Adding subcards from NeMo model cards

Signed-off-by: taejinp <tango4j@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
+Bias Metric (If Measured):                                                   |  None

explainability.md

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+Field                                                                                                  |  Response
+:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------
+Intended Task/Domain:                                                                   |  Speaker Diarization (Speaker Tagging in Speech Recognition)
+Model Type:                                                                                            |  FastConformer Encoder, Transformer Encoder, and RNNT Decoder
+Intended Users:                                                                                        |  People working with conversational AI models that transcribe speech-to-text for multiple users.
+Output:                                                                                                |  Text with speaker tags
+Describe how the model works:                                                                          |  The model incorporates a novel mechanism, the Arrival-Order Speaker Cache (AOSC). This cache management technique dynamically adjusts each speaker’s cache size, prioritizing the speech frames most valuable to cache. The model is fine-tuned with increased weighting on far-field datasets to perform better for meeting-style speech.
+Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of:  |  Not Applicable
+Technical Limitations & Mitigation:                                                                    |  This model can detect up to four speakers; performance degrades in recordings with five or more speakers. The model was trained on publicly available English speech datasets. As a result, it is not suitable for non-English audio. Performance may also degrade on out-of-domain data, such as recordings in noisy conditions.
+Verified to have met prescribed NVIDIA quality standards:  |  Yes
+Performance Metrics:                                                                                   |  Concatenated minimum-permutation word error rate (cpWER) and time-constrained minimum-permutation word error rate (tcpWER)
+Potential Known Risks:                                                                                 |  Transcripts may not be 100% accurate in instances with background noise. Punctuation/capitalization may not be 100% accurate.
+Licensing:                                                                                             |  GOVERNING TERMS: Use of this model is governed by the NVIDIA Open Model License Agreement (found [here](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
+How often is dataset reviewed?                                                                          | Before Release 
+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
+Was data from user interactions with the AI model (e.g. user input and prompts) used to train the model?                                         | No
+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):                               |   Speaker Tagging in Speech Recognition Systems 
+Describe the life critical impact (if present).   |  Not Applicable 
+Use Case Restrictions:                              |  Abide by [NVIDIA Open Model License Agreement](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.