Combining LLMs and Voice Models – Part 1

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This is a guide that I wrote to showcase a new batch inference feature for an OSS framework that I author (nitric.io). I know things like Podcast generation via NotebookLM and also NotebookLlama exist, but wanted to demonstrate a case where an API could be built, and subsequently orchestrated in the cloud. This is just the first part for producing audio using suno/bark via an API. I'm currently working on a part 2 that will introduce an LLM to make scripts from short prompt, which will be piped to the code introduced in Part 1. Looking for feedback on improving this, there are a few things I'd like to clean up but overall am pretty happy with the outputs it produces so far. Thanks in advance for any feedback given.

Visit product View on Hacker NewsOct 30, 2024

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

92%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.

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

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, llama, ide · Missing: https docs, excited, just released

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

42%42% predicted probability of success on AppSumo, 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, introduce · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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