speakerverification_en_titanet_large
Maintainer: nvidia
55
⚙️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The speakerverification_en_titanet_large model is a speaker embedding extraction model developed by NVIDIA. It is a "large" version of the TitaNet model, with around 23 million parameters. The model can be used as the backbone for speaker verification and diarization tasks, extracting speaker embeddings from audio input.
The model is available for use in the NVIDIA NeMo toolkit, and can be used as a pre-trained checkpoint for inference or fine-tuning. Similar models include the parakeet-rnnt-1.1b and parakeet-tdt-1.1b models, which are large ASR models developed by NVIDIA and Suno.ai.
Model inputs and outputs
Inputs
- 16000 kHz Mono-channel Audio (wav files)
Outputs
- Speaker embeddings for an audio file
Capabilities
The speakerverification_en_titanet_large model can extract speaker embeddings from audio input, which are useful for speaker verification and diarization tasks. For example, the model can be used to verify if two audio files are from the same speaker or not.
What can I use it for?
The speaker embeddings produced by the speakerverification_en_titanet_large model can be used in a variety of applications, such as speaker identification, speaker diarization, and voice biometrics. These embeddings can be used as input to downstream models for tasks like speaker verification, where the goal is to determine if two audio samples are from the same speaker.
Things to try
One interesting thing to try with the speakerverification_en_titanet_large model is to use it for large-scale speaker diarization. By extracting speaker embeddings for each audio segment and clustering them, you can automatically identify the different speakers in a multi-speaker audio recording. This could be useful for applications like meeting transcription or content moderation.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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