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38:["$","$L3b",null,{"hasPartnerEndpoint":false}] 41:T19d3,# Speech Recognition: Parakeet ## Description 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. ## License/Terms of Use ### 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 10/09/2025 via [URL]
NGC 10/09/2025 via [URL]
## References [1] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084)
[2] [Fast-Conformer-CTC Model](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/asr/models.html)
[3] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)
## Model Architecture **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](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer) architecture.**
**This model has 600 million model parameters.** ## Input **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 **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. ## How to Use this Model The [Riva Quick Start Guide](https://ngc.nvidia.com/catalog/resources/nvidia:riva:riva_quickstart) 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](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/asr/asr-overview.html). ## Suggested Reading Refer to the [Riva documentation](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/index.html) for more information. ## Software Integration **Runtime Engine(s):** * Riva 2.19.0 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): Parakeet-CTC-XL-0.6b_zh-TW_1.0 # Training and Evaluation Datasets: ## Training Dataset **Data Modality**
* Other: Speech
**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: * Common Voice Corpus 20.0 * TechOrange-Podcast **Data Collection Method by dataset**
* Human
**Labeling Method by dataset**
* Human
**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. ## Evaluation Dataset **Data Modality**
* Other: Speech
**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: * Common Voice Corpus 20.0 * TechOrange-Podcast * NV-zh-tw-subtitle **Data Collection Method by dataset**
* Human
**Labeling Method by dataset**
* Human
**Properties:** A dynamic blend of public and internal proprietary datasets. ## Inference **Acceleration Engine:** Triton
**Test Hardware:**
* NVIDIA A10 * NVIDIA A100 * NVIDIA A30 * NVIDIA H100 * NVIDIA Jetson Orin * NVIDIA L4 * NVIDIA L40 * NVIDIA Turing T4 * NVIDIA Volta V100 ## 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++ Bias, Explainability, Safety & Security, and Privacy Subcards [here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/riva/models/parakeet-ctc-riva-0-6b-unified-zh-cn/bias). Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).42:T9db,Field | Response :------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------- Intended Task/Domain: | Speech Transcription Model Type: | Transformer Intended Users: | This model is intended for developers and data scientists building interactive call centers, virtual assistants, and language learning assistants. Output: | Transcribed text with timestamps and confidence scores. Describe how the model works: | Model transcribes audio input into text for the input language. Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Age, Gender, National Origin Technical Limitations & Mitigation: | Transcripts may not be 100% accurate. Accuracy varies based on the characteristics of input audio (domain, use case, accent, noise, speech type, context of speech, etc.). Verified to have met prescribed NVIDIA quality standards: | Yes Performance Metrics: | Word error rate (WER), silence robustness (characters per minute of silent audio), latency (in milliseconds), and throughput (total audio processed per unit of time). Potential Known Risks: | Not recommended for word-for-word transcription as accuracy varies based on the characteristics of input audio (domain, use case, accent, noise, speech type, and context of speech). Licensing: | [https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/)43:T5b8,Field | Response :----------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------- Generatable or reverse engineerable personal data? | No Personal data used to create this model? | No Was consent obtained for any personal data used? | Not Applicable How often is dataset reviewed? | Before Release Is there data provenance for all datasets used in training? | Yes Does data labeling (annotation, metadata) comply with privacy laws? | Yes Is data compliant with data subject requests for data correction or removal, if such a request was made? | The data is compliant where applicable, but is not applicable for all data. Applicable Privacy Policy | [https://www.nvidia.com/en-us/about-nvidia/privacy-policy/](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/)44:T68b,### Getting Started Riva uses gRPC APIs. Instructions below demonstrate usage of <%- name %> model using Python gRPC client. ### Prerequisites You will need a system with Git and Python 3+ installed. ### Install Riva Python Client ```bash pip install -U nvidia-riva-client ``` ### Download Python Client Download Python client code by cloning Python Client Repository. ```bash git clone https://github.com/nvidia-riva/python-clients.git ``` ### Run Python Client Open a command terminal and execute below command to transcribe audio. Make sure you have a speech file in 16-bit Mono format in WAV/OGG/OPUS container. If you have generated the API key, it will be auto-populated in the command. ```bash python python-clients/scripts/asr/transcribe_file.py \ --server grpc.nvcf.nvidia.com:443 --use-ssl \ --metadata function-id "<%- nvcfFunctionId %>" \ --metadata "authorization" "Bearer <%- apiKey %>" \ --language-code zh-TW \ --input-file ``` ### Support for gRPC clients in other languages Riva uses gRPC APIs. Proto files can be downloaded from Riva gRPC Proto files and compiled to