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--- |
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dataset_info: |
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features: |
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- name: name |
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dtype: string |
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- name: speaker_embeddings |
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sequence: float32 |
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splits: |
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- name: validation |
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num_bytes: 634175 |
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num_examples: 305 |
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download_size: 979354 |
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dataset_size: 634175 |
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license: mit |
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language: |
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- ar |
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size_categories: |
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- n<1K |
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task_categories: |
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- text-to-speech |
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- audio-to-audio |
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pretty_name: Arabic(M) Speaker Embeddings |
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--- |
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# Arabic Speaker Embeddings extracted from ASC and ClArTTS |
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There is one speaker embedding for each utterance in the validation set of both datasets. The speaker embeddings are 512-element X-vectors. |
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[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. |
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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. |
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Usage: |
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```python |
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from datasets import load_dataset |
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embeddings_dataset = load_dataset("herwoww/arabic_xvect_embeddings", split="validation") |
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speaker_embedding = torch.tensor(embeddings_dataset[1]["speaker_embeddings"]).unsqueeze(0) |
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``` |
