mirror of
https://github.com/wassname/TTS.git
synced 2026-10-26 12:20:32 +08:00
notebook update
This commit is contained in:
Eren Golge committed
1 parent
c86a7f9152
commit
87f11ab941
1 file changed
+29
-783
@@ -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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@@ -45,7 +37,7 @@
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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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"outputs": [],
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"source": [
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@@ -61,7 +53,7 @@
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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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"outputs": [],
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"source": [
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@@ -76,274 +68,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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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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" | > Number of characted : 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, bias=True)\n",
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" (1): Linear(in_features=256, out_features=128, bias=True)\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, track_running_stats=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, track_running_stats=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, track_running_stats=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, track_running_stats=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, track_running_stats=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, track_running_stats=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, track_running_stats=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, track_running_stats=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, track_running_stats=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, track_running_stats=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, track_running_stats=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, track_running_stats=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, track_running_stats=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, track_running_stats=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, track_running_stats=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, track_running_stats=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",
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" (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, track_running_stats=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, track_running_stats=True)\n",
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" )\n",
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" )\n",
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" (pre_highway): Linear(in_features=128, out_features=128, bias=False)\n",
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" (highways): ModuleList(\n",
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" (0): Highway(\n",
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" (H): Linear(in_features=128, out_features=128, bias=True)\n",
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" (T): Linear(in_features=128, out_features=128, bias=True)\n",
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" (relu): ReLU()\n",
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" (sigmoid): Sigmoid()\n",
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" )\n",
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" (1): Highway(\n",
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" (H): Linear(in_features=128, out_features=128, bias=True)\n",
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" (T): Linear(in_features=128, out_features=128, bias=True)\n",
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" (relu): ReLU()\n",
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" (sigmoid): Sigmoid()\n",
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" )\n",
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" (2): Highway(\n",
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" (H): Linear(in_features=128, out_features=128, bias=True)\n",
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" (T): Linear(in_features=128, out_features=128, bias=True)\n",
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" (relu): ReLU()\n",
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" (sigmoid): Sigmoid()\n",
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" )\n",
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" (3): Highway(\n",
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" (H): Linear(in_features=128, out_features=128, bias=True)\n",
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" (T): Linear(in_features=128, out_features=128, bias=True)\n",
|
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" (relu): ReLU()\n",
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" (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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" (prenet): Prenet(\n",
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" (layers): ModuleList(\n",
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" (0): Linear(in_features=400, out_features=256, bias=True)\n",
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" (1): Linear(in_features=256, out_features=128, bias=True)\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): AttentionRNN(\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, bias=True)\n",
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" (annot_layer): Linear(in_features=256, out_features=256, bias=True)\n",
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" (v): Linear(in_features=256, out_features=1, bias=False)\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, bias=True)\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, bias=True)\n",
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" )\n",
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" (postnet): 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(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, track_running_stats=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(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, track_running_stats=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, track_running_stats=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",
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" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True, track_running_stats=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(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, track_running_stats=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, track_running_stats=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, track_running_stats=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, track_running_stats=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",
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" (0): BatchNormConv1d(\n",
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" (conv1d): Conv1d(640, 256, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
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" (bn): BatchNorm1d(256, eps=0.001, momentum=0.99, affine=True, track_running_stats=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(256, 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, track_running_stats=True)\n",
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" )\n",
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" )\n",
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" (pre_highway): Linear(in_features=80, out_features=80, bias=False)\n",
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" (highways): ModuleList(\n",
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||||
" (0): Highway(\n",
|
||||
" (H): Linear(in_features=80, out_features=80, bias=True)\n",
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" (T): Linear(in_features=80, out_features=80, bias=True)\n",
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" (relu): ReLU()\n",
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" (sigmoid): Sigmoid()\n",
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" )\n",
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" (1): Highway(\n",
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" (H): Linear(in_features=80, out_features=80, bias=True)\n",
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" (T): Linear(in_features=80, out_features=80, bias=True)\n",
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" (relu): ReLU()\n",
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" (sigmoid): Sigmoid()\n",
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" )\n",
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" (2): Highway(\n",
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" (H): Linear(in_features=80, out_features=80, bias=True)\n",
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" (T): Linear(in_features=80, out_features=80, bias=True)\n",
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" (relu): ReLU()\n",
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" (sigmoid): Sigmoid()\n",
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" )\n",
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" (3): Highway(\n",
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" (H): Linear(in_features=80, out_features=80, bias=True)\n",
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" (T): Linear(in_features=80, out_features=80, bias=True)\n",
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" (relu): ReLU()\n",
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" (sigmoid): Sigmoid()\n",
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" )\n",
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" )\n",
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" (gru): GRU(80, 80, batch_first=True, bidirectional=True)\n",
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" )\n",
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" (last_linear): Linear(in_features=160, out_features=1025, bias=True)\n",
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")"
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]
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},
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"execution_count": 8,
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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.num_freq, CONFIG.num_mels, CONFIG.r)\n",
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@@ -376,7 +103,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -386,59 +113,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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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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"Hosty took the necessary steps to have the Dallas office of the FBI, rather than the New Orleans office, reestablished as the office with principal responsibility.\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/projects/TTS/notebooks/utils.py:23: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
