--- license: other license_name: mixed license_link: LICENSE.md library_name: onnx pipeline_tag: automatic-speech-recognition base_model: - nvidia/parakeet-tdt-0.6b-v3 - nvidia/Nemotron-3-Diarization - urchade/gliner_multi_pii-v1 tags: - parakeet - sherpa-onnx - int8 - speaker-diarization - gliner - pii --- # Parafoil speech models The models the Parafoil desktop app downloads to transcribe meetings on the user's own computer, and, in private mode, to remove other people's details from the transcript before it leaves that computer. Each folder holds one model pack. The app pins every file by size and SHA-256 and refuses any file that does not match. ## parakeet-tdt-0.6b-v3-pc8-2026-10-15 Speech recognition in 25 European languages, for [sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx) (`nemo_transducer`). - `encoder.int8.onnx`, `decoder.onnx`, `joiner.onnx`, `tokens.txt` are derived from **[NVIDIA Parakeet TDT 0.6B v3](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3)**, by NVIDIA, licensed under [CC-BY-4.0](licenses/CC-BY-4.0.txt). The source was the sherpa-onnx ONNX export of that model. - **What we changed:** the encoder was quantized with ONNX Runtime dynamic quantization, per-channel int8 weights on MatMul nodes only; convolution layers stay fp32. The decoder, joiner and tokens are unchanged. On AMI meeting audio it scored 13.7% word error rate in our tests. - `silero_vad.onnx` is **[Silero VAD](https://github.com/snakers4/silero-vad)**, by the Silero Team, under the [MIT licence](licenses/Silero-VAD-MIT.txt). Unchanged. ## nemotron-3-diarization-q8-2026-09-23 - `Nemotron-3-Diarization.q8_0.gguf` is **[NVIDIA Nemotron 3 Diarization](https://huggingface.co/nvidia/Nemotron-3-Diarization)**, by NVIDIA, under the [OpenMDW License Agreement 1.1](licenses/OpenMDW-1.1.txt). It is an unchanged copy of NVIDIA's file at commit `f667ed73aee57d40cc39428eb768b4fd87a0a29e`. NVIDIA's model card, with its notices, is kept in [licenses/Nemotron-3-Diarization-README.md](licenses/Nemotron-3-Diarization-README.md). ## gliner-multi-pii-v1-wo8-2026-10-24 The redaction model private mode uses, replacing `gliner-multi-pii-v1-pc8e-2026-10-23`. It finds names, dates, phone numbers, places, ID numbers and other personal details in a transcript, so private mode can replace them on the device. It runs with ONNX Runtime 1.28 or later. - `model.onnx` is derived from **[GLiNER multi PII v1](https://huggingface.co/urchade/gliner_multi_pii-v1)** (commit `1fcf13e85f4eef5394e1fcd406cf2ca9ea82351d`), by Urchade Zaratiana, licensed under the [Apache License 2.0](licenses/Apache-2.0.txt). `gliner_config.json` is that model's configuration, unchanged. - **What we changed:** the model was exported to ONNX with the `gliner` 0.2.29 library and quantized with ONNX Runtime 1.31's tools, **weights only**: - the vocabulary table is per-channel int8, dequantized on load (`quantize_dynamic`, `op_types_to_quantize=["Gather"]`); - every MatMul is 8-bit `MatMulNBits`, block size 32, symmetric, `accuracy_level=1`. - **Why it replaces the earlier pack:** this model computes in float32 throughout. The earlier pack quantized activations to int8 at run time, which depends on the processor's int8 instructions. On processors without them (Apple M1, for one), it removed only about half the identifiers. On our synthetic test set this pack removes 97.0% of personal identifiers. - `spm.model` and `tokenizer.json` are the tokenizer of **[mDeBERTa-v3 base](https://huggingface.co/microsoft/mdeberta-v3-base)** (commit `a0484667b22365f84929a935b5e50a51f71f159d`), by Microsoft, under the [MIT licence](licenses/mdeberta-v3-MIT.txt). `spm.model` is unchanged. `tokenizer.json` was converted