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canary-1b-asr

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Multi-lingual model supporting speech-to-text recognition and translation.

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Speech Recognition/Translation: Canary

Description

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.

License/Terms of 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.

Deployment Geography:

Global

Use Case:

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]

References

[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

Model Architecture

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

Input Type(s): Audio
Input Format: wav
Input Parameters: One-Dimensional (1D)
Other Properties Related to Input: Mono channel is required

Output

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.

Software Integration

Runtime Engine(s):

  • Riva 2.23.0 or higher

Supported Hardware Microarchitecture Compatibility:

  • NVIDIA Ampere
  • NVIDIA Hopper
  • NVIDIA Jetson
  • NVIDIA Turing
  • NVIDIA Volta

[Preferred/Supported] Operating System(s):

  • Linux
  • Linux 4 Tegra

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.

Model Version(s):

Canary-1B-Flash 2.0

Training & Evaluation

Training Dataset

Data Modality

[Audio]
[Text]

Audio Training Data Size

[10,000 to 1 Million Hours]

Data Collection Method by dataset

  • Human

Labeling Method by dataset

  • ASR: Human
  • AST: Human, Synthetic

Properties:

Mixture of organic ASR data aligned with human voices and machine generated translations to create AST pairings.

Evaluation Dataset

Data Collection Method by dataset

  • Human

Labeling Method by dataset

  • Hybrid: Human, Synthetic

Properties:

A dynamic blend of public and internal proprietary and customer datasets aligning text with human audio data.

Inference

Acceleration Engine: Triton
Test Hardware:

  • NVIDIA A10
  • NVIDIA A100
  • NVIDIA A30
  • NVIDIA H100
  • NVIDIA L4
  • NVIDIA L40
  • NVIDIA Turing T4
  • NVIDIA Volta V100

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Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.

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