Speaker Embeddings extractor This model produces speaker embeddings for automatic speaker verification (ASV). Speaker verification is performed by computing embeddings vectors by applying this model to any two voice signals. Then the cosine similarity between the two embeddings can be used to compare the two voices. The model has been derived from the self supervised pretrained model WavLM large. Usage The following code snippet uses the file spk embeddings.py to build the architecture of the model. Its weights are then downloaded from this repository. The model produces normalized vectors as embeddings. The python file also contains the function to compute the embeddings vector of an audio file. In this tutorial version, the audio file is expected to be sampled at 16kHz. Depending on the available memory (cpu or gpu), you may change the value of the max size parameter, which is used to truncate the long audio signals. finally, we can compute two embeddings from two different files and compare them with a cosine similarity: Evaluations The model has been evaluated on the standard ASV VoxCeleb1 clean test set. It results in an Equal Error Rate (EER, lower value denotes a better iden…
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