{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline\n",
"# only display first 30 seconds\n",
"from pyannote.core.notebook import set_notebook_crop\n",
"from pyannote.core import Segment\n",
"set_notebook_crop(Segment(0, 30)) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Big Bang Theory TVD Plugin"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Install"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following command will install **The Big Bang Theory TVD plugin** (and **TVD** if it is missing)\n",
"```bash\n",
"pip install TVDTheBigBangTheory\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download all resources"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following command will download all resources for **The Big Bang Theory** into `/tmp/` directory.\n",
"```bash\n",
"python -m tvd.create metadata /tmp/ TheBigBangTheory\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Available resources"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Initialize TheBigBangTheory TVD plugin"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"IN CASE YOU USE 'speaker' RESOURCES, PLEASE CONSIDER CITING:\n",
"@inproceedings{Tapaswi2012\n",
" title = {{``Knock! Knock! Who is it?'' Probabilistic Person Identification in TV Series}},\n",
" author = {Makarand Tapaswi and Martin B\\\"{a}uml and Rainer Stiefelhagen},\n",
" booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},\n",
" year = {2012},\n",
" month = {June},\n",
"}\n",
"\n",
"\n",
"IN CASE YOU USE 'outline' RESOURCES, PLEASE CONSIDER CITING:\n",
"@misc{the-big-bang-theory.com,\n",
" title = {{The Big Bang Theory Wiki}},\n",
" howpublished = \\url{http://wiki.the-big-bang-theory.com/}\n",
"}\n",
"\n",
"\n",
"IN CASE YOU USE 'transcript' RESOURCES, PLEASE CONSIDER CITING:\n",
"@misc{bigbangtrans,\n",
" title = {{big bang theory transcripts}},\n",
" howpublished = \\url{http://bigbangtrans.wordpress.com/}\n",
"}\n",
"\n",
"\n",
"IN CASE YOU USE 'transcript_www' RESOURCES, PLEASE CONSIDER CITING:\n",
"@misc{bigbangtrans,\n",
" title = {{big bang theory transcripts}},\n",
" howpublished = \\url{http://bigbangtrans.wordpress.com/}\n",
"}\n",
"\n",
"\n",
"IN CASE YOU USE 'outline_www' RESOURCES, PLEASE CONSIDER CITING:\n",
"@misc{the-big-bang-theory.com,\n",
" title = {{The Big Bang Theory Wiki}},\n",
" howpublished = \\url{http://wiki.the-big-bang-theory.com/}\n",
"}\n",
"\n"
]
}
],
"source": [
"from tvd import TheBigBangTheory\n",
"theBigBangTheory = TheBigBangTheory('/tmp')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get first episode"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Episode(series='TheBigBangTheory', season=1, episode=1)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"episode = theBigBangTheory.episodes[0]\n",
"episode"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Outlines"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Source: http://wiki.the-big-bang-theory.com \n",
"Provides \n",
" - scene summary\n",
" - scene location"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n"
],
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"outline = theBigBangTheory.get_resource('outline', episode)\n",
"outline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transcripts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Source: http://foreverdreaming.org/ \n",
"Provides:\n",
"\n",
" - speaker label\n",
" - speech content\n",
"\n",
"Does not provide timestamps."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n"
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"transcript = theBigBangTheory.get_resource('transcript', episode)\n",
"transcript"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Force-aligned transcripts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Source: [LIMSI](http://www.limsi.fr) \n",
"Provides:\n",
"\n",
" - word-level timestamps (start & end time)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n"
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"transcript_aligned = theBigBangTheory.get_resource('transcript_aligned', episode)\n",
"transcript_aligned.crop(0, 30)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Speaker"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Source: [Makarand Tapaswi](https://cvhci.anthropomatik.kit.edu/~mtapaswi/projects/personid.html) \n",
"Provides:\n",
"\n",
" - speaker label with manual timestamps (five main characters + *other*)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"speaker = theBigBangTheory.get_resource('speaker', episode)\n",
"speaker"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}