ParthBhuva97/TTS
Updated
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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_a0009
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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_a0016
|
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cmu_us_awb_arctic-wav-arctic_a0017
|
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in Data Studio
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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Size of downloaded dataset files:
17.9 MB
Size of the auto-converted Parquet files:
21.3 MB
Number of rows:
7,931
Models trained or fine-tuned on Matthijs/cmu-arctic-xvectors
