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/)