| ---
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| language:
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| - en
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| tags:
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| - onnx
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| - android
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| - qnn
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| - automatic-speech-recognition
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| - text-classification
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| license: other
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| license_name: component-specific
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| license_link: https://huggingface.co/Fugi1128/ScamScan/blob/main/README.md#attribution-and-licenses
|
| ---
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| # ScamScan Android NPU runtime assets
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|
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| Runtime model package for ScamScan code commit `37af8ac`. Parakeet TDT 0.6B v3 is
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| the default speech model and Whisper Small is the alternative. Both run on the
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| Snapdragon NPU and feed the existing fine-tuned MiniLM scam classifier. If an NPU
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| session fails to load, the app falls back to Whisper on the CPU with the same
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| classifier and shows the reason. Bodhan is not part of this package. The
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| repository is gated: request access on this page, then use a token that can read
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| public gated repositories.
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|
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| ## Required files and hardware
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|
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| - `models/parakeet_qnn/`: Parakeet encoder/decoder ONNX wrappers and HTP contexts.
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| - `models/whisper_small_qnn/`: Whisper Small wrappers, HTP contexts and metadata.
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| - `android/app/src/main/assets/`: shared classifier, frontend, vocabularies,
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| Whisper generation settings and keyword rules.
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| - `runtime-manifest.json`: exact sizes and SHA-256 checksums for every runtime file.
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| - `models/minilm_classifier/`: the trained PyTorch MiniLM (`BertForSequenceClassification`,
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| label 0 benign, label 1 scam) that `scamscan_classifier_htp.onnx` was exported from,
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| with its config and tokenizer. The app does not download it; use it to re-export
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| with `pipeline/export_classifier_htp.py` or to fine-tune further.
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|
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| `scam_keywords_in.json`, `parakeet_vocab.txt` and `whisper_small_generation.json`
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| are also tracked in the code repository. The keyword rules here match code commit
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| `37af8ac`. The two text files here use CRLF line endings; their content matches the
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| code repository, and the app reads either.
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|
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| The CPU fallback uses the root-level `whisper_encoder.onnx` and
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| `whisper_decoder.onnx` from revision `37464c16fa9e276a9c72d1c0374e6b76a7518ff4`.
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| The root-level `scamscan_classifier.onnx` is a legacy graph that returns the same
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| logits for every input. The app rejects it at load time; the NPU pipelines and the
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| CPU fallback both use `android/app/src/main/assets/scamscan_classifier_htp.onnx`.
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| The other root-level files remain for older code.
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| Validated on Qualcomm SM8850, Hexagon HTP v81, QAIRT 2.50.0 and ONNX Runtime
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| Android QNN 1.29.0. These precompiled contexts are device-specific; compatibility
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| with other chips is not established. Both ASR neural models and the classifier
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| use strict QNN sessions. Signal processing, tokenization and decoding control
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| run on CPU; Parakeet's small STFT/mel ONNX frontend also runs on CPU.
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|
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| ## Restore into the code checkout
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|
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| From the ScamScan source repository root:
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|
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| ```sh
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| python3 scripts/setup_model_assets.py
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| ```
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| The script downloads `runtime-manifest.json` at a pinned revision, restores every
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| listed file that Git does not track, checks each file's size and SHA-256, and
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| fetches the two CPU fallback Whisper files. `--skip-npu` leaves out the 1.8 GB
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| context packages.
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| To download by hand, pin a revision and exclude the files the code repository
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| tracks so the download does not overwrite them:
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|
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| ```sh
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| hf download Fugi1128/ScamScan --revision <Hub commit SHA> --include "models/parakeet_qnn/*" --include "models/whisper_small_qnn/*" --include "android/app/src/main/assets/*" --include "runtime-manifest.json" --exclude "android/app/src/main/assets/scam_keywords_in.json" --exclude "android/app/src/main/assets/parakeet_vocab.txt" --exclude "android/app/src/main/assets/whisper_small_generation.json" --local-dir .
