| # Speaker Identification with ECAPA-TDNN embeddings on Voxceleb | |
| This repository provides a pretrained ECAPA-TDNN model using SpeechBrain. The system can be used to extract speaker embeddings as well. It is trained on Voxceleb 2 development data only. | |
| # 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. | |
| # Compute the speaker embeddings | |
| The system is trained with recordings sampled at 16kHz (single channel). | |
| ```python | |
| import torchaudio | |
| from speechbrain.pretrained import EncoderClassifier | |
| classifier = EncoderClassifier.from_hparams( | |
| source="yangwang825/ecapa-tdnn-vox2" | |
| ) | |
| signal, fs = torchaudio.load('spk1_snt1.wav') | |
| embeddings = classifier.encode_batch(signal) | |
| ``` | |
| You can find our training results (models, logs, etc) here. |