Transcription Editor

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 present. One experimental 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 year and half internally and now have opened it up for everyone. Please try it out and let us know your feedback and comments. Thanks in advance!

Visit product View on Hacker NewsJun 17, 2014

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5points

3comments

Did not reach leaderboard

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

89%89% 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

71%71% 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

66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive

50%50% 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

43%43% 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

17%17% 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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