Nvidia has become one of the most valuable companies in the world in recent years thanks to the stock market noticing how much demand there is for graphics processing units (GPUs), the powerful chips Nvidia makes that are used to render graphics in video games but also, increasingly, train AI large language and diffusion models.

But Nvidia does far more than just make hardware, of course, and the software to run it. As the generative AI era wears on, the Santa Clara-based company has also been steadily releasing more and more of its own AI models — mostly open source and free for researchers and developers to take, download, modify and use commercially — and the latest among them is Parakeet-TDT-0.6B-v2, an automatic speech recognition (ASR) model that can, in the words of Hugging Face’s Vaibhav “VB” Srivastav, “transcribe 60 minutes of audio in 1 second [mind blown emoji].”

This is the new generation of the Parakeet model Nvidia first unveiled back in January 2024 and updated again in April of that year, but this version two is so powerful, it currently tops the Hugging Face Open ASR Leaderboard with an average “Word Error Rate” (times the model incorrectly transcribes a spoken word) of just 6.05% (out of 100).

To put that in perspective, it nears proprietary transcription models such as OpenAI’s GPT-4o-transcribe (with a WER of 2.46% in English) and ElevenLabs Scribe (3.3%).

And it’s offering all this while remaining freely available under a commercially permissive Creative Commons CC-BY-4.0 license, making it an attractive proposition for commercial enterprises and indie developers looking to build speech recognition and transcription services into their paid applications.

Performance and benchmark standing

The model boasts 600 million parameters and leverages a combination of the FastConformer encoder and TDT decoder architectures.

It is capable of transcribing an hour of audio in just one second, provided it’s running on Nvidia’s GPU-accelerated hardware.

The performance benchmark is measured at an RTFx (Real-Time Factor) of 3386.02 with a batch size of 128, placing it at the top of current ASR benchmarks maintained by Hugging Face.

Use cases and availability

Released globally on May 1, 2025, Parakeet-TDT-0.6B-v2 is aimed at developers, researchers, and industry teams building applications such as transcription services, voice assistants, subtitle generators, and conversational AI platforms.

The model supports punctuation, capitalization, and detailed word-level timestamping, offering a full transcription package for a wide range of speech-to-text needs.

Access and deployment

Developers can deploy the model using Nvidia’s NeMo toolkit. The setup process is compatible with Python and PyTorch, and the model can be used directly or fine-tuned for domain-specific tasks.

The open-source license (CC-BY-4.0) also allows for commercial use, making it appealing to startups and enterprises alike.

Training data and model development

Parakeet-TDT-0.6B-v2 was trained on a diverse and large-scale corpus called the Granary dataset. This includes around 120,000 hours of English audio, composed of 10,000 hours of high-quality human-transcribed data and 110,000 hours of pseudo-labeled speech.

Sources range from well-known datasets like LibriSpeech and Mozilla Common Voice to YouTube-Commons and Librilight.

Nvidia plans to make the Granary dataset publicly available following its presentation at Interspeech 2025.

Evaluation and robustness

The model was evaluated across multiple English-language ASR benchmarks, including AMI, Earnings22, GigaSpeech, and SPGISpeech, and showed strong generalization performance. It remains robust under varied noise conditions and performs well even with telephony-style audio formats, with only modest degradation at lower signal-to-noise ratios.

Hardware compatibility and efficiency

Parakeet-TDT-0.6B-v2 is optimized for Nvidia GPU environments, supporting hardware such as the A100, H100, T4, and V100 boards.

While high-end GPUs maximize performance, the model can still be loaded on systems with as little as 2GB of RAM, allowing for broader deployment scenarios.

Ethical considerations and responsible use

NVIDIA notes that the model was developed without the use of personal data and adheres to its responsible AI framework.

Although no specific measures were taken to mitigate demographic bias, the model passed internal quality standards and includes detailed documentation on its training process, dataset provenance, and privacy compliance.

The release drew attention from the machine learning and open-source communities, especially after being publicly highlighted on social media. Commentators noted the model’s ability to outperform commercial ASR alternatives while remaining fully open source and commercially usable.

Developers interested in trying the model can access it via Hugging Face or through Nvidia’s NeMo toolkit. Installation instructions, demo scripts, and integration guidance are readily available to facilitate experimentation and deployment.