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---
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](https://huggingface.co/datasets/arabic_speech_corpus) has 100 files for a single male speaker and [ClArTTS](https://www.clartts.com/) has 205 files for a single male speaker.
The X-vectors were extracted using [this script](https://huggingface.co/mechanicalsea/speecht5-vc/blob/main/manifest/utils/prep_cmu_arctic_spkemb.py), which uses the `speechbrain/spkrec-xvect-voxceleb` model.
Usage:
```python
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)
``` |