added bais, explainability, privacy, safety model cards

Signed-off-by: Kunal Dhawan <kunaldhawan97@gmail.com>

bias.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
+Measures taken to mitigate against unwanted bias:                                                   |  None

explainability.md

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+Field                                                                                                  |  Response
+:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------
+Intended Task/Domain:                                                                   |  Speech Recognition
+Model Type:                                                                                            |  FastConformer-RNNT
+Intended Users:                                                                                        |  People who work with conversational AI models and need to transcribe speech to text with low latency in streaming scenarios.
+Output:                                                                                                |  Text tokens
+Describe how the model works:                                                                          |  Raw audio is passed into the model, and the model outputs text.
+Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of:  |  Not Applicable
+Technical Limitations & Mitigation:                                                                    |  The model is trained on only a limited amount of English speech data; therefore, it may not work well for other languages, and its performance may degrade in noisy environments.
+Verified to have met prescribed NVIDIA quality standards:  |  Yes
+Performance Metrics:                                                                                   |  Word Error Rate (WER)
+Potential Known Risks:                                                                                 |  The model may produce incorrect transcriptions if the audio is noisy or the speech is not clear, and predicted text may be inaccurate in domains that are not well-represented in the training data.
+Licensing:                                                                                             | [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):                               | Customer Service
+Describe the life critical impact (if present).   |  Not Applicable 
+Use Case Restrictions:                              |  Abide by [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.
+