File size: 3,529 Bytes
9953363 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | ---
license: other
license_name: mixed-open-licenses
license_link: LICENSE.md
language:
- en
tags:
- onnx
- speech-recognition
- speaker-diarization
- speech-language-pathology
pipeline_tag: automatic-speech-recognition
---
# SLP Sidekick speech models
These are the ten model files that SLP Sidekick downloads the first time it runs. SLP Sidekick turns a recording of a speech-therapy session into a listening sheet for speech-language pathologists. Everything runs inside the user's browser, and recordings never leave their computer. The only thing downloaded is this model pack, once.
The files are unchanged copies of the ONNX conversions published by [sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx) (k2-fsa, Apache-2.0). Only the file names are changed, so they can sit side by side in one folder. The app checks every file against the SHA-256 checksums below before using it.
| File | What it does | Original model | License | Size | SHA-256 |
|---|---|---|---|---|---|
| `parakeet-tdt-0.6b-v2-encoder.int8.onnx` | Speech recognition (main) | [NVIDIA Parakeet TDT 0.6B v2](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2) | CC-BY-4.0 | 652,184,296 | `a32b12d17bbbc309d0686fbbcc2987b5e9b8333a7da83fa6b089f0a2acd651ab` |
| `parakeet-tdt-0.6b-v2-decoder.int8.onnx` | Speech recognition (main) | same | CC-BY-4.0 | 7,257,753 | `b6bb64963457237b900e496ee9994b59294526439fbcc1fecf705b31a15c6b4e` |
| `parakeet-tdt-0.6b-v2-joiner.int8.onnx` | Speech recognition (main) | same | CC-BY-4.0 | 1,739,080 | `7946164367946e7f9f29a122407c3252b680dbae9a51343eb2488d057c3c43d2` |
| `parakeet-tdt-0.6b-v2-tokens.txt` | Speech recognition (main) | same | CC-BY-4.0 | 9,384 | `ec182b70dd42113aff6c5372c75cac58c952443eb22322f57bbd7f53977d497d` |
| `whisper-small.en-encoder.int8.onnx` | Second opinion on unclear lines | [OpenAI Whisper small.en](https://github.com/openai/whisper) | MIT | 112,442,483 | `8bdac288f369aa94ee2194059238c465ed82ea9d47ee8fa4a8c0a891873e462f` |
| `whisper-small.en-decoder.int8.onnx` | Second opinion on unclear lines | same | MIT | 262,223,042 | `710ccf890e10f3faa15f51ec346081a2723c9f3adb6e4da81c6573a5a6f877fb` |
| `whisper-small.en-tokens.txt` | Second opinion on unclear lines | same | MIT | 835,554 | `306cd27f03c1a714eca7108e03d66b7dc042abe8c258b44c199a7ed9838dd930` |
| `pyannote-segmentation-3.0.onnx` | Who is speaking when | [pyannote segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) | MIT | 5,992,913 | `220ad67ca923bef2fa91f2390c786097bf305bceb5e261d4af67b38e938e1079` |
| `titanet-small.onnx` | Telling voices apart | [NVIDIA TitaNet small](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/titanet_small) | NeMo toolkit license (Apache-2.0) | 40,257,283 | `ad4a1802485d8b34c722d2a9d04249662f2ece5d28a7a039063ca22f515a789e` |
| `silero-vad-v4.onnx` | Finding the speech in the recording | [Silero VAD v4](https://github.com/snakers4/silero-vad) | MIT | 643,854 | `9e2449e1087496d8d4caba907f23e0bd3f78d91fa552479bb9c23ac09cbb1fd6` |
Total: 1,083,585,642 bytes (about 1.1 GB).
## Using them without internet
Download the ten files into one folder (a USB stick works). In SLP Sidekick, choose **Use a models folder** and pick that folder. The checksums are checked the same way.
## Credits
See [LICENSE.md](LICENSE.md). The models belong to their authors; SLP Sidekick only redistributes them under their licenses. These models are not medical devices, and a listening sheet is a starting point for the clinician's own judgment, not a diagnosis.
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