 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('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*.  