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levicu
/
whisat-base

Automatic Speech Recognition
Transformers
Safetensors
whisper
child_speech
classroom_speech
asr
Model card Files Files and versions
xet
Community

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

  • Libraries
  • Transformers

    How to use levicu/whisat-base with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("automatic-speech-recognition", model="levicu/whisat-base")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
    
    processor = AutoProcessor.from_pretrained("levicu/whisat-base")
    model = AutoModelForSpeechSeq2Seq.from_pretrained("levicu/whisat-base")
  • Notebooks
  • Google Colab
  • Kaggle
whisat-base
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  • 1 contributor
History: 7 commits
rosyvs's picture
rosyvs
upload yaml used fro training
9c7b12b verified 8 months ago
  • .gitattributes
    1.52 kB
    initial commit 9 months ago
  • README.md
    3 kB
    updae readme 8 months ago
  • added_tokens.json
    2.08 kB
    base model upload - processor only 9 months ago
  • config.json
    1.32 kB
    base model upload 8 months ago
  • generation_config.json
    3.79 kB
    base model upload 8 months ago
  • merges.txt
    494 kB
    base model upload - processor only 9 months ago
  • model.safetensors
    290 MB
    xet
    base model upload 9 months ago
  • normalizer.json
    52.7 kB
    base model upload - processor only 9 months ago
  • preprocessor_config.json
    339 Bytes
    base model upload - processor only 9 months ago
  • publicKS_ig_hf_base.yaml
    1.69 kB
    upload yaml used fro training 8 months ago
  • special_tokens_map.json
    2.19 kB
    base model upload - processor only 9 months ago
  • tokenizer_config.json
    20.5 kB
    base model upload - processor only 9 months ago
  • vocab.json
    1.04 MB
    base model upload - processor only 9 months ago