audium-md 0.1.0

Audio-to-Markdown transcription optimized for AI consumption

Audium

🎧 Audio → AI‑optimized Markdown
_{Transcribe MP3/WAV/FLAC into clean, token‑efficient Markdown — ready for any LLM.}

English · Русский · 中文

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✨ Why Audium?

Feed audio to an LLM. Get answers. Simple.

But raw transcripts burn tokens on noise: long timestamps, filler words, silent segments, markup that adds nothing.

Audium turns speech into the minimum viable Markdown: every character counts, nothing wasted.

🎯	⚡	🪙	👁️	🌍
3 formats	GPU‑accelerated	Token‑aware	Watch mode	~97 languages
compact, minimal, structured	2–10× real‑time on CUDA	[MM:SS] + VAD + filler‑strip	drop files → auto‑transcribe	tiny to large‑v3

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📦 Install

pip install audium-md

Requires ffmpeg on your system: sudo apt install ffmpeg / brew install ffmpeg

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🚀 Quick Start

# Process a folder
audium run ./my-recordings/

# Single file
audium run lecture.mp3

# Watch folder — auto‑transcribe new files
audium watch ./incoming/

# See what you've transcribed
audium list

# Change model
audium config set model large-v3

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📝 Formats

compact (default)

# lecture.mp3 (01:23:45)

[00:00] Neural networks learn hierarchical representations
[00:04] Each layer detects increasingly abstract features
[00:08] Early layers find edges and textures
[00:12] Later layers detect objects and scenes

minimal

Neural networks learn hierarchical representations
Each layer detects increasingly abstract features
Early layers find edges and textures
Later layers detect objects and scenes

structured (requires speaker diarization)

# interview.mp3 (00:45:12)

## Alice [00:00-00:30]
Neural networks are a powerful tool. It's important to understand their limitations.

## Bob [00:30-01:15]
I completely agree. Let me walk through an example to make this concrete.

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⚙️ Commands

Command	Description
audium run <path>	Transcribe audio files or folders
audium watch <path>	Watch folder and auto‑process new files
audium list [dir]	Show processed transcripts with file sizes
audium config	Show current configuration
audium config set <key> <value>	Change a setting
audium config reset	Reset to factory defaults
audium config path	Show config file location

Common flags for run and watch

Flag	Default	Description
-o, --output-dir	./transcripts	Where to save .md files
-f, --format	compact	compact / minimal / structured
-r, --recursive	off	Search subdirectories
--model	small	tiny / base / small / medium / large-v3
--language	auto	Force language code: ru, en, zh, ...
--strip-fillers	off	Remove "um", "uh", "like", "мм", "ээ", etc.
--no-vad	off	Disable voice activity detection
--no-progress	off	Hide the progress bar

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🔧 Configuration

Settings are merged: CLI flags > .audium.yaml (project) > ~/.config/audium/config.yaml > defaults

# Set default model
audium config set model large-v3

# Always strip filler words
audium config set strip_fillers true

# Custom output folder
audium config set output_dir ~/Documents/transcripts

# See what you changed
audium config

# Example .audium.yaml (place in project root)
model: medium
language: ru
format: minimal
output_dir: ./transcripts

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🪙 Token Optimization

Audium is built to minimize LLM token cost:

Technique	Savings
[MM:SS] instead of [HH:MM:SS.mmm]	~30% on timestamps
VAD filtering (skip silence)	15–40% on meeting recordings
Filler‑word stripping	5–10% on conversational speech
min_segment_duration threshold	skip noise fragments
One line per segment, no blank lines	~8% vs paragraph output

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📊 Model Sizes

Model	Parameters	Speed (GPU)	Best for
tiny	39M	~32× real‑time	Quick drafts, low‑resource
base	74M	~16× real‑time	Dictation, clean audio
small	244M	~6× real‑time	General purpose
medium	769M	~2× real‑time	Accents, noisy audio
large‑v3	1.5B	~1× real‑time	Maximum accuracy

All multilingual models support the same ~97 languages. The size trades accuracy for speed.

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📄 License

MIT — do whatever you want. Attribution appreciated.