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[Facebook's Wav2Vec2 Conformer (TODO-add link)]()
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Wav2Vec2 Conformer with relative position embeddings, pretrained on 960h hours of Librispeech and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
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Authors
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**Abstract**
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The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
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# Usage
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To transcribe audio files the model can be used as a standalone acoustic model as follows:
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[Facebook's Wav2Vec2 Conformer (TODO-add link)]()
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Wav2Vec2 Conformer with relative position embeddings, pretrained on 960h hours of Librispeech and and fine-tuned on **100 hours of Librispeech** on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
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**Paper**: [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171)
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**Authors**: Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino
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The results of Wav2Vec2-Conformer can be found in Table 3 and Table 4 of the [official paper](https://arxiv.org/abs/2010.05171).
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The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
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# Usage
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To transcribe audio files the model can be used as a standalone acoustic model as follows:
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