Metadata-Version: 2.4
Name: livechord-beat-refiner
Version: 0.1.0
Summary: Bidirectional Transformer beat / downbeat / chord-boundary refiner — denoises beat_this, madmom, or librosa output using full audio context.
Author: LiveChord Project
License:                                  Apache License
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Project-URL: Homepage, https://livechord.org
Project-URL: Hugging Face, https://huggingface.co/livechord-music/livechord-beat-refiner
Project-URL: Source, https://github.com/JJ110112/LiveChord
Keywords: music-information-retrieval,beat-tracking,downbeat-tracking,chord-recognition,audio,transformer,pytorch
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Multimedia :: Sound/Audio :: Analysis
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch>=2.0
Requires-Dist: numpy>=1.23
Requires-Dist: librosa>=0.10
Requires-Dist: soundfile>=0.12
Requires-Dist: huggingface_hub>=0.20
Requires-Dist: safetensors>=0.4
Dynamic: license-file

---
license: apache-2.0
library_name: pytorch
tags:
  - music
  - music-information-retrieval
  - beat-tracking
  - downbeat-tracking
  - chord-recognition
  - audio
  - transformer
pipeline_tag: audio-classification
model-index:
  - name: livechord-beat-refiner
    results:
      - task:
          type: beat-tracking
          name: Beat tracking refinement
        metrics:
          - type: f1
            value: 0.920
            name: Beat F1 (gold-quality holdout, ±93ms)
          - type: f1
            value: 0.936
            name: Downbeat F1 (gold-quality holdout, ±93ms)
          - type: f1
            value: 0.881
            name: Beat F1 (full holdout, ±93ms)
          - type: f1
            value: 0.914
            name: Downbeat F1 (full holdout, ±93ms)
---

# livechord-beat-refiner

> Bidirectional Transformer that refines a prior beat tracker's output
> (beat_this, madmom, or librosa) using full-context audio features.
> Drops in as a post-processing pass to fix phase drift and bar misalignment
> without re-running the expensive front-end model.

## 🎹 Try it live

This model powers the beat / bar grid behind the chord ribbon, 88-key
waterfall, and AI accompaniment at **[livechord.org](https://livechord.org?utm_source=huggingface&utm_medium=model_card&utm_campaign=beat_refiner)**
— upload an MP3 and play along on a virtual piano with chord cards
that follow the music.

## Quickstart

```bash
pip install livechord-beat-refiner
```

```python
from livechord_beat_refiner import refine

# 1. Run any beat tracker (beat_this, madmom, librosa, ...) → beats / downbeats lists
# 2. Pass them to refine() together with the audio file:
out = refine(
    audio_path="song.flac",
    beats=[0.0, 0.52, 1.05, 1.57, ...],        # seconds
    downbeats=[0.0, 2.10, 4.21, ...],          # seconds
)

print(out["refined_beats"])       # list[float]  — refined beat times
print(out["refined_downbeats"])   # list[float]
print(out["applied"])             # bool         — False if model couldn't run; input echoed back
```

The checkpoint is downloaded from this Hub repo on first call (cached
under `~/.cache/huggingface/hub`). To use a local file, pass
`checkpoint_path="path/to/model.safetensors"`.

## What the model does

```
audio (22050 Hz, mono)
   │
   ▼
[ CQT 84 + chroma 12 + onset + RMS ]                  ← 98 audio channels
[ initial beat grid ]                                  ← 1 channel  (from prior tracker)
[ initial downbeat grid ]                              ← 1 channel
   │ concat → (T, 100)
   ▼
[ Linear(100→256) + sinusoidal pos enc ]
   │
   ▼
[ TransformerEncoder × 6 (d=256, h=4, ffn=512, GELU, pre-LN) ]
   │
   ▼              ┌────────────┐
   ├──────────────┤ beat head  ├──→ sigmoid → peak-pick → beat times (s)
   │              └────────────┘
   │              ┌────────────┐
   ├──────────────┤ db head    ├──→ sigmoid → peak-pick → downbeat times (s)
   │              └────────────┘
   │              ┌────────────┐
   └──────────────┤ cb head    ├──→ aux supervision (not production-ready, see Limitations)
                  └────────────┘
```

- **Parameters**: ≈ 3.0 M
- **Frame rate**: 10.766 fps (sr=22050, hop=2048)
- **Max audio length**: ≈ 15 min (longer truncated)
- **Inference budget**: ~1.8 s per minute of audio on CPU; ~0.3 s/min on a recent NVIDIA GPU.

## Why a refiner

State-of-the-art beat trackers (beat_this, madmom) are strong on percussive
genres but drift in three failure modes that show up over and over in
real-world libraries:

1. **Phase shift on slow ballads** — the tracker locks onto an offbeat or jumps half a beat partway through.
2. **Doubletime / halftime confusion** — pop ballads tracked at 138 BPM that are actually 69 BPM.
3. **Bar misalignment** — beats are right, downbeats off by one.

Re-running the front-end tracker rarely helps (same audio, same answer).
The refiner sees both **the audio** AND **the prior tracker's grid as input
hints**, then re-emits a cleaner grid by attending across the full song with
chord-boundary cues as auxiliary supervision.

