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README.md

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 ---
+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)
+
+<style>
+img {
+ display: inline;
+}
+</style>
+
+| [![Model architecture](https://img.shields.io/badge/Model_Arch-TitaNet--Large-lightgrey#model-badge)](#model-architecture)
+| [![Model size](https://img.shields.io/badge/Params-23M-lightgrey#model-badge)](#model-architecture)
+| [![Language](https://img.shields.io/badge/Language-en--US-lightgrey#model-badge)](#datasets)
+
+
+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](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/speaker_recognition/models.html#titanet) for complete architecture details.
+
+## NVIDIA NeMo: Training
+
+To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/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
+
+```python
+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:
+
+```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.