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facebook
/
data2vec-audio-base-960h

Automatic Speech Recognition
Transformers
PyTorch
English
data2vec-audio
speech
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Model card Files Files and versions
xet
Community
6

Instructions to use facebook/data2vec-audio-base-960h with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use facebook/data2vec-audio-base-960h with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("automatic-speech-recognition", model="facebook/data2vec-audio-base-960h")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCTC
    
    tokenizer = AutoTokenizer.from_pretrained("facebook/data2vec-audio-base-960h")
    model = AutoModelForCTC.from_pretrained("facebook/data2vec-audio-base-960h")
  • Notebooks
  • Google Colab
  • Kaggle
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

GGUF + pure-C++ runtime in CrispASR (Data2Vec on the wav2vec2 backend)

#6 opened 6 days ago by
cstr

Add Open ASR Leaderboard evaluation results

#5 opened 30 days ago by
SaylorTwift

Add Open ASR Leaderboard evaluation results

#4 opened 30 days ago by
SaylorTwift

Adding `safetensors` variant of this model

#3 opened about 3 years ago by
SFconvertbot

Update README.md

#2 opened almost 4 years ago by
mazharsaif

Transcribe Streaming audio and very long audio files(Out of Memory:how to read in chunks)

#1 opened almost 4 years ago by
mazharsaif
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