Upload safety, privacy, explainability and bias.md

bias_subcard.md

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+Field                                                                                               |  Response
+:---------------------------------------------------------------------------------------------------|:---------------
+Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing:   |   None
+Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/protected-classes) in model design and testing | None
+Measures taken to mitigate against unwanted bias:                                                   |  None

explainability.md

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+Field                                                                                                  |  Response
+:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------
+Intended Domain:                                                                   |  Speech to text transcription
+Model Type:                                                                                            |  ASR
+Intended Users:                                                                                        |  This model is intended for developers, researchers, academics, and industries building conversational based applications.
+Output:                                                                                                |  Text 
+Describe how the model works:                                                                          |  The model transcribes audio input into text in the input language.
+Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of:  |  Not Applicable
+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 (milliseconds), Throughput (total audio processed per unit of time).
+Potential Known Risks:                                                                                 |  Not recommended for word-for-word transcription, as accuracy varies with the characteristics of the input audio (domain, use case, accent, noise level, speech type, and speech context).
+Licensing:                                                                                             |  GOVERNING TERMS: Use of this model is governed by the NVIDIA Open Model License Agreement (found at [https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/))
+

privacy.md

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+Field                                                                                                                              |  Response
+:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------
+Generatable or reverse engineerable personal data?                                                     |  No
+Personal data used to create this model?                                                                                       |  Yes - Voice
+Was consent obtained for any personal data used?                                                                                             |  Yes
+Is a mechanism in place to honor data subject right of access or deletion of personal data?                                        |  Yes 
+If personal data was collected for the development of the model, was it collected directly by NVIDIA?                                            |  Yes
+If personal data was collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects?  |  Yes
+If personal data was collected for the development of this AI model, was it minimized to only what was required?                                 |  Yes
+Is there 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/]
+

safety.md

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+Field                                               |  Response
+:---------------------------------------------------|:----------------------------------
+Model Application Field(s):                               |  Speech to Text Transcription
+Describe the life critical impact (if present).   |  Not Applicable
+Use Case Restrictions:                              |  Abide by the NVIDIA Open Model License Agreement (found at [https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/)).
+Model and dataset restrictions:            |  The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.