parakeet-rnnt-1.1b
Maintainer: nvlabs
1
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
The parakeet-rnnt-1.1b is an advanced speech recognition model developed by NVIDIA and Suno.ai. It features the FastConformer architecture and is available in both RNNT and CTC versions, making it well-suited for transcribing English speech in noisy audio environments while maintaining accuracy in silent segments. This model outperforms the popular OpenAI Whisper model on the Open ASR Leaderboard, reclaiming the top spot for speech recognition accuracy.
Model inputs and outputs
Inputs
- audio_file: The input audio file to be transcribed by the ASR model, in a supported audio format.
Outputs
- Output: The transcribed text output from the speech recognition model.
Capabilities
The parakeet-rnnt-1.1b model is capable of high-accuracy speech transcription, particularly in challenging audio environments. It has been trained on a diverse 65,000-hour dataset, enabling robust performance across a variety of use cases. Compared to the OpenAI Whisper model, the parakeet-rnnt-1.1b achieves lower Word Error Rates (WER) on benchmarks like AMI, Earnings22, Gigaspeech, and Common Voice 9.
What can I use it for?
The parakeet-rnnt-1.1b model is designed for precision ASR tasks in voice recognition and transcription, making it suitable for a range of applications such as voice-to-text conversion, meeting minutes generation, and closed captioning. It can be integrated into the NeMo toolkit for a broader set of use cases. However, users should be mindful of data privacy and potential biases in speech recognition, ensuring fair and responsible use of the technology.
Things to try
Experimenting with the parakeet-rnnt-1.1b model in various audio scenarios, such as noisy environments or recordings with silent segments, can help evaluate its performance and suitability for specific use cases. Additionally, testing the model's accuracy and efficiency on different benchmarks can provide valuable insights into its capabilities.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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