What is Parrot Speech-to-text API?

Parrot Speech-to-text API is RinggAI’s proprietary speech recognition model built for production-grade voice agents and real-time transcription workflows. It delivers low-latency streaming inference with strong support for Hindi, English, and code-mixed speech — the kind of mixed-language conversation that dominates real-world voice interactions in India. The model achieves a typical streaming latency of 60ms and outperforms alternatives like Deepgram and Sarvam on multiple benchmark datasets, including noisy and code-mixed audio.

Who it's for

• Voice agent developers — building AI-powered assistants that need fast, reliable Hindi-English transcription in real time.
• Contact center operators — deploying speech-to-text for call analytics, quality monitoring, and agent coaching in Hindi-heavy environments.
• Product teams working on multilingual voice products — creating voice search, subtitling, or meeting intelligence tools that must handle code-mixed speech accurately.

Key features

Low-latency streaming inference

Parrot STT V1 delivers a typical streaming latency of 60ms, making it suitable for real-time voice products and conversational AI agents. The model is designed to process audio as it arrives, enabling natural turn-taking and responsive voice interfaces.

Hindi-English code-mixed recognition

The model is purpose-built for Hindi-English code-mixed speech — the dominant mode of spoken communication in many Indian business and consumer contexts. It outperforms ElevenLabs, Deepgram, and Sarvam on benchmarks like Kathbath and Common Voice, with an overall Word Error Rate of 7.27% compared to 8.94% for the next best alternative.

Production-grade accuracy on noisy audio

Parrot achieves a 13.09% WER on the Kathbath noisy dataset, significantly lower than Deepgram (15.93%) and Sarvam (17.53%). This makes it a strong choice for contact centers, field recordings, and other environments where background noise is unavoidable.

Python SDK with Pipecat compatibility

Ringg provides a Python SDK available through the ringglabs package on PyPI. The SDK integrates with the Pipecat toolkit using built-in VAD events, making it straightforward to plug into modern voice-agent orchestration pipelines.

What stands out

Parrot delivers the lowest overall Word Error Rate among major speech-to-text providers — 7.27% — while maintaining 60ms streaming latency.

That combination of accuracy and speed is rare. Most providers trade one for the other. Parrot beats ElevenLabs, Deepgram, and Sarvam on overall WER while keeping latency low enough for real-time voice agents. The model is also proprietary and private, meaning your audio data and transcriptions stay within RinggAI’s controlled deployment environment — a meaningful consideration for businesses handling sensitive conversations.

Worth checking out if…

You’re building a voice product that needs reliable Hindi-English transcription in real time, especially in noisy or code-mixed environments. Parrot is a strong fit for contact centers, AI agents, and meeting intelligence tools where accuracy and latency both matter. If you’re currently using Deepgram or Sarvam for Hindi-heavy workloads, the benchmark data suggests Parrot may deliver noticeably better results. Production access requires contacting RinggAI directly, but you can evaluate the model in the playground at ringg.ai.