from it by the Hugging Face `transformers` library and adds GLiNER's two tokens, `<>` and `<>`. ## gliner-multi-pii-v1-pc8e-2026-10-23 Superseded by `gliner-multi-pii-v1-wo8-2026-10-24`, and kept unchanged, as every folder here is. Finds names, dates, phone numbers, places, ID numbers and other personal details in a transcript, so private mode can replace them on the device. It runs with ONNX Runtime 1.28 or later. - `model.onnx` is derived from **[GLiNER multi PII v1](https://huggingface.co/urchade/gliner_multi_pii-v1)** (commit `1fcf13e85f4eef5394e1fcd406cf2ca9ea82351d`), by Urchade Zaratiana, licensed under the [Apache License 2.0](licenses/Apache-2.0.txt). `gliner_config.json` is that model's configuration, unchanged. - **What we changed:** the model was exported to ONNX with the `gliner` 0.2.29 library, then quantized with ONNX Runtime 1.31 dynamic quantization: per-channel int8 weights on MatMul and Gather nodes (`quantize_dynamic(..., weight_type=QInt8, per_channel=True, op_types_to_quantize=["MatMul", "Gather"])`). Plain per-tensor int8 quantization breaks this model; per-channel weights keep it accurate. On our synthetic test set it removed 97.8% of personal identifiers under ONNX Runtime 1.28. - `spm.model` and `tokenizer.json` are the tokenizer of **[mDeBERTa-v3 base](https://huggingface.co/microsoft/mdeberta-v3-base)** (commit `a0484667b22365f84929a935b5e50a51f71f159d`), by Microsoft, under the [MIT licence](licenses/mdeberta-v3-MIT.txt), `spm.model` is unchanged; `tokenizer.json` was converted from it by the Hugging Face `transformers` library and adds GLiNER's two tokens, `<>` and `<>`. ## Checksums (SHA-256) | File | Bytes | SHA-256 | |---|---|---| | parakeet-tdt-0.6b-v3-pc8-2026-10-15/encoder.int8.onnx | 881498555 | c5cdff28d4ac8690b3e3f7780ac5089586d87a2800cebf0ed5d5a128e94f2b86 | | parakeet-tdt-0.6b-v3-pc8-2026-10-15/decoder.onnx | 47233743 | d593cdb0e571f5a457ec2219af9968cbf6b0e8198e8f7839b40a8754593bf68c | | parakeet-tdt-0.6b-v3-pc8-2026-10-15/joiner.onnx | 25286330 | b9b0bcf88ac571902e69a6536223ed2d94885e981b85045410f1403d53121a63 | | parakeet-tdt-0.6b-v3-pc8-2026-10-15/tokens.txt | 93939 | d58544679ea4bc6ac563d1f545eb7d474bd6cfa467f0a6e2c1dc1c7d37e3c35d | | parakeet-tdt-0.6b-v3-pc8-2026-10-15/silero_vad.onnx | 643854 | 9e2449e1087496d8d4caba907f23e0bd3f78d91fa552479bb9c23ac09cbb1fd6 | | nemotron-3-diarization-q8-2026-09-23/Nemotron-3-Diarization.q8_0.gguf | 107012128 | 08456d9e22cd9a323c0364d98375f3746d6e68507ebb705cd46438c534c7a3a1 | | gliner-multi-pii-v1-wo8-2026-10-24/model.onnx | 365533791 | b7569cce986a70955ba1b3da7b4c98e9cbef2c7e91b657ccf6498a7077110e8b | | gliner-multi-pii-v1-wo8-2026-10-24/tokenizer.json | 16034824 | c5b8041501fcdee792b9b112dd592861f04391a24633a73fd9f05aaaf6e8eff1 | | gliner-multi-pii-v1-wo8-2026-10-24/spm.model | 4305025 | 13c8d666d62a7bc4ac8f040aab68e942c861f93303156cc28f5c7e885d86d6e3 | | gliner-multi-pii-v1-wo8-2026-10-24/gliner_config.json | 2312 | 5645ea576d26777a204be05a8cb97d9c2b15d04496c7712a6bb094c828ef1d9d | | gliner-multi-pii-v1-pc8e-2026-10-23/model.onnx | 354286656 | 92fdb25ad1bcc07476cb86d0e3aeafe38a549ad4f2664328daaab00e26237696 | | gliner-multi-pii-v1-pc8e-2026-10-23/tokenizer.json | 16034824 | c5b8041501fcdee792b9b112dd592861f04391a24633a73fd9f05aaaf6e8eff1 | | gliner-multi-pii-v1-pc8e-2026-10-23/spm.model | 4305025 | 13c8d666d62a7bc4ac8f040aab68e942c861f93303156cc28f5c7e885d86d6e3 | | gliner-multi-pii-v1-pc8e-2026-10-23/gliner_config.json | 2312 | 5645ea576d26777a204be05a8cb97d9c2b15d04496c7712a6bb094c828ef1d9d | The models are provided as is, with no warranty, under their own licences. A new version of a model is published as a new folder; existing folders are never changed.