nvidia
/

canary-1b-flash

@@ -273,6 +273,15 @@ img {
 NVIDIA NeMo Canary Flash [1] is a family of multilingual multi-tasking models based on Canary architecture [2] that achieve state-of-the-art performance on multiple speech benchmarks. With 883 million parameters and an inference speed of more than 1000 RTFx (on open-asr-leaderboard datasets), canary-1b-flash supports automatic speech-to-text recognition (ASR) in four 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). Additionally, canary-1b-flash offers an experimental feature for word-level and segment-level timestamps in English, German, French, and Spanish.
 This model is released under the permissive CC-BY-4.0 license and is available for commercial use.
 ## Model Architecture:
 Canary is an encoder-decoder model with FastConformer [3] Encoder and Transformer Decoder [4]. With audio features extracted from the encoder, task tokens such as \<target language\>, \<task\>, \<toggle timestamps\> and \<toggle PnC\> are fed into the Transformer Decoder to trigger the text generation process. Canary uses a concatenated tokenizer [5] from individual SentencePiece [6] tokenizers of each language, which makes it easy to scale up to more languages. The canary-1b-flash model has 32 encoder layers and 4 decoder layers, leading to a total of 883M parameters. For more details about the architecture, please refer to [1].
@@ -616,8 +625,6 @@ As outlined in the paper "Towards Measuring Fairness in AI: the Casual Conversat
 (Error rates for fairness evaluation are determined by normalizing both the reference and predicted text, similar to the methods used in the evaluations found at https://github.com/huggingface/open_asr_leaderboard.)
-## License/Terms of Use:
-canary-1b-flash is released under the CC-BY-4.0 license. By using this model, you are agreeing to the [terms and conditions](https://choosealicense.com/licenses/cc-by-4.0/) of the license. <br>
 ## References:
 [1] [Training and Inference Efficiency of Encoder-Decoder Speech Models](https://arxiv.org/abs/2503.05931)

 NVIDIA NeMo Canary Flash [1] is a family of multilingual multi-tasking models based on Canary architecture [2] that achieve state-of-the-art performance on multiple speech benchmarks. With 883 million parameters and an inference speed of more than 1000 RTFx (on open-asr-leaderboard datasets), canary-1b-flash supports automatic speech-to-text recognition (ASR) in four 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). Additionally, canary-1b-flash offers an experimental feature for word-level and segment-level timestamps in English, German, French, and Spanish.
 This model is released under the permissive CC-BY-4.0 license and is available for commercial use.
+## Discover more from NVIDIA:
+For documentation, deployment guides, enterprise-ready APIs, and the latest open models—including Nemotron and other cutting-edge speech, translation, and generative AI—visit the NVIDIA Developer Portal at [developer.nvidia.com](developer.nvidia.com).
+Join the community to access tools, support, and resources to accelerate your development with NVIDIA’s NeMo, Riva, NIM, and foundation models.<br>
+### Explore more from NVIDIA:  <br>
+What is [Nemotron](https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/)?<br>
+NVIDIA Developer [Nemotron](https://developer.nvidia.com/nemotron)<br>
+[NVIDIA Riva Speech](https://developer.nvidia.com/riva?sortBy=developer_learning_library%2Fsort%2Ffeatured_in.riva%3Adesc%2Ctitle%3Aasc#demos)<br>
+[NeMo Documentation](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/asr/models.html)<br>
 ## Model Architecture:
 Canary is an encoder-decoder model with FastConformer [3] Encoder and Transformer Decoder [4]. With audio features extracted from the encoder, task tokens such as \<target language\>, \<task\>, \<toggle timestamps\> and \<toggle PnC\> are fed into the Transformer Decoder to trigger the text generation process. Canary uses a concatenated tokenizer [5] from individual SentencePiece [6] tokenizers of each language, which makes it easy to scale up to more languages. The canary-1b-flash model has 32 encoder layers and 4 decoder layers, leading to a total of 883M parameters. For more details about the architecture, please refer to [1].
 (Error rates for fairness evaluation are determined by normalizing both the reference and predicted text, similar to the methods used in the evaluations found at https://github.com/huggingface/open_asr_leaderboard.)
 ## References:
 [1] [Training and Inference Efficiency of Encoder-Decoder Speech Models](https://arxiv.org/abs/2503.05931)