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 feature_extractor_config.json from facebook/wav2vec2-large-960h-lv60-self: direct link, hf CLI and curl.
- Browser
- Download file 157 Bytes
-
https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self/resolve/main/feature_extractor_config.json
- Command line
-
hf download hf://facebook/wav2vec2-large-960h-lv60-self/feature_extractor_config.json
-
curl -L -o feature_extractor_config.json https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self/resolve/main/feature_extractor_config.json
157 Bytes
| { | |
| "do_normalize": true, | |
| "feature_dim": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| } | |