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	---
	language: "en"
	thumbnail:
	tags:
	- embeddings
	- Speaker
	- Verification
	- Identification
	- pytorch
	- xvectors
	- TDNN
	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},
	}
	```


	#### Referencing SpeechBrain

	```
	@misc{SB2021,
	author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
	title = {SpeechBrain},
	year = {2021},
	publisher = {GitHub},
	journal = {GitHub repository},
	howpublished = {\url{https://github.com/speechbrain/speechbrain}},
	}
	```

	#### About SpeechBrain
	SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.

	Website: https://speechbrain.github.io/

	GitHub: https://github.com/speechbrain/speechbrain