| | --- |
| | 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://huggingface.co/datasets/MBZUAI/ClArTTS) 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_xvector_embeddings", split="validation") |
| | speaker_embedding = torch.tensor(embeddings_dataset[1]["speaker_embeddings"]).unsqueeze(0) |
| | ``` |
