Metadata-Version: 2.4
Name: wav2caption
Version: 0.1.2
Summary: Audio → instrument-aware caption for AI music generation (ACE-Step, Suno, Udio prompts)
Project-URL: Homepage, https://github.com/hinanohart/wav2caption
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Project-URL: Issues, https://github.com/hinanohart/wav2caption/issues
Project-URL: Changelog, https://github.com/hinanohart/wav2caption/blob/main/CHANGELOG.md
Author: wav2caption contributors
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Keywords: ace-step,audio-analysis,audio-captioning,caption-generation,essentia,instrument-detection,mir,mtg-jamendo,music-captioning,music-generation,music-information-retrieval,prompt-generation,suno,text-to-music,udio
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Description-Content-Type: text/markdown

# wav2caption

[![PyPI](https://img.shields.io/pypi/v/wav2caption.svg)](https://pypi.org/project/wav2caption/)
[![Python](https://img.shields.io/pypi/pyversions/wav2caption.svg)](https://pypi.org/project/wav2caption/)
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[![Downloads](https://static.pepy.tech/badge/wav2caption)](https://pepy.tech/project/wav2caption)

**Audio → instrument-aware caption for AI music generation.**
Suno / Udio / ACE-Step prompt generator that describes *what is playing*
**and how it is used* (rhythm / bass / harmony / lead / strings / brass /
synth / vocal).

Point it at a WAV/MP3/FLAC file and get back a structured analysis *and* a
ready-to-paste prompt for [ACE-Step](https://github.com/ace-step/ACE-Step),
[Suno](https://suno.com/), [Udio](https://www.udio.com/), or any other
prompt-conditioned music model.

```
live drums, electric guitar, piano, bass, string section, brass section,
D major, 140 BPM, dynamic build-up, breakdown section
```

Under the hood it combines Essentia's TensorFlow graphs (MTG-Jamendo 40-class
instrument head + Discogs-EffNet embeddings) with classical MIR features
(BPM, key, loudness, spectral centroid, pitch range) and a small role
taxonomy, so the caption describes both **what is playing** and **how it is
used** (rhythm / bass / harmony / lead / strings / brass / synth / vocal).

---

## Why this exists

Most "audio → tag" tools stop at a flat list of instruments. When you feed
that into a prompt-conditioned music model, the arrangement gets lost —
instruments are named but their *role* is missing, and dynamics are dropped
entirely. `wav2caption` was factored out of a production pipeline that
captioned hundreds of reference tracks for ACE-Step Lego-mode generation, and
it keeps two things other tools don't:

- **Role grouping.** `drums` and `bass` are not just instruments; they are
  the *rhythm* and *bass* roles. A section that also has `strings` + `brass`
  gets tagged as "string section, brass section" rather than five
  indistinguishable labels.
- **Section features.** Per-window loudness, centroid, and pitch-range give
  you "quiet (breakdown/interlude)", "peak energy (chorus/climax)",
  "staccato stabs", "metallic percussion accents" — the kind of descriptors
  music LLMs actually condition on.

## Install

```bash
pip install wav2caption
# Then opt in to the (AGPL-3.0) Essentia runtime — required for analysis.
pip install "wav2caption[essentia]"
```

> Essentia is distributed under **AGPL-3.0** (or a commercial license
> from MTG-UPF). If you ship a network service built on `wav2caption`,
> you may need to release your source under AGPL-3.0 or buy a commercial
> license. The `wav2caption` code itself is Apache-2.0.

### Models

The pretrained weights are **not** bundled (they are CC-BY-NC-SA 4.0 and
non-commercial). Download them once, then verify the SHA-256 digests:

```bash
mkdir -p ~/.cache/wav2caption/models
cd ~/.cache/wav2caption/models
curl -LO https://essentia.upf.edu/models/feature-extractors/discogs-effnet/discogs-effnet-bs64-1.pb
curl -LO https://essentia.upf.edu/models/classification-heads/mtg_jamendo_instrument/mtg_jamendo_instrument-discogs-effnet-1.pb

# Captured 2026-04-18 against https://essentia.upf.edu/models/
sha256sum -c <<'EOF'
3ed9af50d5367c0b9c795b294b00e7599e4943244f4cbd376869f3bfc87721b1  discogs-effnet-bs64-1.pb
2e8c3003c722e098da371b6a1f7ad0ce62fac0dcfc09c7c7997d430941196c2a  mtg_jamendo_instrument-discogs-effnet-1.pb
EOF
```

The same check is available programmatically:

```python
from wav2caption import resolve_models, verify_digests
verify_digests(resolve_models())
```

or automatically on every `analyze(...)` call by setting
`WAV2CAPTION_VERIFY_DIGESTS=1` in your environment.

> ⚠️ **Supply-chain note.** The `.pb` files are TensorFlow GraphDefs and
> a maliciously crafted graph can influence what runs inside Essentia.
> Always download over HTTPS from `essentia.upf.edu` and verify the
> digests before first load.

