ST
STT –> LLM –> TTS pipeline in C
Hacker NewsSTT –> LLM –> TTS pipeline in C
For speech-to-text, large-language-model inference and text-to-speech I created three wrapper libraries in C/C++ (using Whisper.cpp, Llama.cpp and Piper). Follow the URL to see an example that shows how to use these libraries for a speech-to-text, LLM inference, text-to-speech pipeline. Windows and Linux are supported.
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, using · Missing: mac, agents, macos92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: llama, pipe · Missing: https docs, excited, just released75%75% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting36%36% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto24%24% predicted probability of success on BetaList, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Correct prediction on native model
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