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https://github.com/wassname/TTS.git
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guided attn
This commit is contained in:
Eren Golge committed
1 parent
ff54a4d39b
commit
3176b606ef
2 files changed
+867
-35
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@@ -2,9 +2,17 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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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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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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@@ -37,7 +45,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -53,7 +61,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -68,9 +76,274 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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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)\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",
|
||||
" (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",
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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)\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",
|
||||
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=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)\n",
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||||
" (highways): ModuleList(\n",
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||||
" (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",
|
||||
" )\n",
|
||||
" (1): 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",
|
||||
" (sigmoid): Sigmoid()\n",
|
||||
" )\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",
|
||||
" (sigmoid): Sigmoid()\n",
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||||
" )\n",
|
||||
" (3): 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",
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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",
|
||||
" (prenet): Prenet(\n",
|
||||
" (layers): ModuleList(\n",
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" (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): 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)\n",
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" (annot_layer): Linear(in_features=256, out_features=256)\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",
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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)\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)\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",
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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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" (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",
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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)\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)\n",
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" )\n",
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" )\n",
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" (pre_highway): Linear(in_features=80, out_features=80)\n",
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" (highways): ModuleList(\n",
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" (0): Highway(\n",
|
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" (H): Linear(in_features=80, out_features=80)\n",
|
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" (T): Linear(in_features=80, out_features=80)\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)\n",
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" (T): Linear(in_features=80, out_features=80)\n",
|
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" (relu): ReLU()\n",
|
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" (sigmoid): Sigmoid()\n",
|
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" )\n",
|
||||
" (2): Highway(\n",
|
||||
" (H): Linear(in_features=80, out_features=80)\n",
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" (T): Linear(in_features=80, out_features=80)\n",
|
||||
" (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)\n",
|
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" (T): Linear(in_features=80, out_features=80)\n",
|
||||
" (relu): ReLU()\n",
|
||||
" (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)\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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"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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@@ -103,7 +376,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -113,15 +386,49 @@
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},
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{
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"cell_type": "code",
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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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"execution_count": 6,
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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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"Latona's findings were also confirmed by Ronald G. Wittmus, another FBI fingerprint expert.\n",
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" > Run-time: 11.577292203903198\n"
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]
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},
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{
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"data": {
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"text/html": [
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"\n",
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" <audio controls=\"controls\" >\n",
|
||||
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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": [
|
||||
"<matplotlib.figure.Figure at 0x7fdb863bb358>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sentence = df.iloc[2, 1]\n",
|
||||
"print(sentence)\n",
|
||||
"model.decoder.max_decoder_steps = len(sentence)\n",
|
||||
