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Turn native language audio into flashcards and shadowing practice

Hacker News

Turn native language audio into flashcards and shadowing practice

Here is a tool I built initially for myself to help with my German and Greek language studies. It started as a hack for creating Anki cards from native language audio. It extracts the words, finds their base forms (lemmas) and groups the examples by the lemma. At some point I realised that I have a transcription with word level timestamps that opens a lot of other opportunities. So I added a mode to click the first and last word in the transcript and it starts looping with the right gap and repeat count. Another feature I use a lot is selecting an audio fragment, sending a predefined prompt to an AI to "explain grammar" or "explain nuances of meaning" and I still experimenting with prompts. And because shadowing is so easy I also use it as a player to improve my English pronunciation. (I am not a native English speaker.) I made a quick video showing the workflow for creating Anki cards and shadowing: https://youtu.be/TaR58uuDBvU?si=o5aGLAi2S-BZ7Zy9 The app supports 15 input languages (Japanese and Chinese are the latest experimental additions), and more than 30 output languages. I would really appreciate it if you could try it https://lingochunk.com/try . I know there are other tools with similar functionality but I created something that fits my workflow and it is fun to build. Also I struggled to find public domain audio for the try page. I'd be grateful if anyone could point me to public domain sources (I used LibriVox, Wikimedia and FSI courses), or if you're a creator, let me feature some of your own recordings with credits and links.

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, created, started · Missing: reddit linkedin, podcasting, latex
98%98% 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: recordings, open, plain · Missing: mac, agents, macos
63%63% 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
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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