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	---
	language: "en"
	thumbnail:
	tags:
	- embeddings
	- Speaker
	- Verification
	- Identification
	- pytorch
	- xvectors
	- TDNN
	- speechbrain
	- audio-classification
	license: "apache-2.0"
	datasets:
	- voxceleb
	metrics:
	- EER
	- min_dct
	---

	<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
	<br/><br/>

	# Speaker Verification with xvector embeddings on Voxceleb

	This repository provides all the necessary tools to extract speaker embeddings with a pretrained TDNN model using SpeechBrain.
	The system is trained on Voxceleb 1+ Voxceleb2 training data.

	For a better experience, we encourage you to learn more about
	[SpeechBrain](https://speechbrain.github.io). The given model performance on Voxceleb1-test set (Cleaned) is:

	\| Release \| EER(%)
	\|:-------------:\|:--------------:\|
	\| 05-03-21 \| 3.2 \|


	## Pipeline description
	This system is composed of a TDNN model coupled with statistical pooling. The system is trained with Categorical Cross-Entropy Loss.

	## Install SpeechBrain

	First of all, please install SpeechBrain with the following command:

	```
	pip install speechbrain
	```

	Please notice that we encourage you to read our tutorials and learn more about
	[SpeechBrain](https://speechbrain.github.io).

	### Compute your speaker embeddings

	```python
	import torchaudio
	from speechbrain.pretrained import EncoderClassifier
	classifier = EncoderClassifier.from_hparams(source="speechbrain/spkrec-xvect-voxceleb", savedir="pretrained_models/spkrec-xvect-voxceleb")
	signal, fs =torchaudio.load('samples/audio_samples/example1.wav')
	embeddings = classifier.encode_batch(signal)
	```

	### Inference on GPU
	To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.

	### Training
	The model was trained with SpeechBrain (aa018540).
	To train it from scratch follows these steps:
	1. Clone SpeechBrain:
	```bash
	git clone https://github.com/speechbrain/speechbrain/
	```
	2. Install it:
	```
	cd speechbrain
	pip install -r requirements.txt
	pip install -e .
	```

	3. Run Training:
	```
	cd recipes/VoxCeleb/SpeakerRec/
	python train_speaker_embeddings.py hparams/train_x_vectors.yaml --data_folder=your_data_folder
	```

	You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1RtCBJ3O8iOCkFrJItCKT9oL-Q1MNCwMH?usp=sharing).

	### Limitations
	The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.

	#### Referencing xvectors
	```@inproceedings{DBLP:conf/odyssey/SnyderGMSPK18,
	author = {David Snyder and
	Daniel Garcia{-}Romero and
	Alan McCree and
	Gregory Sell and
	Daniel Povey and
	Sanjeev Khudanpur},
	title = {Spoken Language Recognition using X-vectors},
	booktitle = {Odyssey 2018},
	pages = {105--111},
	year = {2018},
	}
	```


	# Citing SpeechBrain
	Please, cite SpeechBrain if you use it for your research or business.


	```bibtex
	@misc{speechbrain,
	title={{SpeechBrain}: A General-Purpose Speech Toolkit},
	author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
	year={2021},
	eprint={2106.04624},
	archivePrefix={arXiv},
	primaryClass={eess.AS},
	note={arXiv:2106.04624}
	}
	```