metadata

language:
  - en
library_name: nemo
datasets:
  - VOXCELEB-1
  - VOXCELEB-2
  - FISHER
  - switchboard
  - librispeech_asr
  - SRE (2004-2010)
thumbnail: null
tags:
  - speaker
  - speech
  - audio
  - speaker-verification
  - speaker-recognition
  - speaker-diarization
  - titanet
  - NeMo
  - pytorch
license: cc-by-4.0
widget:
  - example_title: Librispeech sample 1
    src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
  - example_title: Librispeech sample 2
    src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
model-index:
  - name: speakerverification_en_titanet_large
    results:
      - task:
          name: Speaker Verification
          type: speaker-verification
        dataset:
          name: VoxCeleb-1 (Cleaned)
          type: voxceleb1-O
          config: clean
          split: test
          args:
            language: en
        metrics:
          - name: Test EER
            type: eer
            value: 0.66
      - task:
          type: Speaker Diarization
          name: speaker-diarization
        dataset:
          name: AMI (MixHeadset)
          type: ami_diarization
          config: oracle-vad-known-number-of-speakers
          split: test
          args:
            language: en
        metrics:
          - name: Test DER
            type: der
            value: 1.73
      - task:
          type: Speaker Diarization
          name: speaker-diarization
        dataset:
          name: AMI (MixHeadset)
          type: ami_diarization
          config: oracle-vad-unknown-number-of-speakers
          split: test
          args:
            language: en
        metrics:
          - name: Test DER
            type: der
            value: 1.89
      - task:
          type: Speaker Diarization
          name: speaker-diarization
        dataset:
          name: AMI (Lapel)
          type: ami_diarization
          config: oracle-vad-known-number-of-speakers
          split: test
          args:
            language: en
        metrics:
          - name: Test DER
            type: der
            value: 2.03
      - task:
          type: Speaker Diarization
          name: speaker-diarization
        dataset:
          name: AMI (Lapel)
          type: ami_diarization
          config: oracle-vad-unknown-number-of-speakers
          split: test
          args:
            language: en
        metrics:
          - name: Test DER
            type: der
            value: 2.03
      - task:
          type: Speaker Diarization
          name: speaker-diarization
        dataset:
          name: CH109
          type: callhome_diarization
          config: oracle-vad-known-number-of-speakers
          split: test
          args:
            language: en
        metrics:
          - name: Test DER
            type: der
            value: 1.19
      - task:
          type: Speaker Diarization
          name: speaker-diarization
        dataset:
          name: CH109
          type: callhome_diarization
          config: oracle-vad-unknown-number-of-speakers
          split: test
          args:
            language: en
        metrics:
          - name: Test DER
            type: der
            value: 1.63
      - task:
          type: Speaker Diarization
          name: speaker-diarization
        dataset:
          name: NIST SRE 2000
          type: nist-sre_diarization
          config: oracle-vad-known-number-of-speakers
          split: test
          args:
            language: en
        metrics:
          - name: Test DER
            type: der
            value: 6.73
      - task:
          type: Speaker Diarization
          name: speaker-diarization
        dataset:
          name: NIST SRE 2000
          type: nist-sre_diarization
          config: oracle-vad-unknown-number-of-speakers
          split: test
          args:
            language: en
        metrics:
          - name: Test DER
            type: der
            value: 5.38

NVIDIA TitaNet-Large (en-US)

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This model extracts speaker embeddings from given speech, which are 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 latest Pytorch version.

pip install nemo_toolkit['all']

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

First, let's get a sample

wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav

Then do:

emb = speaker_model.get_embedding('2086-149220-0033.wav']

Verifying two utterances (Speaker Verification)

let's get another sample

wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav

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

speaker_model.verify_speakers('path/to/one/audio_file','path/to/other/audio_file')

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": "<absolute path to dataset>/audio_file.wav", "duration": "duration of file in sec", "label": "speaker_id"}

Then running following script will extract embeddings and writes to current working directory:

python <NeMo_root>/examples/speaker_tasks/recognition/extract_speaker_embeddings.py --manifest=manifest.json

Input

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

Output

This model provides speaker embeddings for an audio file.

Model Architecture

TitaNet model is a depth-wise separable conv1D model [1] for Speaker Verification and diarization tasks. You may find more info on the detail of this model here: TitaNet-Model.

Training

The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this example script and this base config.

Datasets

All the models in this collection are trained on a composite dataset comprising several thousand hours of English speech:

Voxceleb-1
Voxceleb-2
Fisher
Switchboard
Librispeech
SRE (2004-2010)

Performance

Performances of the these models are reported in terms of Equal Error Rate (EER%) on speaker verification evaluation trial files and as Diarization Error Rate (DER%) on diarization test sessions.

Speaker Verification (EER%)

Version Model Model Size VoxCeleb1 (Cleaned trial file)

1.10.0 TitaNet-Large 23M 0.66

Version	Model	Model Size	VoxCeleb1 (Cleaned trial file)
1.10.0	TitaNet-Large	23M	0.66

Speaker Diarization (DER%)

Version	Model	Model Size	Evaluation Condition	NIST SRE 2000	AMI (Lapel)	AMI (MixHeadset)	CH109
1.10.0	TitaNet-Large	23M	Oracle VAD KNOWN # of Speakers	6.73	2.03	1.73	1.19
1.10.0	TitaNet-Large	23M	Oracle VAD UNKNOWN # of Speakers	5.38	2.03	1.89	1.63

Limitations

This model is trained on both telephonic and non-telephonic speech from voxceleb datasets, Fisher and switch board. If your domain of data differs from trained data or doesnot show relatively good performance consider finetuning for that speech domain.

NVIDIA Riva: Deployment

NVIDIA Riva, is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded. Additionally, Riva provides:

World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours
Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization
Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support

Although this model isn’t supported yet by Riva, the list of supported models is here.
Check out Riva live demo.

References

[1] TitaNet: Neural Model for Speaker Representation with 1D Depth-wise Separable convolutions and global context [2] NVIDIA NeMo Toolkit

Licence

License to use this model is covered by the CC-BY-4.0. By downloading the public and release version of the model, you accept the terms and conditions of the CC-BY-4.0 license.