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UsefulSensors
/
moonshine-tiny

Automatic Speech Recognition
Transformers
Safetensors
English
moonshine
Eval Results
Model card Files Files and versions
xet
Community
8

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

  • Libraries
  • Transformers

    How to use UsefulSensors/moonshine-tiny with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("automatic-speech-recognition", model="UsefulSensors/moonshine-tiny")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
    
    processor = AutoProcessor.from_pretrained("UsefulSensors/moonshine-tiny")
    model = AutoModelForSpeechSeq2Seq.from_pretrained("UsefulSensors/moonshine-tiny")
  • Notebooks
  • Google Colab
  • Kaggle
moonshine-tiny
110 MB
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  • 2 contributors
History: 16 commits
SaylorTwift's picture
SaylorTwift HF Staff
Add Open ASR Leaderboard evaluation results
5129eac verified about 1 month ago
  • .eval_results
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  • .gitattributes
    1.52 kB
    initial commit over 1 year ago
  • README.md
    7.96 kB
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  • config.json
    897 Bytes
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  • generation_config.json
    189 Bytes
    update max tokens based on 30sec (max trained sample) * 6.5 toks/sec over 1 year ago
  • model.safetensors
    108 MB
    xet
    Upload folder using huggingface_hub (#2) over 1 year ago
  • preprocessor_config.json
    215 Bytes
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  • tokenizer.json
    1.99 MB
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