In this notebook we illustrate the power of pliers converters and extractors in a single pipeline. Specifically, we first run a state-of-the-art speech recognition API to transcribe the text of an audio clip. Then, we run a sentiment analysis API to extract the emotion ratings of the spoken words. The audio clip of this example is a short clip of an Obama administration press conference.
Note: the analysis is not using any audio features to assist emotion extraction. It is simply only using the text transcribed from the audio
In [1]:
from pliers.tests.utils import get_test_data_path
from os.path import join
from pliers.stimuli import AudioStim
from pliers.graph import Graph
In [2]:
# Configure our stimulus and extraction graph
stim = AudioStim(join(get_test_data_path(), 'video', 'obama_speech.wav'))
nodes = [
{
'transformer':'IBMSpeechAPIConverter',
'parameters':{'resolution':'phrases'},
'children':[
{
'transformer':'IndicoAPITextExtractor',
'parameters':{'models':['emotion']}
}
]
}
]
graph = Graph(nodes)
Parameters:
IBMSpeechAPIConverter - resolution specifies how we should chunk the text; using phrases provides better results for emotion analysis, as opposed to word-by-word analysis
IndicoAPITextExtractor - models specifies which analysis models to run using the Indico API; 'emotion' will give back five emotion ratings (anger, joy, fear, sadness, surprise) of the text
In [3]:
results = graph.run(stim)
results
Out[3]:
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