tapeback 0.8.2

Local meeting recorder with transcription and speaker diarization for Obsidian

tapeback

Local meeting recorder for Linux. Records system audio + microphone via PipeWire/PulseAudio, transcribes with Whisper, identifies speakers, saves Markdown to your Obsidian vault. Everything runs on your machine, no cloud services or API calls needed for transcription.

Works with any video call platform: Google Meet, Zoom, Teams, Telegram, Discord, Slack huddles.

Features

• Platform-agnostic: captures OS-level audio, works with any app
• Local transcription: faster-whisper on CPU or CUDA GPU
• Speaker diarization: pyannote identifies who said what
• Stereo channel separation: your mic (left) vs. others (right) for accurate "You" attribution
• Obsidian-native output: Markdown with YAML frontmatter, wikilinks to audio files
• LLM summarization: optional, via Anthropic, OpenAI, Groq, Gemini, DeepSeek, OpenRouter, Qwen (with automatic provider fallback)
• CLI-first: tapeback start, Ctrl+C to stop, done

Requirements

• Linux (PipeWire or PulseAudio)
• Python 3.13+
• ffmpeg
• parecord (usually comes with pulseaudio-utils or pipewire-pulse)
• NVIDIA GPU (optional, for faster transcription and diarization)

Installation

1. System dependencies

# Arch / Manjaro
sudo pacman -S python uv ffmpeg pipewire-pulse

# Ubuntu / Debian
sudo apt install python3 pipx ffmpeg pulseaudio-utils
pipx ensurepath  # adds ~/.local/bin to PATH

# Fedora
sudo dnf install python3 pipx ffmpeg pipewire-pulseaudio
pipx ensurepath

2. Install tapeback

The base package records audio and transcribes locally. Optional extras add speaker diarization and LLM summaries:

Extra	What it adds	Size
(none)	Recording + transcription	~150 MB
[llm]	LLM summarization (Anthropic, OpenAI, Gemini, etc.)	+50 MB
[diarize]	Speaker diarization (pyannote + PyTorch)	+2 GB
[llm,diarize]	Everything	+2 GB

With uv (recommended)

uv tool install tapeback                  # basic
uv tool install "tapeback[llm]"           # + summaries
uv tool install "tapeback[diarize]"       # + speaker diarization
uv tool install "tapeback[llm,diarize]"   # everything

With pipx

pipx install tapeback                     # basic
pipx install "tapeback[llm]"              # + summaries
pipx install "tapeback[diarize]"          # + speaker diarization
pipx install "tapeback[llm,diarize]"      # everything

Arch Linux (AUR)

yay -S tapeback                  # basic
yay -S tapeback-llm              # + summaries
yay -S tapeback-diarize          # + speaker diarization (~2 GB PyTorch)

Nix

nix run github:yastcher/tapeback              # basic
nix run github:yastcher/tapeback#llm          # + summaries
nix run github:yastcher/tapeback#diarize      # + speaker diarization
nix run github:yastcher/tapeback#full         # everything

From source (development)

git clone https://github.com/yastcher/tapeback
cd tapeback
uv sync --group dev    # all dependencies + dev tools

3. Uninstall

# Remove tapeback
uv tool uninstall tapeback    # if installed with uv
pipx uninstall tapeback       # if installed with pipx

# Remove cached ML models (~2-5 GB)
# ⚠ Skip if you have other HuggingFace projects
rm -rf ~/.cache/huggingface/

# Arch Linux
yay -R tapeback tapeback-diarize tapeback-llm

Quick start

1. Configure

# Required: set your Obsidian vault path
export TAPEBACK_VAULT_PATH=~/Documents/obsidian/vault

# Or create a .env file in the project root:
echo 'TAPEBACK_VAULT_PATH=~/Documents/obsidian/vault' > .env

2. Record a meeting

# Start recording (blocks, Ctrl+C to stop and transcribe)
tapeback start

# Optionally give the session a name
tapeback start "weekly-standup"

3. Check your vault

The recording is saved as a Markdown note with audio attachment:

vault/
  meetings/2026-03-23_14-30-00.md
  attachments/audio/2026-03-23_14-30-00.wav

