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:

json
{
  "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.segment first, then run appreciate.py on each segment.
• Multi-track separation: use demucs or spleeter (heavier deps — install on demand via pip).
• Pitch tracking (melody extraction): use librosa.pyin or crepe.
• 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-cli reference; for journal-inspired music, use dj.
• Human asked you to describe a video or image — use the sibling vision reference (../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-report skill and follow its instructions to report it.