXezIKSch1J+fvrkNgNnjM5k4vi/FVfX8ZMNicnIq2VdQyeYiE7fZj8hyH6tRyNTft/9hDLgBsKYGzvz9J5Q0NgAwNN6HN384mV+/u5Vl6zbiSyNlZhCjB2byt1nDob4Syo6x5dgJ/vz+Noz6crLCTX4xKYFaw49H31nNNFYxKKSaPGJYUhaLb+owvhmXQ82BlRw5UUc9Thpw0i/SRmBDMTgDrMAUGENpYQ4RJ7bSyzgOldZQm0wbDsON0VhtbWXQ/POsyie26qMz/sybOYAsz6XZ94GrfaKJXVsBa04GfMJ7WFW4qgIIjIaQRCjPgaZ6q7OmT4AV/kKTrMcqcqE0G05Yf0fq/GOwlcVyiasPRz5ayoiBkdBQAw4favxjiS3Nw26EEEo1+5bl0n9wvDW103Tj8o9kSWEAwzJSiQgL9UyXPXNFrKahCQOjzfV2IiIiFysFL7noRAf7Euzn8G64fPPwZF5ZcwSAAZ7glR4TzIyBCby3NY8ZgxLaPE9qZCDbcsoxTet2RkwwvWODCPZ1UFHXxM68chbuKgBgapa1xmtEjwgOFlWTFh3IvFuGcsVjywH45lmGxvZcPTCe1zYcY/n+YoamhPHNUamtHvdz2pnQJwaAzLgQ1h0uZamnS+Qlnvb84zKieXrZIVYcKKJ3bJD3sZRIq3KSEOpHXnmdd+rkN0YmU1LVwOCUMO4e33p6mZ/TzgPT+vC9f27itQ05AAxLDWdav1ge+mAP23PLaXKbNLpMLk2P4oFpfQCICvJlaEo46w6X8tRnB3F7PtuS6gYq6xoJ9nNimiavbThGSkTgaVsLgFV1KaluwGk3sNsMNh1v4O5/beajnflAPE/eMpShqeHENK8V9A2G2CwGx8Kf0oZwxWPLWFni4tLgkeScqOH5hiA+i5nJonsvo/jICX731GqCcxzcOHsuz356iL9+sg+7zcDlNulvC+G/91/a6rN4edE+3li8ikSjmClZcczf08QhVyR+NNA3qAZHdSEGJqOHDmb7zu2kN+4jMtif747vxRufrMTZUMb4zETMY+uIqD1MqS2S8NHf5NmVR+ntPsRY+y6aTIO17kyG2/aSYisCE0yHH3VN4E+dN0AB1hTI6qKTt4v3nrx+fEubf7/8GrO50pbNlbY1kI918QgA5rdcdrnXc/GwA5cDfNx8jwFOf6vRis3pmV5ps+43bJhAWWUD1UYgvXr3x+YXYj1u2MBmB8MO/uEQ3Qd8gsDd6JlCGtj6UlMCJ45YHTptDkgZY00Dbai2zuU8wy81akqtY5orhefp89b+nY1Glxu3aeLrUAgVEbnYKXjJRccwDNJjgth8tIyIQB++PvL04AXwyI0D+f7EXt4piqdKjQjwXrcZ1kbODruNUWmRfLK7gHlLD7A9txybAVM8weubo1PZmlPOT6/oQ9+4EH42vS/55XXnvKlyW+/przcP5pXVR/jGyJQzftnrGx/MusOl3tvN4WV4j3B8HTYKKuq9n8f43tHe43rHBZNXXneySpcRzfQB8e2+zrR+cQxODmPLsTJ87DYe/toASqqtKtSO3HLvOrkrB8S3Gu+4jCjWHS7l3S15rc53pKSG/omh7Myr4KdvbgfgznE9uXlEMtHBfoT6W1W5zcesCmS/hFAGJIbyypojntAF37m05xnHnBwRwM0jUnhhZTZ/XbSPnBNW85Wvj0jGMAyGpYQTF+JHfkUdy/YVs+GI9Tn+cFI6zyw7xI7cClYfLOGS9JOdN7OLq8klmlwzmiPHfClwWZWoWvzYXOUHWNP21m2sB3qzlN7YKwym9LqMn7zbwzp/VDoryoo5duIIvsGRLBg7kYeWLAJg98/G85dPDvDs6lz6hDSSVr2ZA+4E/ucbM7n9xfWEUcmEsAJ+d+MogmJ7Me+dT9m1dw9zb5hEWnw0L328ki3ZBVTVNXJjWhPTU02r6hXeEyJ68v+WHmLV5q1cEXyIxJq9NGJnQr9U/AOtNXB5xw5SV3KMOHsFJe4gSsxgeidEEODjxMQk59gRYt0F+BjNU3tNKww11rT5MzCABM9h7Mlu92d1XgKioMbaD5DgeOs9BkRA3hYrjCaPtMaVu9E6xi8UUsdCz8t4Z28NJ6obuGVIJD5OBzhbBr0gCIyEsB5gs4Fp8vHKNXy4cCFzRgSRERcGmTOsKug5uv2FdewrqOSTueMJC/DprE/igmeaJs8tz2ZQchgje3Z8P0MRkS8DBS+5KKVHW8FraEo4mXEh9EsIoaKusdXaFF+Hvd3QBXgrQWBVv/yc1m+kvzEymU92F/DxTqvaNTw1gijPvlT9E0P58MfjvM/73vhenfaeooJ8uffy3p97XGb8yffUMyrQO53Rz2lnZM8Ilu8vJueE1cu+ZfDqExvs3UsNrGBzJoZh8Ntr+3H3Pzfx3fFpZMQGE1dnha288jrvWrfRaa2/NI3rHc2ji/bhai53eTQHr30Fld77nl2ezbPLs7EZ8MIdI5jQJ4bNR8sAq5vlz6b3ZXRaJNtyy7AZBvdMyfjcz2f22B68tCqbLces8/SNC+YWTwXRZjO4ckA8L6zM5pU1R7yvdXlWLCVVDbyy5ggvrDzcKngdLjm5y3ZBhfWeByWFkldeR1FlPWPTI2lsMluFYZfb5PWNx7y339qUS155LSahUNnEXs9nEBPsi39gED+bMYh+KdEMTArlO/8I41BRtTc8lxHMO2XB7PlvA4OSjjN/RwAwlN5F0YwLi+a3O2MAKxCsOWrnslunEOhpzFFSVc/ftx2k1t2Pu6+fzZ8/3sOO3AqeyBrC1QOtSvCfXt3Mu8fzuH9qb3bkVvDRznxmhify15sHs+XoCWb+fRVg4mO42PyzcQTaGq2NyBvrrE3EMa1tB0wTMHlncw4vrThEuFHJ7CyI8XPz4bYcxvaKZGRqqDWFsaoAiveRX1pObkUjfjQQQD0RzkaCbfXYGqvBL8TanNw3GGrLIH/bydAFUHncurR0eHnr23Xl3oYu1zXf9zHt8wmywlp9FdPqy5lmAzwZzv3BT6gO60twaIT1Hpo/A8MG0b2tAFd2FHyCIaInuJuoqq7ikiOVpJpR7NniZHRknTXG4HhIHAphqdbUUZvDCnxgTZ0tOWht4eC4eIPa2uxS/vDBbuJC/Fj980md1tClrtGF3Wbg9DRWEhH5Iil4yUXp2sGJLN5TyKxRydhsBm9/fyxgrfc6Wz1arMnKiDm5bmxyZiy/vDKTP3ywG4Cp/TpWzepsfVu03z91qt6l6VEs3299OU2O8G+1L1rLtv3Bvg6Sws+8/gxgYFIYK3826eTz/JykRQVyqLiaJrdJbIjvaXuvDUgMJSzASVmNFdKignwprqrnSKkVYA57tgAYkBhKo8tN7olaKuub+O1/dzE2PYrNnsA0JCUMP6edqwbGc9XA9qtcp0qOCGD6gHje33acsAAnz942vNVao1mjUnhlzWGW7bNCaJCvg75xIdwxtgevrDnC4j0FHC2pISUyANM0yS6qPu01MuOt49/alMtDMwdQWt3ADU+told0EBGBPqw6WMIbnimaALne/eQsKzw/o1RP+LfbDK4bkmidOy6EQ0XVLPZ0GpwxKIHl+4vYk1/JnvyToXVXXgURgdYX87HpkeSeqOVwSQ0f7cjn+mFJALy06jC1jS4GJoUyLiOKJXsi2JFbwYbDJ+gVHUSTy/Q21uiXGMr43jF8tDOfd7bk8v0JvXhzU/N7MGgwHewpg2GpsbjdJr9dsIvoYL/T2s8vWmqyxbRZhbG6KCrLGtnaUM7fdsNv0rO4Y2xP77HffWIFW4vLvdNgaYBgPweL772MmFO6oK7fsZtjhw9wzYSxOGx41rBlQ1UhxPazqlFHVrH9eBXfWhWF3S+YVd9JxnZoMdvXf8qJsjIMTKrwZ3TPCMIdDda0xYZqa7uCynzrzwZr24lGHOx0p1Drn8CIiFocxzcSfGInnOB0hTvbuBOCgB80/1944fNtHuPlG2Jten4i2wq0gdHW9gu1ZdZtvzBr6qRfaIvrYVZoqyvHrCvneGER0aH+OB0+0FRnTceMH2w1drH7WOsEG2utkOgXYgXNbupgecjz7yq/wqrCp8cEfc4zPl9do4sr/7YcDFh4z2U4FL5E5Aum4CUXpUszotj0q8u9t88lcDVLbVHxyoht/T/9Oy9Lo8lt8tm+Qr42NOn8B9oF+sQFYxhWgeGSU4LX2BaVmssyolv9Frl3i42qMxNCznvtSv/EUA4VW1+aRqdFnvabarvNYGx6FO9vs6oRVw2I4+XVRzhSbAWuw57nXjUwnu+N70VlXSMT/+9TDhVX89AHu9mVZwWBoWfocPl5fnZFX+yGwe2X9CC5xZRSsDpffm98L/7fEmsX6iEpYdhtBr2igxjfO5rP9hXx8urD/OrqLGttWn0ThgHRQb7eKl+fuGBmDkli5hDr70ZyRABL7ptAiL+Tvy89wKqDJd5pmQ6bQdMp1b/l+4u8zztVZnww728/7l17OGtkCr+ZkcVjn+znjY05jE2P5JPdhew6XkG4Z+ra0JRwRveM5NFF+3hzUw7XD0vieHktL606DMD3J6RjGAYjekTw0qrDLNiWxz/XHGk1rv4JoUQH+3JFvzg+2pnPfa9vJdvzs4oM9KGkuoGdeRUMS41gS06Z99yZ8cEkhgXw/rY87hjb01tpBGuT65av8Zv/7iIzPoRRaZGUVjewzRP63vZ0AG3u7vnxrgJubdFcxjRNfvheHvkVNnL9S/jh5AxrimHSsNYfXkwmSz7ZTxH7oA72OXoRO6wf1y/sR0OTmyEpYWw+WkZaaSAf/Hict8oNUFhWxa9eeI9e4QYPXDWESc8e4lilSYjNweMThvCbl96jp5HPjQPCuHJgIlUuJ0FBwVaDk8Jd1p/hqVBXYa3Jc/jy350lFBflk2HkMNxxCL/gSEifTH1FAY252wisK8AwPVM46yusC1hr3qqLYPM/T/v70R7vFM9zYdjA7mv9GZZsbazu9LOmiZpurGqmVckEA6LSIawHK9aupq68iAmDMnA4fawKYHAsRPSyqpShSdZ6PrcbSg5AYJT182rh2ImTU1VXHSzulOC1eHeh979NhZX1n9vcSESksyl4ibQjJtgXP6eNukZ3q1DS7O4Jvbh7QudNJewsAT4Oruwfz67jFYzLiG71WFZ8iPdLcstphmAFDpsBbhP6JbQ/BfPzDEgM5b2t1vqtMWmnN8cAuCzDCl6JYf4MSQnn5dVHvFP2jnj+bK44Bvs5uffy3vzy7R28uPKwd6xnU5FrT3JEAI9/Y0i7j8+ZmM6CbcfJLq5mZIvW6neM7cFn+4p4bf0x7r28tzckJoT60ycu2LvfVZ82/r40h6isUz7bm0ck86+1RwEr7B8pqfEGjtSI0/etazk91mk3vJW/313Xn99e24+K2iYG/XYhOSdqWZNtdajslxBC/8RQHl20j1UHS9h09AT/+99dVNY1MSAx1NscZngPK8wWVzW0es24ED/vZuNzp/bm41353g6biWH+XDs4gb9/epCduVYw2NBiWuXP39pOTb2Lyvom1h8+QW5ZLTYDQv2dnPBUPadkxhLi7+CtTbk8u/wQo9IiWb6/CNO0KrixIX7Ehvhx8/Bk/vjhHhaeErxyy2rJr7C2kfjb4v1Myoxpd6rsvsKTVcF12aVU17toaHLTLyGEl2aPZOpfP+NQcTWPLtzLL6+yekrWNrj4zj+3sK0wFArhuzf3IqfqIAAVdU0s2VPIYTOew2Y8+474sycykceXHGBqViCP3HAJob2nnjaOJpebXyxfRGWT1QQoPsCP1XMnA/Cbt7bxnxPHmDu5Fz+6NNYKKDUlUJHDlqpwfrW0jK+H7mRSWD7xCSnWlMPaMqgrx117AltdOdSVkXv8OE1NjcTHxbPyWAMlTb4YuJmYHk5EaCjUnrC2TagutpqY2H2tYNVQbVXRTLc1ZRKgaI91OQuXNl9Z2c4BNqcVQquLoa7Mui+qj9XZNCASKnK5OucEyY4A8s1I2LSJ3RXxrDtSzszhqYT4+1mVPP8wa1omWGMNSbCauoAVCN0usJ/8mtPcOAjgeHmtgpeIfOEUvETa0VwBWHOohGGp519d6Q7zbhna5v02m8Ffbh7M1mNlTDml4Yef006PSGua4Oet7zqTluvoRrcTvK4ZlMi67BNM7BtNoufLz9FST8XLM9WwR9TJas/Nw5N5fUMOO/PK+dqQJH44Ob1LN/H1c9p57vbhvLExh9vH9vDePz4j2juV8s2NOd4pij2jAsmIDToZvOJOD17NWobaIF8H35+Yztubc4kJ9uXKAfH8/dOD3mpWy6prs8wWzx+QGNqqKmMYBqEBThLD/Mktq+WI57PslxBKUngAl/SKZNXBEr7291UAhAU4+fstQ73VzdgQP2/4G5MWyb2X9+bvnx5gWr8472v0jg3mz9cPZNPRMtKiArk8K5bdx63Atcvz57rsk/Ptmte9Aaw+VOI9R0ZsMP/1BPQbhiXSOzaYtzblsnhPIdnF1Xzmmeo5vs/JXxBM7RfHHz/cw+qDJZTXNBIa4Gm44lmLB9DkNrnz5Q38YFIG1w9LPK1b4IGCKu/1VQdK2O4Jubdf0oNQfyd//NoAvvXSBp5bkc0V/eMYlhrBz9/a5g2aze/DbFGkbP5FA1gh8HFPtXThrgK2/20ZkzNjmNw3lgl9TlaZtxwro7K+iWA/a3P44+V1FFfVExXky9Zj1mt9sLOQH13e1zpxYCRE9+aPT69me34N2/N7Aj25bUwqv7gyEz+nnTc35vDTJdt4cEYW43tHM/6RTwGINn0pqj35cxhRG85rt41p/W/I7T65jsw0obGWktJibK56wv1sVmWqaI8VyDxdKvPK63hh1VFMwAcXl0cW0de/gv/mBlJIGCPi7IzuEQoYUJEHpQetap+rwTof0GTzxeGut7pvtujA2Q/o1/wNpci6ZAK8xxkY1pRMV4M1lRLTCnKhyTQ11vOzonLu9AmhyvQn6YNgSOoFkb0gf4e1/UJUhrX+zjfECnHN+wg6A6yA2FADdqc1hfMiXl8nIl1HwUvkDJ69bTgVtY3EhJzfprRfRuN7R59W7Wo2d2pvPtqRz5UD4tp8/GwMTg6jR2QAMZ4v8W3x97Hz6E2DACj1TLk7Xl7H8fJabzfEltUeh93Ga98dQ5PbTYDPF/OfrV7RQfz0ir6t7rPZDO4Y24MH393JS6sOewNJz6hAMmKssBUV5ENkkO9p52vWMyrIW0ntnxhCYpg/H99zGf4+dpZ6gluztqYaJoT6EeJnbWlw6kbHzTLjQ7zrxkL9nd7q4MNfG8gv39nOqoMlGMDjXx9y2mv89tr+fLa3iHsuzyDEz8nIniNPO/+Nw5O5cXjyyc/F8wV+b34lDU1uNnq6Qd4/tTd///Qg4zKiqG9ye5u3DEkJY2BSGP/dmkdYgJOJfWPwddiZ1DeGJXsKeWLJAZbts9a5tfy7an3OQewvrGLp3kKm9ovF32n3Bq+rBsaz5WgZuWW1/OLt7by9OYdXvj3KG06bXG4OFZ8MXh/vysc0ITzAyTWebSUm9Y3l+qFJvLkph/95ZycPf20A72zJw2ZYW1UUVNSz8kCLJh7gXa/YNy7Yu85u5pBENh45wdHSGv655ij/XHOUqwbGM7lvDHvzK1mbbX1Gl/WOZvfxCg4VVbMjt5xL06M4UGSNcU9+JcdKa7w/o2OlNazNLsUw4KoB8SzYdpx/rD7CpqMnuO/yPvzyne00uU3mLT3g/XcFUOSZAvujSek8vewQ6w+fYNn+4tb/HbC1mI5tGOwoauTrz+ymye3m99cN4PKsSRSEXkJ6dJA3qL+wYBfPubK96zTfbwrgxyMz+OnrW62fV10gS6+eAMC7W3J56IPd/OLq3lzbEyg9xKpj9dz6YT3DYwzmX+0LxfugthRCk/jf9/cR0lBIvFFCjFGGkyYcuLEbbgYlBOFrc1lVwMp8TxDEClv1JwMyYB1TU4IDSDcg3fCE5AKgYFnrY4+soG3Nu68337R5OoP2sBqgBIRbFbjqYqvK5vSzwp+rCeIHQcJga6opWCEuf5vVaTO2H2RMtcKe7eLaTmD1wRIKK+u4dnBidw9F5EtFwUvkDPyc9lYVhYvd1QMTvN3szpe/j51PfzIR0zTPqioVHuD07ru23PNlOy7E77TNdX0cNnzo/sXwXxuaxCMf7SW7uJpX11tTBHtEBTIuI4rYEN/P/aJhtxn0jQthy7EyBiWHAScDVo9TGpG0FVwNw2BkT2tLg5bVoJay4oP5ZLfVdbNfQoj355ASGcAr3x5FSVU9dU1ub7WxpTMF8/YkR/h7f4Yf78znRE0jfk4bd13WizsvS8PXYWd7TvnJ4JUczlUDrU2/J2fGeqtS3760J0v2FHqbdgT42Bme2jpcTu0Xy/7CKn7x9nbumb+F716W5t1i4PLMWP58/UDmrz/GXz/Zx/rDJ/jhfzYzODmM+kYXVw6Mp9Fl4u+043KbNLisDcK/PjKl1b/zX16VycKd+ew+XsF3/rEBgOsGJ2IYBm9uymHFKcGr2UNfG8A3n1vL2PQo/u/GQdQ3ufh0bxGrDhbzn3XHeH/bce/axmZTMmNw2AwOFVWzM6+C5IgA78blYFXNvn2p1XDkHc9UuUt6RfLErKFcP7SQua9tYUduBbNfWu99TkFFPU9+ak2FvGpgPB9sP05yeAA/mJTB8fI6Xt+Yw4bDpaf9nIur6nl7Uy5hAU7+b+FequqtaZD3e4IUwI8nZ3Dv5b0xTZMPPRvI//SKPvzkjW0cLa1h5cGTn012cTU5J2pICg/grU25FFTUc+9r27F/YwhXD5zAxuz9uNjHuiIoT55Ifdxl7CuoYkhKGC++YbWXHNUzwhs206Ot0H1zVDJ/umFg6w/fNKGmBLOmlPmbCwkLCeKKAclQkQPlufxu4WF25VeR7FOF2VjLuPRIrkmqtTY4j+5jrT0r3mdV5uoqoPyY1T2yqRZv6LI5Tk7BLD9mXTilU+aptr165sc//rk1xTM4zmpwYndar2N3gsPPWv8WFGc1hwmKtZqquBqsIBeTaV1sdjBNiipqCDZq8XNVWc/1C221n11NQxNPfXaIrw1JPO2/NZ2pur6Jb7+8npoGF71jg1t12hX5qlPwEpEucbZTAQ3DoEdkINtzy1m4y/oi116l7MsgyNfBLaNTeeqzg95KR+/YIGJD/Fjz88ln9b6/MTKZgoo6Zg5pHdJavu8AHzuRgW1PZ/rzDQPJLq5udwpsy3Vkba3XO1NF7nwYhkFWfAhrs0t5fPF+wKp8tmxqMyAplFmjUvhsbxET+8YQ6Ovgb19vvc7ukl6RXD0wnnXZpYT4O5k1MuW0xjhX9Itn3tKD1DRYTSdeWHlyL7AhKWEE+jr41qU9yUoI4bbn17FoVwGLPBudN3fEzIgNws9hZ93hUmyGtf9eSxGBPsyZlM7DH+6hqLIemwE/mJTuDRrNTUWaK49g/eyGpoSz5cGpOO0GhmFY6y0HxHPlgHhuHJbMnz/eQ0OTm6z4EJLCA0iNDGBKZizFlQ28uyWPHbnlp3UBXbQrn29f2hPTNHnLE7y+5mnaMrFvDB/8eBw//PdmNhw5QViAkymZsbyxMYf6Jjc+dhsPzRzAvVMyCPX3wcdh8zapaJ7S29LDH+7hjY0nu232jg3iiv7xPLFkv3ez8+eWH+KOS3pwtLSG3LJaAnzszBiUwJOfHeRQUTUfbG8dLFfsL+brI1O801DdJtzz6hYGJYV5x2CasPnoCeYtPcD6wyf4xZVWpTkswMnXRyazNruUOy7pwdUD47n+ydW8sSmH+6b2bj0TwTAgMIqPsxv52ZL92G0VbB7cj5D4QZhxA3n91YVUuJtI7p/E6xtyqHLGcc3lVvOVmoYmjpXW0mdw6ynCh4uquPefK8gtKOZ/b76U6UN6WIOtKrSmTJ44DOVHrfV1rkYrFNmdVmdIh481ffPYGmtapTMQ03RTXlZKU2gqUX3HWvvJHV0DrnooO3Laz+NctfXrEndoCm7/SByNVZQ1BRNSEsveXaH0yIyG0GSr0YlfmKcbpufiG9yhbpYLd+V7/32uOVSi4CXSgoKXiHS7S9Ij2Z5bzie7ral2LVv5fxndN7U3o9Mi2JtfidNu41JPt8izDZs3j0jh5hEpp90fHeRLgI+dmgYXKREB7Z4vItDH2yq+LS2/6LRcc9eVbhiWxNrsUvYXWtPk2poG+dDMAWc8h2EYPDGr7fWJzQYkhfLkLUOpaXDx2oZj3il7EYE+pLSYNjk6LZLHvzGY/1u4Dz+njR25Fd6tFNJjgkgOD2Dd4VKm9Ytrs/J3xyU9eGX1EXLLarlucCJp0UGknRKKxvWO9lawmj/n9jqoDkoO41/fGd3mY/0SrZ/X1mNlZHgas4xJi2T1oRLWZZdytKSGnXnlZBdX4++0c0X/k1OB40P9+c9do3l/23H6J4bi57Tx5qYcTNNaHxfq7/RuPg7WnoQARz1NbJ767CDhAU6uHZzIx55gOcBznr/ePJik8ACr4mbCN55dw67jFby46jD1TdYX60l9Y/Bz2hmcbO0vV9doVesm9olm6d4ilh8oZlJmDEWV9RiG1XhmT34l67JLW+2B99+tx1l/2KpcvuRpopMcHsB1gxMZlhJBcoQ/hmEwMCmUbTnlfLaviGsHJ/LvtUeIDfFjdFokQX4O/vSRtU7M5TZZc7CEqf3iOFHT6A3IY3pF8tqGHI6XW81YDhZVcceL6zhWWsv/+8YQZnimnO7Nr+SGp1ZRWecCwpm34hhXDE61/k0Gx1qXlFFt/jzbs3RPAd96aQPBjQ42T7wch93G7txSvvfEO0RSTv/4AH46NZ1Au+mpatVa0xcr86GqELOqgIriXPz8A/B1OiF/u3d7g5ZMhz+46jFMN7byo9jKrcp8AvAdB9aWB6vOMFDD1jqItQploVYDE6e/FTRNN1TkWp02w5IhMJpNawuIo4lSQlh/uJTZLbaIEPmqU/ASkW73zVGpPLPskLdhQVdOg+kMTruNCX1imNAnplPPaxgGKREB7MmvbBUizlVyeABRQVb3ykFJYZ03wDO4cXgyx0prvI0lhrez/qwzTB9gdbJLCvfn5mfWADAkOey0oHpF/3iu6B9PaXUDox9a7J1a2Ds2mNvGpBIe4PTuj3YqP6edx78xhH+vPcpPr+gDQM/o1n8vL02PYuHOfBpdJgM6EHAHJYXh57SRV17H25utitPkzBjqm1xsOlrG155cRYVn7ePXRyZ7N8Bu5rTbWr2PKz371H1jZDKnaq6qHimt4UBhJQ9/aHUqLKyop7K+ibgQP96dM7bVdhLNwe2Hk9K5+1+beHbZIW9B5ErPz2JIchhvbbIqcjYDvnVpT5buLWLlgWLvXnBpUYGM6hnBnvxK9hZUeruCAry1+WSlLc8Tipp/+dByM/vxvaO9wauoqp4/e4KWzbACdXaLc644UMzUfnFke9b1JYT6kRZlVfyOl9eyv6CSG59e7a1cP/zhHi7PisXPaefFldlU1jUxMCmUvfmV7MitYNPRsjarzOU1jRRV1ZEe035THYDdx631f5V1TWzNKWNYagSPfnKII2YsR4hlUx4cXBHIv74zGpfb5EhJNWnRJ9vov7b+KD/dsZ2JfaJ5cfZIaz1ZdTEYNv53wW7e3pJHNf68+O2xbDxcykuLN9LbyCXYqOHuqYP5eOVaYmsPYmI1k3FWHLM2Ga+rsDYTryuzAp/ptjpe1ra1Kd3n+x3wO08xsmJ/IOaf/TGa6q2w1ry/XGAUhKVYe8a5Gqzpln4h1pq5sBSrwQlYwdI32Gp24lQXSrnwKXiJSLdLjghgct+YFhWvL+9Uw67WIzKQPfmVHZpuabMZvHDHCEqqGr7QEHvv5b3xddo5VFR92h5yXWFUWqR3DdCInu0HvYhAH6b1j/N2UcyICSLAx9Fqs+a2DEsNb/VF+9RKbGpEAP0TQ9l8tIwRPc6/82mgr4Mr+sXxzpY8jpVaTVH6xAVz1cB47nhhPXsLrC/s0/vH8csrMz/3fI/eOIgfT85ocxuM5kBfVtPI6oMlJ5+zaB9gBan29vCb1i+O3rFB7PN0hkyO8GeCZ51h83pFsH5xMiYtklB/a6P0f66xKi5ZCaH09VRj1x8u9e5lB7TqEtksKaLtNYj/b8kBVhwo9nakjA2xmp40j6t5v73mtXjNmzH3jA4kPtRKBIWV9bywMpuymkYGJIZSVFlPblktr6w+wuyxPVjomZ76syv68vbmXF7fmMPLqw4zLDWc6vomXllzhEl9Y0iPDmLWc2vYk1/J+z+6tNV2D6dqnm4J8Nm+YmyGwSe7C7AZ8NjXhzB3/hZWHijhQGEl/1l3jOdXZHurcKZperfT2JHnOY/NblXegPVFeyjD+nkv21/Ef9YdpZIQckJjyS2rJbI4mfmVduASAEYOHMvAU38pY5pWg5K68taX2jIrlNWVW3vJNTVAYzVUWWs2CU2ypleWH6W06DhNlUVEGJU4cBFCNdR4wnBDJVS3biB0TvzCrHVuDl/rYvexLs3r2fzDrYtvkLfzJja7p7NlktUQJSjGWkPXTRuDiyh4iciXwm1jeniDV+qXfKphV7puSCK7jldwRf/4Dp3ntC9VXwDDMJgzMf0Lfc3/N2sI/916nFkjT5+62dI3RiS3CF5nrky0x89p97bqB0gI8+fxrw/hUHE1w1I7VuG7flgS72w52Za+T1wwMcF+vPa9MfzmvZ0E+Tr41dVZOOyf32DGz2lvM3SBFfKig30pqqz3rllr6aqB7f+9s9kMnr1tOJ/tK6JfQggDEk+u4+sbF4KPw0ZDk5vM+BAcdhuT+sbw9uZc7zYLWfEh3q0WmjtRRgb60NDkptLTyCMtOtAblJLDT//lw+DkMIL9HJTVNFJW00iQr4Ol90+guLKB/27Lw+U2uXV0KsN+v4hDRdXkldV6q2A9owKJCvL1blr+kef9z5nYi4q6Jh54Yxv/b8l+YkJ8Ka1uIDzAycieEYT4O3l9Yw4fbD/OrFEpPLf8EJ/sLuQ/645y39Q+7PQEocW7C88YvHa3DF57C1njCb5fG5rENYMSeMfzWb2y+gjzNxwD4PWNOcwYlMD6wye8HTOLKuspr230ViIbXW725Z+ccvjyqsPUN7lJCPXjx1MyeOCNbbyz5eQeZmB1zGz534gV+4v57YKd3Damh7XmMfj8OtvOeWYNq4tLePCqTFbu2M+Ro4e5b0o6Vwzuwe/fXs+uQ0e5c3gEk5JMqzlJU721Lq6pwaqwnciG8lxoqMLlNilusBNq1OJHvSf8lZ3XuFozPIHN1wpioZ7KsGGzqm3BcVZ1LSTRarzSvIl4YJQV8BTapAMUvETkS+HS9CjG946mrLbR2wDgq+iK/nGt1vDImcUE+3m7/p3J6LRIbzOT5DYqKWerZ1SgN3jFh/nh67C32fb/XF3SK4q4ED/yK+oID3AS7WmAEurv5K83D+7w+VtKjQigqLKeNZ591cICrMpUYpg/Q1PCzvzcyEBuG3P6L0Z8HDb6JYSw+WgZmZ5wNSUzttWmxVkJIadtLt4zKpAAXwfL9hUxMCmUaf3ieORja/pgW5+rw7Omsjk0zhgUT4CPg5RIR6vQPyg5jM1Hy1ixv7hF8LJa4ceG+JFbVuvdwHt4jwjCA3x4aeVhdh2v8HZxnNYvDofdRv/EUCb3jWHxnkK+7pnaCnCkpKZVx8eVB4qZMzGd2gYXvg4bNpvB8fJa9uRXMrpnZKuplVs9+8L5O+3ce3lvAK4dnMCSPYW8vPpko43VB4upqGvkH6sPt/ocDhVVMSTFqrIeKKyiweX2Bsp6T1fMa4ckMrqnVXmub9EpE6w1bACmafKvtUf59Xs7cblNHl+8n2+MTOHNjTmsPlTCH2b2P+MWHqZp8sqaI2TGhzA4OczbYXRc72hO1Day+EgTHxVFULTPzvMHgoF+HNjty/IZEz+3Y/AP/7WRD7bnAyb/b2YaM3oA1UVWIxNXvRXamvdsq6vwTI8stTYAN00w3TQ01uOsLcaoOA6VeVZXSkzr+a56q4Ln2VPurDj8rLDm8LOmZLpdYLqsRipOP6s7pl+YdV9QnBXkXA3WBuV+YdZUS/9wz/Vw67ZvaOvtHOSipuAlIl8KNpvBy986fc8okc5gsxmdEmB6RgWy4kAx0cG+p23O3BF2m8HMoYk8+elB+saFnHWjlvOREhnAhiMnvJ0Km1vwf21oUode9wcT03lueTY3DLMqCJf1jsJpN2h0WS+UFR9CoK/Du0k3WEFuVM8Ilu0r4pujUslKCPEGr/bWOY7vHe0NXi33k2tpXHoUm4+W8dn+Im/wam6OEh/q5w3PaZ4qGMCjNw3imidWeMfbvJYQ4IlZQ5nz703e6t3XhiTy1uZcGppOBp4NR06wLruU219Yx3VDEvjj1wZyz6tbWJtdyncvS8NtWhW+qCBf7/TRH03O8DZ3uTzL2peuttFqXGIzoNFl8sxnh7zVuYRQP/LK6zhYVO0NXs0Vt6Gp4RwrrfE2DvnakESSI/y9UzGb3++h4mr2FVRysKiKX769nTWHSr3vs7CynkW78vnVuzuob3IzvEc4t4xq3fWzpQ935PPguzuJCvLl6VuHUdfoJizASa/oIEZ6pv++uyWPdz3VXB+7jaLKel7bcIzbxvRo97x5ZbV8vLPAc8vg4aX5XH7fePxis9p9zqk2HinlpqfXcOvoVH7z7X5WOKovt6przYGtIo+i3EP836L9uF2N3NrXYGB4o7VZ9olsK5S5XVbIqq+wnlN2tP0XPZcQ52W0mCoZdnowa3kbE+qrrCmUTn+rqYnTz/qzuemJw9e6bXdY0yptTquqqErdl4KCl4iIyFlqbvee0EYnxI763mW9KKtp4PqhSZ1+7pZarlWzGdYmzlP7dbzKOjkzlsmZsd7bwX5ORqdFsny/FVSjg62A0yc22Bu8ekYFcOPwJC7PiiU80Ae32+SSXpHUNbpIDm/7M56cGUtYwB56xwQzpMXaspYm9o3h8SUHWLy7wBsw0zzNUeLD/OGIVZkZ3mJtXmZ8CPdM6c0jH+8l1N/JmLST6xT9few8feswnl+RTWKYP1cPjCevvJY1h0r55uhUPtxxnIKKen7w703UNrp4Z3Me917em/WHrVDz/Ips72tkJYSwt6CS9JigVtXaAB8HU/vF8u6WPIL9HFw/NImXVh3miaXWl/kpmTHEh/rzypojHCw6ObVwlyd49UsIISHUj3e25NE/McTbIXNEjwgWeLpvXj8siUc+3suO3HJufnoNxVX1+Dps3DOlNwcKq3hzUw4/fXO7t0L23pa8MwavV9dbUyKLq+p5+jNr77jhqeHYbAajekYyoU80K/YX0+Q2mdw3hvF9onnw3Z089elBvj7C2i5i45FSHDYbfeKCvVWwf645gsttMiw1nNwTteSW1fL8iuxzmso8f/0xXG6TNzfl8MurMnHabZ7wclJTeC/u+sjB5garUdIJdyzPXT287RM21lpbCVQVWMHNsFsByLBZl4Yqa++3hmor5FTkWdMpHX5WAGpeL1d7grrKEtw1Jwgw6gHz5DTK8+tn8vmcAVbjEt9gzwbg8dYUS98Qq8GJ9xLU/nW7w6o21lu/NLBCnd3zp8PzOSjcfR4FLxERkbM0qW8Mz6/I5tpBHdtovC2hAU7++LWBn39gB7Vs3JIWHdSlm8Rf0T+O5fuLGdwiIPWND/E2r0iNDMQwDMI92yPYbAb/vrPttvvNooN9WfWzSdhtRrsVusHJYa3Wiznthrey1NxgA07vvvndy9LwsdvIjA85bWsAp93G98b38t7++y3DeH9bHjcMS6aitpG3NudSWGlVlmobXfztk5P7nzV5rvSNC+a7l6XR0OTmm6NP36du9lhrE/EfTkpnVM9IXlp1GLD2jPvDzAF86Nkn7WBhFQUVdWw9VsbGo9a39az4ENKiA9l49AT3TuntPWfL4DVzSCKPfLzXO80yLTqQl2ePJDkigE/3WpuXl3s6aAKsO1xKfnkdcS0+s2Y5J2pYvr/Ie7v5Z9r8mfo4bLw0eyR1jS4OFVWTHhOE2zR5fPEB8srrWH2oBLth8M3n13p/RjcNT6ZvfAgve973neN6Ul3v4r7Xt/LXRfsYlhrOjtxy7DbjjG3qm1xu75rhyromNh45wei00xv+/GP1ETYfLcPHbqPB5WbVwWLqm1xtV7Od/hCeal3a0/Oy9