Audio Classification
LiteRT
LiteRT
language-identification
language-detection
langid
spoken-language-identification
speech
on-device
edge-ai
mobile
offline
core-ml
apple-neural-engine
multilingual
Eval Results (legacy)
Instructions to use desert-ant-labs/ear with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use desert-ant-labs/ear with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Commit History
18 days ago
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Weights v0.2.0 on main, with the model card and third-party notices
Verified signature16aeb02
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Move the frontend into the SDK; one artifact per runtime
Verified signatureThe log-mel frontend is now host code rather than part of the graph. It cannot run in float16 -- the power spectrum is squared magnitudes floored at 1e-10 and 80% of its bins fall below float16's smallest normal number -- so shipping it as a Core ML program meant either destroying the features (routing 97.5% to 84.2%) or a float32 program that cannot use the Neural Engine at all. Both runtimes now take log-mel and return logits, so every platform feeds the same tensor to the same graph. mel_filters.f32 carries the filterbank so host code does not reimplement slaney-normalized librosa filters and drift from it silently. ear.mlmodelc: 256 ops, 100% Neural Engine, palette6-group32. ear.tflite: int8 with Hadamard rotation, signature input 'mel'.5ac5bec