"model.decoder.max_decoder_steps = 250\n",
|
||||
"align, spec = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
]
|
||||
},
|
||||
@@ -134,11 +441,46 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 11.755356550216675\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7fdb865257f0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"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",
|
||||
@@ -147,9 +489,44 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 4.867138147354126\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7fdb86261550>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sentence = \"Be a voice, not an echo.\" # 'echo' is not in training set. \n",
|
||||
"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
@@ -157,9 +534,45 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" !! Decoder stopped with 'max_decoder_steps'. Something is probably wrong.\n",
|
||||
" > Run-time: 29.618252754211426\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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"
|
||||
},
|
||||
{
|
||||
"data": {
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"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7fdb85ae52b0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sentence = \"The human voice is the most perfect instrument of all.\"\n",
|
||||
"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
@@ -167,9 +580,44 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 9.181858539581299\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <audio controls=\"controls\" >\n",
|
||||
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|
||||
" Your browser does not support the audio element.\n",
|
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||||
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||||
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|
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"metadata": {},
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"output_type": "display_data"
|
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7fdb861a6160>"
|
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]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sentence = \"I'm sorry Dave. I'm afraid I can't do that.\"\n",
|
||||
"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
@@ -177,9 +625,44 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 10.925827741622925\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
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"\n",
|
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" <audio controls=\"controls\" >\n",
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|
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truncated
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"text/plain": [
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]
|
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},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sentence = \"This cake is great. It's so delicious and moist.\"\n",
|
||||
"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
@@ -194,9 +677,44 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 8.598266839981079\n"
|
||||
]
|
||||
},
|
||||
{
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"data": {
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"\n",
|
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" <audio controls=\"controls\" >\n",
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truncated
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7fdb85b11e48>"
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]
|
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},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sentence = \"Generative adversarial network or variational auto-encoder.\"\n",
|
||||
"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
@@ -204,19 +722,89 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 14.10420560836792\n"
|
||||
]
|
||||
},
|
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{
|
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"data": {
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"\n",
|
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" <audio controls=\"controls\" >\n",
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7fdb86560240>"
|
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]
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},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sentence = \"Scientists at the CERN laboratory say they have discovered a new particle. \"\n",
|
||||
"sentence = \"Scientists at the CERN laboratory say they have discovered a new particle.\"\n",
|
||||
"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 12.206540584564209\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
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"\n",
|
||||
" <audio controls=\"controls\" >\n",
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"text/plain": [
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},
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"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sentence = \"here’s a way to measure the acute emotional intelligence that has never gone out of style.\"\n",
|
||||
"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
@@ -224,9 +812,44 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 9.559785842895508\n"
|
||||
]
|
||||
},
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{
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"data": {
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"\n",
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" <audio controls=\"controls\" >\n",
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|
||||
" Your browser does not support the audio element.\n",
|
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" </audio>\n",
|
||||
" "
|
||||
],
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||||
"text/plain": [
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"<IPython.lib.display.Audio object>"
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]
|
||||
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|
||||
"metadata": {},
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||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