## Metrics

Held-out test set: **7,389 songs** (15% stratified holdout from a 13,017-song
corpus). Tolerance: **±1 frame ≈ ±93 ms** (mir_eval-style F-measure; standard
is 70 ms).

### Headline (gold-quality subset, n=510)

Songs whose prior-tracker output passed `coefficient-of-variation < 0.05` AND
`chord-change alignment ≥ 0.5` filters — i.e. the most reliable references.

| Metric | F1 | Precision | Recall |
|---|---:|---:|---:|
| Beat | **0.920** | 0.910 | 0.933 |
| Downbeat | **0.936** | 0.925 | 0.952 |

### Full holdout (n=7,389)

| Metric | F1 | Precision | Recall |
|---|---:|---:|---:|
| Beat | 0.881 | 0.859 | 0.914 |
| Downbeat | 0.914 | 0.906 | 0.930 |

The "skip" quality bucket (6,087 / 7,389 songs) consists of tracks whose
prior-tracker reference didn't pass the gold/ok filters, so their "labels"
are noisy by construction. The lower full-holdout F1 reflects label noise,
not model regression — quality is monotonic (gold > ok > skip), which is
the structural sanity check.

### Per-bucket

| beat_quality | n | beat F1 | downbeat F1 |
|---|---:|---:|---:|
| gold | 510 | 0.920 | 0.936 |
| ok | 792 | 0.906 | 0.925 |
| skip | 6,087 | 0.874 | 0.910 |

Reproduce with the eval script in the
[LiveChord repo](https://github.com/JJ110112/LiveChord/blob/master/scripts/eval_beat_refiner_holdout.py).

### Note on baseline

The training corpus uses each song's own prior-tracker output as the
supervision target whenever that output passed the filter. So the F1 reported
here measures **how well the refiner preserves correct beats while also
absorbing chord-boundary auxiliary signal** — not "refiner > beat_this" head
to head. An independent ground-truth comparison (MIREX Beatles, GTZAN_rhythm)
is on the v2 roadmap.

## Training data

- **Corpus**: 13,017 songs from a personal music library, predominantly
  East Asian pop, Western pop / rock / R&B, jazz standards, and a smaller
  classical / folk tail.
- **Labels**: prior-tracker output (beat_this `final0` checkpoint) filtered
  to gold / ok quality bands by coefficient-of-variation and chord-alignment
  heuristics.
- **Split**: 70% train / 15% val / 15% test, stratified by
  (beat_quality × chord_quality).
- **Augmentation**: random initial-grid corruption during training
  (jitter / drop / insert / phase-shift) so the model learns to denoise rather
  than copy the input grid verbatim.

## Limitations

- **Chord-boundary head is auxiliary supervision, not a production target.**
  cb F1 on gold = 0.243. v1 didn't tune class weights or peak-picking
  thresholds for this head; treat the cb output as exploratory.
- **No genre rebalancing.** Folk / classical / EDM are underrepresented vs
  pop; performance there is more variable.
- **Long songs (> 15 min) are truncated.** v1 has no chunked-overlap
  inference. If you need this, please file a GitHub issue.
- **Label noise.** Because labels are filtered prior-tracker output, the
  model inherits any systematic biases that survived the filter.

## How to use with beat_this

`beat_this` ([CPJKU 2024](https://github.com/CPJKU/beat_this)) is a strong
front-end choice, especially for percussive genres. Pipeline:

```python
from beat_this.inference import File2Beats
from livechord_beat_refiner import refine

# Front-end (GPU-friendly, run once per song)
predictor = File2Beats(checkpoint_path="final0", device="cuda", float16=True)
init_beats, init_downbeats = predictor("song.flac")

# Refine
out = refine(
    audio_path="song.flac",
    beats=init_beats,
    downbeats=init_downbeats,
)
final_beats, final_downbeats = out["refined_beats"], out["refined_downbeats"]
```

## Citation

```bibtex
@misc{livechord-beat-refiner,
  title  = {livechord-beat-refiner: a bidirectional Transformer for
            beat / downbeat / chord-boundary refinement},
  author = {LiveChord Project},
  year   = {2026},
  url    = {https://huggingface.co/livechord-music/livechord-beat-refiner},
  note   = {Refines beat\_this / madmom / librosa output using full
            audio context. Trained on 13,017 songs.},
}
```

## License

[Apache License 2.0](LICENSE) — code AND weights. The training data is not
redistributed; only the trained model artifact is released.

The LiveChord product (full FastAPI server, frontend, and AI pipeline) is
released separately under AGPL v3 at
[github.com/JJ110112/LiveChord](https://github.com/JJ110112/LiveChord). This
package is the standalone inference release.

## Related

- **[livechord-bar-arbitrator](https://huggingface.co/livechord-music/livechord-bar-arbitrator)** —
  companion phase-correction post-processor that runs after this model to
  fix bar / beats-per-bar / doubletime confusion using `chords[]` as an
  additional signal.
- **[CPJKU/beat_this](https://github.com/CPJKU/beat_this)** —
  recommended upstream beat tracker.
- **[livechord.org](https://livechord.org?utm_source=huggingface&utm_medium=model_card&utm_campaign=beat_refiner_related)** —
  the live application this model powers.
