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--- |
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language: vi |
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datasets: |
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- youtube-vi-13k-hours |
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tags: |
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- speech |
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license: cc-by-nc-4.0 |
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--- |
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# Vietnamese Self-Supervised Learning Wav2Vec2 model |
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## Model |
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We use wav2vec2 architecture for doing Self-Supervised learning |
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<img src="https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/wav2vec2.png" width=50% height=50%> |
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## Data |
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Our self-supervised model is pre-trained on a massive audio set of 13k hours of Vietnamese youtube audio, which includes: |
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- Clean audio |
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- Noise audio |
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- Conversation |
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- Multi-gender and dialects |
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## Download |
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We have already upload our pre-trained model to the Huggingface. |
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- [Based version](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vi) ~ 95M params |
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- [Large version](https://huggingface.co/nguyenvulebinh/wav2vec2-large-vi) ~ 317M params |
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## Usage |
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```python |
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from transformers import Wav2Vec2ForPreTraining, Wav2Vec2Processor |
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model_name = 'nguyenvulebinh/wav2vec2-base-vi' |
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# model_name = 'nguyenvulebinh/wav2vec2-large-vi' |
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model = Wav2Vec2ForPreTraining.from_pretrained(model_name) |
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processor = Wav2Vec2Processor.from_pretrained(model_name) |
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``` |
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Since our model has the same architecture as the English wav2vec2 version, you can use [this notebook](https://colab.research.google.com/drive/1FjTsqbYKphl9kL-eILgUc-bl4zVThL8F?usp=sharing) for more information on how to fine-tune the model. |
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## Contact |
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[email protected] / [email protected] |
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[](https://twitter.com/intent/follow?screen_name=nguyenvulebinh) |
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