---
license: other
license_name: per-file
tags: [onnx, audio, singing, source-separation, pitch-estimation, speech-recognition, forced-alignment, beat-tracking]
---

# singcheck models

The networks the singcheck app runs on the user's own computer to prepare a song for singing along: the singer's voice
alone, her melody, the words with their times, the exact word starts, who sings each line, the beats. ONNX exports
(made with `scripts/export_onnx.py` of the singcheck project, each checked against the original PyTorch model); the app
fetches them once (about 2.4 GB) and runs them with ONNX Runtime — no audio leaves the computer. Big weights are stored
in float16 and turned into float32 when loaded (half the download, the same results).

| File | What | Source | License |
|---|---|---|---|
| `htdemucs.onnx` | the voice alone: htdemucs between its STFT and iSTFT (its 1D convolutions as 2D) | [facebookresearch/demucs](https://github.com/facebookresearch/demucs) | MIT |
| `crepe_full.onnx` | the singer's melody: CREPE full | [marl/crepe](https://github.com/marl/crepe) via [torchcrepe](https://github.com/maxrmorrison/torchcrepe) | MIT |
| `parakeet-fp16w/` | words with timestamps: Parakeet TDT 0.6B v3; `nemo128.onnx` is onnx-asr's preprocessor | [nvidia/parakeet-tdt-0.6b-v3](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3) via [istupakov/parakeet-tdt-0.6b-v3-onnx](https://huggingface.co/istupakov/parakeet-tdt-0.6b-v3-onnx), [onnx-asr](https://github.com/istupakov/onnx-asr) | CC-BY-4.0 (model), MIT (onnx-asr) |
| `mms_fa.fp16w.onnx` (+ `.data`) | the lyrics aligner: MMS_FA (wav2vec2, with the star token) | [torchaudio MMS_FA](https://pytorch.org/audio/stable/generated/torchaudio.pipelines.MMS_FA.html), Pratap et al. 2023, *Scaling Speech Technology to 1,000+ Languages* | CC-BY-NC-4.0 — non-commercial use only |
| `ecapa.onnx` | a speaker print (with its Fbank): ECAPA-TDNN | [speechbrain/spkrec-ecapa-voxceleb](https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb) | Apache-2.0 |
| `beat_this.onnx` | the beats: Beat This! (final0) on 1500-frame chunks | [CPJKU/beat_this](https://github.com/CPJKU/beat_this) | MIT |

Each file keeps its upstream license; with MMS_FA in it the set as a whole is for non-commercial use.