Record-setting accuracy and performance for Mandarin Taiwanese English transcriptions.
NVIDIA Parakeet-CTC-0.6B ASR Taiwanese Mandarin (600M parameters) is trained on an ASR dataset with around 90 hours of Taiwanese Mandarin (zh-TW) speech. The model transcribes speech in Taiwanese Mandarin (Traditional Chinese), in upper case and lower case alphabets along with spaces. While the model does not transcribe text with punctuation (period, comma, and question mark), the fused Language Model (LM) decoding may attempt to provide punctuation capabilities.
This model is ready for commercial use.
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.
Build.Nvidia.com 10/09/2025 via [URL]
NGC 10/09/2025 via [URL]
[1] Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition
[2] Fast-Conformer-CTC Model
[3] Conformer: Convolution-augmented Transformer for Speech Recognition
Architecture Type: Parakeet-CTC (also known as FastConformer-CTC) [1], [2], which is an optimized version of the Conformer model [3], features 8x depthwise-separable convolutional downsampling with CTC loss.
Network Architecture: Parakeet-CTC-XL-0.6B
This model was developed based on FastConformer architecture.
This model has 600 million model parameters.
Input Type(s): Audio
Input Format: wav
Input Parameters: One-Dimensional (1D)
Other Properties Related to Input: Maximum Length in seconds specific to GPU Memory, No Pre-Processing Needed, Mono channel is required.
Output Type(s): Text
Output Format: String (in Mandarin and English)
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.
The Riva Quick Start Guide is recommended as the starting point for trying out Riva models. For more information on using this model with Riva Speech Services, see the Riva User Guide.
Refer to the Riva documentation for more information.
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.
Parakeet-CTC-XL-0.6b_zh-TW_1.0
Data Modality
Text Training Data Size
Less than a Billion Tokens
Non-Audio, Image, Text Training Data Size
The model was trained approximately 90 hours of Taiwanese Mandarin speech data:
Data Collection Method by dataset
Labeling Method by dataset
Properties:
This model is trained on around 90 hours of Taiwanese Mandarin (zh-TW) speech, comprised of a dynamic blend of public and internal proprietary datasets.
Data Modality
Text Evaluation Data Size
Less than a Billion Tokens
Non-Audio, Image, Text Evaluation Data Size
The model was evaluated approximately 17.5 hours of Taiwanese Mandarin speech data:
Data Collection Method by dataset
Labeling Method by dataset
Properties:
A dynamic blend of public and internal proprietary datasets.
Acceleration Engine: Triton
Test Hardware:
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