metadata
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 | MIT |
crepe_full.onnx |
the singer's melody: CREPE full | marl/crepe via 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 via istupakov/parakeet-tdt-0.6b-v3-onnx, 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, 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 | Apache-2.0 |
beat_this.onnx |
the beats: Beat This! (final0) on 1500-frame chunks | 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.