Multi-lingual model supporting speech-to-text recognition and translation.
NVIDIA Canary‑1B is a 1‑billion‑parameter multilingual model for both automatic speech recognition (ASR) and speech‑to‑text translation (AST). It’s designed to transcribe speech and translate between English and a wide range of other languages for production use cases.
This model is ready for commercial use.
GOVERNING TERMS: The NIM container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for AI Products. Use of this model is governed by the NVIDIA Community Model License.
Global
This model serves developers, researchers, academics, and industries building applications that require speech-to-text capabilities, including but not limited to: conversational AI, voice assistants, transcription services, subtitle generation, and voice analytics platforms.
#Release Date: Build.Nvidia.com [10/15/2025] via [https://build.nvidia.com/nvidia/canary-1b-asr] Hugging Face [10/15/2025] via [URL] NGC [10/15/2025] via [https://build.stg.ngc.nvidia.com/nvidia/canary-1b-asr]
[1] Less is More: Accurate Speech Recognition & Translation without Web-Scale Data
[2] Training and Inference Efficiency of Encoder-Decoder Speech Models
[3] New Standard for Speech Recognition and Translation from the NVIDIA NeMo Canary Model
[4] Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition
[6] Attention Is All You Need
Architecture Type: This model was developed based on the Canary Flash Architecture [2]. An encoder-decoder model with FastConformer [4] encoder and Transformer decoder [5]
Network Architecture: 42 layer encoder, 8 layer decoder, Number of model parameters: 918M
Input Type(s): Audio
Input Format: wav
Input Parameters: One-Dimensional (1D)
Other Properties Related to Input: Mono channel is required
Output Type(s): Text
Output Format: String
Output Parameters: One-Dimensional (1D)
Other Properties Related to Output: No Maximum Character Length, Does not handle special characters
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
Runtime Engine(s):
Supported Hardware Microarchitecture Compatibility:
[Preferred/Supported] Operating System(s):
The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
Canary-1B-Flash 2.0
Data Modality
[Audio]
[Text]
Audio Training Data Size
[10,000 to 1 Million Hours]
Data Collection Method by dataset
Labeling Method by dataset
Properties:
Mixture of organic ASR data aligned with human voices and machine generated translations to create AST pairings.
Data Collection Method by dataset
Labeling Method by dataset
Properties:
A dynamic blend of public and internal proprietary and customer datasets aligning text with human audio data.
Acceleration Engine: Triton
Test Hardware:
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