canary-1b
Maintainer: nvidia
191
👁️
| 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 canary-1b model is a part of the NVIDIA NeMo Canary family of multi-lingual, multi-tasking models. With 1 billion parameters, the Canary-1B model supports automatic speech-to-text recognition (ASR) in 4 languages (English, German, French, Spanish) and translation from English to German/French/Spanish and from German/French/Spanish to English with or without punctuation and capitalization (PnC). The model uses a FastConformer-Transformer encoder-decoder architecture.
Model inputs and outputs
Inputs
- Audio files or a jsonl manifest file containing audio data
Outputs
- Transcribed text in the specified language (English, German, French, Spanish)
- Translated text to/from the specified language pair
Capabilities
The Canary-1B model demonstrates state-of-the-art performance on multiple benchmarks for ASR and translation tasks in the supported languages. It can handle various accents, background noise, and technical language well.
What can I use it for?
The canary-1b model is well-suited for research on robust, multi-lingual speech recognition and translation. It can also be fine-tuned on specific datasets to improve performance for particular domains or applications. Developers may find it useful as a pre-trained model for building ASR or translation tools, especially for the supported languages.
Things to try
You can experiment with the canary-1b model by loading it using the NVIDIA NeMo toolkit. Try transcribing or translating audio samples in different languages, and compare the results to your expectations or other models. You can also fine-tune the model on your own data to see how it performs on specific tasks or domains.
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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