target language using Protoc compiler. Example Riva clients in C++ and Python languages are provided below. * Python Client Repository * C++ Client Repository 45:T71a,## Step 1. Generate API Key ::generate-api-key ## Step 2. Pull and Run the NIM ```bash $ docker login nvcr.io Username: $oauthtoken Password: ``` Refer [Supported Models](https://docs.nvidia.com/nim/riva/asr/latest/getting-started.html#supported-models) for full list of models. ```bash export NGC_API_KEY= docker run -it --rm --name=parakeet-ctc-0.6b-zh-tw \ --runtime=nvidia \ --gpus '"device=0"' \ --shm-size=8GB \ -e NGC_API_KEY \ -e NIM_HTTP_API_PORT=9000 \ -e NIM_GRPC_API_PORT=50051 \ -p 9000:9000 \ -p 50051:50051 \ -e NIM_TAGS_SELECTOR=mode=str,vad=silero,diarizer=sortformer \ nvcr.io/nim/nvidia/parakeet-ctc-0.6b-zh-tw:latest ``` It may take a up to 30 minutes depending on your network speed, for the container to be ready and start accepting requests from the time the docker container is started. ## Step 3. Test the NIM Open a new terminal and run following command to check if the service is ready to handle inference requests ```bash curl -X 'GET' 'http://localhost:9000/v1/health/ready' ``` If the service is ready, you get a response similar to the following. ```bash {"ready":true} ``` Install the Riva Python client package ```bash sudo apt-get install python3-pip pip install -U nvidia-riva-client ``` Download Riva sample clients ```bash git clone https://github.com/nvidia-riva/python-clients.git ``` Run Speech to Text inference in streaming modes. Riva ASR supports Mono, 16-bit audio in WAV, OPUS and FLAC formats. ```bash python3 python-clients/scripts/asr/transcribe_file.py --server 0.0.0.0:50051 --input-file --language-code zh-TW ``` For more details on getting started with this NIM, visit the [Riva ASR NIM Docs](https://docs.nvidia.com/nim/riva/asr/latest/overview.html).39:[["$","div",".3",{"className":"relative","children":[["$","div",null,{"className":"relative flex min-h-[194px] w-full flex-col overflow-hidden p-md sm:p-ml bg-manitoulinLightBlack","children":[["$","$L3c",null,{"alt":"","className":"object-cover object-center !left-1/2 z-10 !w-1/2 brightness-20 lg:brightness-75","fill":true,"priority":true,"src":"https://assets.ngc.nvidia.com/products/api-catalog/images/parakeet-ctc-0_6b-zh-tw.jpg","style":{"maskImage":"linear-gradient(to left, rgba(0,0,0,1), rgba(0,0,0,0))"}}],["$","div",null,{"className":"z-30 mx-auto flex w-full max-w-screen-xl min-w-0 flex-1 flex-col items-start justify-between gap-md lg:flex-row lg:gap-ml","children":["$","section",null,{"aria-describedby":"resource-description","aria-labelledby":"resource-name","className":"flex w-full min-w-0 flex-col lg:flex-1","children":["$","div",null,{"className":"flex flex-col gap-1.5 whitespace-pre-wrap","children":[["$","$L3d",null,{"className":"w-fit text-ml text-n300 lowercase hover:text-n050","href":"/nvidia","children":"NVIDIA"}],["$","div",null,{"className":"flex flex-col items-start gap-3 lg:flex-row lg:items-center","children":[["$","h1",null,{"className":"max-w-full text-lg leading-heading font-medium break-words text-primary","data-testid":"resource-name","id":"resource-name","children":"parakeet-ctc-0.6b-zh-tw"}],[["$","span","AVAILABLE",{"className":"nv-badge nv-badge--kind-solid h-[18px] py-0","data-testid":"nv-badge","ref":"$undefined","children":["$undefined","Downloadable"]}]],"$undefined"]}],["$","p",null,{"className":"max-w-[85ch] text-ms text-primary","id":"resource-description","children":"Record-setting accuracy and performance for Mandarin Taiwanese English transcriptions."}],["$","div",null,{"className":"flex w-full flex-col items-start gap-md lg:flex-row lg:items-end lg:justify-between","children":[["$","div",null,{"className":"flex w-full min-w-0 flex-wrap gap-sm lg:flex-1","children":["$","$L3e",null,{"artifactType":"ENDPOINT","density":"standard","labels":["ASR","NVIDIA NIM","Streaming","Taiwanese","nimType:endpoint:nim_type_run_anywhere","usecase:endpoint:usecase_speech_to_text"],"searchRoutePath":"/models"}]}],["$","div",null,{"className":"z-30 flex w-full flex-row justify-start gap-ms sm:justify-end lg:w-auto lg:shrink-0","children":["$undefined",["$","$L3f",null,{"cta":{"text":"Run Anywhere - Notify Me","url":"https://www.nvidia.com/en-us/ai/nim-notifyme/"},"isNIMAccessRestricted":"$undefined","isNIMAvailable":true}]]}]]}]]}]}]}],["$","div",null,{"className":"pointer-events-none absolute right-0 bottom-0 left-0 z-20 h-30 bg-gradient-to-t to-transparent from-manitoulinLightBlack via-manitoulinLightBlack/70"}]]}],["$","div",null,{"className":"px-4 mx-auto flex max-w-screen-xl flex-col pt-0 lg:px-0","data-body-background":"light-black","children":["$","section",null,{"className":"flex w-full flex-col justify-between rounded-4 border-manitoulinBorderColor bg-manitoulinDarkBlack sm:rounded-6 lg:rounded-8 p-ms lg:p-lg","children":["$","$L40",null,{"artifact":{"artifactType":"ENDPOINT","name":"parakeet-ctc-0_6b-zh-tw","displayName":"parakeet-ctc-0.6b-zh-tw","publisher":"nvidia","shortDescription":"Record-setting accuracy and performance for Mandarin Taiwanese English transcriptions.","logo":"https://assets.ngc.nvidia.com/products/api-catalog/images/parakeet-ctc-0_6b-zh-tw.jpg","labels":"$39:0:props:children:0:props:children:1:props:children:props:children:props:children:3:props:children:0:props:children:props:labels","attributes":[{"key":"AVAILABLE","value":"true"},{"key":"PREVIEW","value":"false"},{"key":"last_month_api_invocation_count","value":"294"},{"key":"last_month_download_count","value":"0"}],"updatedDate":"2025-10-16T01:36:31.358Z","bias":"Field | Response\n:---------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nWhat is the language balance of the model validation data? 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