h9r4aevbubdLXlMzgjl+ZsyrNBVe8IbzNq9bdisEGS6rCDYfGmqt6pxzRdXQ+sXbKyBot1nNbZ22ZzWlMnPOyYoBgIirJAG1ufmDPD86Q/OwNb3+QScvO7wt8IcnAxyzY1STrt96vVTjrXZW4fj5tutAqLnT8M45fopj9V37KNrScFLRETkLPWICmTlzyZ19zA6pOUUvqwu3tz26yNScNptjPXsdQdW+Gh2vnv2nWndEViNXm4clsyfPrLa5adEBHgbk7QMXqfuN+ew27jzsrSzGkNEoA+3eqbLXZIe5d3g2mZYG0U3V4NayowPITLIl99c06/Ncw5ODmP7b6YB4HabJIX7k3Oilt9c04/YED96eda/HiiqYvaL61t1SuyXEEpWQgjLH2j993NchjXlMzM+hIQwf5Ij/L3dMx+aOcC7lm5sepR3vV9UkA+J4QFsPVbGgm15fPvSnjyz7BBPfXaQh2YOYPqAeF7fYO0X13IjceszbV1V8nPaW23qPr53NG9uymHtoRLvRssOmzUl9V9rT07jG54azpTMWOw2gyV7Cnl/+/FWa+yGp0YwICmUspoGQvycrbpxbjhygtIWXTOX7i1kQGIo+RV19PK06G90uXlu+SEAfjUji8cX76eosp4Nh0+0+vvarLS6gfLaxtM2OT8fWzybue8sqD+5L1xncrutkORqtP6sKbU2/G6qA1cjTy9Yjr0yl2HxPgyJdVpVuoYqz5/VLW5XedbF8fmhq/mYilzrcjHpc3OnnUrBS0RE5CukZdhp+YW4K9htVhWjpZbBKzWq67aOsKo7e3CbVmONZs3BKyrIp9O2rhibHknz9/77pvbhkY/34vJUuW4dncora6yGGX3jz76jps1m8NLskRwrrfG27W8ODc2VPLvNwG2apEQEkBHbdlOitOgg3v/ROMIDrKpi/4RQjpXWcsOwpFZVIKfdxrWDEnh59RFuH9OD0AAnW4+V8eLKw2w4fIKPdlrrzJ787CCXZ8Xyuqfz4v9clcWv39tJbaMLH4ftczdtH5UWYQWv7FKqPIHtb18fQlSQD3/6aA/7C6r40eQMZo/t4Q3Lf7x+ANtyy7yBEeCzfYUUV9XzrZfX88NJGcy9/GSV72PPWKOCfCiuamDhzgIW7SrgcHE1C344jqyEED7ckU9eeR1RQT7cOCyJLUfLeHNTDp/uLTwteBVX1XPFY8spqa7n6W8O69DU3NLqBu9U2/yKOsprGgkNcH7Os86RzQY2T9t9sKZYRvbyvv4fT1iv16chmI9v+JwqXVODFcAaa6zKVPMea+4mT2ORJmsdnNtlNSypKoCaE+Cw/r7RWGc913uptdbQNV9veX9jrbWezvQ0gzHd3kYpYHK0pIqSqnoyogMI8rW3esy6bp58vunC7XZRUV1HgNPAx2aeXK+HpxTt3cfCbH3d+4fnevPn2AkUvERERL5CwgKcBPs5qKxrIrOLK15t6RkVyDdGJhPi5yTEr5O/cLYQF+rHuAxrT69eLTa/Ht87himZsUzJjOm0Jibxof48e9twnHYbg5LD+MuifbjcJk67wU+u6MOBwirqm1zttvlvT3pMUKsur3EhfgT42L2VopuGJ/GLKzPxdditNUztaPm6P72iL4OSw6y28af4+ZWZTMqMZVx6FKU1Dfz+/d3klllrrAwDbIbBtpxy/vrJPvLK64gI9OGawQl8uq+QD7bnMzgp7HObzjR3Wtx6rMw7BXNMr0giAn146/tjcbvN0/aSC/Fz8tp3x7D6YAklVQ384YPdfLaviLXZpZgm/GvNEX40KR2H3YZpmiz0NOb46RV9+emb21ptrP3xznwy44O91a5bR/fAz2lnQh+rEvfZviJ+edXJ1zZNk1+8tZ3iKmu+2Y9f3cIDV/ThRHUD1w5J9IZhsKpojjNMgW1+3y3tLagkLsSPwso6hqWG43KbbM0pJys+BH+f1p9laXUDP31zGzcMS2LaeYa/5m6mza99vLyW+NC211Oapsnflx/F12Hj25f2bP2+7O1EiNCuW6N66yNLOdJQwxVhcTx167DPPf7VtUf5xdvb6REZwOJ7J2BvY4/ClQeKeeCNbTxyw0AuaaPSCUBFBfBExwbvoeAlIiLyFWIYBt+fkM6Gw6WMOsPG0135+l/EWjaAB2dk8eSnB7n9kh7e+/x97Dx3ezsNFDqgZWORIclhbDhygiEp4YT4OfnPXWdet3a2bDaDtOhAduRaUwy/OTqV4HMMrz2iAlutVWvJz2lnfG+ruhYV5Mub37uEVQeLOV5ex+TMGF5ZfYSFuwqYt9RqpDH7Eiu0fPvSnqw/fIJZo868nx5Y2znEh/p5uy/2jQsmwrPGr/k9tiU+1J+vDU3iWGkNf/hgN5uOluH2VClKqhtYm13K2PQo9uRXkltWi7/TzoxBCby24RjrD5/sWrF8fxHjMqLYllOOr8PGN0dbYx6XEYXNgH0FVeScqCHJs4/cu1vyWLirAKfdoF9CKFuOlfG//90FwMajJ/jXd6yfbXFVPTP/vpJQfyfvzbm03fex+ZTgtSO3nB/8exOFlfX0jQumvLaR4+V1pEUF8v9mDaFfwskK4n/WHfVW7s4leJmm6Q1Nqw4Wt3ps+b5ibhrRdnfQVQdLvF1GA30dfONz9kvsSo0uNzknrIrn4j0FlFTVExl05krU3nzr38nhkhoW7Spoc6uWF1Zkk1tWy+sbc9oPXp1IGweIiIh8xdw9oRfP3zGiSxtrfBn0ig7i/24c1CVdKM+keXrlDV3QobK5wjIsNbzVl/KuMCAplO+O78VvrunHuIxorh928v0E+ti9LeGHpUaw/pdTuM6zV96ZGIbRKvBf0uvcvuwmRwSQFh2Iy22enB0GLNhmtaxfts9q+DGmVyR+TjvfGtuTxDB/fj3DakW/5VgZ8zydIq8bnOj98h4W4MNwz0boiz2NOQCeW2FVxn4wMYOXvzWSKZkxDEu11rGtyy6lpsGaLvnguzs4VlrLjtwKtuSUtTv+5vVdYZ7pha+sOeJtyrInv9IbSA8VVzNz3ipW7D8ZlJrf2/5Ca8rd5zFNk1ufX8vlf13m7YS5yrNxd/9Eq9r92b6idp//lKdTJcCv3915WrXubCzfX8R+z/YJHXGstMY7fbfRZbbacL49+wtPdv981lPhbKnR5fZWAJs3KO9qCl4iIiIinejG4Uls+tXl3Di884PXDcOSSIsO5IFpfTr93J9nYp8Yb3XqGyNTzntt0sieJ9eWXdLr9G6Dn6e5KgdwjafD6Ic78ml0uVnm6bQ4LsMKdNMHxLPyZ5OYPbYnvaIDcZuwdK91zKnTLS/PsqqWizwdGnPLrCBlM+Cbo1MI9Xfy3O0jeON7Y0gK96fRZbLmUAnvbzvu2ezZsnBngbVW7OEl9P/1xwz57UK+9dJ6XliR7Q0vzRu6N0+DvG5wAr+7th9/+/pg1v5iMhP7RNPgcjP3tS2U1TRQWdfIxiMnK3frsk/uv3aq/PI6TNPkQGEVy/cXc6CwihufWs1rG45xqKgamwE/mdYXsIJRy0YkzXbklrN8fzF2m8GYtEgaXG5+t2DXGX8up9pXUMltL6zjG8+upeGUTbzP1eGS6la3X99wDLNl8vaY8+9NTPy/TymvbWwVvDYeOdHq8wMrBFd7pu0eKKyk0dWxMZ4NBS8RERGRTmQYBhGBPl2yEfa4jGiW3DeBUW20R+9qPg4bv7mmH1f0i+P757Cn1qnGeMKWw2YwMu3cp7s2By9fh43fXtuPqCAfymoa+WhHPuuzrS/Xl7UIZ81a3jcwKZQBSa0rhlM8wWvNoRIq6hpZ5GnSMTw1otW0NsMwvOf6eEcBv/nvTgAGebZN+HhnPr95bye5ZbVU1TdxoqaRJXsK+e2CXZTXNuLrsHHt4NbVwRuHJ3PrmB5cOziR2BA//n7LMNKiAymsrOeX7+xg1cES75o4gLXtBK8/frib0X9czDPLDvHp3pPVrNLqBh54YxsA/RNDuTQ9iuhgXyrqmhj/56W8u6V1J8Knl1kVoqsHxvPoTYMA2HT0BGU1rUOaaZq8vTnHu59cS8v3F2Oa1jTMpXsLvce3VFxVz/2vb/U+3p7sYqshydj0SHwdNvbkV7I9t7zVMUWV9by/7TjZxdUs2JZHkaeSONXzc124K7/V8ctbVBMbXWartYBdRcFLRERERM7KNYMSeOrWYa3WZZ2rnlGB/PmGgTwxa8h5NVi5LCOaH05K5/9uHERYgA/XeULMT9/cRoPLTWKYP2lttH2/LONk8LqljfVoPaMCSY8Joslt8uneIu/eZFP7nd7u/TJPRW3+hmMUVdaTHOHPi3eMwMduI7u4msMlNUQG+vDJ3Mt4d85YfnllJkNTwgAr4PWNC/Y2ewgPcJ623tLfx85jNw/GYTN4f9txfutZV5bg6crZsklGs1fWHOHpz6zA9MLKbD7ZbY1/7uW9+eboFNJjgvB32pk1MgW7zeCZW4eRFR9CZX0Tv3hru3cqX1V9k7cz5J3j0kgI86d3bBBuE1YcaL1G7NO9Rdw7fyt3vLiO+iYXqw4U88AbWymvaWRd9skxvrkxh3lLDzDofxd6K0+maXLfa1t5Y2MO//P2Du/rt+WwJxQNSAxjumet1msbWm/Z0HL92j/XWFsTJIb5M6GPtUH2qeFwxf7W0yx3Hz89PHY2NdcQERERkS/UqdsMnAubzeC+qSenWv5wcgbvbc3zrpW6rHd0m9XG0WmRxIX4YTNgRjuboE/JjOVAYRUvrzrsXY/VPAWxpTG9orDbDG9YuH9qHyICfbgkPdJbabp7Qi/SY6yOkoOSw7jzsjTKaxsJ9LHjsNvoERnAwaJqpmbFeVvntzQwKYxfXJnJbxfsIrfMaizx4ykZ/PTN7ewtqKSspoEwzzYBBwor+c17VuXNbjMoqKinoML6PK4aGO9dG9iy0caQlHDe+8FY+v36Y6obXBwrraFHVCBL9hTS0OSmZ1Qg/TxbTlyWEc2+giqW7Svi6oEnP7vmSllhZT3/XnuUJ5YcoKS6gehg31bTIRfvKfQG2QXb8hiWGs4ra45415jlltWy6mAx4zJOr1TCyamGPaMCGJcRxTtb8nh3Sx7/c1WWd63qyhahsDlEpccEed/DrrwK7/uvqGtka45VMRvf2+p+uje/kt3HK2h0uRmYFNbmODpKFS8RERERuWCF+jv53XX9vbebq1Gn8vex8/E9l/Hhjy9rdxPuqwfGYzOsNUEut0nfuGBS29joO9TfyWDP1ML+iSHM8ISR5m6DMcG+bbbsD/V3ekPW1H5x+NhtfOMM3SC/dWlPvjve2tTbx2Hj6oEJpEUHYpqt13l9tq8Yl9tkdFoE3xnX03t/ckTr6t+pgdRht3n3gNvraYLx0Y7jAFzRP857/HjPXnKf7SvC5TaprGukrtHlXQ8H8LsFuyjxrBd7YcVhTtQ04ue00TcuuFU1a1tOOWU1DTz0wW7vGAFe25DDsn1FzFt6gLpGV6txNgevHpGBjEmLJDHMn8q6k5U50zRZeeD0KmBGTBB94oKxGVb3y+ZwvuqA9Xn1iAxgcqZVEVu2v4iZf1/JTU+vprymkU/3FjL7xXXePes6g4KXiIiIiFzQpvWL43vjezGxT7R3allbQgOcZ2wK0j8xlJe/NZLLs2IJC3DynXFp7R773cvSyIwP4Q/XDfC2j79hWBL3TunNM7cN/9yuoQ9M68O230z1Brj2/OyKvvzu2n7MmzWUQF8HYz2dIP+yaB+1nuYQzU07xvaKYlaLtu8Ten/+fnXN+7zty6+ktsHF0j1WFWp6i/brI3pE4Oe0UVBRz2V/Xsqw333CL9/eQXWDy7u/XHO28rHbqPUEp2Gp4d5pnUnhVsDamVfOZ/uKqGt00ys6kHmzhgLwwfbj3PbCOh75eC/3v74Vt+eEDU1ucj2t5HtGBWKzGd7GNb9bsJv/eWc7n+wuJLesFh+7jdQWG6NnxAbh57R7K34786wq10c7rMA2OTOWPp73vyO3grpGN3WNbnbklbM2u5Sle4vYccpaso7QVEMRERERueD9bHrfTjnPuIzodqe8tTS1XxxTT9lPy2m38eMpGWf1OoZhnNWWDoZhcKundT/ADyal8+GOfPbkV/KLt7fzl5sGeadFDk4JIzUykGn9Yvl4ZwFXD4z/3PM3B4+9BZV8tq+Q2kYXiWH+DEg82XzEz2lndJo1jbJ52uObm3IAuHZwAvVNbl5adZgxaZH0SwjhuRXZAIzsEckto1KJCfFjdM9ILv3TEirrm3hp1WHAmhY6IDGUzPgQ7/RAw4AF246TFhXI3Kl9OFpag9u0tjCIDraanNw8IpnnV2RTXFXPP9cc9a7pGpISRu/YYF4pOQJAhue99UsIYX9hFbvyKrg0Pdq7ZcD0/nFkxJy+sfmO3HK2ebYF6J/Yeds2qOIlIiIiInKBiA3x44lZQ7DbDN7enMt7W/M4Wmp1/Wtem/TYzUNYct/4s+p+2TvOU/EqqPS2xZ/eYpphszvHpZEc4c+3L+3J11rs2Xb1wAQeuKIPv56Rxf+bNYSvjzy5fm9UWgQ2m8G0fnGEBji9nSQ3Hy0DrAqdYRj8eHIGSeH+/GFmf/50vbXB+hNLD1BUWe9trJEaGegdU3yoP6t+NolnbxvOpL4nK5yXpkcxusV7To+xKl1Zzeu8jlew8mAxlfVNxAT7MjQlnNAAJ/GepiUOT+VyR14F245Zla7mPc86gypeIiIiIiIXkNFpkdwyKoV/rD7C79+31kqlRQcS6m9No/T3sZPmmV73eZorXoeKqsnxTOm7qo1K2dj0KJY/MAkAl9skPswKK/0TQzAMg9ljrbVlUUG+fG98L/LKar2bTTcbmBTm3cTZbjMY5dlO4Ir+cVzRYmrjP1YfZkduBZ/tK+KEZ91Yz1M6VQb7Obk8K5YpmTG8v/04y/YVMWuU1bExOtiXHpEB3q6ZzZuN78yrINgTLqf1i/NOEb1uSCKvrT/GXZel8ccP97B0TyFV9U34OW3eBimdQcFLREREROQC842RVvBq3q/q89aKtSc+1I9gXweV9U00NbhIiQj43HPZbYZ3E+a2tDftc1CLvdMGJYUS3M52AhP7xLAjt4KlewsprKgD2p/yZxgGVw9MaNVt8bOfTMBhOzmxLyveqlodKamh2PN5tVzD9tMr+vLAtD4UVdXzxw/3UFXfBFiBzdlGx8nzpamGIiIiIiIXmMz4kFZBZsh5Bi/DMLzTDcHaq60rNv+Gk5tMgzUtsD3NDVIW7y5g/eET2AyYOSSx3eNPFeDjwMdxMuaEB/qQGGY196j2hMuRp+ydZhgGMcF+3nVkAIM6ua28gpeIiIiIyAXo5hEnOxgOOs/gBSc7GwJcM7jtPc46Q3yon3c91bje7TcwGZwcRniAk7pGN2BVwOI8zztfD18/gG+N7cmTtwzlgx+Pa3PvNID+CSfXdA1K7rzGGqCphiIiIiIiF6QZg+J57JN9OO02+sadfxOIvp6KV9+44FYhrLMZhsG8W4aSXVTNiB4R7R5ntxmM7x3NO1vyAKuLYUedbbfKfgmhLPVsgm1VvFxnPP5cKHiJiIiIiFyAgv2cfHzPZdgMo9XUunN1w7AkDhRW8bWhZz+d73wNTQlnaEr45x43sW8M72zJIyrIl4l929+brbP181S8Qv2dpEYGUFlZ2WnnVvASEREREblAhQf6dPgcgb4Ofndd/04YTee5akA8h4qqGZ0W2akNLj7PhD4xTOsXy9j0qE5f62aYpml26hkvcBUVFYSGhlJeXk5ISOf17RcRERERkQtLZ2YDNdcQERERERHpYgpeIiIiIiIiXUzBS0REREREpIspeImIiIiIiHQxBS8REREREZEupuAlIiIiIiLSxRS8RET+f3t3Hh9lfe9//zUzSWayTkhCNhJICGFfgiwh4IKKomArbW3RnqPctj2e+hOP3Got1u1naw9StdW23mI3seeUo+VYaYuKYFSsGFkCCAECBLKSneyTZGYyc91/IFMDAQlkmIS8n4/HPGKu63vNfK75ejF5z3Vd36+IiIiInyl4iYiIiIiI+JmCl4iIiIiIiJ8peImIiIiIiPiZgpeIiIiIiIifBQW6gP7GMAwAWlpaAlyJiIiIiIgE0slMcDIjXAgFr1McP34cgNTU1ABXIiIiIiIi/UFrayt2u/2CnkPB6xQxMTEAlJWVXfCbKxdPS0sLqamplJeXExUVFehypBfUdwOT+m1gUr8NXOq7gUn9NnCd7LuysjJMJhPJyckX/JwKXqcwm0/c9ma323WADEBRUVHqtwFKfTcwqd8GJvXbwKW+G5jUbwNXX2YCDa4hIiIiIiLiZwpeIiIiIiIifqbgdQqr1coTTzyB1WoNdCnSC+q3gUt9NzCp3wYm9dvApb4bmNRvA5c/+s5k9MXYiCIiIiIiInJGOuMlIiIiIiLiZwpeIiIiIiIifqbgJSIiIiIi4mcKXiIiIiIiIn6m4CUiIiIiIuJngzJ4vfjii6SlpWGz2cjOzmbbtm1nbb927VrGjh2LzWZj0qRJvP322xepUvmi3vTb6tWrMZlM3R42m+0iVisAH330EV/5yldITk7GZDKxbt26L93mww8/5LLLLsNqtTJq1ChWr17t9zrldL3tuw8//PC0Y85kMlFdXX1xChZWrFjBjBkziIyMJD4+nkWLFnHw4MEv3U6fcYF3Pn2nz7nAe+mll5g8eTJRUVFERUWRk5PDO++8c9ZtdLz1D73tu7463gZd8Hr99de5//77eeKJJ9i5cydTpkxh/vz51NbW9tj+k08+4bbbbuO73/0uu3btYtGiRSxatIiCgoKLXPng1tt+A4iKiqKqqsr3KC0tvYgVC4DD4WDKlCm8+OKL59S+uLiYhQsXcvXVV7N7926WLVvG9773Pd59910/Vyqn6m3fnXTw4MFux118fLyfKpRTbd68mXvuuYdPP/2UTZs24Xa7uf7663E4HGfcRp9x/cP59B3ocy7QUlJSePrpp8nPz2fHjh1cc8013Hzzzezbt6/H9jre+o/e9h300fFmDDIzZ8407rnnHt/vHo/HSE5ONlasWNFj+29961vGwoULuy3Lzs42/v3f/92vdUp3ve23V155xbDb7RepOjkXgPHmm2+etc1DDz1kTJgwoduyxYsXG/Pnz/djZfJlzqXvPvjgAwMwGhsbL0pN8uVqa2sNwNi8efMZ2+gzrn86l77T51z/NGTIEON3v/tdj+t0vPVvZ+u7vjreBtUZL5fLRX5+PvPmzfMtM5vNzJs3j7y8vB63ycvL69YeYP78+WdsL33vfPoNoK2tjREjRpCamvql32JI/6DjbeDLysoiKSmJ6667ji1btgS6nEGtubkZgJiYmDO20THXP51L34E+5/oTj8fDa6+9hsPhICcnp8c2Ot76p3PpO+ib421QBa/6+no8Hg8JCQndlickJJzxPoTq6upetZe+dz79NmbMGP7whz/w17/+lf/+7//G6/Uye/ZsKioqLkbJcp7OdLy1tLTQ0dERoKrkXCQlJbFq1SreeOMN3njjDVJTU5k7dy47d+4MdGmDktfrZdmyZcyZM4eJEyeesZ0+4/qfc+07fc71D3v37iUiIgKr1cr3v/993nzzTcaPH99jWx1v/Utv+q6vjregvihcpL/Jycnp9q3F7NmzGTduHC+//DI/+clPAliZyKVpzJgxjBkzxvf77NmzOXLkCL/4xS/4r//6rwBWNjjdc889FBQU8PHHHwe6FOmlc+07fc71D2PGjGH37t00Nzfzv//7vyxZsoTNmzef8Q946T9603d9dbwNquAVFxeHxWKhpqam2/KamhoSExN73CYxMbFX7aXvnU+/nSo4OJipU6dSVFTkjxKlj5zpeIuKiiI0NDRAVcn5mjlzpv7wD4ClS5eyfv16PvroI1JSUs7aVp9x/Utv+u5U+pwLjJCQEEaNGgXAtGnT2L59Oy+88AIvv/zyaW11vPUvvem7U53v8TaoLjUMCQlh2rRp5Obm+pZ5vV5yc3PPeE1nTk5Ot/YAmzZtOus1oNK3zqffTuXxeNi7dy9JSUn+KlP6gI63S8vu3bt1zF1EhmGwdOlS3nzzTd5//33S09O/dBsdc/3D+fTdqfQ51z94vV6cTmeP63S89W9n67tTnffxdsHDcwwwr732mmG1Wo3Vq1cb+/fvN+666y4jOjraqK6uNgzDMG6//XZj+fLlvvZbtmwxgoKCjGeffdY4cOCA8cQTTxjBwcHG3r17A7ULg1Jv++3JJ5803n33XePIkSNGfn6+ceuttxo2m83Yt29foHZhUGptbTV27dpl7Nq1ywCMn//858auXbuM0tJSwzAMY/ny5cbtt9/ua3/06FEjLCzM+MEPfmAcOHDAePHFFw2LxWJs2LAhULswaPW2737xi18Y69atMw4fPmzs3bvXuO+++wyz2Wy89957gdqFQefuu+827Ha78eGHHxpVVVW+R3t7u6+NPuP6p/PpO33OBd7y5cuNzZs3G8XFxcaePXuM5cuXGyaTydi4caNhGDre+rPe9l1fHW+DLngZhmH86le/MoYPH26EhIQYM2fOND799FPfuquuuspYsmRJt/Z//vOfjdGjRxshISHGhAkTjLfeeusiVyyG0bt+W7Zsma9tQkKCsWDBAmPnzp0BqHpwOznE+KmPk321ZMkS46qrrjptm6ysLCMkJMQYOXKk8corr1z0uqX3fbdy5UojIyPDsNlsRkxMjDF37lzj/fffD0zxg1RP/QV0O4b0Gdc/nU/f6XMu8L7zne8YI0aMMEJCQoyhQ4ca1157re8Pd8PQ8daf9bbv+up4MxmGYfTuHJmIiIiIiIj0xqC6x0tERERERCQQFLxERERERET8TMFLRERERETEzxS8RERERERE/EzBS0RERERExM8UvERERERERPxMwUtERERERMTPFLxERERERGRA+uijj/jKV75CcnIyJpOJdevW9fo5DMPg2WefZfTo0VitVoYNG8ZPf/rTPq81qM+fUURERERE5CJwOBxMmTKF73znO3z9618/r+e477772LhxI88++yyTJk2ioaGBhoaGPq4UTIZhGH3+rCIiIiIiIheRyWTizTffZNGiRb5lTqeTRx55hP/5n/+hqamJiRMnsnLlSubOnQvAgQMHmDx5MgUFBYwZM8av9elSQxERERERuSQtXbqUvLw8XnvtNfbs2cM3v/lNbrjhBg4fPgzA3//+d0aOHMn69etJT08nLS2N733ve34546XgJSIiIiIil5yysjJeeeUV1q5dyxVXXEFGRgYPPvggl19+Oa+88goAR48epbS0lLVr1/LHP/6R1atXk5+fzy233NLn9egeLxERERERueTs3bsXj8fD6NGjuy13Op3ExsYC4PV6cTqd/PGPf/S1+/3vf8+0adM4ePBgn15+qOAlIiIiIiKXnLa2NiwWC/n5+Vgslm7rIiIiAEhKSiIoKKhbOBs3bhxw4oyZgpeIiIiIiMhZTJ06FY/HQ21tLVdccUWPbebMmUNXVxdHjhwhIyMDgEOHDgEwYsSIPq1HoxqKiIiIiMiA1NbWRlFREXAiaP385z/n6quvJiYmhuHDh/Ov//qvbNmyheeee46pU6dSV1dHbm4ukydPZuHChXi9XmbMmEFERATPP/88Xq+Xe+65h6ioKDZu3NintSp4iYiIiIjIgPThhx9y9dVXn7Z8yZIlrF69GrfbzVNPPcUf//hHjh07RlxcHLNmzeLJJ59k0qRJAFRWVnLvvfeyceNGwsPDufHGG3nuueeIiYnp01oVvERERERERPxM93idwuv1UllZSWRkJCaTKdDliIiIiIhIgBiGQWtrK8nJyZjNFzYTl4LXKSorK0lNTQ10GSIiIiIi0k+Ul5eTkpJyQc9x3sHrxRdf5JlnnqG6upopU6bwq1/9ipkzZ56x/dq1a3nssccoKSkhMzOTlStXsmDBAt96wzB44okn+O1vf0tTUxNz5szhpZdeIjMz09fmpz/9KW+99Ra7d+8mJCSEpqam016nrKyMu+++mw8++ICIiAiWLFnCihUrCAo6t12NjIwETry5UVFR5/huiIiIiIjIpaalpYXU1FRfRrgQ5xW8Xn/9de6//35WrVpFdnY2zz//PPPnz+fgwYPEx8ef1v6TTz7htttuY8WKFdx0002sWbOGRYsWsXPnTiZOnAjAz372M375y1/y6quvkp6ezmOPPcb8+fPZv38/NpsNAJfLxTe/+U1ycnL4/e9/f9rreDweFi5cSGJiIp988glVVVXccccdBAcH85//+Z/ntG8nLy+MiopS8BIRERERkT65Bem8BtfIzs5mxowZ/PrXvwZO3BeVmprKvffey/Lly09rv3jxYhwOB+vXr/ctmzVrFllZWaxatQrDMEhOTuaBBx7gwQcfBKC5uZmEhARWr17Nrbfe2u35Vq9ezbJly0474/XOO+9w0003UVlZSUJCAgCrVq3ihz/8IXV1dYSEhHzpvrW0tGC322lublbwEgkwt8tJu6MVd4cDr+EFwDC8+P7Z+vynJSgYsyWIoKBgLMEhJ34GBRMUFIzpAq/HFhERkcGrL7NBr894uVwu8vPzefjhh33LzGYz8+bNIy8vr8dt8vLyuP/++7stmz9/PuvWrQOguLiY6upq5s2b51tvt9vJzs4mLy/vtOB1Jnl5eUyaNMkXuk6+zt13382+ffuYOnXqads4nU6cTqfv95aWlnN6LRHpPcPr5XjtMerLD9JWVURXcyW01RLUUY/VeZxwdwNh3jZsdBJmdBJi6sJ+ga/ZZZjxYMGDmS5TEB4suAnCabLhNoXgNtvoMlvpsljxWGx4LDa8FhtGUChGkA2CQzGFhGG22bGE2gkOiyIkIprQ8GhskdGERQ4hPMKO2WLpk/dIRERELk29Dl719fV4PJ5u4QYgISGBwsLCHreprq7usX11dbVv/cllZ2pzLs70Ol98jVOtWLGCJ5988pxfQ0S+nNvlpKJoD8eP7MRdVYCtqYjozmMkeKqJMzmJO5cn+cIZfY9hwsuJM1fGF1YamDBhYMZLkMnb49MEmbwEcXLdP79kwfj80fNmveI1TLRio90URoc5nE5zOM7gKFwhQ/DYhkBoDKbwWIIjY7FGDiVsSDyR0fFExSYQYrVdeAEiIiLS7w36UQ0ffvjhbmfjTt5AJyLnpsvtovTADuoLt2A+toO41kKGecpJN3lIP7Wx6USIqjXF0RCSRIctnq7QoRA+FEtUAlZ7Ijb7UKxhkVjDowgNiyQ0wk6I1caXnU/yejx4PF14utx0dbnxuF10dbnxerrocjvxfr7O0+Wmy9mB29mOx9lOl7Mdj6sdw9WB192B4WrHcHdCVwcmdwdmTydmdztBXQ5Cutqweh2EetsJNdqJMNoJNnkwmwwi6SCSDvAePxHmuoAOoPnsdTsMG83mKNos0ThC4nDZ4vCGx2OOTCDYnkRYTBJRcSnEJKRgC4s4324SERGRAOt18IqLi8NisVBTU9NteU1NDYmJiT1uk5iYeNb2J3/W1NSQlJTUrU1WVtY515aYmMi2bdtOe50vvsaprFYrVqv1nF9DZLDrcLRSlP8ebYXvY6/fRZrzEBkmJxlfbGSCViOUYyHpNEeNhvhxhCZkMiRlNAmpmSRZbSSd6QXOk9liwWyxEBxy8Y5nw+uls7OdtpZGOtqa6GxtxOlowu1oxN3WgMfRgKn9OObORoJdTdjcTYR5WojythBltGIxGYSbOgk3OqGrFroOQTvQ0PPrtRqhNJmH0BocS4c1DndYIthTsMamEj40jZjENGISUnTZo4iISD/U6+AVEhLCtGnTyM3NZdGiRcCJwTVyc3NZunRpj9vk5OSQm5vLsmXLfMs2bdpETk4OAOnp6SQmJpKbm+sLWi0tLWzdupW77777nGvLycnhpz/9KbW1tb7RFTdt2kRUVBTjx4/v7a6KCCfOaB3e+QFN+97DXp3HKOd+Jpk8/2xgghbCKLWOxTF0Kra0GSRkXkZiaiZjL/GBLUxmM7awiM/PRPXuTLnX46G5uYGWhmocTbV0NtbgbKrC21qD2VFDSGc9oc7jRHkaiPE2YDO5iTR1EGl0gKsSXEAr0P07LVyGhXpzLE1B8bTbEnBHJGG2pxASk0pU0ijih48m0h7TV2+BiIiInKPzutTw/vvvZ8mSJUyfPp2ZM2fy/PPP43A4uPPOOwG44447GDZsGCtWrADgvvvu46qrruK5555j4cKFvPbaa+zYsYPf/OY3wInhGZctW8ZTTz1FZmambzj55ORkX7iDE3N0NTQ0UFZWhsfjYffu3QCMGjWKiIgIrr/+esaPH8/tt9/Oz372M6qrq3n00Ue55557dFZLpBeaG+o4/MmbcHADo1s/ZRyOf640QQ2xlNlnwIjZxI+/gtTMKUzSWZZeMVss2GOGYo8Z+qVtDa+X1tYmmmrLaa2vpKOxEnfjMWipJMRRSXhnDdFddcQZDYSYPCQbtSS7a8FdcCKcVXV/viYiqAtKotWWjDMyFXNMOmHxI4kelklCaqbuOxMREfGD8wpeixcvpq6ujscff5zq6mqysrLYsGGDbyCLsrIyzF/4pnv27NmsWbOGRx99lB/96EdkZmaybt063xxeAA899BAOh4O77rqLpqYmLr/8cjZs2OCbwwvg8ccf59VXX/X9fnKUwg8++IC5c+disVhYv349d999Nzk5OYSHh7NkyRJ+/OMfn89uigwqtceKObr5T0SWbGCMcx/TvzBYRRMRHImYTteIKxg29QaGjRxPwiV+Nqs/MZnNRNpjTpypypxyxnZdbhfV1WU0VRXjqC/D3VAOLccIcVQR6awmrquGIbQQTRvRXYeh7TC0cSKY7TvxHB7DRLUploaQJNrCUvEMGYktcTTRKWNJSp+g+8xERETO03nN43Up0zxeMpjUVZZwZPMa7EfXM869r9u6EvNwqhLnMmTKV8icdg2WoEE/Fs8loa2lkbryQzQdO4yzvhgaS7G1lRPtrCTBU02oyXXW7asZSr11GI6INIyYDEKTRhMzfAKJw0df1PvrRERELoa+zAYKXqdQ8JJLXVtLI/tz/4vwA2sZ59yL2fTPfwIOBE+gOX0Bw3NuITl9bACrlEDwzbNWcYi2qiLcdUcIbjpKVHspiV3HiPriJaencBsWqs0J1Iem0RmdSXDiOKJHTCI5YxJhERc6G5uIiEhgKHj5kYKXXIq8Hg/7P1lPx47/ZkLTZsJM/5zPqjBoHE0jbyL9yttISMk4y7PIYGZ4vTQdr6GmuIDWyoN01R3G2nwUe0c5yV3HznqmrNIUT50tjQ57JuaEsdhTJ5KcmaVBPkREpN9T8PIjBS+5lNRXl3H4nRdJL32DROp8y8tNyRwb8TVGXL2EpBFjAlihXAq8Hg91VSXUFRfgOLYf6gqJaD1CkquUGFrOuF0NsdTYRuIYMobgpEnEjppGyqjJumRRRET6DQUvP1LwkoHO8Ho5sPVdOj55mcktHxH8+dDvLYRxIPY67LPuYMy0azBpcAy5CBpqj1F9ZA+t5QVQV0h4SxEJzlKG0thje5dhoTxoOA0RmXiGjic8dQpJY2YQl6iJ7UVE5OJT8PIjBS8ZqFqbG9i/4TckHPwTad4y3/LCoHG0TrqDSdfdoRHppN9obqij6vAumsv2QPVeoloOkeoqJsLU0WP749iptI7EET0WS9JEYjKmM3zMVJ0dExERv1Lw8iMFLxloKksOUvb2s0yq+Rvhpk4A2g0re2PnEzf3bjImzw5whSLnxvB6qSo7TO3hfDoqPsN6/ABx7UWkeCq7DQJzktMIpiw4jQb7eEicQsyoGQwfNx2rLSwA1YuIyKVIwcuPFLxkoDi8+x+05D5HVsuHWD7/o7TUnEpV5rcZf+O/ExUdG+AKRfpGh6OV8oM7aSrehbe6gMjmQlJdR4ii/bS2bsNCWdAIjkeOxUjKwp4xnRHjZhIaHhmAykVEZKBT8PIjBS/pz7weD3s3v0HQp79igmuPb/le62Uw5z+YePnNundLBgWvx0NVaSHVhdtwle8koqGAVOchomk7ra3HMFFmGU595Fg8iVMYkpnDiAnZ2ELDA1C5iIgMJApefqTgJf1Rl9vF7nd+T9zuF0nzlgMnvtnfHX0tMfMeIGPSrABXKBJ4htdLdflhqgq34SzLJ+x4ASmdh4il+bS2LsNCafBIGqInYU