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Discover trending topics and level up your research game without having to trawl through hours of audio. Download transcripts for your favourite podcasts today.
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Tapesearch is a search engine that allows users to quickly search podcast transcripts and find exact moments in a recording without having to listen to hours of audio.
Unlock spoken content and explore a previously opaque source of information. Ideal for market research, open-source intelligence, podcasters, and the curious listener.
Instantly locate key discussions across podcasts. Search thousands of transcripts and jump to precise timestamps.
Stay ahead of podcast conversations. Get alerts within hours when your tracked topics appear in podcasts.
Extract insights through natural conversation. Ask questions across multiple podcasts and get comprehensive answers instantly.
Visualize conversation patterns over time. See how topics evolve across the podcast landscape.
Tapesearch can be used in various ways, including:
Search through your previous episodes to help create engaging new content. Distribute transcripts to your listeners.
Track market sentiment via conversations and grab the next bull by the horns!
Find out which podcasts are offering sponsored slots for specific brands. Track ad volume over time.
Follow real-time discussions about brands, products, and consumer behavior across thousands of authentic conversations.
Find expert commentary and uncover emerging narratives across podcasts. Get timestamped quotes and source accurate context for your stories.
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Tapesearch is a search engine to quickly search podcast transcripts. Find what was said in a podcast and go straight to that point in a recording without having to listen to hours of audio.
Currently, the most popular podcasts based on the number of iTunes ratings are included (not the rating itself). The long-term aim is to include as many podcasts as possible.
Generally, the AI is very good at transcribing, but you might find small errors with people's names, fast speech, and when people are talking over music. Long periods of silence and/or music can also introduce errors in the timestamps which can cumulate over long recordings.
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