Model Overview

Description:

This model is used to transcribe short-form audio files and is designed to be compatible with OpenAI's sequential long-form transcription algorithm. Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labeled data, Whisper models demonstrate a strong ability to generalize to many datasets and domains without the need for fine-tuning. Whisper-large-v3 is one of the 5 configurations of the model with 1550M parameters.
\nThis model version is optimized to run with NVIDIA TensorRT-LLM. This model is ready for commercial use.

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see the Whisper Model Card on GitHub.(https://github.com/openai/whisper/blob/main/model-card.md).

License/Terms of Use:

This model is governed by the NVIDIA RIVA License Agreement.

Disclaimer: AI models generate responses and outputs based on complex algorithms and machine learning techniques, and those responses or outputs may be inaccurate or offensive. By downloading a model, you assume the risk of any harm caused by any response or output of the model.\nBy using this software or model, you are agreeing to the terms and conditions of the license, acceptable use policy and Whisper’s privacy policy. Whisper is released under the Apache 2.0 License.

References:

Whisper website
\nWhisper paper:

@misc{radford2022robust,\n      title={Robust Speech Recognition via Large-Scale Weak Supervision}, \n      author={Alec Radford and Jong Wook Kim and Tao Xu and Greg Brockman and Christine McLeavey and Ilya Sutskever},\n      year={2022},\n      eprint={2212.04356},\n      archivePrefix={arXiv},\n      primaryClass={eess.AS}\n}\n

Model Architecture:

Architecture Type: Transformer (Encoder-Decoder)
\nNetwork Architecture: Whisper

Input:

Input Type(s): Audio, Text-Prompt
\nInput Format(s): Linear PCM 16-bit 1 channel (Audio), String (Text Prompt)
\nInput Parameters: One-Dimensional (1D)

Output:

Output Type(s): Text\nOutput Format: String\nOutput Parameters: 1D

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Blackwell

Supported Operating System(s):

Linux

Model Version(s):

Large-v3: Whisper large-v3 has the same architecture as the previous large and large-v2 models, except for the following minor differences:

The spectrogram input uses 128 Mel frequency bins instead of 80.
A new language token for Cantonese.

Training Dataset:

Data Collection Method by dataset: [Hybrid: human, automatic]

Labeling Method by dataset: [Automated]

Dataset License(s): NA

Inference:

Engine: Tensor(RT)-LLM, Triton
\nTest Hardware:

A100
H100

For more detail on model usage, evaluation, training data set and implications, please refer to Whisper Model Card.

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 internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
\nPlease report security vulnerabilities or NVIDIA AI Concerns here.

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\nThis model version is optimized to run with NVIDIA TensorRT-LLM. This model is ready for commercial use.\n\n### Third-Party Community Consideration\n\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see the Whisper Model Card on GitHub.(https://github.com/openai/whisper/blob/main/model-card.md).\n\n#### License/Terms of Use:\n\nThis model is governed by the [NVIDIA RIVA License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/).\n\nDisclaimer: AI models generate responses and outputs based on complex algorithms and machine learning techniques, and those responses or outputs may be inaccurate or offensive. By downloading a model, you assume the risk of any harm caused by any response or output of the model.\nBy using this software or model, you are agreeing to the terms and conditions of the license, acceptable use policy and Whisper’s privacy policy. Whisper is released under the [Apache 2.0 License](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md).\n\n### References:\n\nWhisper [website](https://openai.com/index/whisper/)
\nWhisper paper:
\n\n```\n@misc{radford2022robust,\n title={Robust Speech Recognition via Large-Scale Weak Supervision}, \n author={Alec Radford and Jong Wook Kim and Tao Xu and Greg Brockman and Christine McLeavey and Ilya Sutskever},\n year={2022},\n eprint={2212.04356},\n archivePrefix={arXiv},\n primaryClass={eess.AS}\n}\n```\n\n### Model Architecture:\n\n**Architecture Type:** Transformer (Encoder-Decoder)
\n**Network Architecture:** Whisper
\n\n### Input:\n\n**Input Type(s):** Audio, Text-Prompt
\n**Input Format(s):** Linear PCM 16-bit 1 channel (Audio), String (Text Prompt)
\n**Input Parameters:** One-Dimensional (1D)
\n\n### Output:\n\n**Output Type(s):** Text\n**Output Format:** String\n**Output Parameters:** 1D\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Ampere
\n* NVIDIA Blackwell
\n\n**Supported Operating System(s):**
\n* Linux
\n\n### Model Version(s):\n\n**Large-v3:** Whisper large-v3 has the same architecture as the previous large and large-v2 models, except for the following minor differences:\n- The spectrogram input uses 128 Mel frequency bins instead of 80.\n- A new language token for Cantonese.\n\n### Training Dataset:\n\n**Data Collection Method by dataset:** [Hybrid: human, automatic]
\n\n**Labeling Method by dataset:** [Automated]
\n\n**Dataset License(s):** NA\n\n### Inference:\n\n**Engine:** Tensor(RT)-LLM, Triton
\n**Test Hardware:**\n- A100\n- H100\n\nFor more detail on model usage, evaluation, training data set and implications, please refer to [Whisper Model Card](https://github.com/openai/whisper/blob/main/model-card.md).\n\n### Ethical Considerations:\n\nNVIDIA 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 internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).",["D",1727776931770],"3.0","/api/image/nvidia/riva/models/whisper_large","https://github.com/kbojo/images/raw/master/Nvidia-Centric-RIVA.png",3089131696,"bias","explainability","privacy","safetyAndSecurity",[],[],[],[],[],{"_170":1228,"_172":1229},"ngcApps",[1181],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1162":1163,"_1164":1165,"_1166":1167,"_1168":1169,"_1170":259,"_202":287,"_1171":1172,"_1173":51,"_1174":213,"_1175":213,"_1176":1177,"_1178":213,"_1179":1180},"amplitude",{"_1159":1160,"_801":-7,"_1161":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1153":1154,"_1155":1156,"_1157":1158},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1785969994241,"1.11.0","defaultSearchSuggestions",{"_1041":369,"_1042":1043,"_1044":1045,"_1046":1047},"announcements",[],"consentState",{"_325":1031,"_1032":213,"_1033":26,"_1034":26,"_1035":1036},"consentConfig",{"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326},"showContactAdmin",1785969994242,[],[],[301,302,303,304,305,306],{"_40":55,"_42":66,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":63,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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