6ZTvy4OaSOmoTZYglA5SIi0l8pePmRgpf0J26Xk11v/YbkPS+SYlQB0GaEUpD0ddIW3k9i6qgAVyjSvxleL3VVpVQe+JSO0nzC6vcwvOMAQ3oY5r7VCKXUNobWuCxsI2aQOvEK4pJHBKBqERHpLxS8/EjBS/oDl7OT3etXMazg/2OYUQNAI5EcTLuD8Yse0P1bIhfA8HqpKj1E1f6PcZftwN6whzTX4R4nga4mjsrw8bgSpxI9+nLSJs/RJYoiIoOIgpcfKXhJILmcnez6268Zvm8VSZ9PeNxAFIcy7mTSovsJj4wObIEilyi3y0lZYT71B/MwVeYztLmAEZ6y00ZTdBkWioMzaYydSkj6LIZPvlpnxURELmEKXn6k4CWB4OnqYuf6VQz77AWSjVoA6ommKPM7TL55GWER9gBXKDL4tLU0UlqQR2tRHtaanaQ6Coij6bR2laZ4KiMn4xk2g9ixV5A2fgZBwSEXv2AREelzCl5+pOAlF5Ph9bJr05+I2brSN2hGPdEUjf43shYt04THIv2I4fVSWXKQqoIP8ZR+SlzTZ6R1lfimczip3bBy1DaO1qGXEZYxm7Qpc7HHDA1Q1SIiciEUvPxIwUsuloJ//JWQzU8xuusQAM2Es3/kd5ny9Qd1hktkgGhraaTks49oLfqE8Jp80jr39Ti/WIk5lZoh07CkzSZ16jwSUjICUK2IiPSWgpcfKXiJvx3e9RHODY8x0bkbOPHt+Gcp32b8LY9iHxIX2OJE5IJ4PR7KDu2idt9HmCq2kdj8GalG5WntKk0JHIvKwhg+m6Qp15IycoLm4BMR6YcUvPxIwUv8pbrsMBX/u5zpLe8BJ27S3xX/NTK+8X+JS0wNcHUi4i8Ntcco3f0BziMfE3t8ByPdRaddnlhPNGURU3Cn5BA34WrSx8/QnGIiIv2AgpcfKXhJX2ttbqDg9f/L1GNrsJncAOyIuo7kr/+U5LQxAa5ORC62tpZGind9QNuhj7DXbifDdRDr5/82nNRCOEdDJ9GZNJPo8XPJmHw5wSHWAFUsIjJ4KXj5kYKX9JUut4v8N19g1P5fEUszAPtCJhGyYAWZWVcEuDoR6S86Oxwc/ewftBR+RFj1VjI6Cgg3dXZr4xuwIyEb+/hrGDV1LiFWW2AKFhEZRBS8/EjBSy6U4fWy58M/E/3xU4z4fKTCclMy9TmPkDXv27qPQ0TOqsvtonjfVo7v+wDrsU9Ja9/DEFq7tekwQiiyTcCRNJvoCdeQMeUKnRETEfEDBS8/UvCSC1G8byttf/shk5y7AGgkkoNj/g/TvvGA/igSkfPi9XgoP7Sb6r3vE1T2MWltu3xn0U9yGDaOhE6iPTmHmInzGDkpR3OJiYj0AQUvP1LwkvPR3FBH4f8sZ1rtXwgyeXEZQexMWsy4xT/WSIUi0qcMr5fSgzup+WwTweVbGOnYRTRt3dq0GqEcCZtCZ8ps4ibOY+TEWRqsQ0TkPCh4+ZGCl/SGp6uL/HW/JLPgFwyhBYCd4VeSeMszJKePDXB1IjIYeD0eivdvp27vJqwVn5DRvvu0ucSaCedo2BScKXOIn3wdaeOmK4iJiJwDBS8/UvCSc1W4/T0sG35IpqcIgFJzKi1zf8qkK28OcGUiMph5uro4WpDH8YL3CD32CRnte4kwdXRr00gUxRFZuEdcxbCpNzBs5Hjdfyoi0gMFLz9S8JIvU19ZSvHrP2BG87vAiUt69o25h2m3PKT7uESk3+lyuzjy2cc07MslvCqPUR0FhJmc3dpUMZTyITMxj7qa9Ok3EpuQEqBqRUT6FwUvP1LwkjNxOTvZuXYFEw+/7Pv2eFv0AjJue0Z/pIjIgOFydnJ090c07s/FXrWFUc79hJg83docsaRTFzeL0LHXkjnjesIi7AGqVkQksBS8/EjBS3qyd/NfsG9+lOHeYwAcChoNC55h9GVzA1uYiMgFcrQ2UbRjEx2FuQyt/5QMT3G39S7DQpF1PM1Jcxgy8TpGZV2pERNFZNBQ8PIjBS/5otpjxVT8z39wWdtHABzHzpEpDzL9q/foxnQRuSQdr6mgeMc7eIs+YHjjNhKp67a+zQilKDyLztQrSMq6geFjpur+MBG5ZCl4+ZGCl8CJeyJ2/PlpJh16kXBTJ12GmR0J32T8t1cQFR0b6PJERC4Kw+vl2NH9HNu1geDSzWS05WPH0a1NHUMosc+A9LmMmLGA+GHpgSlWRMQPFLz8SMFLCre/R/CGB32X2xQGjSP45ufJmDQrwJWJiATWyRET6/dsJOLYP8jsLMBmcndrU2JOpTpuNqHjrtP9YSIy4Cl4+ZGC1+DVfLyGg396gJkNfwegiQgOTXqQ6Yv+Q5cVioj0oLPDQVF+Lq373yO2No9R7sOYTf/8s8JlBHHINpHWYVcydMoNmshZRAYcBS8/UvAafAyvl+1/fZHMz37mmwR5W/QCRn37WWLihwW4OhGRgaP5eA1Htr1N1+Fchjd8etr9YQ1EcTRqJsbIq0mbeRNDk9MCU6iIyDlS8PIjBa/BpXj/djrfvI9x7n0AlJiH0379M4yfdUOAKxMRGdgMr5eKI3upzH8ba9lmMh27CDd1dmtTbB5BzdDZhH1+WWJoeGSAqhUR6ZmClx8peA0OjtYm9v7pR0yreo1gk4d2w8qeUd9n2uJHNAmyiIgfuJydFO38gOaCd4mt2XLaZYlOI5hDtok4Uq5kaNYC0sfP0GWJIhJwCl5+pOB1aTO8Xna/t4akT54gkXoAdoXNIenWF0gcnhng6kREBo+m+mqObHsLz+FcRjR+SgLHu62vJ5qSqBkYGdeQPmMhcckjAlSpiAxmCl5+pOB16aosOUjt6/9BVsenJ343xVN7+U/IuvbWAFcmIjK4GV4vZYf3ULXzLULLNpPZvpswk7Nbm6PmNGrj5xA+/joyp1+HLSwiQNWKyGCi4OVHCl6XHpezk53/82OmFP+WUJMLl2EhP+V2sr79lO4nEBHph5yd7RTlf0DLvneJq9lCpqeo2/pOI5hDoZNpT7mC+M8vS9QkziLiD32ZDc77X6kXX3yRtLQ0bDYb2dnZbNu27azt165dy9ixY7HZbEyaNIm3336723rDMHj88cdJSkoiNDSUefPmcfjw4W5tGhoa+Jd/+ReioqKIjo7mu9/9Lm1tbb71JSUlmEym0x6ffvrp+e6mDHD7trxF1cppzCp5kVCTi30hk6m67T1y/u0FhS4RkX7KagtjwpyF5Nz1SzIfy6fh/+xnx4xn2Ra9gFpisJncTO7MZ1bR84z83+s5/uN0tv/im+z42yrqq8sDXb6ISI/O64zX66+/zh133MGqVavIzs7m+eefZ+3atRw8eJD4+PjT2n/yySdceeWVrFixgptuuok1a9awcuVKdu7cycSJEwFYuXIlK1as4NVXXyU9PZ3HHnuMvXv3sn//fmw2GwA33ngjVVVVvPzyy7jdbu68805mzJjBmjVrgBPBKz09nffee48JEyb4Xj82Npbg4OBz2jed8bo0HK+p4Oia/5cZzRtP/I6d4sseZtpN/65vRUVEBjDD66Xs4C6qdr1NaNlmRnd8RqjJ1a3NEctIauNnEzn+ejJnXIfVFhagakVkoAv4pYbZ2dnMmDGDX//61wB4vV5SU1O59957Wb58+WntFy9ejMPhYP369b5ls2bNIisri1WrVmEYBsnJyTzwwAM8+OCDADQ3N5OQkMDq1au59dZbOXDgAOPHj2f79u1Mnz4dgA0bNrBgwQIqKipITk72Ba9du3aRlZV1Pu+HgtcA5/V42P6XXzBu38+JwoHXMLE97mbG/suz2GOGBro8ERHpY87Odg5v30Tr/vcYWruFUZ4j3dZ3GCEcCp1Cx/CrSJq6gOFjpuoLOBE5Z32ZDYJ6u4HL5SI/P5+HH37Yt8xsNjNv3jzy8vJ63CYvL4/777+/27L58+ezbt06AIqLi6murmbevHm+9Xa7nezsbPLy8rj11lvJy8sjOjraF7oA5s2bh9lsZuvWrXzta1/zLf/qV79KZ2cno0eP5qGHHuKrX/3qGffH6XTidP7zBt6WlpZzeyOk3zmy5xO6/raM7K6DABRZMvAu/DnZl80NbGEiIuI3VlsYE6+4Ga64GThxxUPx1vUYR94nvXkrcaYmpnRuh0Pb4dCz1BJDSfQsLJnXMnLmQoYMTQrwHojIYNHr4FVfX4/H4yEhIaHb8oSEBAoLC3vcprq6usf21dXVvvUnl52tzamXMQYFBRETE+NrExERwXPPPcecOXMwm8288cYbLFq0iHXr1p0xfK1YsYInn3zyXHZd+qm2lkYK/vuHzKj5MxaTQZsRSsHYe5l+yw8ICg4JdHkiInIRxSakEPvV7wPfx/B6KT6wnZpd7xBW8RGjO/YQb2ogvult2P423m0Pcjh4FPUJc4iaMJ/MadcQYrUFehdE5BLV6+DVn8XFxXU7szZjxgwqKyt55plnzhi8Hn744W7btLS0kJqa6vda5cIZXi+73n2VlK0/ZhYNYIL8iLkM//YLzEpOC3R5IiISYCazmfQJ2aRPyAags72Nvds34ti/kYS6PNK9JWR2HSbz2GE4thrHuzb2h0/FOfwqkqctICVjki5LFJE+0+vgFRcXh8VioaamptvympoaEhMTe9wmMTHxrO1P/qypqSEpKalbm5P3aiUmJlJbW9vtObq6umhoaDjj68KJ+9E2bdp0xvVWqxWr1XrG9dI/HTu6j+N/vo/LOrcDUGFKpHHuCqZd9fUAVyYiIv2VLSyCSVd9HT7/rKirLKFk21uYjr7PyJZtxJhayGrPg8I8KHyaKoZSHjOLoMxryci+SfcKi8gF6fXXOCEhIUybNo3c3FzfMq/XS25uLjk5OT1uk5OT0609wKZNm3zt09PTSUxM7NampaWFrVu3+trk5OTQ1NREfn6+r83777+P1+slOzv7jPXu3r27W5iTgc3Z2U7eKz8k9tWrmNy5HZcRRF7q94j7Qf6JD1MREZFzNDQ5jRmL7mH6/W8Q/VgJRV97m7yR/0GBNQuXEUQSdcxs+DuXbV1GxAuZHHwqm7zfP0Dh1o10uV1f/gIiIl9wXpca3n///SxZsoTp06czc+ZMnn/+eRwOB3feeScAd9xxB8OGDWPFihUA3HfffVx11VU899xzLFy4kNdee40dO3bwm9/8BgCTycSyZct46qmnyMzM9A0nn5yczKJFiwAYN24cN9xwA//2b//GqlWrcLvdLF26lFtvvZXk5GQAXn31VUJCQpg6dSoAf/nLX/jDH/7A7373uwt6k6R/KPj4b0S9v5wc7zEwwV7rVKJveYGczCmBLk1ERAY4s8XCqClzGDVlDgDtbc0c2PYuHYWbSKrPY4S3nDFdhVBeCOW/o/XtUIrCL8OVdjUp029i2MhxAd4DEenvzit4LV68mLq6Oh5//HGqq6vJyspiw4YNvsExysrKMH/hmujZs2ezZs0aHn30UX70ox+RmZnJunXrfHN4ATz00EM4HA7uuusumpqauPzyy9mwYYNvDi+AP/3pTyxdupRrr70Ws9nMN77xDX75y192q+0nP/kJpaWlBAUFMXbsWF5//XVuueWW89lN6Sfqq8spWbOM6S3vnfidaEqmP8K0Bd/TtfciIuIXYRF2plzzLbjmWwBUlxdRvv0tzMUfkNG6nWhTG1Pbt8D+LbD/KSpMiRyLmUXImOvImHkjUdGxAd4DEelvzmser0uZ5vHqPzxdXex44znGHXieKNpPzMk19OuM+9dn9IEmIiIB4+nq4ujeT6j/7G3slR+T6dxPsMnjW99lmDkcMo6m5CuImTSfUVlXYgm6pMYzExk0Aj6B8qVMwat/KNy6keCNPyTDcxSAw5ZRcNMvyJx6ZYArExER6a6tpZGibRtwFm4i+XgeqUZlt/XNhHMkYhpdaVeTOmMhSSPGBKhSEektBS8/UvAKrPrKUopff5AZzRuBEx9WhePuY/o3HtC3hSIiMiBUFhdSvuMtgks+ZJRjB1G0d1tfZh5GVWwO1rHXkTnzBsIjowNTqIh8KQUvP1LwCgy3y0n+2qeZeOglIkwdJy4rjP0Ko2/7GUOGalRKEREZmLrcLoo++weNe95lSNU/GOUqJMjk9a13GRYOW8fTMuxK4qbcSMak2ZgtlgBWLCJfpODlRwpeF1/BP/5K5AePMMJbDsDBoDGYFz6rywpFROSS09xYz5Ft7+A+9B6pDXkkG93nOW0kiqMRl9E14kpSpi3QaIkiAabg5UcKXhdPdXkRla/fz2VtmwFoIIojkx9k2s1L9W2fiIgMCseO7qNi+3pCSj8k07GLCFNHt/WVpngqomdgHnU16dNvJDYhJUCVigxOCl5+pODlf50dDna//lMmF/+OMJMTj2FiR/w3GHvb09hjhga6PBERkYBwu5wU7fqQpn3vEV21hVGuwm6jJQIUm9OoGTqL0DFXkzF9PhFRQwJUrcjgoODlRwpe/mN4vezc8ApJ258m2agFYH/wRGw3/5yRE7MDXJ2IiEj/4mhtomjHJjoKcxla/ykZnuJu692GhaKQMTQlzsY+fh6jLruaEKvtDM8mIudDwcuPFLz849DOzXjeWc44934Aaoil/LKHmHbTXZoEWURE5Bw01B6jeMcGPEUfMKxxG8NOuT+s3bBSFDqZ9pQ5DJ08n/QJ2bp0X+QCKXj5kYJX36qpOEL5n3/I9JZNwIkPhc9G/D9kLX6M0PDIAFcnIiIycFUWF1Kx8x0sJR+R3rqDGFq6re8+UMcNJKeN05edIr2k4OVHCl59o72tmc9e/zFZZX8k1OQCYLv9BkZ862nih6UHuDoREZFLi9fjoeTADmo/e5fQio8Z1f4Z4abObm0qTfFU2KdjSruc1MuuJ3F4ZoCqFRk4FLz8SMHrwni6uti5fhUjdj9HPA0AHAieQNCCpzU8vIiIyEXidjk5smszjfvew161hVGuA4ScMlDHMVMCx+zTMKVfcSKIpY4KULUi/ZeClx8peJ0fw+tlz4d/Jurj/yTdWwpApSmB6pkPM3X+El3aICIiEkDtbc0Ubd+E49BmYuq2kuE+3G0iZ/hnEDOPvJKUqdcpiImg4OVXCl69V7gjF+/GJxjv2gtAC+HsH/ldsr65HFtoeICrExERkVO1tTRyND/3rEGswpRIZfQ0zJ+fEUtIyQhQtSKBo+DlRwpe5660cCcNf3uUqe1bAHAawexK+hbjvvkE9tiEAFcnIiIi56q1uYGjO3NpP/QhsXXbyHAfxmLq/ifiP4PYlaRedp2CmAwKCl5+pOD15SpLDlLx1yeZ1vA2FpOBxzCRP+RGhn/jJ7osQURE5BLQ2tzA0fz36Dj0ITH1288YxKqisjCG55A0+WpSMibp1gK55Ch4+ZGC15lVlx2m9K8/YWr9et8NurvCZhPzlacYMW5agKsTERERfzmXIHYcO6Xhk3ENyyZ23FWkT5xFUHBIgCoW6RsKXn6k4HW6moojlLz5Y6bW/90XuAqsWQRd+yhjZ14X4OpERETkYmtpOk7Jrg9wFP0De+0OMlwHsZrc3dq0G1aO2MbTljCTyDFXkJE1V3N4yoCj4OVHCl7/VFdZwtG//JipdX8lxNQFwL6QKZiu+RHjZ90Q4OpERESkv3B2tlP82cc0Fm4mrHo76R0FROHo1sZtWDgaPIrG2MuwZlxO+mXziI5LDFDFIudGwcuPFLygoqiAY+/8jKz6t33fXu0PmYRx1cNMmLMwwNWJiIhIf3dyQue6fR8SVPEpqa27ffN7flGpOZUa+xRMqTOJH3c5qaOzMFssAahYpGcKXn40mINX0WdbaH7vGbJaPvRdt30geAKeq5YzYfZNumFWREREzovh9VJVdphjn+XiLc0jsWkXI7zlp7VrIZwS2zgc8ZcRkZHDiClXERUdG4CKRU5Q8PKjwRa8DK+X/Xnv4PnHz5ncucO3/LPQmQRf9QDjZl6vwCUiIiJ9rrGuipJduXQWb8V+fBfpzoOEmlzd2ngNE2WWFGrtkyFlJgnjr9BZMbmoFLz8aLAEL7fLyWeb/kjkrt8ypusgAB7DxC77tQy57gdkTJoV4ApFRERkMHG7nJQe2M7xwo+xHNtOUutehhk1p