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| ```
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|
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| Then verify each file's size and SHA-256 against `runtime-manifest.json`.
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|
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| Build the Android APK with the restored assets and install it without
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| uninstalling the previous app. Then provision the contexts with the source
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| repository scripts (use `python` on Windows):
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|
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| ```sh
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| python3 pipeline/provision_parakeet_qnn.py
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| python3 pipeline/provision_whisper_qnn.py
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| ```
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| They need Python 3.9 or newer and a debuggable installed app. They find `adb`
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| through `$ADB`, `PATH`, `ANDROID_HOME` or `ANDROID_SDK_ROOT`; pass `--adb <path>`
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| to use another one. Each file is verified on the device. The APK alone does not
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| provision the large ASR context binaries. Qualcomm SDK and runtime libraries are
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| not distributed in this repository; obtain the required runtime/toolchain
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| separately as configured in the app build.
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|
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| ## Validation and limitations
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|
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| With code commit `37af8ac`, end-to-end calls from the ScamScan caller portal to the
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| phone ran Parakeet and the classifier on the NPU at 83 to 106 ms per audio chunk.
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| Only the classifier moves the risk score. A recorded digital-arrest scam call
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| reached 97% (Critical) within 14 seconds, and a recorded food-delivery call stayed
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| at 0% under a police caller name.
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| The classifier in this revision was retrained on public call datasets and
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| generated Indian-context scam calls. On held-out calls it missed 2.0% of scam
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| windows (the previous classifier missed 46.6%) and flagged 1.4% of benign windows
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| (previously 2.8%). With the app's smoothing, every held-out scam call raised an
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| alert and 313 of 314 reached Critical, while 2.7% of benign calls reached a false
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| Critical (previously 7.7%).
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|
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| Foreground device tests also passed three Parakeet/Whisper round trips with ASR
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| and classification after each switch. Tests used English recordings and synthetic
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| fixtures; this is not an independent human-call WER benchmark or a sustained
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| background test. The classifier is a development scam-detection model, not a
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| guarantee of safety.
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|
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| ## Attribution and licenses
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|
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| This repository is a bundle; licenses apply per component rather than a single
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| blanket license for all files.
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|
|
| - Parakeet: NVIDIA [parakeet-tdt-0.6b-v3](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3), CC BY 4.0.
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| Frontend, vocabulary and source ONNX export from
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| [istupakov/parakeet-tdt-0.6b-v3-onnx](https://huggingface.co/istupakov/parakeet-tdt-0.6b-v3-onnx/tree/8f23f0c03c8761650bdb5b40aaf3e40d2c15f1ce).
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| Changes: fixed-shape specialization, FP16 conversion and QAIRT HTP compilation.
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| - Whisper: [OpenAI Whisper](https://github.com/openai/whisper), MIT license.
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| Compiled package from Qualcomm AI Hub Models whisper_small release v0.63.0,
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| `whisper_small-precompiled_qnn_onnx-float-qualcomm_snapdragon_8_elite_gen5_for_galaxy.zip`.
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| Original archive SHA-256: `c02d8e86b541f5b259b3b0f2b300b400a6a2e2828ceff10b668f3b3909e9f074`.
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| - MiniLM classifier: fine-tuned from
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| [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
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| (Apache 2.0) and exported to a static QNN-compatible ONNX graph. Training data:
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| the BothBosu scam, single-agent, multi-agent and YouTube conversation sets
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| (Apache 2.0), the scam half of shakeleoatmeal/phone-scam-detection-synthetic
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| (MIT), Ngadou/social-engineering-convo (Apache 2.0), the Talkmap telecom and
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| banking corpora (MIT), Lakshan2003/customer-support-client-agent-conversations
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| (MIT), the AppTek call-centre dialogues (CC BY-SA 4.0), Google Taskmaster
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| (CC BY 4.0), 3nesdeniz/english-daily-dialogues-10k (CC BY 4.0), banking77 (MIT)
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| and ScamScan's generated Indian-context examples.
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|
|
