speakerverification en titanet large EU AI Act Compliance Profile

NVIDIA TitaNet-Large (en-US)

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This model extracts speaker embeddings from given speech, which is the backbone for speaker verification and diarization tasks. It is a "large" version of TitaNet (around 23M parameters) models.
See the model architecture section and NeMo documentation for complete architecture details.

NVIDIA NeMo: Training

To train, fine-tune or play with the model you will need to install NVIDIA NeMo. We recommend you install it after you've installed the latest Pytorch version.

How to Use this Model

The model is available for use in the NeMo toolkit [3] and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Automatically instantiate the model

import nemo.collections.asr as nemo_asr
speaker_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained("nvidia/speakerverification_en_titanet_large")

Embedding Extraction

Using

emb = speaker_model.get_embedding("an255-fash-b.wav")

Verifying two utterances (Speaker Verification)

Now to check if two audio files are from the same speaker or not, simply do:

speaker_model.verify_speakers("an255-fash-b.wav","cen7-fash-b.wav")

Extracting Embeddings for more audio files

To extract embeddings from a bunch of audio files:

Write audio files to a manifest.json file with lines as in format:

{"audio_filepath": "

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