franjamonga
/

speakerverification_en

@@ -1,143 +0,0 @@
----
-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.