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NbAiLab
/
nb-whisper-tiny

Automatic Speech Recognition
Transformers
PyTorch
google-tensorflow TensorFlow
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Model card Files Files and versions
xet
Metrics Training metrics Community
2

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

  • Libraries
  • Transformers

    How to use NbAiLab/nb-whisper-tiny with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-tiny")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
    
    processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-tiny")
    model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-tiny")
  • Notebooks
  • Google Colab
  • Kaggle
nb-whisper-tiny / onnx
Ctrl+K
Ctrl+K
  • 2 contributors
History: 1 commit
pere's picture
pere
test submit
e1f27a9 over 2 years ago
  • added_tokens.json
    34.6 kB
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  • config.json
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  • decoder_model.onnx
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    xet
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  • decoder_with_past_model.onnx
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  • encoder_model.onnx
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  • generation_config.json
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  • merges.txt
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  • normalizer.json
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  • preprocessor_config.json
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  • special_tokens_map.json
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  • tokenizer.json
    2.48 MB
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  • tokenizer_config.json
    283 kB
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  • vocab.json
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