filename
stringlengths
28
34
xvector
sequence
cmu_us_awb_arctic-wav-arctic_a0001
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cmu_us_awb_arctic-wav-arctic_a0002
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cmu_us_awb_arctic-wav-arctic_a0003
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cmu_us_awb_arctic-wav-arctic_a0004
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cmu_us_awb_arctic-wav-arctic_a0005
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cmu_us_awb_arctic-wav-arctic_a0006
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cmu_us_awb_arctic-wav-arctic_a0007
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cmu_us_awb_arctic-wav-arctic_a0008
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cmu_us_awb_arctic-wav-arctic_a0009
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cmu_us_awb_arctic-wav-arctic_a0010
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cmu_us_awb_arctic-wav-arctic_a0011
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cmu_us_awb_arctic-wav-arctic_a0012
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cmu_us_awb_arctic-wav-arctic_a0013
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cmu_us_awb_arctic-wav-arctic_a0014
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cmu_us_awb_arctic-wav-arctic_a0015
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cmu_us_awb_arctic-wav-arctic_a0016
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cmu_us_awb_arctic-wav-arctic_a0017
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cmu_us_awb_arctic-wav-arctic_a0018
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Speaker embeddings extracted from CMU ARCTIC

There is one .npy file for each utterance in the dataset, 7931 files in total. The speaker embeddings are 512-element X-vectors.

The CMU ARCTIC dataset divides the utterances among the following speakers:

  • bdl (US male)
  • slt (US female)
  • jmk (Canadian male)
  • awb (Scottish male)
  • rms (US male)
  • clb (US female)
  • ksp (Indian male)

The X-vectors were extracted using this script, which uses the speechbrain/spkrec-xvect-voxceleb model.

Usage:

from datasets import load_dataset
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")

speaker_embeddings = embeddings_dataset[7306]["xvector"]
speaker_embeddings = torch.tensor(speaker_embeddings).unsqueeze(0)
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