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	---
	language: "en"
	thumbnail:
	tags:
	- speechbrain
	- embeddings
	- Speaker
	- Verification
	- Identification
	- pytorch
	- ECAPA
	- TDNN
	license: "apache-2.0"
	datasets:
	- voxceleb
	metrics:
	- EER
	widget:
	- example_title: VoxCeleb Speaker id10003
	src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav
	- example_title: VoxCeleb Speaker id10004
	src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb_00004.wav
	---

	<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 ECAPA-TDNN embeddings on Voxceleb

	This repository provides all the necessary tools to perform speaker verification with a pretrained ECAPA-TDNN model using SpeechBrain.
	The system can be used to extract speaker embeddings as well.
	It 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 model performance on Voxceleb1-test set(Cleaned) is:

	\| Release \| EER(%)
	\|:-------------:\|:--------------:\|
	\| 05-03-21 \| 0.80 \|


	## Pipeline description

	This system is composed of an ECAPA-TDNN model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss. Speaker Verification is performed using cosine distance between speaker embeddings.

	## Install SpeechBrain

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

	```
	pip install git+https://github.com/speechbrain/speechbrain.git@develop
	```

	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.inference.speaker import EncoderClassifier
	classifier = EncoderClassifier.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb")
	signal, fs =torchaudio.load('tests/samples/ASR/spk1_snt1.wav')
	embeddings = classifier.encode_batch(signal)
	```
	The system is trained with recordings sampled at 16kHz (single channel).
	The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling classify_file if needed. Make sure your input tensor is compliant with the expected sampling rate if you use encode_batch and classify_batch.

	### Perform Speaker Verification

	```python
	from speechbrain.inference.speaker import SpeakerRecognition
	verification = SpeakerRecognition.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb", savedir="pretrained_models/spkrec-ecapa-voxceleb")
	score, prediction = verification.verify_files("tests/samples/ASR/spk1_snt1.wav", "tests/samples/ASR/spk2_snt1.wav") # Different Speakers
	score, prediction = verification.verify_files("tests/samples/ASR/spk1_snt1.wav", "tests/samples/ASR/spk1_snt2.wav") # Same Speaker
	```
	The prediction is 1 if the two signals in input are from the same speaker and 0 otherwise.

	### 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_ecapa_tdnn.yaml --data_folder=your_data_folder
	```

	You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1-ahC1xeyPinAHp2oAohL-02smNWO41Cc?usp=sharing).

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

	#### Referencing ECAPA-TDNN
	```
	@inproceedings{DBLP:conf/interspeech/DesplanquesTD20,
	author = {Brecht Desplanques and
	Jenthe Thienpondt and
	Kris Demuynck},
	editor = {Helen Meng and
	Bo Xu and
	Thomas Fang Zheng},
	title = {{ECAPA-TDNN:} Emphasized Channel Attention, Propagation and Aggregation
	in {TDNN} Based Speaker Verification},
	booktitle = {Interspeech 2020},
	pages = {3830--3834},
	publisher = {{ISCA}},
	year = {2020},
	}
	```

	# 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}
	}
	```

	# About SpeechBrain
	- Website: https://speechbrain.github.io/
	- Code: https://github.com/speechbrain/speechbrain/
	- HuggingFace: https://huggingface.co/speechbrain/