README.md

metadata

dataset_info:
  features:
    - name: name
      dtype: string
    - name: speaker_embeddings
      sequence: float32
  splits:
    - name: validation
      num_bytes: 634175
      num_examples: 305
  download_size: 979354
  dataset_size: 634175
license: mit
language:
  - ar
size_categories:
  - n<1K
task_categories:
  - text-to-speech
  - audio-to-audio
pretty_name: Arabic(M) Speaker Embeddings

Arabic Speaker Embeddings extracted from ASC and ClArTTS

There is one speaker embedding for each utterance in the validation set of both datasets. The speaker embeddings are 512-element X-vectors.

Arabic Speech Corpus has 100 files for a single male speaker and ClArTTS has 205 files for a single male speaker.

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("herwoww/arabic_xvect_embeddings", split="validation")
speaker_embedding = torch.tensor(embeddings_dataset[1]["speaker_embeddings"]).unsqueeze(0)