Introducing Parrot: Ringg’s speech-to-text model for production-grade voice agents. Capture Hindi-heavy and noisy real-world conversations with low-latency inference, stronger transcript quality, and Hindi validation built for downstream workflows.
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 benchmarks
For 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!
Our AI has a voice mode. We use ChatGPT (before that, we used speech-to-text recognition and then text-to-speech). How is your service better? Is it only better Hindi recognition, or is there something else?
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.
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.
About Parrot Speech-to-text API on Product Hunt
“Fast, accurate STT for production-grade voice agents”
Parrot Speech-to-text API launched on Product Hunt on May 26th, 2026 and earned 149 upvotes and 19 comments, placing #5 on the daily leaderboard. Introducing Parrot: Ringg’s speech-to-text model for production-grade voice agents. Capture Hindi-heavy and noisy real-world conversations with low-latency inference, stronger transcript quality, and Hindi validation built for downstream workflows.
Parrot Speech-to-text API was featured in API (98.2k followers), Artificial Intelligence (469.4k followers) and Audio (2k followers) on Product Hunt. Together, these topics include over 108.7k products, making this a competitive space to launch in.
Who hunted Parrot Speech-to-text API?
Parrot Speech-to-text API was hunted by Parth Chadha. 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.
Reviews
Parrot Speech-to-text API has received 1 review on Product Hunt with an average rating of 5.00/5. Read all reviews on Product Hunt.
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Parth Chadha
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 benchmarks
For 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!