We Opened Up Our Transcription Tool

Hacker News

Audio/video transcription tools are quite rudimentary. The audio player is separate from the editor which makes it very cumbersome cross-reference between the audio and the text. Our transcription editor solves this problem. It provides the audio and a fully featured text editor on a single interface and tightly couples both of them. Jumping to any position in the audio causes the cursor to move correspondingly, and vice-versa. https://scribie.com/tools/transcription-editor The editor is built on Ace Editor and the audio player flash or native. All standard features are there, with some advanced ones. One feature is text analysis. It performs a trigram match followed by a TF-IDF analysis which highlights the new terms and phrases in the transcript. Those are the most likely places for mistakes. We have been using this tool for the past two years internally and now have opened it up for everyone. Please do try it out once. Thanks in advance!

Visit product View on Hacker NewsJun 17, 2014

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Indie HackersFits the IH revenue-focused audience · Strong signals: mistakes, para · Missing: supports, reddit linkedin, podcasting

93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, new, single · Missing: mac, agents, macos

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, para · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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