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notebook update

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This commit is contained in:
Eren Golge committed 2018-05-18 04:46:09 -07:00
1 parent 288a6b5b1d
commit 9ff240e0ea
1 file changed
+311 -365
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notebooks/TacotronPlayGround.ipynb
+311 -365
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@@ -2,15 +2,13 @@
"cells": [
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n",
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
@@ -57,98 +55,30 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[0m\u001b[01;31mbest_model.pth.tar\u001b[0m \u001b[01;31mcheckpoint_35344.pth.tar\u001b[0m\r\n",
"\u001b[01;31mcheckpoint_10152.pth.tar\u001b[0m \u001b[01;31mcheckpoint_35720.pth.tar\u001b[0m\r\n",
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"\u001b[01;31mcheckpoint_24816.pth.tar\u001b[0m \u001b[01;31mcheckpoint_50008.pth.tar\u001b[0m\r\n",
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"\u001b[01;31mcheckpoint_32336.pth.tar\u001b[0m \u001b[01;31mcheckpoint_7896.pth.tar\u001b[0m\r\n",
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"\u001b[01;31mcheckpoint_33464.pth.tar\u001b[0m \u001b[01;31mcheckpoint_9024.pth.tar\u001b[0m\r\n",
"\u001b[01;31mcheckpoint_3384.pth.tar\u001b[0m \u001b[01;31mcheckpoint_9400.pth.tar\u001b[0m\r\n",
"\u001b[01;31mcheckpoint_33840.pth.tar\u001b[0m \u001b[01;31mcheckpoint_9776.pth.tar\u001b[0m\r\n",
"\u001b[01;31mcheckpoint_34216.pth.tar\u001b[0m \u001b[01;34mcheckpoints\u001b[0m/\r\n",
"\u001b[01;31mcheckpoint_34592.pth.tar\u001b[0m config.json\r\n",
"\u001b[01;31mcheckpoint_34968.pth.tar\u001b[0m events.out.tfevents.1526350247.mlc1\r\n"
"\u001b[0m\u001b[01;31mcheckpoint_33464.pth.tar\u001b[0m \u001b[01;31mcheckpoint_36096.pth.tar\u001b[0m\r\n",
"\u001b[01;31mcheckpoint_33840.pth.tar\u001b[0m \u001b[01;31mcheckpoint_36472.pth.tar\u001b[0m\r\n",
"\u001b[01;31mcheckpoint_34216.pth.tar\u001b[0m \u001b[01;31mcheckpoint_36848.pth.tar\u001b[0m\r\n",
"\u001b[01;31mcheckpoint_34592.pth.tar\u001b[0m \u001b[01;31mcheckpoint_37224.pth.tar\u001b[0m\r\n",
"\u001b[01;31mcheckpoint_34968.pth.tar\u001b[0m \u001b[01;34mcheckpoints\u001b[0m/\r\n",
"\u001b[01;31mcheckpoint_35344.pth.tar\u001b[0m config.json\r\n",
"\u001b[01;31mcheckpoint_35720.pth.tar\u001b[0m events.out.tfevents.1526635608.mlc1\r\n"
]
}
],
"source": [
"ls /data/shared/erogol_models/May-14-2018_07:10PM-stop-token-separate-larger-lr-b2e09f9"
"ls /data/shared/erogol_models/May-18-2018_02:26AM-los_sen_attn-debug "
]
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -164,13 +94,13 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Set constants\n",
"ROOT_PATH = '/data/shared/erogol_models/May-15-2018_04:12PM-stop-token-rnn-40f1a3d/'\n",
"MODEL_PATH = ROOT_PATH + '/checkpoint_16920.pth.tar'\n",
"ROOT_PATH = '/data/shared/erogol_models/May-18-2018_02:26AM-los_sen_attn-debug/'\n",
"MODEL_PATH = ROOT_PATH + '/checkpoint_37224.pth.tar'\n",
"CONFIG_PATH = ROOT_PATH + '/config.json'\n",
"OUT_FOLDER = ROOT_PATH + '/test/'\n",
"CONFIG = load_config(CONFIG_PATH)\n",
@@ -179,7 +109,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -190,17 +120,269 @@
]
},
{
"ename": "TypeError",
"evalue": "super(type, obj): obj must be an instance or subtype of type",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-20-33e7f6a6cc7d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# load the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTacotron\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCONFIG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCONFIG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_freq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCONFIG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_mels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCONFIG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# load the audio processor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/projects/TTS/models/tacotron.