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" torch.from_numpy(seq), volatile=True).unsqueeze(0)\n"
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]
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},
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||||
{
|
||||
"name": "stdout",
|
||||
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|
||||
"text": [
|
||||
" > Run-time: 8.232513427734375\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
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|
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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x1152 with 4 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = df.iloc[175, 1]\n",
|
||||
"print(sentence)\n",
|
||||
@@ -455,54 +132,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/erogol/projects/TTS/notebooks/utils.py:23: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
|
||||
" torch.from_numpy(seq), volatile=True).unsqueeze(0)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 4.864278793334961\n"
|
||||
]
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|
||||
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|
||||
" </audio>\n",
|
||||
" "
|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
||||
},
|
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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x1152 with 4 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.\"\n",
|
||||
"model.decoder.max_decoder_steps = 250\n",
|
||||
@@ -511,52 +145,9 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/erogol/projects/TTS/notebooks/utils.py:23: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
|
||||
" torch.from_numpy(seq), volatile=True).unsqueeze(0)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 11.617222785949707\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
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}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"Be a voice, not an echo.\" # 'echo' is not in training set. \n",
|
||||
"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
@@ -564,52 +155,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": null,
|
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"metadata": {},
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"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
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||||
"text": [
|
||||
"/home/erogol/projects/TTS/notebooks/utils.py:23: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
|
||||
" torch.from_numpy(seq), volatile=True).unsqueeze(0)\n"
|
||||
]
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},
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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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" > Run-time: 7.855222225189209\n"
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]
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},
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|
||||
" Your browser does not support the audio element.\n",
|
||||
" </audio>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.lib.display.Audio object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x1152 with 4 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"The human voice is the most perfect instrument of all.\"\n",
|
||||
"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
@@ -617,52 +165,9 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/erogol/projects/TTS/notebooks/utils.py:23: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
|
||||
" torch.from_numpy(seq), volatile=True).unsqueeze(0)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 2.89864444732666\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
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"text/plain": [
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"<Figure size 1152x1152 with 4 Axes>"
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},
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"metadata": {},
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||||
"output_type": "display_data"
|
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}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"I'm sorry Dave. I'm afraid I can't do that.\"\n",
|
||||
"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
@@ -670,52 +175,9 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/erogol/projects/TTS/notebooks/utils.py:23: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
|
||||
" torch.from_numpy(seq), volatile=True).unsqueeze(0)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 6.8727028369903564\n"
|
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]
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},
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{
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"data": {
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|
||||
" Your browser does not support the audio element.\n",
|
||||
" </audio>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.lib.display.Audio object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
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"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x1152 with 4 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"This cake is great. It's so delicious and moist.\"\n",
|
||||
"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
@@ -730,52 +192,9 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/erogol/projects/TTS/notebooks/utils.py:23: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
|
||||
" torch.from_numpy(seq), volatile=True).unsqueeze(0)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 4.949292421340942\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
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],
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||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"Generative adversarial network or variational auto-encoder.\"\n",
|
||||
"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
|
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@@ -783,53 +202,9 @@
|
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},
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{
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"cell_type": "code",
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"execution_count": 25,
|
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"execution_count": null,
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"metadata": {},
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"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
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||||
"text": [
|
||||
"/home/erogol/projects/TTS/notebooks/utils.py:23: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
|
||||
" torch.from_numpy(seq), volatile=True).unsqueeze(0)\n"
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]
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},
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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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" !! Decoder stopped with 'max_decoder_steps'. Something is probably wrong.\n",
|
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" > Run-time: 19.892284870147705\n"
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]
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},
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|
||||
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|
||||
" Your browser does not support the audio element.\n",
|
||||
" </audio>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.lib.display.Audio object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x1152 with 4 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"Scientists at the CERN laboratory say they have discovered a new particle.\"\n",
|
||||
"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
@@ -837,52 +212,9 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/erogol/projects/TTS/notebooks/utils.py:23: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
|
||||
" torch.from_numpy(seq), volatile=True).unsqueeze(0)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 6.549621105194092\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
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],
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"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"here’s a way to measure the acute emotional intelligence that has never gone out of style.\"\n",
|
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"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
|
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@@ -890,52 +222,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"execution_count": null,
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"metadata": {},
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"outputs": [
|
||||
{
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"name": "stderr",
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"text": [
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"/home/erogol/projects/TTS/notebooks/utils.py:23: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
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" torch.from_numpy(seq), volatile=True).unsqueeze(0)\n"
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]
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},
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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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" > Run-time: 4.370303153991699\n"
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]
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},
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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x1152 with 4 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"President Trump met with other leaders at the Group of 20 conference.\"\n",
|
||||
"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
@@ -943,52 +232,9 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/erogol/projects/TTS/notebooks/utils.py:23: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
|
||||
" torch.from_numpy(seq), volatile=True).unsqueeze(0)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 4.006836891174316\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
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|
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"\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"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x1152 with 4 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"The buses aren't the problem, they actually provide a solution.\"\n",
|
||||
"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
|
||||
Reference in new issue
Block a user