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:
- src: >-
https://huggingface.co/nvidia/speakerverification_en_titanet_large/resolve/main/an255-fash-b.wav
example_title: Speech sample 1
- src: >-
https://huggingface.co/nvidia/speakerverification_en_titanet_large/resolve/main/cen7-fash-b.wav
example_title: Speech sample 2
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)
| 
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
Using
emb = speaker_model.get_embedding('nvidia/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('nvidia/an255-fash-b.wav','nvidia/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": "<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 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.