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biodatlab
/
whisper-th-medium-timestamp

Automatic Speech Recognition
Transformers
Safetensors
Thai
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use biodatlab/whisper-th-medium-timestamp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use biodatlab/whisper-th-medium-timestamp with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("automatic-speech-recognition", model="biodatlab/whisper-th-medium-timestamp")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
    
    processor = AutoProcessor.from_pretrained("biodatlab/whisper-th-medium-timestamp")
    model = AutoModelForSpeechSeq2Seq.from_pretrained("biodatlab/whisper-th-medium-timestamp")
  • Notebooks
  • Google Colab
  • Kaggle
whisper-th-medium-timestamp
3.06 GB
Ctrl+K
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  • 1 contributor
History: 3 commits
tensorops's picture
tensorops
Update README.md
b88b11d verified over 1 year ago
  • .ipynb_checkpoints
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  • .gitattributes
    1.52 kB
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  • README.md
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  • added_tokens.json
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  • config.json
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  • generation_config.json
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  • merges.txt
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  • model.safetensors
    3.06 GB
    xet
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  • normalizer.json
    52.7 kB
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  • preprocessor_config.json
    339 Bytes
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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
    836 kB
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