lingtai-ai listen
Nested swiss-knife reference for local-only audio analysis. No API key, no network. Two actions: transcribe (speech → text) or appreciate (music → numerical critique).
Two Actions
| Action | Backend | When |
|---|---|---|
| transcribe | faster-whisper (local Whisper) |
Spoken word, voice notes, podcasts, lectures. Works on singing too but lyrics may be inaccurate. |
| appreciate | librosa (signal processing) |
Music — tempo, key, frequency bands, dynamics. Returns numerical measurements, not subjective descriptions. |
Both actions are wrappers around the bundled scripts. Run them with bash like any other command-line tool:
python3 <skill-path>/scripts/transcribe.py <audio-file>
python3 <skill-path>/scripts/appreciate.py <audio-file>
The scripts auto-install their dependencies via lingtai.venv_resolve.ensure_package on first run, so the first invocation may take ~30 s.
transcribe — speech to text
python3 <skill-path>/scripts/transcribe.py <audio-path> [--model base] [--device cpu]
| Flag | Default | Notes |
|---|---|---|
--model |
base |
Whisper model size: tiny, base, small, medium, large-v2, large-v3. Larger = more accurate, slower, more RAM. |
--device |
cpu |
Use cuda if you have a GPU. |
--compute-type |
int8 |
CTranslate2 compute type. int8 is the fastest CPU mode. Use float16 on GPU. |
Output: a JSON document on stdout with:
{
"text": "<full transcript>",
"language": "en",
"language_probability": 0.99,
"duration": 42.3,
"segments": [
{"start": 0.0, "end": 4.2, "text": "..."},
...
]
}
Best for: Clear spoken word in any of Whisper's supported languages.
Caveats: Singing lyrics often mistranscribed — Whisper is trained on speech, not singing. Background music degrades accuracy. For very noisy input, try --model medium or large-v3.
appreciate — music analysis
python3 <skill-path>/scripts/appreciate.py <audio-path>
No flags — purely analytical. Output: a JSON document with:
| Field | Meaning |
|---|---|
duration |
Audio length in seconds |
tempo_bpm |
Estimated tempo |
beat_regularity_std |
Std-dev of inter-beat intervals — small (<0.05) = steady, large = rubato/free |
key |
Estimated key (e.g. D minor, G major) |
key_confidence |
0–1, correlation with Krumhansl key profile |
chroma_profile |
Per-pitch-class energy — useful for spotting modal mixture |
spectral_centroid_hz |
Brightness — higher = brighter mix |
spectral_bandwidth_hz |
Spread of spectrum |
spectral_rolloff_hz |
85th-percentile frequency — "where the highs end" |
zero_crossing_rate |
Noisiness measure |
dynamic_range_db |
Loud-vs-quiet contrast in dB |
frequency_bands_pct |
Percentage of energy in sub_bass/bass/low_mid/mid/upper_mid/presence/brilliance |
energy_contour |
RMS energy in 10 equal-time segments (loud-vs-quiet shape over time) |
onset_density_per_sec |
How many note-onsets per second — proxy for "busyness" |
These are measurements, not opinions. Your job is to translate the numbers into a critique:
- "tempo_bpm: 84, beat_regularity_std: 0.012" → "steady mid-tempo, ballad pacing".
- "spectral_centroid_hz: 3500, presence: 22%" → "bright, vocal-forward mix".
- "energy_contour: monotonically increasing" → "builds throughout".
Best for: Music. Useless for speech — gives spectral data with no semantic content.
When to use which
transcribe for anything you want words from — voice notes, lectures, podcasts,
song lyrics (warn: lyrics may be wrong), or round-trip QA of minimax-cli TTS
output. appreciate for anything you want musical measurements from — checking
a track against a brief, or QAing generated music from minimax-cli/dj. Run
both when you need transcript + analysis.
Going Deeper
The bundled scripts are deliberately minimal. If you need:
- Per-section analysis (verse vs chorus): segment the file with
librosa.segmentfirst, then runappreciate.pyon each segment. - Multi-track separation: use
demucsorspleeter(heavier deps — install on demand viapip). - Pitch tracking (melody extraction): use
librosa.pyinorcrepe. - Lyrics alignment: the Whisper segments give you word-level timing if you pass
--word-timestamps.
You can write your own scripts using the same dependencies — librosa and faster-whisper are already installed once the bundled scripts have run.
When NOT to use this skill
- Human asked you to create audio (music, speech, sound effect) — use the sibling
minimax-clireference; for journal-inspired music, usedj. - Human asked you to describe a video or image — use the sibling
visionreference (../vision/SKILL.md). - You only need to play audio for the human — use an OS-native player; this reference only analyzes audio files.
Found a bug or issue? If you encounter any problems with this skill, load the
lingtai-issue-reportskill and follow its instructions to report it.