parakeet-rnnt-1.1b
Maintainer: nvidia
98
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model Overview
The parakeet-rnnt-1.1b is an ASR (Automatic Speech Recognition) model developed jointly by the NVIDIA NeMo and Suno.ai teams. It uses the FastConformer Transducer architecture, which is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling. This XXL model has around 1.1 billion parameters and can transcribe speech in lower case English alphabet with high accuracy.
The model is similar to other high-performing ASR models like Canary-1B, which also uses the FastConformer architecture but supports multiple languages. In contrast, the parakeet-rnnt-1.1b is focused solely on English speech transcription.
Model Inputs and Outputs
Inputs
- 16000 Hz mono-channel audio (WAV files)
Outputs
- Transcribed speech as a string for a given audio sample
Capabilities
The parakeet-rnnt-1.1b model demonstrates state-of-the-art performance on English speech recognition tasks. It was trained on a large, diverse dataset of 85,000 hours of speech data from various public and private sources, including LibriSpeech, Fisher Corpus, Switchboard, and more.
What Can I Use It For?
The parakeet-rnnt-1.1b model is well-suited for a variety of speech-to-text applications, such as voice transcription, dictation, and audio captioning. It could be particularly useful in scenarios where high-accuracy English speech recognition is required, such as in media production, customer service, or educational applications.
Things to Try
One interesting aspect of the parakeet-rnnt-1.1b model is its ability to handle a wide range of audio inputs, from clear studio recordings to noisier real-world audio. You could experiment with feeding it different types of audio samples and observe how it performs in terms of transcription accuracy and robustness.
Additionally, since the model was trained on a large and diverse dataset, you could try fine-tuning it on a more specialized domain or genre of audio to see if you can further improve its performance for your specific use case.
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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The parakeet-tdt-1.1b is an ASR (Automatic Speech Recognition) model that transcribes speech in lower case English alphabet. This model is jointly developed by the NVIDIA NeMo and Suno.ai teams. It uses a FastConformer-TDT architecture, which is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling. The model has around 1.1 billion parameters. Similar models include the parakeet-rnnt-1.1b, which is also a large ASR model developed by NVIDIA and Suno.ai. It uses a FastConformer Transducer architecture and has similar performance characteristics. Model inputs and outputs Inputs 16000 Hz mono-channel audio (wav files) as input Outputs Transcribed speech as a string for a given audio sample Capabilities The parakeet-tdt-1.1b model is capable of transcribing English speech with high accuracy. It was trained on a large corpus of speech data, including 64K hours of English speech from various public and private datasets. What can I use it for? You can use the parakeet-tdt-1.1b model for a variety of speech-to-text applications, such as transcribing audio recordings, live speech recognition, or integrating it into your own voice-enabled products and services. The model can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset using the NVIDIA NeMo toolkit. Things to try One interesting thing to try with the parakeet-tdt-1.1b model is to experiment with fine-tuning it on a specific domain or dataset. This could help improve the model's performance on your particular use case. You could also try combining the model with other components, such as language models or audio preprocessing modules, to further enhance its capabilities.
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