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Premove ITN is an open-source, context-aware inverse text normalizer built for voice agents. It turns messy ASR transcripts into structured values APIs can actually use: phone numbers, dates, times, amounts, IDs, emails, URLs, and more. Instead of generating normalized text directly, it deterministically produces valid candidates, uses a neural model to score them in sentence context, and applies exact decoding to choose compatible outputs. Open weights, Python package, real-time inference.
Hey everyone 👋
I started building Premove ITN while working on a voice agent.
The problem looked simple at first: turn spoken forms like “one hundred twenty three” into 123.
But the harder cases were contextual:
meet me at two thirty → 2:30
room number two thirty → 230
I first tried deterministic rules, then NVIDIA Thutmose, then a BIO-tagging approach. What finally worked was separating the problem into three parts:
1. Deterministic code generates structurally valid candidates
2. A contextual neural model scores them
3. Exact decoding chooses a compatible final path
That became Premove ITN.
On a frozen 1,500-row synthetic benchmark, Premove reached 89.7% overall semantic accuracy, compared with 59.4% for NVIDIA Thutmose and 55.8% for text-processing-rs. On the dedicated voice-agent subset, Premove reached 99.5%.
The code and model weights are open source.
I’d especially love feedback from people building voice agents: what normalization failures are you still seeing in production?
Interested to see how the semantic accuracy holds up, great concept for an issue that is specific and tough to solve.
About Premove ITN on Product Hunt
“Open-source context-aware ITN for voice agents”
Premove ITN was submitted on Product Hunt and earned 10 upvotes and 3 comments, placing #28 on the daily leaderboard. Premove ITN is an open-source, context-aware inverse text normalizer built for voice agents. It turns messy ASR transcripts into structured values APIs can actually use: phone numbers, dates, times, amounts, IDs, emails, URLs, and more. Instead of generating normalized text directly, it deterministically produces valid candidates, uses a neural model to score them in sentence context, and applies exact decoding to choose compatible outputs. Open weights, Python package, real-time inference.
Premove ITN was featured in Open Source (68.9k followers), Developer Tools (519.8k followers), Artificial Intelligence (479k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 252.2k products, making this a competitive space to launch in.
Who hunted Premove ITN?
Premove ITN was hunted by Aryaman Todkar. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Aryaman Todkar