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This model produces embeddings that globally represent the non-timbral traits (prosody, accent, ...) of a speaker's voice. These embeddings can be used the same way as for a classical speaker verification (ASV): in order to compare two voice signals, an embeddings vector must be computed for each of them. Then the cosine similarity between the two embeddings can be used for comparison. The main difference with classical ASV embeddings is that here only the non-timbral traits are compared. The model has been derived from the self-supervised pretrained model WavLM-large. 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.

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This model produces embeddings that globally represent the timbral traits of a speaker's voice. These embeddings can be used the same way as for a classical speaker verification (ASV): in order to compare two voice signals, an embeddings vector must be computed for each of them. Then the cosine similarity between the two embeddings can be used for comparison. The main difference with classical ASV embeddings is that, here, only the timbral traits are compared. 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 python file also contains the function to compute the timbral embeddings 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.

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