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, embedding_dim, linear_dim, mel_dim, r, padding_idx)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" | > Number of characters : {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msymbols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormal_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEncoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membedding_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 20\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecoder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDecoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmel_dim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpostnet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCBHG\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmel_dim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mK\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprojections\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmel_dim\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/projects/TTS/layers/tacotron.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, in_features)\u001b[0m\n\u001b[1;32m 188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0min_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 190\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEncoder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 191\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprenet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPrenet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0min_features\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m128\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 192\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcbhg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCBHG\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mK\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprojections\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m128\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: super(type, obj): obj must be an instance or subtype of type"
]
"data": {
"text/plain": [
"Tacotron(\n",
" (embedding): Embedding(149, 256)\n",
" (encoder): Encoder(\n",
" (prenet): Prenet(\n",
" (layers): ModuleList(\n",
" (0): Linear(in_features=256, out_features=256, bias=True)\n",
" (1): Linear(in_features=256, out_features=128, bias=True)\n",
" )\n",
" (relu): ReLU()\n",
" (dropout): Dropout(p=0.5)\n",
" )\n",
" (cbhg): CBHG(\n",
" (relu): ReLU()\n",
" (conv1d_banks): ModuleList(\n",
" (0): BatchNormConv1d(\n",
" (conv1d): Conv1d(128, 128, kernel_size=(1,), stride=(1,), bias=False)\n",
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (1): BatchNormConv1d(\n",
" (conv1d): Conv1d(128, 128, kernel_size=(2,), stride=(1,), padding=(1,), bias=False)\n",
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (2): BatchNormConv1d(\n",
" (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, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (3): BatchNormConv1d(\n",
" (conv1d): Conv1d(128, 128, kernel_size=(4,), stride=(1,), padding=(2,), bias=False)\n",
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (4): BatchNormConv1d(\n",
" (conv1d): Conv1d(128, 128, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)\n",
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (5): BatchNormConv1d(\n",
" (conv1d): Conv1d(128, 128, kernel_size=(6,), stride=(1,), padding=(3,), bias=False)\n",
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (6): BatchNormConv1d(\n",
" (conv1d): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)\n",
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (7): BatchNormConv1d(\n",
" (conv1d): Conv1d(128, 128, kernel_size=(8,), stride=(1,), padding=(4,), bias=False)\n",
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (8): BatchNormConv1d(\n",
" (conv1d): Conv1d(128, 128, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)\n",
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (9): BatchNormConv1d(\n",
" (conv1d): Conv1d(128, 128, kernel_size=(10,), stride=(1,), padding=(5,), bias=False)\n",
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (10): BatchNormConv1d(\n",
" (conv1d): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,), bias=False)\n",
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (11): BatchNormConv1d(\n",
" (conv1d): Conv1d(128, 128, kernel_size=(12,), stride=(1,), padding=(6,), bias=False)\n",
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (12): BatchNormConv1d(\n",
" (conv1d): Conv1d(128, 128, kernel_size=(13,), stride=(1,), padding=(6,), bias=False)\n",
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (13): BatchNormConv1d(\n",
" (conv1d): Conv1d(128, 128, kernel_size=(14,), stride=(1,), padding=(7,), bias=False)\n",
