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update notebook
This commit is contained in:
Eren Golge committed
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commit
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@@ -2,17 +2,9 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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@@ -47,7 +39,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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@@ -65,14 +57,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Set constants\n",
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"ROOT_PATH = '../result/February-13-2018_01:04AM/'\n",
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"ROOT_PATH = '/data/shared/erogol_models/March-28-2018_06:24PM/'\n",
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"MODEL_PATH = ROOT_PATH + '/best_model.pth.tar'\n",
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"CONFIG_PATH = ROOT_PATH + '/config.json'\n",
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"OUT_FOLDER = ROOT_PATH + '/test/'\n",
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@@ -82,279 +74,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" | > Embedding dim : 149\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"Tacotron(\n",
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" (embedding): Embedding(149, 256)\n",
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" (encoder): Encoder(\n",
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" (prenet): Prenet(\n",
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" (layers): ModuleList(\n",
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" (0): Linear(in_features=256, out_features=256)\n",
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" (1): Linear(in_features=256, out_features=128)\n",
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" )\n",
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" (relu): ReLU()\n",
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" (dropout): Dropout(p=0.5)\n",
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" )\n",
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" (cbhg): CBHG(\n",
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" (relu): ReLU()\n",
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" (conv1d_banks): ModuleList(\n",
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" (0): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(1,), stride=(1,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (1): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(2,), stride=(1,), padding=(1,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (2): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (3): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(4,), stride=(1,), padding=(2,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (4): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (5): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(6,), stride=(1,), padding=(3,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (6): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (7): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(8,), stride=(1,), padding=(4,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (8): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (9): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(10,), stride=(1,), padding=(5,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (10): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(11,), stride=(1,), padding=(5,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (11): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(12,), stride=(1,), padding=(6,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (12): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(13,), stride=(1,), padding=(6,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (13): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(14,), stride=(1,), padding=(7,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (14): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(15,), stride=(1,), padding=(7,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (15): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(16,), stride=(1,), padding=(8,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" )\n",
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" (max_pool1d): MaxPool1d(kernel_size=2, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
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" (conv1d_projections): ModuleList(\n",
|
||||
" (0): BatchNormConv1d(\n",
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" (conv1d): Conv1d (2048, 128, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
|
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" (activation): ReLU()\n",
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" )\n",
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" (1): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
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||||
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" )\n",
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" )\n",
|
||||
" (pre_highway): Linear(in_features=128, out_features=128)\n",
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" (highways): ModuleList(\n",
|
||||
" (0): Highway(\n",
|
||||
" (H): Linear(in_features=128, out_features=128)\n",
|
||||
" (T): Linear(in_features=128, out_features=128)\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (sigmoid): Sigmoid()\n",
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||||
" )\n",
|
||||
" (1): Highway(\n",
|
||||
" (H): Linear(in_features=128, out_features=128)\n",
|
||||
" (T): Linear(in_features=128, out_features=128)\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (sigmoid): Sigmoid()\n",
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||||
" )\n",
|
||||
" (2): Highway(\n",
|
||||
" (H): Linear(in_features=128, out_features=128)\n",
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" (T): Linear(in_features=128, out_features=128)\n",
|
||||
" (relu): ReLU()\n",
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" (sigmoid): Sigmoid()\n",
|
||||
" )\n",
|
||||
" (3): Highway(\n",
|
||||
" (H): Linear(in_features=128, out_features=128)\n",
|
||||
" (T): Linear(in_features=128, out_features=128)\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (sigmoid): Sigmoid()\n",
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" )\n",
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" )\n",
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" (gru): GRU(128, 128, batch_first=True, bidirectional=True)\n",
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" )\n",
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" )\n",
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" (decoder): Decoder(\n",
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||||
" (input_layer): Linear(in_features=256, out_features=256)\n",
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" (prenet): Prenet(\n",
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||||
" (layers): ModuleList(\n",
|
||||
" (0): Linear(in_features=400, out_features=256)\n",
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||||
" (1): Linear(in_features=256, out_features=128)\n",
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" )\n",
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" (relu): ReLU()\n",
