Instructions to use facebook/w2v-bert-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/w2v-bert-2.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="facebook/w2v-bert-2.0")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("facebook/w2v-bert-2.0") model = AutoModel.from_pretrained("facebook/w2v-bert-2.0") - Notebooks
- Google Colab
- Kaggle
Link to "SeamlessM4T v1" paper, where the w2v-BERT 2.0 was presented for the first time.
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# W2v-BERT 2.0 speech encoder
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We are open-sourcing our Conformer-based [W2v-BERT 2.0 speech encoder](#w2v-bert-20-speech-encoder) as described in Section
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This model was pre-trained on 4.5M hours of unlabeled audio data covering more than 143 languages. It requires finetuning to be used for downstream tasks such as Automatic Speech Recognition (ASR), or Audio Classification.
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# W2v-BERT 2.0 speech encoder
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We are open-sourcing our Conformer-based [W2v-BERT 2.0 speech encoder](#w2v-bert-20-speech-encoder) as described in Section 4.1 of the [paper](https://arxiv.org/abs/2308.11596), which is at the core of our Seamless models.
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This model was pre-trained on 4.5M hours of unlabeled audio data covering more than 143 languages. It requires finetuning to be used for downstream tasks such as Automatic Speech Recognition (ASR), or Audio Classification.
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