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (14): BatchNormConv1d(\n",
" (conv1d): Conv1d(128, 128, kernel_size=(15,), stride=(1,), padding=(7,), bias=False)\n",
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (15): BatchNormConv1d(\n",
" (conv1d): Conv1d(128, 128, kernel_size=(16,), stride=(1,), padding=(8,), bias=False)\n",
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" )\n",
" (max_pool1d): MaxPool1d(kernel_size=2, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
" (conv1d_projections): ModuleList(\n",
" (0): BatchNormConv1d(\n",
" (conv1d): Conv1d(2048, 128, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (1): BatchNormConv1d(\n",
" (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, track_running_stats=True)\n",
" )\n",
" )\n",
" (pre_highway): Linear(in_features=128, out_features=128, bias=False)\n",
" (highways): ModuleList(\n",
" (0): Highway(\n",
" (H): Linear(in_features=128, out_features=128, bias=True)\n",
" (T): Linear(in_features=128, out_features=128, bias=True)\n",
" (relu): ReLU()\n",
" (sigmoid): Sigmoid()\n",
" )\n",
" (1): Highway(\n",
" (H): Linear(in_features=128, out_features=128, bias=True)\n",
" (T): Linear(in_features=128, out_features=128, bias=True)\n",
" (relu): ReLU()\n",
" (sigmoid): Sigmoid()\n",
" )\n",
" (2): Highway(\n",
" (H): Linear(in_features=128, out_features=128, bias=True)\n",
" (T): Linear(in_features=128, out_features=128, bias=True)\n",
" (relu): ReLU()\n",
" (sigmoid): Sigmoid()\n",
" )\n",
" (3): Highway(\n",
" (H): Linear(in_features=128, out_features=128, bias=True)\n",
" (T): Linear(in_features=128, out_features=128, bias=True)\n",
" (relu): ReLU()\n",
" (sigmoid): Sigmoid()\n",
" )\n",
" )\n",
" (gru): GRU(128, 128, batch_first=True, bidirectional=True)\n",
" )\n",
" )\n",
" (decoder): Decoder(\n",
" (prenet): Prenet(\n",
" (layers): ModuleList(\n",
" (0): Linear(in_features=400, out_features=256, bias=True)\n",
" (1): Linear(in_features=256, out_features=128, bias=True)\n",
" )\n",
" (relu): ReLU()\n",
" (dropout): Dropout(p=0.5)\n",
" )\n",
" (attention_rnn): AttentionRNN(\n",
" (rnn_cell): GRUCell(384, 256)\n",
" (alignment_model): LocationSensitiveAttention(\n",
" (loc_conv): Conv1d(2, 32, kernel_size=(31,), stride=(1,), padding=(15,), bias=False)\n",
" (loc_linear): Linear(in_features=32, out_features=256, bias=True)\n",
" (query_layer): Linear(in_features=256, out_features=256, bias=True)\n",
" (annot_layer): Linear(in_features=256, out_features=256, bias=True)\n",
" (v): Linear(in_features=256, out_features=1, bias=False)\n",
" )\n",
" )\n",
" (project_to_decoder_in): Linear(in_features=512, out_features=256, bias=True)\n",
" (decoder_rnns): ModuleList(\n",
" (0): GRUCell(256, 256)\n",
" (1): GRUCell(256, 256)\n",
" )\n",
" (proj_to_mel): Linear(in_features=256, out_features=400, bias=True)\n",
" (stopnet): StopNet(\n",
" (rnn): GRUCell(400, 400)\n",
" (relu): ReLU()\n",
" (linear): Linear(in_features=400, out_features=1, bias=True)\n",
" (sigmoid): Sigmoid()\n",
" )\n",
" )\n",
" (postnet): CBHG(\n",
" (relu): ReLU()\n",
" (conv1d_banks): ModuleList(\n",
" (0): BatchNormConv1d(\n",
" (conv1d): Conv1d(80, 80, kernel_size=(1,), stride=(1,), bias=False)\n",
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (1): BatchNormConv1d(\n",
" (conv1d): Conv1d(80, 80, kernel_size=(2,), stride=(1,), padding=(1,), bias=False)\n",
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (2): BatchNormConv1d(\n",
" (conv1d): Conv1d(80, 80, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (3): BatchNormConv1d(\n",
" (conv1d): Conv1d(80, 80, kernel_size=(4,), stride=(1,), padding=(2,), bias=False)\n",
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (4): BatchNormConv1d(\n",
" (conv1d): Conv1d(80, 80, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)\n",
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (5): BatchNormConv1d(\n",
" (conv1d): Conv1d(80, 80, kernel_size=(6,), stride=(1,), padding=(3,), bias=False)\n",
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (6): BatchNormConv1d(\n",
" (conv1d): Conv1d(80, 80, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)\n",
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (7): BatchNormConv1d(\n",
" (conv1d): Conv1d(80, 80, kernel_size=(8,), stride=(1,), padding=(4,), bias=False)\n",
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" )\n",
" (max_pool1d): MaxPool1d(kernel_size=2, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
