Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
English
wav2vec2
speech
audio
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use facebook/wav2vec2-large-960h-lv60-self with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/wav2vec2-large-960h-lv60-self with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-960h-lv60-self")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self") model = AutoModelForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/wav2vec2-large-960h-lv60-self: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/wav2vec2-large-960h-lv60-self/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- e6725a55c0fb20b65d5d5575905cdc7c5811060f9290a7773b576c26354699a4
- Size of remote file:
- 1.26 GB
- SHA256:
- 00b604cf4d28e86559e8adaeb3a186daa89dc37f5ab216771a0a15a26db0de9f
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