DeepSpeech based automated transcription service

Hacker News

We have been building a DeepSpeech model with our data for the past year and we have recently hit 95% accuracy on the LibriSpeech dataset. That puts us close to the published results for DeepSpeech 2. However our dataset is conversational audio and we do much better with our own internal dataset compared to PaddlePaddle. Here's a blog post on the method we followed to build our models. https://scribie.com/blog/2018/03/continual-learning-for-spee... We have been using this internally in our service and it saves a ton of time and effort during the typing stage. It is nowhere near to the accuracy which our transcribers can achieve, but we are getting close. We are offering automated transcripts free for a limited time. Please do try it out. https://scribie.com/transcription/free Thanks in advance!

Visit product View on Hacker NewsJun 28, 2018

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Actual performance

67points

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

80%80% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: model, models, using · Missing: mac, agents, macos

50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

33%33% 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

15%15% 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.

Correct prediction on native model

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