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futo-org
/
asr4all-m-plus

Automatic Speech Recognition
Transformers
ONNX
Safetensors
ExecuTorch
English
asr4all
feature-extraction
speech
ctc
streaming
on-device
edge
custom_code
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use futo-org/asr4all-m-plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use futo-org/asr4all-m-plus with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("automatic-speech-recognition", model="futo-org/asr4all-m-plus", trust_remote_code=True)
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("futo-org/asr4all-m-plus", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
asr4all-m-plus / executorch /xnnpack_fp32
301 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 9 commits
lee-futo's picture
lee-futo
ExecuTorch: delegate the sv/seg LoRA adapters to XNNPACK
a030328 verified 21 days ago
  • asr_encoder.pte
    301 MB
    xet
    ExecuTorch: delegate the sv/seg LoRA adapters to XNNPACK 21 days ago
  • metadata.json
    91 kB
    metadata: sv.normalised -> sv.normalized (consistent spelling) 26 days ago
  • ops.txt
    763 Bytes
    ExecuTorch: delegate the sv/seg LoRA adapters to XNNPACK 21 days ago