2018-02-13 16:33:14 +00:00
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{
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"cells": [
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{
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"cell_type": "code",
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2018-04-13 12:09:14 +00:00
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"execution_count": 1,
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2018-02-13 16:33:14 +00:00
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"metadata": {},
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2018-04-13 12:09:14 +00:00
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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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2018-02-13 16:33:14 +00:00
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"import os\n",
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"import sys\n",
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"import io\n",
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"import torch \n",
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"import time\n",
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"import numpy as np\n",
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"from collections import OrderedDict\n",
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"\n",
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"%pylab inline\n",
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"rcParams[\"figure.figsize\"] = (16,5)\n",
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"sys.path.append('/home/erogol/projects/')\n",
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"\n",
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"import librosa\n",
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"import librosa.display\n",
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"\n",
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2018-02-23 14:20:22 +00:00
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"from torchviz import make_dot, make_dot_from_trace\n",
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"\n",
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2018-02-13 16:33:14 +00:00
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"from TTS.models.tacotron import Tacotron \n",
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"from TTS.layers import *\n",
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"from TTS.utils.data import *\n",
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"from TTS.utils.audio import AudioProcessor\n",
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"from TTS.utils.generic_utils import load_config\n",
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"from TTS.utils.text import text_to_sequence\n",
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"\n",
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"import IPython\n",
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"from IPython.display import Audio\n",
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"from utils import *"
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]
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},
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{
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"cell_type": "code",
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2018-04-13 12:09:14 +00:00
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"execution_count": 2,
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2018-04-10 16:37:15 +00:00
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"metadata": {},
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2018-02-13 16:33:14 +00:00
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"outputs": [],
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"source": [
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"def tts(model, text, CONFIG, use_cuda, ap, figures=True):\n",
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" t_1 = time.time()\n",
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" waveform, alignment, spectrogram = create_speech(model, text, CONFIG, use_cuda, ap) \n",
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" print(\" > Run-time: {}\".format(time.time() - t_1))\n",
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" if figures: \n",
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" visualize(alignment, spectrogram, CONFIG) \n",
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" IPython.display.display(Audio(waveform, rate=CONFIG.sample_rate)) \n",
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" return alignment, spectrogram"
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]
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},
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{
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"cell_type": "code",
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2018-04-13 12:09:14 +00:00
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"execution_count": 3,
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2018-04-10 16:37:15 +00:00
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"metadata": {},
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2018-02-13 16:33:14 +00:00
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"outputs": [],
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"source": [
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"# Set constants\n",
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2018-04-13 12:09:14 +00:00
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"ROOT_PATH = '/data/shared/erogol_models/April-12-2018_06:00AM-06d4b23/'\n",
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"MODEL_PATH = ROOT_PATH + '/checkpoint_118440.pth.tar'\n",
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2018-02-13 16:33:14 +00:00
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"CONFIG_PATH = ROOT_PATH + '/config.json'\n",
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"OUT_FOLDER = ROOT_PATH + '/test/'\n",
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"CONFIG = load_config(CONFIG_PATH)\n",
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"use_cuda = False"
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]
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},
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{
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"cell_type": "code",
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2018-04-13 12:09:14 +00:00
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"execution_count": 4,
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2018-02-13 16:33:14 +00:00
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"metadata": {},
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2018-04-13 12:09:14 +00:00
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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",
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" (activation): ReLU()\n",
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" )\n",
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" (3): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(4,), stride=(1,), padding=(2,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (4): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (5): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(6,), stride=(1,), padding=(3,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (6): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (7): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(8,), stride=(1,), padding=(4,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (8): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (9): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(10,), stride=(1,), padding=(5,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (10): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(11,), stride=(1,), padding=(5,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (11): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(12,), stride=(1,), padding=(6,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (12): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(13,), stride=(1,), padding=(6,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (13): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(14,), stride=(1,), padding=(7,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (14): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(15,), stride=(1,), padding=(7,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" (15): BatchNormConv1d(\n",
