Parrot Speech-to-text API

Building a dedicated validation layer for Hindi downstream workflows is clever. Most generic STT APIs fall apart on code-switching and regional accents. We've hit similar walls where raw transcripts were too noisy for reliable intent parsing in production pipelines. How do you handle Hinglish code-switching, and what's the P95 latency on a 10-second audio chunk?

Congrats on the launch! Building voice sessions into a couples app right now (currently on Deepgram for streaming transcription), so the "voice agents don't get clean audio" framing really lands...clean-audio benchmarks oversell every STT model until you hit a real room. One thing I've run into that I'd love your take on: the hardest case isn't accent or noise, it's two people talking, overlapping speech, interruptions, one person finishing the other's sentence. Most STT degrades badly there. Is Parrot tuned mainly for the single-caller voice-agent case (one human, one agent), or does it hold up on genuine multi-speaker conversations? Curious whether that's a roadmap item or a deliberate scope line.

Congratulation on the launch! Btw, when I mix English with Hindi, I observed its little biased towards transcribing English in Hindi (using Devnagri glyph). Latency is impressive

This looks really solid 🔥Curious about latency and how it performs in noisy real-world calls compared to Whisper.

Try this out with easy to integrate package https://www.ringg.ai/dashboard/stt

Best for voice AI use case!!

Haha, how can something be this useful and this scary simultaneously!? As someone with a name most humans can't spell right, I look forward to the day when this is no longer an issue.

Just tried this out, amazing speed and accuracy. Great work!

Hey Product Hunt 👋Thrilled to introduce Parrot, Ringg’s speech-to-text model built for production-grade voice agents.Most STT models do well on clean audio. Voice agents don’t get clean audio. They deal with compressed phone calls, Hindi-English code-switching, Indian accents, background noise, and conversations where one misheard word can break the next action.What makes it different:🦜 Built for real world calls🦜 Low latency inference for smoother voice agent conversations🦜 Hindi validation and normalization for cleaner downstream workflows🦜 Strong Normalised WER performance on open-source Hindi benchmarksFor teams building voice agents, Parrot helps turn messy speech into cleaner transcripts that LLMs can actually use.Try it out and let us know what you're building with it!