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UsefulSensors
/
moonshine-base

Automatic Speech Recognition
Transformers
Safetensors
English
moonshine
Eval Results
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xet
Community
6

Instructions to use UsefulSensors/moonshine-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use UsefulSensors/moonshine-base with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("automatic-speech-recognition", model="UsefulSensors/moonshine-base")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
    
    processor = AutoProcessor.from_pretrained("UsefulSensors/moonshine-base")
    model = AutoModelForSpeechSeq2Seq.from_pretrained("UsefulSensors/moonshine-base")
  • Notebooks
  • Google Colab
  • Kaggle
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Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

GGUF + pure-C++ runtime in CrispASR — Moonshine base

#6 opened 9 days ago by
cstr

Add Open ASR Leaderboard evaluation results

#5 opened about 1 month ago by
SaylorTwift

Adding ONNX file of this model

#4 opened 10 months ago by
tehkehyong

Word-level timestamps?

#3 opened 10 months ago by
hammeiam
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