From the course: AI Workshop: Building AI Applications with Hugging Face Models
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Post-processing and transcription - Hugging Face Tutorial
From the course: AI Workshop: Building AI Applications with Hugging Face Models
Post-processing and transcription
- [Instructor] So we'll go ahead and start cleaning up words that we don't need. So we'll go ahead and define a new function for clean_transcription, and we'll pass in our text variable. And essentially what this is going to do is just help us remove some filler words. And in your text, if you don't have any filler words, then you can try and remove something else. Let's say you have some words that you just don't want in your text. Well, you can use this to just remove them. Essentially, there's a lot of different scenarios where this can be useful, not including filler words. So in our case, there was a lot of "A"s, which was actually representing "DO." We also had, "You know," as a bit of a filler, so we can get rid of that. We also had "Am," as in "A-M" over here, so we can get rid of that. "Basically" is another filler word. "Like" was used a few times, right here. "And like," instead of just, "and we're talking." And finally, "So ya." Now that we have gotten rid of these filler…
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