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" (conv1d): Conv1d (128, 128, kernel_size=(16,), stride=(1,), padding=(8,), bias=False)\n",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" (activation): ReLU()\n",
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" )\n",
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" )\n",
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" (max_pool1d): MaxPool1d(kernel_size=2, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
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" (conv1d_projections): ModuleList(\n",
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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",
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" (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n",
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" )\n",
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" )\n",
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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",
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" (H): Linear(in_features=128, out_features=128)\n",
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" (T): Linear(in_features=128, out_features=128)\n",
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" (relu): ReLU()\n",
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" (sigmoid): Sigmoid()\n",
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" )\n",
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" (1): Highway(\n",
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" (H): Linear(in_features=128, out_features=128)\n",
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" (T): Linear(in_features=128, out_features=128)\n",
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" (relu): ReLU()\n",
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" (sigmoid): Sigmoid()\n",
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" )\n",
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" (2): Highway(\n",
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" (H): Linear(in_features=128, out_features=128)\n",
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" (T): Linear(in_features=128, out_features=128)\n",
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" (relu): ReLU()\n",
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" (sigmoid): Sigmoid()\n",
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" )\n",
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" (3): Highway(\n",
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" (H): Linear(in_features=128, out_features=128)\n",
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" (T): Linear(in_features=128, out_features=128)\n",
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" (relu): ReLU()\n",
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" (sigmoid): Sigmoid()\n",
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" )\n",
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" )\n",
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" (gru): GRU(128, 128, batch_first=True, bidirectional=True)\n",
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" )\n",
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" )\n",
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" (decoder): Decoder(\n",
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" (prenet): Prenet(\n",
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" (layers): ModuleList(\n",
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" (0): Linear(in_features=400, out_features=256)\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",
|
|
|
|
|
" (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)\n",
|
|
|
|
|
" (activation): ReLU()\n",
|
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|
|
" )\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)\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)\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)\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)\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)\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)\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)\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)\n",
|
|
|
|
|
" )\n",
|
|
|
|
|
" )\n",
|
|
|
|
|
" (pre_highway): Linear(in_features=80, out_features=80)\n",
|
|
|
|
|
" (highways): ModuleList(\n",
|
|
|
|
|
" (0): Highway(\n",
|
|
|
|
|
" (H): Linear(in_features=80, out_features=80)\n",
|
|
|
|
|
" (T): Linear(in_features=80, out_features=80)\n",
|
|
|
|
|
" (relu): ReLU()\n",
|
|
|
|
|
" (sigmoid): Sigmoid()\n",
|
|
|
|
|
" )\n",
|
|
|
|
|
" (1): Highway(\n",
|
|
|
|
|
" (H): Linear(in_features=80, out_features=80)\n",
|
|
|
|
|
" (T): Linear(in_features=80, out_features=80)\n",
|
|
|
|
|
" (relu): ReLU()\n",
|
|
|
|
|
" (sigmoid): Sigmoid()\n",
|
|
|
|
|
" )\n",
|
|
|
|
|
" (2): Highway(\n",
|
|
|
|
|
" (H): Linear(in_features=80, out_features=80)\n",
|
|
|
|
|
" (T): Linear(in_features=80, out_features=80)\n",
|
|
|
|
|
" (relu): ReLU()\n",
|
|
|
|
|
" (sigmoid): Sigmoid()\n",
|
|
|
|
|
" )\n",
|
|
|
|
|
" (3): Highway(\n",
|
|
|
|
|
" (H): Linear(in_features=80, out_features=80)\n",
|
|
|
|
|
" (T): Linear(in_features=80, out_features=80)\n",
|
|
|
|
|
" (relu): ReLU()\n",
|
|
|
|
|
" (sigmoid): Sigmoid()\n",
|
|
|
|
|
" )\n",
|
|
|
|
|
" )\n",
|
|
|
|
|
" (gru): GRU(80, 80, batch_first=True, bidirectional=True)\n",
|
|
|
|
|
" )\n",
|
|
|
|
|
" (last_linear): Linear(in_features=160, out_features=1025)\n",
|
|
|
|
|
")"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 4,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
}
|
|
|
|
|
],
|
2018-02-13 16:33:14 +00:00
|
|
|
"source": [
|
|
|
|
|
"# load the model\n",
|
2018-04-10 16:37:15 +00:00
|
|
|
"model = Tacotron(CONFIG.embedding_size, CONFIG.num_freq, CONFIG.num_mels, CONFIG.r)\n",
|
2018-02-13 16:33:14 +00:00
|
|
|
"\n",
|
|
|
|
|
"# load the audio processor\n",
|
|
|
|
|
"ap = AudioProcessor(CONFIG.sample_rate, CONFIG.num_mels, CONFIG.min_level_db,\n",
|
|
|
|
|
" CONFIG.frame_shift_ms, CONFIG.frame_length_ms, CONFIG.preemphasis,\n",
|
|
|
|
|
" CONFIG.ref_level_db, CONFIG.num_freq, CONFIG.power, griffin_lim_iters=80) \n",
|
|
|
|
|
"\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"# load model state\n",
|
|
|
|
|
"if use_cuda:\n",
|
|
|
|
|
" cp = torch.load(MODEL_PATH)\n",
|
|
|
|
|
"else:\n",
|
|
|
|
|
" cp = torch.load(MODEL_PATH, map_location=lambda storage, loc: storage)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"# load the model\n",
|
|
|
|
|
"model.load_state_dict(cp['model'])\n",
|
|
|
|
|
"if use_cuda:\n",
|
|
|
|
|
" model.cuda()\n",
|
|
|
|
|
"model.eval()"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"source": [
|
|
|
|
|
"### EXAMPLES FROM TRAINING SET"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2018-04-13 12:09:14 +00:00
|
|
|
"execution_count": 5,
|
2018-04-10 16:37:15 +00:00
|
|
|
"metadata": {},
|
2018-02-13 16:33:14 +00:00
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"import pandas as pd\n",
|
2018-04-10 16:37:15 +00:00
|
|
|
"df = pd.read_csv('/data/shared/KeithIto/LJSpeech-1.0/metadata_val.csv', delimiter='|')"
|
2018-02-13 16:33:14 +00:00
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2018-04-13 12:09:14 +00:00
|
|
|
"execution_count": 6,
|
2018-02-13 16:33:14 +00:00
|
|
|
"metadata": {
|
2018-04-13 12:09:14 +00:00
|
|
|
"collapsed": true
|
2018-02-13 16:33:14 +00:00
|
|
|
},
|
2018-04-13 12:09:14 +00:00
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"Latona's findings were also confirmed by Ronald G. Wittmus, another FBI fingerprint expert.\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
"torch.Size([1, 400])\n",
|
|
|
|
|
" > Run-time: 8.135989665985107\n"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/html": [
|
|
|
|
|
"\n",
|
|
|
|
|
" <audio controls=\"controls\" >\n",
|
|
|
|
|
" <source src=\"data:audio/wav;base64,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" Your browser does not support the audio element.\n",
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" </audio>\n",
