## Speaker Diarization: TitaNet Model Card

#### Model Overview

TitaNet is a novel neural network architecture for extracting speaker representations.

#### Model Architecture

TitaNet employs 1D depth-wise separable convolutions with Squeeze-and-Excitation (SE) layers with global context followed by channel attention based statistics pooling layer to map variable-length utterances to a fixed-length embedding (t-vector) [1].

#### Training

These models were trained on a composite dataset comprising of several thousand hours of speech, compiled from various publicly available sources. The NeMo toolkit [2] was used for training this model over few hundred epochs on multiple GPUs.

#### How to Use this Model

To use this model, we can use Riva Skills Quick start guide, it is a starting point to try out Riva models. Information regarding Quick start guide can be found : [here](https://ngc.nvidia.com/catalog/resources/nvidia:riva:riva_quickstart).
To use Riva Speech ASR service using this model, [document](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/asr/asr-overview.html) has the necessary information.

#### Input

This model accepts 16000 KHz Mono-channel Audio (wav files) as input.

#### Output

This model provides embeddings of size 192 from a speaker for a given audio sample.

#### References

[1] [TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context](https://arxiv.org/pdf/2110.04410.pdf)
[2] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)

#### License

By downloading and using the models and resources packaged with Riva Conversational AI, you would be accepting the terms of the Riva [license](https://developer.nvidia.com/riva/ga/license)

diarizer_titanet_small

Embedding Extractor model used in Riva Speaker Diarization

RIVA Diarizer Embedding Extractor

Description

Publisher

Use Case

Framework

Latest Version

Modified

Size