7XznRUbOpXwjBxGTLpc84eK3yh4+dGlHrzqq8s5/M6vGVX6OkNpBE5MfLw7biEpC5czbOS4AFcoIiIicsLxmgrK93xER3EeUfW7SHceIszkPK1dhSmRmojxuBOziMrIJm1iDmER9gBULJcaBS8/ulSD16Gdm2ne/GumNL3vG6GwnmgOD/8WmQv+g7jE1ABXKCIiInJ2XW4XJfv/eVYssbWAFKPqtHYew0SZZTh1URMwkqcSkzmL4eOmY7WFBaBqGcgUvPzoUgpe7W3NFGx6Ffu+PzGmq9C3/GDQGFqnfJfJ1y8hxGoLYIUiIiIiF6a5oY6ygo9xHN2OtXY3w9oP9DiCossIojQ4nQb7BEwp04jLzCZ1dBbBIdYAVC0DhYKXHw304GV4vRze/Q8aP/4dE45vIsLUAYDLsLDHfg1Rc5cy+rK5gS1SRERExI/qK0up2LeFjtLthNfvYXhnIdG0ndbOaQRTFjSCxqgxGImTsadPY/j4mbpMUXwUvPxooAav2mPFHP3wj8Qf+QsjvSW+5RWmRMrTvkHm/O8Tlzg8cAWKiIiIBIjh9VJZcpDqA1twl+0gqrGAVGcRkZ9/Qf1FXsNEhSWZuvAxuIdOJDztMoaNnUlM/LAAVC6BpuDlRwMpeDU31nPw/f8m/OBfGOfcg/nzubc6jWAK7FcRmv0dxs26QUOuioiIiJzC6/FQVVpIzaHtOMt3E9awj+SOw77Bx05VSwyVoaPpiBlHSPJEYkdmMSxjki5VvMQpePlRfw9ezcdrOPSPtQQdeosJjm2+gTIADgSPpyXza4yddyf2mKEBrFJERERkYKqvLqfywFYcZTux1hUQ7zjU4wAecOK+sQpLCg0Ro3DHjiUsdRLxGVNJTM3UPKiXCAUvP+qPwaum4gglW9YSfvQdxnbuIcjk9a0rMQ+nasRXGHHVEpLTxgSwShEREZFLU2tzAxWF22k+mo+5dh/21iJS3CWEmzp7bN9mhFIRnEZzVCbEjycidTLJoy9jyNCki1y5XCgFLz/qD8Grs8PB4e0bcex7l4S6T0j3lnZbf9ScRs2w60jI/ibp42foGxURERGRi8zr8VBdXkRt0U46K/YSdLyQWEcRKZ4Kgk2eHrdpJJLq4FRaI0bijc0kNHkscWmTSBw+BktQ0EXeAzkXCl5+FIjg5XJ2cvSzf9BY+BHhlXlkdnxGqMnlW+81TBwKGUfTiOtJnf0tho2ccFHqEhEREZHecTk7OXZkL8eLd+OuLCC08SDxHUdINmrPuI3TCKbSMozGsBG4okcRlDCG6OETSEgbT6Q95iJWL6dS8PKjixG86itLOVaYR/vRrUTVbifDWYjN5O7Wpo4hlETPwpw5j4zsm4iOS/RLLSIiIiLifx2OViqP7KWxfB/u6oOENB5mSHsJwzzHsJ7yd+AXHcdOXfAwWsNS6YpOJ3hoBlHJY0hIG697+i8CBS8/6ss319nZTlXxfhpKC3BV7CH0eAHDOg4SR9NpbRuJoiRsEs5h2SRk3UDaOF1CKCIiInKp83R1UVNeRF3JXjoq92M+fpiI1mIS3WXE0HLWbZuIoDYomZbQVLrsaZhj0wiLT2dI0ijiU0ZqxMU+oODlR715cz1dXRyvKaeh6ijtdWW4GiuguYLQlqPEdpaR5K3BYjr97fUYJsotKdRFjsNIzSFh0jUMz5ysoCUiIiIiPq3NDdSUHKD52CFcdYexNBYT0V5OnOsY8TScdVuPYaLeFENDcCKO0CTckalYhgwnND6d6KSRDE0ZhS00/CLtycCl4OVHJ9/cTb/4HhEhJkxeNyavG4vbQbC7BVtXC6GeNiKMNiKNtm4jDPakzQilMjiV5ogMvIlTsI+crhnRRUREROSCdDhaqS4tpKniIM7aw5gaiwl1HCPaVUW8p/a021h6Uk80TZZY2qzxOEMT8EYkEmRPxhqTQmT8cGIS04iKjh3UJwcUvPzI9+YujyTKavrS9l2GmXpTLE3BQ3FY43GHJ2GKzSBi2HgS0icSm5g6qP9nFREREZGLy/B6OV57jOMVh2mrLcZdX4KppQKbowK780QwCzM5z+m5OowQjptjaQ6Oo8MaT1doHEb4UIIi4wm2JxA2JJGImCSGDE0mNDzSz3t28fVl8DrvcStffPFFnnnmGaqrq5kyZQq/+tWvmDlz5hnbr127lscee4ySkhIyMzNZuXIlCxYs8K03DIMnnniC3/72tzQ1NTFnzhxeeuklMjMzfW0aGhq49957+fvf/47ZbOYb3/gGL7zwAhEREb42e/bs4Z577mH79u0MHTqUe++9l4ceeqjX+7d96C2ERYSDJQTMQZhCwjGHDSE4fAjWyFhCo2KIHJLIkPhhJAYFoaEvRERERKQ/MJnNxCWmEpeY2uN6w+ul8XgNxyuP0lZfjrOhAqO5EnNbFbbOWiJddQzxHieaNkJNLlKMKlJcVeACWs/8uu2GlSZzNK2WIbSHxOC2xuCxxUBoNJawIQSFDyEkIgZbVBxh9jgiouOIjBoyaE5SnNcZr9dff5077riDVatWkZ2dzfPPP8/atWs5ePAg8fHxp7X/5JNPuPLKK1mxYgU33XQTa9asYeXKlezcuZOJEycCsHLlSlasWMGrr75Keno6jz32GHv37mX//v3YbDYAbrzxRqqqqnj55Zdxu93ceeedzJgxgzVr1gAnEuno0aOZN28eDz/8MHv37uU73/kOzz//PHfdddc57Vt/mMdLRERERCTQOtvbOF5dSnNNGe3Hy+lqqsRw1BHUcZwQ53HC3I1EdjUyxGg+68iMZ9NlmGk1heMwRdBuicRpicAdFI4nKBxvcBje4AgICcNkjcRsjcBiiyAoNIrg0EhCwiKxhkUSbA0j2BpKsDWUEKsNqzW0z8JcwC81zM7OZsaMGfz6178GwOv1kpqayr333svy5ctPa7948WIcDgfr16/3LZs1axZZWVmsWrUKwzBITk7mgQce4MEHHwSgubmZhIQEVq9eza233sqBAwcYP34827dvZ/r06QBs2LCBBQsWUFFRQXJyMi+99BKPPPII1dXVhISEALB8+XLWrVtHYWFhj/vidDpxOv95qrWlpYXU1FQFLxERERGRc2B4vbS1NtFcV0lbQxUdTTW4mmvwttVi6mjE4mwi2N2C1d1CqKeVcG8rkUbbOd2Hdr5cRhAugnGbgn0/u0zBdJlC8JiCMDBjmMx4TWbgxE/DZMHAdOKnyQwmM41xM7n2O/83MJcaulwu8vPzefjhh33LzGYz8+bNIy8vr8dt8vLyuP/++7stmz9/PuvWrQOguLiY6upq5s2b51tvt9vJzs4mLy+PW2+9lby8PKKjo32hC2DevHmYzWa2bt3K1772NfLy8rjyyit9oevk66xcuZLGxkaGDBlyWm0rVqzgySef7O3bICIiIiIinLi0MdIe8/lkzxPPebvODgdtjfW0NdfR0XIcZ2sDbkcjXmcbhrMNnG3gdmB2O7C4HVi62gn2OAjxdGA1OrB5Owg1OrDiJsTU1e25Q0xdhNAFdJxYYHz+6KX3vWG93+gMeh286uvr8Xg8JCQkdFuekJBwxrNK1dXVPbavrq72rT+57GxtTr2MMSgoiJiYmG5t0tPTT3uOk+t6Cl4PP/xwt1B48oyXiIiIiIj4jy00HFtoOHHJIy74uQyvF5erE2dnB25nB25XJ25nB12uTrqcHXhcnXS5O/G4OjE8XXi9HjA8GN4TD7zeE/9teMHrAePE7+HRacD/XnB9cAGDa1wqrFYrVqsmlxMRERERGahMZjNWWxhWW9+doYITJ2X6Sq/vOouLi8NisVBTU9NteU1NDYmJPY/tl5iYeNb2J39+WZva2tpu67u6umhoaOjWpqfn+OJriIiIiIiIXGy9PuMVEhLCtGnTyM3NZdGiRcCJwTVyc3NZunRpj9vk5OSQm5vLsmXLfMs2bdpETk4OAOnp6SQmJpKbm0tWVhZwIl1u3bqVu+++2/ccTU1N5OfnM23aNADef/99vF4v2dnZvjaPPPIIbreb4OBg3+uMGTOmx8sMe3JyrJG+TLciIiIiIjLwnMwEfTL1sXEeXnvtNcNqtRqrV6829u/fb9x1111GdHS0UV1dbRiGYdx+++3G8uXLfe23bNliBAUFGc8++6xx4MAB44knnjCCg4ONvXv3+to8/fTTRnR0tPHXv/7V2LNnj3HzzTcb6enpRkdHh6/NDTfcYEydOtXYunWr8fHHHxuZmZnGbbfd5lvf1NRkJCQkGLfffrtRUFBgvPbaa0ZYWJjx8ssvn/O+HTly5OStd3rooYceeuihhx566KGHHkZ5efn5xKZuzuser8WLF1NXV8fjjz9OdXU1WVlZbNiwwTeQRVlZGeYvjJ0/e/Zs1qxZw6OPPsqPfvQjMjMzWbdunW8OL4CHHnoIh8PBXXfdRVNTE5dffjkbNmzwzeEF8Kc//YmlS5dy7bXX+iZQ/uUvf+lbb7fb2bhxI/fccw/Tpk0jLi6Oxx9//Jzn8AKIiYnx7YPdbj+ft0cC4OSgKOXl5ZoGYIBR3w1M6reBSf02cKnvBib128B1su/KysowmUwkJydf8HOe1zxelzJNoDwwqd8GLvXdwKR+G5jUbwOX+m5gUr8NXP7ou76Z0llERERERETOSMFLRERERETEzxS8TmG1WnniiSc0t9cAo34buNR3A5P6bWBSvw1c6ruBSf02cPmj73SPl4iIiIiIiJ/pjJeIiIiIiIifKXiJiIiIiIj4mYKXiIiIiIiInyl4iYiIiIiI+JmCl4iIiIiIiJ8peImIiIiIiPiZgpeIiIiIiIifKXiJiIiIiIj42f8PeicmMxVM7hUAAAAASUVORK5CYII=",
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- },
- "metadata": {},
- "output_type": "display_data"
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- ],
- "source": [
- "model = TSARTransformer(depth=3).cuda()\n",
- "train(\"tsar-140k\", model, train_ds, val_ds, half=True, bs=8, lr=1e-3, epochs=25, warmup=0,\n",
- " table_row_every_iters=80000, run_valid_every_iters=8000)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "282243a2",
- "metadata": {},
- "outputs": [],
- "source": [
- "torch.save(model.state_dict(), 'tsar-140k-25e-ce1.58.pth')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4ec37514",
- "metadata": {},
- "outputs": [
- {
- "data": {
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- },
- "metadata": {},
- "output_type": "display_data"
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- " /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
- " background-size: auto;\n",
- " }\n",
- " progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
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- " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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- " <th>val</th>\n",
- " <th>time</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
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- " <td>80000</td>\n",
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- " <td>04:53</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>160000</td>\n",
- " <td>2.16040</td>\n",
- " <td>2.25076</td>\n",
- " <td>09:42</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>240000</td>\n",
- " <td>2.09020</td>\n",
- " <td>2.14504</td>\n",
- " <td>14:51</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>320000</td>\n",
- " <td>1.95545</td>\n",
- " <td>2.00177</td>\n",
- " <td>19:52</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>400000</td>\n",
- " <td>1.83738</td>\n",
- " <td>1.87890</td>\n",
- " <td>24:42</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>480000</td>\n",
- " <td>1.75185</td>\n",
- " <td>1.82079</td>\n",
- " <td>29:49</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>560000</td>\n",
- " <td>1.72557</td>\n",
- " <td>1.78461</td>\n",
- " <td>34:49</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>640000</td>\n",
- " <td>1.73570</td>\n",
- " <td>1.76621</td>\n",
- " <td>39:40</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>720000</td>\n",
- " <td>1.69044</td>\n",
- " <td>1.75392</td>\n",
- " <td>44:40</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>800000</td>\n",
- " <td>1.67324</td>\n",
- " <td>1.73999</td>\n",
- " <td>49:35</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>880000</td>\n",
- " <td>1.70643</td>\n",
- " <td>1.73158</td>\n",
- " <td>54:24</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>960000</td>\n",
- " <td>1.70786</td>\n",
- " <td>1.72684</td>\n",
- " <td>59:18</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1040000</td>\n",
- " <td>1.71996</td>\n",
- " <td>1.71658</td>\n",
- " <td>1:04:09</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1120000</td>\n",
- " <td>1.67093</td>\n",
- " <td>1.71494</td>\n",
- " <td>1:08:59</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1200000</td>\n",