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" "
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],
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"text/plain": [
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"<IPython.lib.display.Audio object>"
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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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},
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{
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"data": {
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"image/png": "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"text/plain": [
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|
|
|
"<matplotlib.figure.Figure at 0x7f8cef1a9e48>"
|
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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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}
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],
|
2018-02-13 16:33:14 +00:00
|
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"source": [
|
2018-04-10 16:37:15 +00:00
|
|
|
"sentence = df.iloc[2, 1]\n",
|
2018-02-13 16:33:14 +00:00
|
|
|
"print(sentence)\n",
|
2018-04-10 16:37:15 +00:00
|
|
|
"model.decoder.max_decoder_steps = len(sentence)\n",
|
|
|
|
|
"align, spec = tts(model, sentence, CONFIG, use_cuda, ap)"
|
2018-02-13 16:33:14 +00:00
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|
]
|
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|
},
|
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{
|
|
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|
"cell_type": "markdown",
|
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|
"metadata": {},
|
|
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|
|
"source": [
|
2018-04-13 12:09:14 +00:00
|
|
|
"### Comparision with https://mycroft.ai/blog/available-voices/"
|
2018-02-13 16:33:14 +00:00
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|
]
|
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|
},
|
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{
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"cell_type": "code",
|
2018-04-13 12:09:14 +00:00
|
|
|
"execution_count": 7,
|
2018-02-13 16:33:14 +00:00
|
|
|
"metadata": {
|
2018-04-13 12:09:14 +00:00
|
|
|
"scrolled": true
|
2018-02-13 16:33:14 +00:00
|
|
|
},
|
2018-04-13 12:09:14 +00:00
|
|
|
"outputs": [
|
|
|
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|
{
|
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|
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|
"name": "stdout",
|
|
|
|
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"output_type": "stream",
|
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"text": [
|
|
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|
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" > Run-time: 17.50665545463562\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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" </audio>\n",
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f8cee965c18>"
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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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}
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],
|
2018-02-13 16:33:14 +00:00
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"source": [
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2018-04-13 12:09:14 +00:00
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"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",
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"model.decoder.max_decoder_steps = 250\n",
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"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"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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" > Run-time: 13.074267864227295\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",
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" </audio>\n",
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" "
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],
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"<IPython.lib.display.Audio object>"
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{
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"data": {
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f8cf0213828>"
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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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}
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],
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"source": [
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"sentence = \"Be a voice, not an echo.\" # 'echo' is not in training set. \n",
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"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"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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" > Run-time: 12.174984216690063\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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" </audio>\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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}
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],
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"source": [
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"sentence = \"The human voice is the most perfect instrument of all.\"\n",
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"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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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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" > Run-time: 20.04722023010254\n"
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]
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},
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{
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"\n",
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" <audio controls=\"controls\" >\n",
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" </audio>\n",
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" "
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"data": {
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f8cff7df390>"
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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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}
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],
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"source": [
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"sentence = \"I'm sorry Dave. I'm afraid I can't do that.\"\n",
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"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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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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" > Run-time: 19.729145765304565\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",
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" </audio>\n",
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" "
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],
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},
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{
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"data": {
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f8c2453bf60>"
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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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}
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],
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"source": [
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"sentence = \"This cake is great. It's so delicious and moist.\"\n",
|
2018-02-13 16:33:14 +00:00
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"alignment = tts(model, sentence, CONFIG, use_cuda, ap)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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}
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"nbformat_minor": 2
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}
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