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thomasfillon/Serialization example.ipynb

Created February 27, 2014 17:32
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TimeSide Serialization example
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Serialization example.ipynb
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"TimeSide Serialization example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For this example, we first define a Timeside pipe process that processes several audio analyzer on an audio file.\n",
"Then, we get back the results of the analysis and serialize them to json, xml, and yaml."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Running the process pipe"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import timeside\n",
"\n",
"# set-up an audio source and a decoder\\n\",\n",
"audio_file = '/home/thomas/data/CNRSMH_I_2013_401_001_13.wav'\n",
"decoder = timeside.decoder.FileDecoder(audio_file)\n",
"\n",
"# set-up a mp3 encoder\n",
"mp3encoder = timeside.encoder.Mp3Encoder(output='CNRSMH_I_2013_401_001_13.mp3', overwrite=True)\n",
"\n",
"# Set-up some analyzers\n",
"aubio_melenergy = timeside.analyzer.AubioMelEnergy()\n",
"aubio_pitch = timeside.analyzer.AubioPitch()\n",
"aubio_temporal = timeside.analyzer.AubioTemporal()\n",
"\n",
"# create a pipe to run all process\n",
"pipe = (decoder | mp3encoder | aubio_melenergy | aubio_pitch | aubio_temporal)\n",
"\n",
"# run the pipe\n",
"pipe.run()\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Get the results back"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The results of the process are stored both in the 'pipe' and in each analyzers in a structure equivalent to a python dictionary"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# All results are stored in the pipe\n",
"pipe.results.keys()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"['aubio_temporal.beat_confidence',\n",
" 'aubio_temporal.bpm',\n",
" 'aubio_temporal.onset',\n",
" 'aubio_temporal.onset_rate',\n",
" 'aubio_pitch.pitch',\n",
" 'aubio_temporal.beat',\n",
" 'aubio_melenergy',\n",
" 'aubio_pitch.pitch_confidence']"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Individual analyzer results are store in the analyzer\n",
"aubio_temporal.results.keys()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"['aubio_temporal.onset_rate',\n",
" 'aubio_temporal.onset',\n",
" 'aubio_temporal.bpm',\n",
" 'aubio_temporal.beat_confidence',\n",
" 'aubio_temporal.beat']"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"aubio_pitch.results.keys()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"['aubio_pitch.pitch_confidence', 'aubio_pitch.pitch']"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Dictionary-like structure of the results\n",
"aubio_temporal.results"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
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" 'aubio_temporal.beat_confidence': EventValueResult(id_metadata=IdMetadata(id='aubio_temporal.beat_confidence', name='onsets (aubio) Beat confidences', unit='', description='', date='2014-02-27 18:22:54', version='0.5.5', author='TimeSide', uuid='73af8092-4829-449b-93c0-05458abd44a3'), data_object=DataObject(value=array([ 0. , 0. , 0. ,\n",
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" 0.24618912, 0.14739227, 0.26480865, 0.42448807, 0.24898148,\n",
" 0.22904587, 0.27090836, 0.3937645 , 0.32113266, 0.69721222,\n",
" 0.23669052, 0.24058914, 0.27738953, 0.63422012, 0.03065491,\n",
" 0.10360718, 0.70750427, 0.45864105, 0.32244873, 0.32206726,\n",
" 0.2338028 , 0.11560059, 0.71644592, 0.23301697, 0.23972321,\n",
" 0.2927475 , 0.61489868, 0.11434937, 0.72716522, 0.24995422,\n",
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" 0.25238037, 0.43515015, 0.4509964 , 0.3144455 , 0.32194519,\n",
" 0.23916626, 0.13988495, 0.67956543, 0.24199677, 0.20222473,\n",
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" 0.24612427, 0.23535156, 0.3025589 , 0.32106781, 0.29031372,\n",
" 0.12916565, 0.70410156, 0.24267578, 0.18716431, 0.3214798 ,\n",
" 0.36103821, 0.2545166 , 0.14344788, 0.7067337 , 0.25219727,\n",
" 0.22079468, 0.30158997, 0.35509491, 0.22505951, 0.13437653,\n",
" 0.24385834, 0.46459961, 0.24211121, 0.2273941 , 0.29063416,\n",
" 0.37006378, 0.26009369, 0.10090637, 0.73809814, 0.24822998,\n",
" 0.22493744, 0.31238556, 0.32210541, 0.24539948, 0.135849 ,\n",
" 0.69184113, 0.24963379, 0.22186279, 0.03591156, 0.2547226 ,\n",
" 0.37924957, 0.23750305, 0.11442566, 0.23099518, 0.45911407,\n",
" 0.27016449, 0.21723175, 0.30297089, 0.38378143, 0.24415588,\n",
" 0.13478088, 0.25 , 0.43335724, 0.25927734, 0.21217346,\n",
" 0.32437897, 0.62698364, 0.11516571, 0.66478729, 0.24743652,\n",
" 0.20083618, 0.27796173, 0.6098175 , 0.0389328 , 0.10927582,\n",
" 0.73669434, 0.48165131, 0.32032013, 0.32032013])), audio_metadata=AudioMetadata(uri='file:///home/thomas/data/CNRSMH_I_2013_401_001_13.wav', start=0.0, duration=99.706666667, is_segment=False, channels=None, channelsManagement=''), parameters={})}"
]
}
],
"prompt_number": 5
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Serialization"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Serialize all the pipe results\n",
"pipe.results.to_json(output_file='ts_pipe_results.json')\n",
"pipe.results.to_yaml(output_file='ts_pipe_results.yaml')\n",
"pipe.results.to_xml(output_file='ts_pipe_results.xml')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Or serialize individual analyzer results\n",
"aubio_temporal.results.to_json() # it could also be serialized to a file"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
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