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" (dropout): Dropout(p=0.5)\n",
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" )\n",
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" (attention_rnn): AttentionWrapper(\n",
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" (rnn_cell): GRUCell(384, 256)\n",
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" (alignment_model): BahdanauAttention(\n",
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" (query_layer): Linear(in_features=256, out_features=256)\n",
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" (tanh): Tanh()\n",
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" (v): Linear(in_features=256, out_features=1)\n",
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" )\n",
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" )\n",
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||||
" (project_to_decoder_in): Linear(in_features=512, out_features=256)\n",
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" (decoder_rnns): ModuleList(\n",
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" (0): GRUCell(256, 256)\n",
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" (1): GRUCell(256, 256)\n",
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" )\n",
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" (proj_to_mel): Linear(in_features=256, out_features=400)\n",
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" )\n",
|
||||
" (postnet): CBHG(\n",
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" (relu): ReLU()\n",
|
||||
" (conv1d_banks): ModuleList(\n",
|
||||
" (0): BatchNormConv1d(\n",
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||||
" (conv1d): Conv1d (80, 80, kernel_size=(1,), stride=(1,), bias=False)\n",
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||||
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True)\n",
|
||||
" (activation): ReLU()\n",
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" )\n",
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||||
" (1): BatchNormConv1d(\n",
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" (conv1d): Conv1d (80, 80, kernel_size=(2,), stride=(1,), padding=(1,), bias=False)\n",
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" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (2): BatchNormConv1d(\n",
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" (conv1d): Conv1d (80, 80, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
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" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True)\n",
|
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" (activation): ReLU()\n",
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" )\n",
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" (3): BatchNormConv1d(\n",
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" (conv1d): Conv1d (80, 80, kernel_size=(4,), stride=(1,), padding=(2,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True)\n",
|
||||
" (activation): ReLU()\n",
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" )\n",
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" (4): BatchNormConv1d(\n",
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" (conv1d): Conv1d (80, 80, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)\n",
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" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (5): BatchNormConv1d(\n",
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" (conv1d): Conv1d (80, 80, kernel_size=(6,), stride=(1,), padding=(3,), bias=False)\n",
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" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True)\n",
|
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" (activation): ReLU()\n",
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" )\n",
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||||
" (6): BatchNormConv1d(\n",
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" (conv1d): Conv1d (80, 80, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)\n",
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" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True)\n",
|
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" (activation): ReLU()\n",
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" )\n",
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||||
" (7): BatchNormConv1d(\n",
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" (conv1d): Conv1d (80, 80, kernel_size=(8,), stride=(1,), padding=(4,), bias=False)\n",
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" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True)\n",
|
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" (activation): ReLU()\n",
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" )\n",
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" )\n",
|
||||
" (max_pool1d): MaxPool1d(kernel_size=2, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
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" (conv1d_projections): ModuleList(\n",
|
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" (0): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d (640, 256, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(256, eps=0.001, momentum=0.99, affine=True)\n",
|
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" (activation): ReLU()\n",
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" )\n",
|
||||
" (1): BatchNormConv1d(\n",
|
||||
" (conv1d): Conv1d (256, 80, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
|
||||
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True)\n",
|
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" )\n",
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" )\n",
|
||||
" (pre_highway): Linear(in_features=80, out_features=80)\n",
|
||||
" (highways): ModuleList(\n",
|
||||
" (0): Highway(\n",
|
||||
" (H): Linear(in_features=80, out_features=80)\n",
|
||||
" (T): Linear(in_features=80, out_features=80)\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (sigmoid): Sigmoid()\n",
|
||||
" )\n",
|
||||
" (1): Highway(\n",
|
||||
" (H): Linear(in_features=80, out_features=80)\n",
|
||||
" (T): Linear(in_features=80, out_features=80)\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (sigmoid): Sigmoid()\n",
|
||||
" )\n",
|
||||
" (2): Highway(\n",
|
||||
" (H): Linear(in_features=80, out_features=80)\n",
|
||||
" (T): Linear(in_features=80, out_features=80)\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (sigmoid): Sigmoid()\n",
|
||||
" )\n",
|
||||
" (3): Highway(\n",
|
||||
" (H): Linear(in_features=80, out_features=80)\n",
|
||||
" (T): Linear(in_features=80, out_features=80)\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (sigmoid): Sigmoid()\n",
|
||||
" )\n",
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||||
" )\n",
|
||||
" (gru): GRU(80, 80, batch_first=True, bidirectional=True)\n",
|
||||
" )\n",
|
||||
" (last_linear): Linear(in_features=160, out_features=1025)\n",
|
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")"
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]
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},
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"execution_count": 4,
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"metadata": {},
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||||
"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
