sherpa
1.3
  • Introduction
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  • Pre-trained models

k2-fsa/sherpa

  • sherpa

k2-fsa/sherpa-ncnn

  • sherpa-ncnn

k2-fsa/sherpa-onnx

  • sherpa-onnx
    • Tutorials
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    • Frequently Asked Question (FAQs)
    • Python
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    • VAD
    • Pre-trained models
      • Online transducer models
      • Online paraformer models
      • Online CTC models
      • Offline transducer models
      • Offline paraformer models
      • Offline CTC models
        • NeMo
        • yesno
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      • Small models
    • Moonshine
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    • Speaker Diarization
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    • rknn
    • Text-to-speech (TTS)

Triton

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sherpa
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  • Pre-trained models »
  • Offline CTC models
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Offline CTC models

This section lists available offline CTC models.

  • NeMo
    • How to export models from NeMo to sherpa-onnx
      • Step 1: Export model.onnx
      • Step 2: Add metadata
      • Step 3: Obtain model.int8.onnx
      • Step 4: Obtain tokens.txt
    • English
      • stt_en_citrinet_512
        • Download the model
        • Decode wave files
      • stt_en_conformer_ctc_small
        • Download the model
        • Decode wave files
      • stt_en_conformer_ctc_medium
        • Download the model
        • Decode wave files
      • stt_en_conformer_ctc_large
        • Download the model
        • Decode wave files
    • Russian
      • sherpa-onnx-nemo-ctc-giga-am-v2-russian-2025-04-19
        • Download the model
        • Decode wave files
        • Speech recognition from a microphone
        • Speech recognition from a microphone with VAD
      • sherpa-onnx-nemo-ctc-giga-am-russian-2024-10-24
        • Download the model
        • Decode wave files
        • Speech recognition from a microphone
        • Speech recognition from a microphone with VAD
  • yesno
    • Decode wave files
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