Or point `WAV2CAPTION_MODELS_DIR` (or `--models-dir`) at an existing folder.

## Quick start

### CLI

```bash
wav2caption song.wav
wav2caption song.wav --json > analysis.json
wav2caption song.wav --section-seconds 5
```

### Example output

On a 3:32 record-grand-prix reference instrumental, `wav2caption
song.wav` produces:

```
=== song.wav ===
duration: 3:32  tempo: 132.9 BPM  key: Eb major (conf 0.87)  danceability: 1.10

[ detected instruments ]
  drums                0.402  ################
  electricguitar       0.308  ############
  bass                 0.286  ###########
  guitar               0.274  ##########
  piano                0.222  ########
  acousticguitar       0.177  #######
  synthesizer          0.176  #######
  violin               0.126  #####
  ...

[ role scores ]
  rhythm             0.468
  acoustic_guitar    0.450
  harmony            0.377
  lead_guitar        0.308
  bass               0.286
  strings            0.219
  synth              0.176
  brass              0.118
  vocal              0.067
  woodwind           0.061

[ sections ]
  0:20-0:30  loud=1301  bright=1019Hz  Eb major
    roles: rhythm=drums(0.44) / lead_guitar=electricguitar(0.37) / bass=bass(0.34) / ...
    features: metallic percussion accents, string harmonies, brass accents
  0:30-0:40  loud=1224  bright=1278Hz  Eb major
    roles: rhythm=drums(0.38) / lead_guitar=electricguitar(0.31) / bass=bass(0.29) / ...
    features: metallic percussion accents, staccato stabs

[ caption ]
  live drums, electric guitar, piano, bass, string section, acoustic guitar,
  Eb major, 133 BPM, dynamic build-up, breakdown section
```

### Python

```python
from wav2caption import analyze, build_caption

result = analyze("song.wav")
print(build_caption(result))

for s in result.sections:
    roles = {r: name for r, (name, _score) in s.roles.items()}
    print(f"{s.start:>5.1f}s  {roles}  {s.features}")
```

`AnalysisResult` is a typed dataclass:

```python
@dataclass
class AnalysisResult:
    path: Path
    duration_sec: float
    bpm: float
    key: str
    scale: str  # "major" | "minor"
    key_confidence: float
    danceability: float
    detected_instruments: list[tuple[str, float]]   # (label, probability)
    role_scores: dict[str, float]                   # aggregated per role
    sections: list[Section]
```

## Role taxonomy

| role              | instruments                                                                 |
| ----------------- | --------------------------------------------------------------------------- |
| `rhythm`          | drums, drummachine, beat, percussion, bongo                                 |
| `bass`            | bass, acousticbassguitar, doublebass                                        |
| `harmony`         | piano, electricpiano, keyboard, rhodes, organ, pipeorgan, accordion         |
| `lead_guitar`     | electricguitar                                                              |
| `acoustic_guitar` | acousticguitar, classicalguitar, guitar                                     |
| `strings`         | strings, violin, viola, cello, orchestra                                    |
| `brass`           | brass, trumpet, trombone, horn, saxophone                                   |
| `woodwind`        | flute, clarinet, oboe                                                       |
| `synth`           | synthesizer, pad, sampler, computer                                         |
| `bells`           | bell, harp, harmonica                                                       |
| `vocal`           | voice                                                                       |

The mapping is intentionally opinionated and biased toward *production
arrangement* labels rather than strict orchestration (e.g. `guitar` goes to
`acoustic_guitar` because the MTG-Jamendo label is ambiguous and the
acoustic interpretation is safer for caption conditioning). Override
`ROLE_MAP` if you disagree — it's just a `dict[str, tuple[str, ...]]`.

## Project layout

```
src/wav2caption/
    __init__.py       # public API
    analyzer.py       # analyze() + build_caption() + dataclasses
    constants.py      # INSTRUMENTS, ROLE_MAP, get_role()
    models.py         # model-path discovery
    cli.py            # wav2caption console script
tests/                # no-Essentia unit tests
```

## Development

```bash
git clone https://github.com/hinanohart/wav2caption
cd wav2caption
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest
ruff check .
mypy src
```

The unit tests intentionally do **not** require Essentia, so CI stays fast
and free of TensorFlow. Real-audio smoke tests belong in `examples/`.

## License

- Source code: **Apache 2.0** (see [LICENSE](LICENSE)).
- Runtime dep Essentia: **AGPL-3.0** (opt-in via `pip install "wav2caption[essentia]"`).
- Pretrained models: **CC-BY-NC-SA 4.0** (user-downloaded, non-commercial).

Full third-party notices: [`NOTICE.md`](NOTICE.md).

If you need a commercial pipeline you will have to either license Essentia
from MTG-UPF or swap in a different backend. The Apache-2.0-licensed code
in this repo is backend-agnostic enough that a `torch` / `onnxruntime`
port is straightforward — PRs welcome.
