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The skills directory was getting disorganized — mlops alone had 40 skills in a flat list, and 12 categories were singletons with just one skill each. Code change: - prompt_builder.py: Support sub-categories in skill scanner. skills/mlops/training/axolotl/SKILL.md now shows as category 'mlops/training' instead of just 'mlops'. Backwards-compatible with existing flat structure. Split mlops (40 skills) into 7 sub-categories: - mlops/training (12): accelerate, axolotl, flash-attention, grpo-rl-training, peft, pytorch-fsdp, pytorch-lightning, simpo, slime, torchtitan, trl-fine-tuning, unsloth - mlops/inference (8): gguf, guidance, instructor, llama-cpp, obliteratus, outlines, tensorrt-llm, vllm - mlops/models (6): audiocraft, clip, llava, segment-anything, stable-diffusion, whisper - mlops/vector-databases (4): chroma, faiss, pinecone, qdrant - mlops/evaluation (5): huggingface-tokenizers, lm-evaluation-harness, nemo-curator, saelens, weights-and-biases - mlops/cloud (2): lambda-labs, modal - mlops/research (1): dspy Merged singleton categories: - gifs → media (gif-search joins youtube-content) - music-creation → media (heartmula, songsee) - diagramming → creative (excalidraw joins ascii-art) - ocr-and-documents → productivity - domain → research (domain-intel) - feeds → research (blogwatcher) - market-data → research (polymarket) Fixed misplaced skills: - mlops/code-review → software-development (not ML-specific) - mlops/ml-paper-writing → research (academic writing) Added DESCRIPTION.md files for all new/updated categories.
80 lines
2.3 KiB
Markdown
80 lines
2.3 KiB
Markdown
---
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name: songsee
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description: Generate spectrograms and audio feature visualizations (mel, chroma, MFCC, tempogram, etc.) from audio files via CLI. Useful for audio analysis, music production debugging, and visual documentation.
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version: 1.0.0
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author: community
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license: MIT
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metadata:
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hermes:
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tags: [Audio, Visualization, Spectrogram, Music, Analysis]
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homepage: https://github.com/steipete/songsee
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---
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# songsee
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Generate spectrograms and multi-panel audio feature visualizations from audio files.
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## Prerequisites
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Requires [Go](https://go.dev/doc/install):
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```bash
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go install github.com/steipete/songsee/cmd/songsee@latest
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```
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Optional: `ffmpeg` for formats beyond WAV/MP3.
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## Quick Start
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```bash
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# Basic spectrogram
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songsee track.mp3
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# Save to specific file
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songsee track.mp3 -o spectrogram.png
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# Multi-panel visualization grid
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songsee track.mp3 --viz spectrogram,mel,chroma,hpss,selfsim,loudness,tempogram,mfcc,flux
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# Time slice (start at 12.5s, 8s duration)
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songsee track.mp3 --start 12.5 --duration 8 -o slice.jpg
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# From stdin
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cat track.mp3 | songsee - --format png -o out.png
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```
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## Visualization Types
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Use `--viz` with comma-separated values:
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| Type | Description |
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|------|-------------|
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| `spectrogram` | Standard frequency spectrogram |
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| `mel` | Mel-scaled spectrogram |
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| `chroma` | Pitch class distribution |
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| `hpss` | Harmonic/percussive separation |
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| `selfsim` | Self-similarity matrix |
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| `loudness` | Loudness over time |
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| `tempogram` | Tempo estimation |
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| `mfcc` | Mel-frequency cepstral coefficients |
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| `flux` | Spectral flux (onset detection) |
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Multiple `--viz` types render as a grid in a single image.
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## Common Flags
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| Flag | Description |
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|------|-------------|
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| `--viz` | Visualization types (comma-separated) |
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| `--style` | Color palette: `classic`, `magma`, `inferno`, `viridis`, `gray` |
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| `--width` / `--height` | Output image dimensions |
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| `--window` / `--hop` | FFT window and hop size |
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| `--min-freq` / `--max-freq` | Frequency range filter |
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| `--start` / `--duration` | Time slice of the audio |
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| `--format` | Output format: `jpg` or `png` |
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| `-o` | Output file path |
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## Notes
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- WAV and MP3 are decoded natively; other formats require `ffmpeg`
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- Output images can be inspected with `vision_analyze` for automated audio analysis
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- Useful for comparing audio outputs, debugging synthesis, or documenting audio processing pipelines
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