- " <td>1.68071</td>\n",
- " <td>1.70685</td>\n",
- " <td>1:13:52</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1280000</td>\n",
- " <td>1.67647</td>\n",
- " <td>1.69975</td>\n",
- " <td>1:18:41</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1360000</td>\n",
- " <td>1.68778</td>\n",
- " <td>1.69497</td>\n",
- " <td>1:23:45</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1440000</td>\n",
- " <td>1.63308</td>\n",
- " <td>1.69156</td>\n",
- " <td>1:28:50</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1520000</td>\n",
- " <td>1.62949</td>\n",
- " <td>1.68304</td>\n",
- " <td>1:33:56</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1600000</td>\n",
- " <td>1.65336</td>\n",
- " <td>1.67676</td>\n",
- " <td>1:39:02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1680000</td>\n",
- " <td>1.64270</td>\n",
- " <td>1.67212</td>\n",
- " <td>1:44:08</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1760000</td>\n",
- " <td>1.60869</td>\n",
- " <td>1.66234</td>\n",
- " <td>1:48:54</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1840000</td>\n",
- " <td>1.63936</td>\n",
- " <td>1.65975</td>\n",
- " <td>1:53:43</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1920000</td>\n",
- " <td>1.60129</td>\n",
- " <td>1.65080</td>\n",
- " <td>1:58:49</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2000000</td>\n",
- " <td>1.61278</td>\n",
- " <td>1.64941</td>\n",
- " <td>2:03:41</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2080000</td>\n",
- " <td>1.59714</td>\n",
- " <td>1.64003</td>\n",
- " <td>2:08:33</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2160000</td>\n",
- " <td>1.60850</td>\n",
- " <td>1.63501</td>\n",
- " <td>2:13:21</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2240000</td>\n",
- " <td>1.55892</td>\n",
- " <td>1.62777</td>\n",
- " <td>2:18:18</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2320000</td>\n",
- " <td>1.55027</td>\n",
- " <td>1.62194</td>\n",
- " <td>2:23:05</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2400000</td>\n",
- " <td>1.53199</td>\n",
- " <td>1.61445</td>\n",
- " <td>2:27:54</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2480000</td>\n",
- " <td>1.55937</td>\n",
- " <td>1.60859</td>\n",
- " <td>2:33:05</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2560000</td>\n",
- " <td>1.50396</td>\n",
- " <td>1.60217</td>\n",
- " <td>2:38:00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2640000</td>\n",
- " <td>1.54471</td>\n",
- " <td>1.59542</td>\n",
- " <td>2:43:14</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2720000</td>\n",
- " <td>1.55641</td>\n",
- " <td>1.58846</td>\n",
- " <td>2:48:25</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2800000</td>\n",
- " <td>1.50863</td>\n",
- " <td>1.57974</td>\n",
- " <td>2:53:37</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2880000</td>\n",
- " <td>1.49681</td>\n",
- " <td>1.57561</td>\n",
- " <td>2:58:43</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2960000</td>\n",
- " <td>1.50676</td>\n",
- " <td>1.57008</td>\n",
- " <td>3:03:52</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3040000</td>\n",
- " <td>1.50988</td>\n",
- " <td>1.56511</td>\n",
- " <td>3:08:52</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3120000</td>\n",
- " <td>1.45172</td>\n",
- " <td>1.56008</td>\n",
- " <td>3:13:49</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3200000</td>\n",
- " <td>1.44757</td>\n",
- " <td>1.55511</td>\n",
- " <td>3:18:36</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3280000</td>\n",
- " <td>1.46662</td>\n",
- " <td>1.55356</td>\n",
- " <td>3:23:34</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3360000</td>\n",
- " <td>1.44749</td>\n",
- " <td>1.55062</td>\n",
- " <td>3:28:34</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3440000</td>\n",
- " <td>1.47749</td>\n",
- " <td>1.55033</td>\n",
- " <td>3:33:28</td>\n",
- " </tr>\n",
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",
- "text/plain": [
- "<Figure size 1000x600 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Whisper tiny sized model\n",
- "model = TSARTransformer(depth=4).cuda()\n",
- "train(\"tsar-140k-4l\", model, train_ds, val_ds, half=True, bs=16, lr=2e-3, epochs=25, warmup=0, pct_start=0.05,\n",
- " table_row_every_iters=80000, run_valid_every_iters=8000, chkpt_every_iters=80000)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a21eb3ed",
- "metadata": {},
- "outputs": [],
- "source": [
- "torch.save(model.state_dict(), 'tsar-140k-4l-25e-ce1.55.pth')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "843d6703",
- "metadata": {},
- "source": [
- "# Sample from the model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "46afee25",
- "metadata": {},
- "outputs": [],
- "source": [
- "model = TSARTransformer(depth=4).cuda()\n",
- "model.load_state_dict(torch.load('tsar-140k-4l-25e-ce1.55.pth')) #'/scrach/tsar-checkpoints/1480000.pt')) # tsar-32k-60e-ce1.87.pth\n",
- "model.eval().cuda();"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8c4e37f8",
- "metadata": {},
- "outputs": [],
- "source": [
- "whmodel = whisper.load_model('tiny.en')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b26f93b3",
- "metadata": {},
- "outputs": [],
- "source": [
- "from spear_tts_pytorch.extract_stoks import RQBottleneckTransformer\n",
- "vqmodel = RQBottleneckTransformer(codebook_dim=16, vq_codes=1024, q_depth=1, n_head=6, depth=1,\n",
- " threshold_ema_dead_code=0.1)\n",
- "vqmodel.load_state_dict(torch.load('./vqmodel2-tiny-1000h.pth'))\n",
- "vqmodel.eval().cuda();"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "da227a3b",
- "metadata": {},
- "outputs": [],
- "source": [
- "from torch.utils.data import DataLoader\n",
- "dl = DataLoader(val_ds, batch_size=16)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f9f8ae84",
- "metadata": {},
- "outputs": [],
- "source": [
- "bx, by = [x.cuda() for x in next(iter(dl))]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "145d3f4b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(torch.Size([16, 200]), torch.Size([16, 1500]))"
- ]
- },
- "execution_count": null,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "bx.shape, by.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ca57316b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tensor(1.4946, device='cuda:0')"
- ]
- },
- "execution_count": null,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "with torch.no_grad():\n",
- " logits, loss = model(bx, by)\n",
- "loss"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d7cea6e2",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tensor(141, device='cuda:0')"
- ]
- },
- "execution_count": null,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "(by[5] == 1024).sum()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3d49a5c2",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tensor([ 78, 980, 980, ..., 216, 690, 216], device='cuda:0')"
- ]
- },
- "execution_count": null,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "by[5,:-141]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7b437e0e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[DecodingResult(audio_features=tensor([[-1.7002, -1.3105, -0.0891, ..., -1.5332, 0.6606, -3.5156],\n",
- " [-1.5342, -0.3982, 1.1299, ..., -1.8730, -0.1315, -4.1523],\n",
- " [-1.5029, -0.5972, 0.6626, ..., -1.5752, -0.1542, -4.1172],\n",
- " ...,\n",
- " [-0.2218, 1.3877, 0.6792, ..., 1.5840, -1.5488, -0.7349],\n",
- " [-0.4180, 1.5811, 0.3328, ..., 2.4277, -1.4521, -0.7466],\n",
- " [-0.7339, 1.4521, -0.0561, ..., 2.8633, -1.2754, -0.6807]],\n",
