Automatic Speech Recognition
Transformers
ONNX
Safetensors
ExecuTorch
English
asr4all
feature-extraction
speech
ctc
streaming
on-device
edge
custom_code
Eval Results (legacy)
Eval Results
Instructions to use futo-org/asr4all-s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use futo-org/asr4all-s with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="futo-org/asr4all-s", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("futo-org/asr4all-s", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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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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metadata: BPE merges beside the vocab (tokenizer buildable from metadata.json)
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metadata schema v1: vad as method list, no benchmark figures in metadata, provenance paths removed; benchmarks.json beside the card
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Update release: trimmed VAD section in the model card, schema_version added to onnx/metadata.json (all metadata now v8)
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fir25 release: card overhaul + session(commit_chunk, right_ctx) tier-aligned API
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