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Eren Golge committed 2018-05-16 05:09:15 -07:00
1 parent 70beccf328
commit 520ba9551f
1 file changed
+137 -383
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notebooks/TacotronPlayGround.ipynb
+137 -383
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@@ -2,13 +2,15 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 16,
"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"
]
},
@@ -55,40 +57,88 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"\u001b[01;31mcheckpoint_31584.pth.tar\u001b[0m \u001b[01;31mcheckpoint_752.pth.tar\u001b[0m\r\n",
"\u001b[01;31mcheckpoint_31960.pth.tar\u001b[0m \u001b[01;31mcheckpoint_7520.pth.tar\u001b[0m\r\n",
"\u001b[01;31mcheckpoint_32336.pth.tar\u001b[0m \u001b[01;31mcheckpoint_7896.pth.tar\u001b[0m\r\n",
"\u001b[01;31mcheckpoint_32712.pth.tar\u001b[0m \u001b[01;31mcheckpoint_8272.pth.tar\u001b[0m\r\n",
"\u001b[01;31mcheckpoint_33088.pth.tar\u001b[0m \u001b[01;31mcheckpoint_8648.pth.tar\u001b[0m\r\n",
"\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"
]
}
],
@@ -98,7 +148,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
@@ -114,12 +164,12 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# Set constants\n",
"ROOT_PATH = '/data/shared/erogol_models/May-14-2018_07:10PM-stop-token-separate-larger-lr-b2e09f9/'\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",
"CONFIG_PATH = ROOT_PATH + '/config.json'\n",
"OUT_FOLDER = ROOT_PATH + '/test/'\n",
@@ -129,7 +179,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -140,269 +190,17 @@
]
},
{
"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): BahdanauAttention(\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): Sequential(\n",
" (0): Linear(in_features=656, out_features=80, bias=True)\n",
" (1): ReLU()\n",
" (2): Linear(in_features=80, out_features=656, bias=True)\n",
" (3): ReLU()\n",
" (4): Linear(in_features=656, out_features=1, bias=True)\n",
" (5): 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": 5,
"metadata": {},
"output_type": "execute_result"
"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"
]
}
],
"source": [
@@ -438,7 +236,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
@@ -448,7 +246,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 22,
"metadata": {
"scrolled": false
},
@@ -472,7 +270,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
" > Run-time: 5.755854845046997\n"
" > Run-time: 6.3198254108428955\n"
]
},
{
@@ -480,7 +278,7 @@
"text/html": [
"\n",
" <audio controls=\"controls\" >\n",
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" "
@@ -494,7 +292,7 @@
},
{
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"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
@@ -519,7 +317,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 23,
"metadata": {
"scrolled": false
},
@@ -536,7 +334,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
" > Run-time: 4.917491436004639\n"
" > Run-time: 4.629634857177734\n"
]
},
{
@@ -544,7 +342,7 @@
"text/html": [
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" "
@@ -558,7 +356,7 @@
},
{
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truncated
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truncated
"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
@@ -575,7 +373,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 24,
"metadata": {},
"outputs": [
{
@@ -590,7 +388,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
" > Run-time: 1.1238880157470703\n"
" > Run-time: 1.2954251766204834\n"
]
},
{
@@ -598,7 +396,7 @@
"text/html": [
"\n",
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" "
@@ -612,7 +410,7 @@
},
{
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truncated
"image/png": 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truncated
"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
@@ -628,7 +426,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 25,
"metadata": {},
"outputs": [
{
@@ -643,7 +441,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
" > Run-time: 2.4607677459716797\n"
" > Run-time: 2.442563772201538\n"
]
},
{
@@ -651,7 +449,7 @@
"text/html": [
"\n",
" <audio controls=\"controls\" >\n",
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" </audio>\n",
" "
@@ -665,7 +463,7 @@
},
{
"data": {
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truncated
"image/png": "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 truncated
"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
@@ -681,7 +479,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 26,
"metadata": {},
"outputs": [
{
@@ -696,7 +494,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
" > Run-time: 6.053100109100342\n"
" > Run-time: 2.959503412246704\n"
]
},
{
@@ -704,7 +502,7 @@
"text/html": [
"\n",
" <audio controls=\"controls\" >\n",
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@@ -718,7 +516,7 @@
},
{
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truncated
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truncated
"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
@@ -734,7 +532,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 27,
"metadata": {},
"outputs": [
{
@@ -749,7 +547,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
" > Run-time: 2.6189444065093994\n"
" > Run-time: 3.3856382369995117\n"
]
},
{
@@ -757,7 +555,7 @@
"text/html": [
"\n",
" <audio controls=\"controls\" >\n",
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@@ -771,7 +569,7 @@
},
{
"data": {
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truncated
"image/png": "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 truncated
"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
@@ -794,7 +592,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 28,
"metadata": {},
"outputs": [
{
@@ -809,8 +607,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
" !! Decoder stopped with 'max_decoder_steps'. Something is probably wrong.\n",
" > Run-time: 13.515002012252808\n"
" > Run-time: 3.539504289627075\n"
]
},
{
@@ -818,7 +615,7 @@
"text/html": [
"\n",
" <audio controls=\"controls\" >\n",
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@@ -832,7 +629,7 @@
},
{
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truncated
"image/png": "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 truncated
"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
@@ -848,7 +645,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 29,
"metadata": {},
"outputs": [
{
@@ -863,7 +660,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
" > Run-time: 4.4747514724731445\n"
" > Run-time: 3.4460177421569824\n"
]
},
{
@@ -871,7 +668,7 @@
"text/html": [
"\n",
" <audio controls=\"controls\" >\n",
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@@ -885,7 +682,7 @@
},
{
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truncated
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truncated
"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
@@ -901,7 +698,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 30,
"metadata": {},
"outputs": [
{
@@ -916,7 +713,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
" > Run-time: 5.41440224647522\n"
" > Run-time: 3.5448789596557617\n"
]
},
{
@@ -924,7 +721,7 @@
"text/html": [
"\n",
" <audio controls=\"controls\" >\n",
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" "
@@ -938,7 +735,7 @@
},
{
"data": {
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truncated
"image/png": "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 truncated
"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
@@ -954,7 +751,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 31,
"metadata": {},
"outputs": [
{
@@ -969,7 +766,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
" > Run-time: 2.631229877471924\n"
" > Run-time: 2.8492112159729004\n"
]
},
{
@@ -977,7 +774,7 @@
"text/html": [
"\n",
" <audio controls=\"controls\" >\n",
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" </audio>\n",
" "
@@ -991,7 +788,7 @@
},
{
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truncated
"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
@@ -1007,52 +804,9 @@
},
{
"cell_type": "code",
"execution_count": 17,
"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: 3.031719923019409\n"
]
},
{
"data": {
"text/html": [
"\n",
" <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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truncated
"text/plain": [
"<Figure size 1152x1728 with 5 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"sentence = \"The buses aren't the problem, they actually provide a solution.\"\n",
"align, spec, stop_tokens = tts(model, sentence, CONFIG, use_cuda, ap)"