- " device='cuda:0', dtype=torch.float16), language='en', language_probs=None, tokens=[50363, 1002, 673, 15847, 612, 11, 262, 34692, 10846, 561, 423, 284, 1394, 2491, 736, 290, 6071, 287, 262, 3024, 50619, 50619, 4252, 393, 287, 262, 23147, 6290, 286, 262, 937, 36194, 13, 50807, 50807, 1318, 318, 645, 406, 6, 46, 293, 321, 11, 691, 257, 21151, 11, 30690, 7815, 4314, 11, 290, 612, 318, 645, 14595, 13, 51145, 51145, 1439, 262, 670, 10616, 1660, 318, 1760, 319, 262, 4314, 416, 257, 14782, 12656, 13, 51371, 51371, 843, 3360, 611, 262, 3159, 3011, 5445, 625, 262, 5422, 286, 262, 14782, 12656, 11, 284, 5643, 1282, 51633, 51633], text=\"If she cooked there, the missionary lady would have to keep running back and forth in the hot sun or in the pouring rain of the monsoon. There is no L'Oleam, only a damp, uneven stone floor, and there is no sink. All the work requiring water is done on the floor by a drain pipe. And sometimes if the screen gets broken over the mouth of the drain pipe, toads come\", avg_logprob=-0.29323856198057835, no_speech_prob=0.04910319671034813, temperature=0.0, compression_ratio=1.6177777777777778)]"
- ]
- },
- "execution_count": null,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "x = vqmodel.rq.layers[0]._codebook.embed[0,by[5,:-141].to(torch.long).view(-1)]\n",
- "x = F.pad(x, (0, 0, 0, 1500-len(x)))\n",
- "orig_embs = vqmodel.ln_post(vqmodel.out_blocks((vqmodel.rq.layers[0].project_out(x) + vqmodel.positional_embedding).unsqueeze(0)))\n",
- "whmodel.decode(orig_embs, whisper.DecodingOptions(language='en'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "61e0e6e5",
- "metadata": {},
- "outputs": [],
- "source": [
- "def decode_stoks(stoks):\n",
- " stoks = stoks[:-(stoks == 1024).sum()]\n",
- " x = vqmodel.rq.layers[0]._codebook.embed[0,stoks.to(torch.long).view(-1)]\n",
- " x = F.pad(x, (0, 0, 0, 1500-len(x)))\n",
- " embs = vqmodel.ln_post(vqmodel.out_blocks((vqmodel.rq.layers[0].project_out(x) + vqmodel.positional_embedding).unsqueeze(0)))\n",
- " return whmodel.decode(embs, whisper.DecodingOptions(language='en'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f51e1772",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " If she cooked there, the missionary lady would have to keep running back and forth in the hot sun or in the pouring rain of the monsoon. There is no linoleum, only a damp, uneven stone floor, and there is no sink. All the work requiring water is done on the floor by a drainpipe, and sometimes, if the screen gets broken over the mouth of the drainpipe, toads come hopping in, and sometimes even\n"
- ]
- }
- ],
- "source": [
- "print(tokenizer.decode(bx[5][torch.where(bx[5] != tokenizer.eot)]))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5adc41c9",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "If she cooked there, the missionary lady would have to keep running back and forth in the hot sun or in the pouring rain of the monsoon. There is no L'Oleam, only a damp, uneven stone floor, and there is no sink. All the work requiring water is done on the floor by a drain pipe. And sometimes if the screen gets broken over the mouth of the drain pipe, toads come\n"
- ]
- }
- ],
- "source": [
- "# decode the quantized semantic tokens (they have some errors!)\n",
- "print(decode_stoks(by[5])[0].text)\n",
- "torch.save(by[5], 'gt-tokens.pth')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "702121bc",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "<style>\n",
- " /* Turns off some styling */\n",
- " progress {\n",
- " /* gets rid of default border in Firefox and Opera. */\n",
- " border: none;\n",
- " /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
- " background-size: auto;\n",
- " }\n",
- " progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
- " background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
- " }\n",
- " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
- " background: #F44336;\n",
- " }\n",
- "</style>\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "\n",
- " <div>\n",
- " <progress value='1500' class='' max='1500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
- " 100.00% [1500/1500 00:35<00:00]\n",
- " </div>\n",
- " "
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from fastprogress import progress_bar\n",
- "# generate output using sampling, one token at a time\n",
- "T=0.7\n",
- "toks = []\n",
- "for i in progress_bar(range(1500)):\n",
- " p, loss = model(bx[5:6], torch.tensor([toks]).cuda(), loss=None)\n",
- " last_p = p[0,-1]\n",
- " toks.append(torch.multinomial((last_p / T).softmax(-1), 1).item())\n",
- "toks = torch.tensor(toks).cuda()\n",
- "# btw. this is stupidly slow since it reruns the whole sequence every time, to be optimized later"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c43f4edc",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "If she cooked there, the missionary lady would have to keep running back and forth in the hot sun or in the pouring rain of the monsoon. There is no L'Alim, only a damp, uneven stone floor, and there is no sink. All the work requiring water is done on the floor by a drying pipe. And sometimes, if the screen gets broken over the mouth of the drain pipe, towards\n"
- ]
- }
- ],
- "source": [
- "# decode the semantic tokens generated by the model (they have some more errors)\n",
- "print(decode_stoks(toks)[0].text)\n",
- "torch.save(toks, 'gen-tokens-T0.7.pth')"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "python3",
- "language": "python",
- "name": "python3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/spear_tts_pytorch/t2s.py b/spear_tts_pytorch/t2s.py
deleted file mode 100644
index fed0f768ceff0fd06a3ec45a569dfaa3b1acda19..0000000000000000000000000000000000000000
--- a/spear_tts_pytorch/t2s.py
+++ /dev/null
@@ -1,79 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/5. Text to semantic token modeling.ipynb.
-
-# %% auto 0
-__all__ = ['load_datasets', 'TSARTransformer', 'make_model']
-
-# %% ../nbs/5. Text to semantic token modeling.ipynb 1
-import torch
-import torch.nn as nn
-from torch.profiler import record_function
-
-# %% ../nbs/5. Text to semantic token modeling.ipynb 2
-from pathlib import Path
-import pylab as plt
-import pandas as pd
-
-# %% ../nbs/5. Text to semantic token modeling.ipynb 3
-import whisper
-from spear_tts_pytorch.train import *
-from spear_tts_pytorch.modules import *
-
-# %% ../nbs/5. Text to semantic token modeling.ipynb 11
-def load_data(path):
- data = pd.DataFrame(dict(stoks=[str(x) for x in Path(path).rglob('*.stoks')]))
- data['text'] = data['stoks'].apply(lambda x: Path(x).with_suffix('.txt').read_text())
- return data
-
-# %% ../nbs/5. Text to semantic token modeling.ipynb 14
-import torch.nn.functional as F
-
-class SADataset(torch.utils.data.Dataset):
- def __init__(self, data, tokenizer):
- self.data = data
- self.tokenizer = tokenizer
-
- def __len__(self):
- return len(self.data)
-
- def __repr__(self):
- return f"<Dataset: {len(self)} samples>"
-
- def __getitem__(self, idx):
- row = self.data.iloc[idx]
- Stoks = torch.load(row['stoks'], map_location='cpu')[0,:,0]
- Ttoks = self.tokenizer.encode(row['text'])
- return F.pad(torch.tensor(Ttoks), (0, 200 - len(Ttoks)), value=self.tokenizer.eot).to(torch.long), F.pad(Stoks, (0, 1500 - len(Stoks)), value=1024).to(torch.long)
-
-# %% ../nbs/5. Text to semantic token modeling.ipynb 19
-def load_datasets(path):
- tokenizer = whisper.tokenizer.get_tokenizer(multilingual=True)
- data = load_data(path)
-
- val_data, train_data = data[:300], data[300:]
-
- return SADataset(train_data, tokenizer), SADataset(val_data, tokenizer)
-
-# %% ../nbs/5. Text to semantic token modeling.ipynb 21
-class TSARTransformer(nn.Module):
- def __init__(self, width=384, depth=6, n_head=6):
- super(TSARTransformer, self).__init__()
-
- self.encoder = Encoder(length=200, codes=50364, width=width, n_head=n_head, depth=depth)
- self.decoder = Decoder(length=1500, codes=1024, width=width, n_head=n_head, depth=depth)
-
- def forward(self, Ttoks, Stoks, loss=True):
- with record_function("encoder"):
- xenc = self.encoder(Ttoks.to(torch.long))
- with record_function("decoder"):
- logits = self.decoder(Stoks, xenc)
- if loss is not None:
- with record_function("loss"):
- loss = F.cross_entropy(logits.reshape(-1,logits.shape[-1]), Stoks.view(-1))
- return logits, loss
-
-# %% ../nbs/5. Text to semantic token modeling.ipynb 22
-def make_model(size):
- if size == 'micro':
- return TSARTransformer(depth=3)
- elif size == 'tiny':
- return TSARTransformer(depth=4)
