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--- |
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language: lt |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- voxpopuli |
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datasets: |
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- voxpopuli |
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license: cc-by-nc-4.0 |
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inference: false |
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--- |
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# Wav2Vec2-base-VoxPopuli |
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[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **lt** on **14.4k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). |
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The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. |
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**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **lt**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. |
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**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation |
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Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)* |
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**Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*. |
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See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/). |
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