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README.md

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+---
+language:
+- en
+license: cc-by-4.0
+library_name: nemo
+datasets:
+- librispeech_asr
+- VOXCCELEB-1
+- VOXCCELEB-2
+- FISHER
+- Switchboard
+- SRE(2004-2010)
+thumbnail: null
+tags:
+- speaker-recognition
+- speech
+- audio
+- speaker-verification
+- titanet
+- speaker-diarization
+- NeMo
+- pytorch
+model-index:
+- name: speakerverification_en
+  results: []
+
+---
+
+
+## Model Overview
+
+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](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user
+
+## 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
+
+```python
+import nemo.collections.asr as nemo_asr
+speaker_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained("nvidia/speakerverification_en_titanet_large")
+```
+
+### Embedding Extraction
+
+Using 
+
+```python
+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:
+
+```python
+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:
+
+```json
+{"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:
+```shell
+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](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speaker_recognition/models.html). 
+
+## Training
+
+The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_tasks/recognition/speaker_reco.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_tasks/recognition/conf/titanet-large.yaml).
+
+### 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](https://developer.nvidia.com/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](https://huggingface.co/models?other=Riva).  
+Check out [Riva live demo](https://developer.nvidia.com/riva#demos). 
+
+## References
+[1] [TitaNet: Neural Model for Speaker Representation with 1D Depth-wise Separable convolutions and global context](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9746806) 
+[2] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
+
+## Licence
+
+License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/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](https://creativecommons.org/licenses/by/4.0/) license.
+