" (conv1d_projections): ModuleList(\n",
" (0): BatchNormConv1d(\n",
" (conv1d): Conv1d(640, 256, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
" (bn): BatchNorm1d(256, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" (activation): ReLU()\n",
" )\n",
" (1): BatchNormConv1d(\n",
" (conv1d): Conv1d(256, 80, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
" (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (pre_highway): Linear(in_features=80, out_features=80, bias=False)\n",
" (highways): ModuleList(\n",
" (0): Highway(\n",
" (H): Linear(in_features=80, out_features=80, bias=True)\n",
" (T): Linear(in_features=80, out_features=80, bias=True)\n",
" (relu): ReLU()\n",
" (sigmoid): Sigmoid()\n",
" )\n",
" (1): Highway(\n",
" (H): Linear(in_features=80, out_features=80, bias=True)\n",
" (T): Linear(in_features=80, out_features=80, bias=True)\n",
" (relu): ReLU()\n",
" (sigmoid): Sigmoid()\n",
" )\n",
" (2): Highway(\n",
" (H): Linear(in_features=80, out_features=80, bias=True)\n",
" (T): Linear(in_features=80, out_features=80, bias=True)\n",
" (relu): ReLU()\n",
" (sigmoid): Sigmoid()\n",
" )\n",
" (3): Highway(\n",
" (H): Linear(in_features=80, out_features=80, bias=True)\n",
" (T): Linear(in_features=80, out_features=80, bias=True)\n",
" (relu): ReLU()\n",
" (sigmoid): Sigmoid()\n",
" )\n",
" )\n",
" (gru): GRU(80, 80, batch_first=True, bidirectional=True)\n",
" )\n",
" (last_linear): Linear(in_features=160, out_features=1025, bias=True)\n",
")"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
@@ -236,7 +418,7 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -246,7 +428,7 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 9,
"metadata": {
"scrolled": false
},
@@ -255,22 +437,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
"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"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/erogol/projects/TTS/notebooks/utils.py:19: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
" torch.from_numpy(seq), volatile=True).unsqueeze(0).cuda()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" > Run-time: 6.3198254108428955\n"
"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",
" > Run-time: 9.547307014465332\n"
]
},
{
@@ -278,7 +446,7 @@
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" "
@@ -292,7 +460,7 @@
},
{
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truncated
"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
@@ -317,24 +485,16 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 10,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/erogol/projects/TTS/notebooks/utils.py:19: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
" torch.from_numpy(seq), volatile=True).unsqueeze(0).cuda()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" > Run-time: 4.629634857177734\n"
" > Run-time: 5.965982913970947\n"
]
},
{
@@ -342,7 +502,7 @@
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" "
@@ -356,7 +516,7 @@
},
{
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truncated
"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
@@ -373,22 +533,14 @@
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/erogol/projects/TTS/notebooks/utils.py:19: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
" torch.from_numpy(seq), volatile=True).unsqueeze(0).cuda()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" > Run-time: 1.2954251766204834\n"
" > Run-time: 1.3593213558197021\n"
]
},
{
@@ -396,7 +548,7 @@
"text/html": [
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" "
@@ -410,7 +562,7 @@
},
{
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truncated
"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
@@ -426,22 +578,14 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/erogol/projects/TTS/notebooks/utils.py:19: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
" torch.from_numpy(seq), volatile=True).unsqueeze(0).cuda()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" > Run-time: 2.442563772201538\n"
" > Run-time: 3.3218443393707275\n"
]
},
{
@@ -449,7 +593,7 @@
"text/html": [
"\n",
" <audio controls=\"controls\" >\n",
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" Your browser does not support the audio element.\n",
" </audio>\n",
" "
@@ -463,7 +607,7 @@
},
{
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truncated
"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
