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Field | Response
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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/)
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