|
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"# load the model\n",
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"model = Tacotron(CONFIG.embedding_size, CONFIG.hidden_size,\n",
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" CONFIG.num_mels, CONFIG.num_freq, CONFIG.r)\n",
|
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"model = Tacotron(CONFIG.embedding_size, CONFIG.num_mels, CONFIG.num_freq, CONFIG.r)\n",
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"\n",
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"# load the audio processor\n",
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"ap = AudioProcessor(CONFIG.sample_rate, CONFIG.num_mels, CONFIG.min_level_db,\n",
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@@ -391,7 +116,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": null,
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"metadata": {
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"collapsed": true
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||||
},
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@@ -403,69 +128,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": null,
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"metadata": {
|
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"scrolled": false
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},
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"outputs": [
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{
|
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
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"that he has a 5 an 8 or a 3 before him unless the press work is of the best:\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
|
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"/home/erogol/miniconda3/envs/pytorch/lib/python3.6/site-packages/librosa/util/utils.py:1725: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
|
||||
" if np.issubdtype(x.dtype, float) or np.issubdtype(x.dtype, complex):\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 8.921625852584839\n"
|
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]
|
||||
},
|
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{
|
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"name": "stderr",
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"output_type": "stream",
|
||||
"text": [
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"/home/erogol/miniconda3/envs/pytorch/lib/python3.6/site-packages/librosa/display.py:656: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
|
||||
" if np.issubdtype(data.dtype, np.complex):\n"
|
||||
]
|
||||
},
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||||
"text/plain": [
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"<matplotlib.figure.Figure at 0x7f11b28ab5f8>"
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]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = df.iloc[120, 1].lower().replace(',','')\n",
|
||||
"print(sentence)\n",
|
||||
@@ -481,62 +148,11 @@
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"/home/erogol/miniconda3/envs/pytorch/lib/python3.6/site-packages/librosa/util/utils.py:1725: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
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"/home/erogol/miniconda3/envs/pytorch/lib/python3.6/site-packages/librosa/display.py:656: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
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" if np.issubdtype(data.dtype, np.complex):\n"
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7fb03226fb38>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"Will Donald Trump Jr. offer the country’s business leaders a peek into a new U.S.-India relationship in trade? Defense? Terrorism?\"\n",
|
||||
"model.decoder.max_decoder_steps = 300\n",
|
||||
|
||||
@@ -1,212 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"import sys\n",
|
||||
"import io\n",
|
||||
"import torch \n",
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"from collections import OrderedDict\n",
|
||||
"\n",
|
||||
"%pylab inline\n",
|
||||
"rcParams[\"figure.figsize\"] = (16,5)\n",
|
||||
"sys.path.append('/home/erogol/projects/')\n",
|
||||
"\n",
|
||||
"import librosa\n",
|
||||
"import librosa.display\n",
|
||||
"\n",
|
||||
"from TTS.models.tacotron import Tacotron \n",
|
||||
"from TTS.layers import *\n",
|
||||
"from TTS.utils.data import *\n",
|
||||
"from TTS.utils.audio import AudioProcessor\n",
|
||||
"from TTS.utils.generic_utils import load_config\n",
|
||||
"from TTS.utils.text import text_to_sequence\n",
|
||||
"\n",
|
||||
"import IPython\n",
|
||||
"from IPython.display import Audio\n",
|
||||
"from utils import visualize"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Constants\n",
|
||||
"ROOT_PATH = '../result/February-03-2018_08:32PM/'\n",
|
||||
"MODEL_PATH = ROOT_PATH + '/checkpoint_3800.pth.tar'\n",
|
||||
"CONFIG_PATH = ROOT_PATH + '/config.json'\n",
|
||||
"OUT_FOLDER = ROOT_PATH + '/test/'\n",
|
||||
"CONFIG = load_config(CONFIG_PATH)\n",
|
||||
"use_cuda = False\n",
|
||||
"hop_length = 250"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"(13099, 3)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# read dataset meta file\n",
|
||||
"WAV_PATH = '/data/shared/KeithIto/LJSpeech-1.0/wavs/'\n",
|
||||
"df = pd.read_csv('/data/shared/KeithIto/LJSpeech-1.0/metadata.csv', delimiter='|')\n",
|
||||
"print(df.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ap = AudioProcessor(CONFIG.sample_rate, CONFIG.num_mels, CONFIG.min_level_db,\n",
|
||||
" CONFIG.frame_shift_ms, CONFIG.frame_length_ms, CONFIG.preemphasis,\n",
|
||||
" CONFIG.ref_level_db, CONFIG.num_freq, CONFIG.power, griffin_lim_iters=80)\n",
|
||||
"\n",
|
||||
"def compute_spectrogram(file_name):\n",
|
||||
" w = librosa.load(file_name, CONFIG.sample_rate)[0]\n",
|
||||
" spec = ap.spectrogram(w)\n",
|
||||
" return spec, w\n",
|
||||
" \n",
|
||||
"def plot_spectrogram(spec):\n",
|
||||
" figure(figsize=(16, 8))\n",
|
||||
" librosa.display.specshow(spec.T, sr=CONFIG.sample_rate,\n",
|
||||
" hop_length=hop_length, x_axis='time',\n",
|
||||
" y_axis='linear')\n",
|
||||
" xlabel('Time', fontsize=16)\n",
|
||||
" ylabel(\"HZ\", fontsize=16)\n",
|
||||
" tight_layout()\n",
|
||||
" colorbar()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"file_name = df.iloc[1,0] \n",
|
||||
"file_name = WAV_PATH + file_name + '.wav'\n",
|
||||
"spec, w = compute_spectrogram(file_name)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7f2a5f0f9f28>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"spec2 = np.concatenate([spec, spec], axis=1)\n",
|
||||
"hop_length=hop_length*2\n",
|
||||
"plot_spectrogram(spec2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <audio controls=\"controls\" >\n",
|
||||
" <source src=\"data:audio/wav;base64,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 truncated
|
||||
" Your browser does not support the audio element.\n",
|
||||
" </audio>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.lib.display.Audio object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"IPython.display.display(Audio(w, rate=CONFIG.sample_rate))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7f2a5e368f60>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plot_spectrogram(spec)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Reference in new issue
Block a user