@@ -479,22 +623,14 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/erogol/projects/TTS/notebooks/utils.py:19: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
" torch.from_numpy(seq), volatile=True).unsqueeze(0).cuda()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" > Run-time: 2.959503412246704\n"
" > Run-time: 3.3077054023742676\n"
]
},
{
@@ -502,7 +638,7 @@
"text/html": [
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" "
@@ -516,7 +652,7 @@
},
{
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truncated
"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
@@ -532,22 +668,14 @@
},
{
"cell_type": "code",
"execution_count": 27,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/erogol/projects/TTS/notebooks/utils.py:19: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
" torch.from_numpy(seq), volatile=True).unsqueeze(0).cuda()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" > Run-time: 3.3856382369995117\n"
" > Run-time: 3.859889507293701\n"
]
},
{
@@ -555,7 +683,7 @@
"text/html": [
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" <audio controls=\"controls\" >\n",
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" Your browser does not support the audio element.\n",
" </audio>\n",
" "
@@ -566,16 +694,6 @@
},
"metadata": {},
"output_type": "display_data"
},
{
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truncated
"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
@@ -592,52 +710,9 @@
},
{
"cell_type": "code",
"execution_count": 28,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/erogol/projects/TTS/notebooks/utils.py:19: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
" torch.from_numpy(seq), volatile=True).unsqueeze(0).cuda()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" > Run-time: 3.539504289627075\n"
]
},
{
"data": {
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},
{
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truncated
"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"sentence = \"Generative adversarial network or variational auto-encoder.\"\n",
"align, spec, stop_tokens = tts(model, sentence, CONFIG, use_cuda, ap)"
@@ -645,52 +720,9 @@
},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/erogol/projects/TTS/notebooks/utils.py:19: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
" torch.from_numpy(seq), volatile=True).unsqueeze(0).cuda()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" > Run-time: 3.4460177421569824\n"
]
},
{
"data": {
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"metadata": {},
"output_type": "display_data"
},
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truncated
"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"sentence = \"Scientists at the CERN laboratory say they have discovered a new particle.\"\n",
"align, spec, stop_tokens = tts(model, sentence, CONFIG, use_cuda, ap)"
@@ -698,52 +730,9 @@
},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/erogol/projects/TTS/notebooks/utils.py:19: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
" torch.from_numpy(seq), volatile=True).unsqueeze(0).cuda()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" > Run-time: 3.5448789596557617\n"
]
},
{
"data": {
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"\n",
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},
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truncated
"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"sentence = \"here’s a way to measure the acute emotional intelligence that has never gone out of style.\"\n",
"align, spec, stop_tokens = tts(model, sentence, CONFIG, use_cuda, ap)"
@@ -751,52 +740,9 @@
},
{
"cell_type": "code",
"execution_count": 31,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/erogol/projects/TTS/notebooks/utils.py:19: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
" torch.from_numpy(seq), volatile=True).unsqueeze(0).cuda()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" > Run-time: 2.8492112159729004\n"
]
},
{
"data": {
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"metadata": {},
"output_type": "display_data"
},
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"data": {
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truncated
"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"sentence = \"President Trump met with other leaders at the Group of 20 conference.\"\n",
"align, spec, stop_tokens = tts(model, sentence, CONFIG, use_cuda, ap)"