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"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7fdb860977b8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sentence = \"President Trump met with other leaders at the Group of 20 conference.\"\n",
|
||||
"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
@@ -234,13 +857,203 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" > Run-time: 8.320220232009888\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
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"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"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7fdb85af7e80>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sentence = \"The buses aren't the problem, they actually provide a solution.\"\n",
|
||||
"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"69"
|
||||
]
|
||||
},
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"len(\"President Trump met with other leaders at the Group of 20 conference.\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(67, 74)"
|
||||
]
|
||||
},
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"alignment[0].shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0.5278257\n",
|
||||
"0.5526603\n",
|
||||
"0.9558787\n",
|
||||
"0.92132413\n",
|
||||
"0.81367326\n",
|
||||
"0.4858333\n",
|
||||
"0.5570528\n",
|
||||
"0.63596785\n",
|
||||
"0.8613073\n",
|
||||
"0.62610185\n",
|
||||
"0.5429014\n",
|
||||
"0.67617726\n",
|
||||
"0.6459313\n",
|
||||
"0.55958015\n",
|
||||
"0.3836815\n",
|
||||
"0.6041045\n",
|
||||
"0.6867788\n",
|
||||
"0.8378071\n",
|
||||
"0.79800814\n",
|
||||
"0.7517049\n",
|
||||
"0.72250843\n",
|
||||
"0.7627181\n",
|
||||
"0.95597124\n",
|
||||
"0.61695683\n",
|
||||
"0.7325122\n",
|
||||
"0.74086845\n",
|
||||
"0.8736562\n",
|
||||
"0.5194566\n",
|
||||
"0.920689\n",
|
||||
"0.5987127\n",
|
||||
"0.82588756\n",
|
||||
"0.63186014\n",
|
||||
"0.7214943\n",
|
||||
"0.8175821\n",
|
||||
"0.66797817\n",
|
||||
"0.7466511\n",
|
||||
"0.48579764\n",
|
||||
"0.5320349\n",
|
||||
"0.43590546\n",
|
||||
"0.36981428\n",
|
||||
"0.45671675\n",
|
||||
"0.6435481\n",
|
||||
"0.84083676\n",
|
||||
"0.61167735\n",
|
||||
"0.44248602\n",
|
||||
"0.8262451\n",
|
||||
"0.8528732\n",
|
||||
"0.6941957\n",
|
||||
"0.7164757\n",
|
||||
"0.58785707\n",
|
||||
"0.6581132\n",
|
||||
"0.66425616\n",
|
||||
"0.62861\n",
|
||||
"0.9175123\n",
|
||||
"0.7020908\n",
|
||||
"0.53642195\n",
|
||||
"0.51358885\n",
|
||||
"0.60365206\n",
|
||||
"0.8154945\n",
|
||||
"0.94948417\n",
|
||||
"0.57743055\n",
|
||||
"0.92082053\n",
|
||||
"0.44779623\n",
|
||||
"0.6089538\n",
|
||||
"0.44988328\n",
|
||||
"0.60405654\n",
|
||||
"0.46145117\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for i in range(alignment[0].shape[0]):\n",
|
||||
" a = alignment[0][i].max()\n",
|
||||
" print(a)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.image.AxesImage at 0x7fdb86305630>"
|
||||
]
|
||||
},
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7fdb8651ef60>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plt.imshow(alignment[0])"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
@@ -66,6 +66,7 @@ def train(model, criterion, data_loader, optimizer, epoch):
|
||||
epoch_time = 0
|
||||
avg_linear_loss = 0
|
||||
avg_mel_loss = 0
|
||||
avg_attn_loss = 0
|
||||
|
||||
print(" | > Epoch {}/{}".format(epoch, c.epochs))
|
||||
progbar = Progbar(len(data_loader.dataset) / c.batch_size)
|
||||
@@ -102,6 +103,18 @@ def train(model, criterion, data_loader, optimizer, epoch):
|
||||
mel_spec_var = mel_spec_var.cuda()
|
||||
mel_lengths_var = mel_lengths_var.cuda()
|
||||
linear_spec_var = linear_spec_var.cuda()
|
||||
|
||||
# create attention mask
|
||||
# TODO: vectorize
|
||||
M = np.zeros([N, T])
|
||||
for t in range(T):
|
||||
for n in range(N):
|
||||
val = 20 * np.exp(-pow((n/N)-(t/T), 2.0)/0.05)
|
||||
M[n, t] = val
|
||||
e_x = np.exp(M - np.max(M))
|
||||
M = e_x / e_x.sum(axis=0) # only difference
|
||||
M = Variable(M)
|
||||
M = torch.stack([M]*32)
|
||||
|
||||
# forward pass
|
||||
mel_output, linear_output, alignments =\
|
||||
@@ -113,7 +126,8 @@ def train(model, criterion, data_loader, optimizer, epoch):
|
||||
+ 0.5 * criterion(linear_output[:, :, :n_priority_freq],
|
||||
linear_spec_var[:, :, :n_priority_freq],
|
||||
mel_lengths_var)
|
||||
loss = mel_loss + linear_loss
|
||||
attention_loss = criterion(, alignments, mel_lengths_var)
|
||||
loss = mel_loss + linear_loss + 0.2 * attention_loss
|
||||
|
||||
# backpass and check the grad norm
|
||||
loss.backward()
|
||||
@@ -132,15 +146,18 @@ def train(model, criterion, data_loader, optimizer, epoch):
|
||||
('linear_loss',
|
||||
linear_loss.data[0]),
|
||||
('mel_loss', mel_loss.data[0]),
|
||||
('attn_loss', attention_loss.data[0]),
|
||||
('grad_norm', grad_norm)])
|
||||
avg_linear_loss += linear_loss.data[0]
|
||||
avg_mel_loss += mel_loss.data[0]
|
||||
avg_attn_loss += attention_loss.data[0]
|
||||
|
||||
# Plot Training Iter Stats
|
||||
tb.add_scalar('TrainIterLoss/TotalLoss', loss.data[0], current_step)
|
||||
tb.add_scalar('TrainIterLoss/LinearLoss', linear_loss.data[0],
|
||||
current_step)
|
||||
tb.add_scalar('TrainIterLoss/MelLoss', mel_loss.data[0], current_step)
|
||||
tb.add_scalar('TrainIterLoss/AttnLoss', attention_loss.data[0], current_step)
|
||||
tb.add_scalar('Params/LearningRate', optimizer.param_groups[0]['lr'],
|
||||
current_step)
|
||||
tb.add_scalar('Params/GradNorm', grad_norm, current_step)
|
||||
@@ -181,12 +198,14 @@ def train(model, criterion, data_loader, optimizer, epoch):
|
||||
|
||||
avg_linear_loss /= (num_iter + 1)
|
||||
avg_mel_loss /= (num_iter + 1)
|
||||
avg_attn_loss /= (num_iter + 1)
|
||||
avg_total_loss = avg_mel_loss + avg_linear_loss
|
||||
|
||||
# Plot Training Epoch Stats
|
||||
tb.add_scalar('TrainEpochLoss/TotalLoss', avg_total_loss, current_step)
|
||||
tb.add_scalar('TrainEpochLoss/LinearLoss', avg_linear_loss, current_step)
|
||||
tb.add_scalar('TrainEpochLoss/MelLoss', avg_mel_loss, current_step)
|
||||
tb.add_scalar('TrainEpochLoss/AttnLoss', avg_attn_loss, current_step)
|
||||
tb.add_scalar('Time/EpochTime', epoch_time, epoch)
|
||||
epoch_time = 0
|
||||
|
||||
|
||||
Reference in new issue
Block a user