Family Guy Dialogue Timeline I made a second-by-second…
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Family Guy Dialogue Timeline
I made a second-by-second breakdown of the first episode of Family Guy so you can see on a timeline who is speaking through the 22 minute episode.
I made a timestamped transcript and built everything around that. For this first go-around, I grouped everybody that isn't a Griffin into the "Other" category.
From this dataset, you can see the total duration spoken by each character and how many lines they spoke.
|speaker|lines|mm:ss|
|:-|:-|:-|
|Brian|24|0:51|
|Chris|7|0:12|
|Lois|59|3:14|
|Meg|12|0:22|
|Peter|105|6:15|
|Stewie|20|1:18|
A "line" here is uninterrupted speech (there's a bit more to it, but that's the gist). A single row of the data, with timestamps (in seconds) from the audio transcription looks like this:
|start|end|duration\_in\_sec|line|character|
|:-|:-|:-|:-|:-|
|221.82|225.76|03.9|Lois, honey, I promise not a drop of alcohol is going to touch these lips tonight.|Peter|
It took a bit longer to clean up the transcription, but otherwise it was a great waste/use of my time!
Data is transcribed audio of the episode itself (I just converted it to mp3 and fed through turboscribe). I used Excel to clean up things and manually tag the speaker, and then Python/Pandas to clean it up and additional processing. The dashboard is in Tableau.
#education
source
I made a second-by-second breakdown of the first episode of Family Guy so you can see on a timeline who is speaking through the 22 minute episode.
I made a timestamped transcript and built everything around that. For this first go-around, I grouped everybody that isn't a Griffin into the "Other" category.
From this dataset, you can see the total duration spoken by each character and how many lines they spoke.
|speaker|lines|mm:ss|
|:-|:-|:-|
|Brian|24|0:51|
|Chris|7|0:12|
|Lois|59|3:14|
|Meg|12|0:22|
|Peter|105|6:15|
|Stewie|20|1:18|
A "line" here is uninterrupted speech (there's a bit more to it, but that's the gist). A single row of the data, with timestamps (in seconds) from the audio transcription looks like this:
|start|end|duration\_in\_sec|line|character|
|:-|:-|:-|:-|:-|
|221.82|225.76|03.9|Lois, honey, I promise not a drop of alcohol is going to touch these lips tonight.|Peter|
It took a bit longer to clean up the transcription, but otherwise it was a great waste/use of my time!
Data is transcribed audio of the episode itself (I just converted it to mp3 and fed through turboscribe). I used Excel to clean up things and manually tag the speaker, and then Python/Pandas to clean it up and additional processing. The dashboard is in Tableau.
#education
source
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