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Download README.md from ggaps/sound-space-models: direct link, hf CLI and curl.
- Browser
- Download file 2.26 kB
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https://huggingface.co/ggaps/sound-space-models/resolve/main/README.md
- Command line
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hf download hf://ggaps/sound-space-models/README.md
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curl -L -o README.md https://huggingface.co/ggaps/sound-space-models/resolve/main/README.md
2.26 kB
metadata
license: other
license_name: mixed-see-below
tags:
- music-source-separation
- music-transcription
- beat-tracking
- music-mood
- onnx
- onnxruntime-web
Sound Space – browser models
ONNX files used by the Sound Space web app to analyse music entirely inside the user's browser (onnxruntime-web, WebGPU). Audio never leaves the user's device; only these model files are downloaded.
Large weights are stored as float16 and cast back to float32 at load time (Cast nodes), so computation is float32.
| File | What | Original project | License |
|---|---|---|---|
htdemucs_6s.onnx |
Demucs v4 6-stem separation core, STFT/iSTFT moved outside the graph (sevagh/demucs.onnx method) | facebookresearch/demucs (htdemucs_6s) | MIT, Copyright (c) Meta Platforms, Inc. and affiliates |
htdemucs_6s_mobile.onnx |
Same htdemucs_6s weights exported with a 2.93 s segment (129024 samples) for phones: CPU (WebAssembly) peak memory during a run drops from about +1.7 GB to about +190 MB | facebookresearch/demucs (htdemucs_6s) | MIT, Copyright (c) Meta Platforms, Inc. and affiliates |
basic_pitch_nmp.onnx |
Basic Pitch note/onset model (unchanged) | spotify/basic-pitch | Apache-2.0, Copyright Spotify AB |
beat_this_final0.onnx, beat_this_melfb.f32 |
Beat This! beat/downbeat tracker (final0) and its mel filterbank | CPJKU/beat_this | MIT (code and published weights) |
drumsep.onnx |
Drum stem split into kick / snare / cymbals / toms (HDemucs), STFT outside the graph | inagoy/drumsep | Repository MIT. The weights are distributed by the author via Google Drive without a separate license statement. |
clap_audio.onnx |
CLAP audio encoder + projection (input: log-mel [1,1,1001,64] at 48 kHz, output: L2-normalised 512-d embedding). Mood text embeddings are precomputed in the app | laion/clap-htsat-unfused (Hugging Face) | Apache-2.0 |
onset_mel_44k.f32 |
librosa mel filterbank (44.1 kHz, n_fft 2048, 128 bands) | librosa | ISC |
Conversion scripts and verification (ONNX vs PyTorch differences, evaluation results) are in the Sound Space project
(scripts/export_demucs_onnx.sh, scripts/export_beat_this_onnx.py, scripts/export_drumsep_onnx.py, scripts/export_clap_onnx.py, scripts/onnx_fp16_weights.py).