Automatic Speech Recognition
Transformers
ONNX
Safetensors
ExecuTorch
English
asr4all
feature-extraction
speech
ctc
streaming
on-device
edge
custom_code
Eval Results (legacy)
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
Commit History
27 days ago
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asr4all_executorch.py: hold back ExecuTorch's own open_text deprecation warning
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asr4all_executorch.py: hold only the selected tier's methods (medium fp32 1.24 GB -> 0.62 GB)
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asr4all_executorch.py: --tier takes a name or an index
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ONNX: one graph per component over one shared weights file; hardened scripts and modeling file
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PCEC keep input: refreshed reference scripts (feed 0 by default), self-contained modeling file, merges in ONNX metadata
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asr4all+ m: full GPTQ coverage on the speaker heads, sv rung ladder, FSMN VAD on all surfaces, Diarizer-first card with through-.pte EER/DER (rel-2026-09-18)
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