Instructions to use facebook/wav2vec2-base-mt-voxpopuli-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/wav2vec2-base-mt-voxpopuli-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-mt-voxpopuli-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForPreTraining processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-mt-voxpopuli-v2") model = AutoModelForPreTraining.from_pretrained("facebook/wav2vec2-base-mt-voxpopuli-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/wav2vec2-base-mt-voxpopuli-v2: direct link, hf CLI and curl.
- Browser
- Download file 380 MB
-
https://huggingface.co/facebook/wav2vec2-base-mt-voxpopuli-v2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/wav2vec2-base-mt-voxpopuli-v2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/wav2vec2-base-mt-voxpopuli-v2/resolve/main/pytorch_model.bin
380 MB
- Xet hash:
- 57142a87aa32b0cc3a2ff5fcebc547d8765f2c09b2630e589d9f734f4b9e33c8
- Size of remote file:
- 380 MB
- SHA256:
- d5417967185c9a5152ad72466ae467476b07b29cb37ddb9bc3b66f8c92e77ef4
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