Process an existing recording

# Transcribe any audio file (mp3, m4a, ogg, wav)
tapeback process meeting.mp3

# With options
tapeback process call.wav --name "client-call" --no-diarize

Add summary to existing transcript

tapeback summarize vault/meetings/2026-03-23.md
tapeback summarize transcript.md --provider gemini

Configuration

All settings via environment variables (prefix TAPEBACK_) or .env file. Copy .env.example to .env and adjust:

cp .env.example .env

Core

Variable	Default	Description
TAPEBACK_VAULT_PATH	(required)	Path to Obsidian vault
TAPEBACK_MEETINGS_DIR	meetings	Subdirectory for meeting notes
TAPEBACK_ATTACHMENTS_DIR	attachments/audio	Subdirectory for audio files

Transcription

Variable	Default	Description
TAPEBACK_WHISPER_MODEL	large-v3-turbo	Whisper model (tiny, base, small, medium, large-v3-turbo)
TAPEBACK_LANGUAGE	en	Transcription language code
TAPEBACK_DEVICE	cuda	cuda or cpu
TAPEBACK_COMPUTE_TYPE	float16	float16, int8, or float32
TAPEBACK_BEAM_SIZE	5	Whisper beam search width
TAPEBACK_PAUSE_THRESHOLD	1.0	Seconds; split segments on silence gaps >= this

Audio

Variable	Default	Description
TAPEBACK_MONITOR_SOURCE	auto	PulseAudio monitor source name
TAPEBACK_MIC_SOURCE	auto	PulseAudio mic source name
TAPEBACK_SAMPLE_RATE	48000	Recording sample rate

Speaker diarization

Variable	Default	Description
TAPEBACK_DIARIZE	true	Enable speaker diarization (requires tapeback[diarize])
TAPEBACK_HF_TOKEN	(empty)	HuggingFace token for pyannote models
TAPEBACK_MAX_SPEAKERS	(auto)	Maximum number of speakers

Speaker diarization requires the diarize extra (uv tool install "tapeback[diarize]") and a HuggingFace token with access to pyannote models:

1. Create account at huggingface.co
2. Accept license at pyannote/speaker-diarization-3.1
3. Accept license at pyannote/segmentation-3.0
4. Accept license at pyannote/speaker-diarization-community-1
5. Create token at huggingface.co/settings/tokens
6. Set TAPEBACK_HF_TOKEN=hf_your_token_here

Without a token, tapeback still works but skips diarization.

LLM summarization

Requires the llm extra: uv tool install "tapeback[llm]"

Variable	Default	Description
TAPEBACK_SUMMARIZE	true	Enable LLM summarization
TAPEBACK_LLM_PROVIDER	anthropic	Primary LLM provider
TAPEBACK_LLM_API_KEY	(empty)	API key (or use provider-specific env var)
TAPEBACK_LLM_MODEL	(provider default)	Override model name

Supported providers and their env vars:

Provider	Env var	Default model
anthropic	ANTHROPIC_API_KEY	claude-sonnet-4-20250514
openai	OPENAI_API_KEY	gpt-4o
groq	GROQ_API_KEY	llama-3.3-70b-versatile
gemini	GEMINI_API_KEY	gemini-2.5-flash
openrouter	OPENROUTER_API_KEY	google/gemini-2.5-flash:free
deepseek	DEEPSEEK_API_KEY	deepseek-chat
qwen	DASHSCOPE_API_KEY	qwen-turbo

If the primary provider fails, tapeback automatically tries the next available provider (any provider with an API key set).

CLI reference

tapeback --help                    Show help and quick start guide
tapeback start [NAME]              Start recording (Ctrl+C to stop)
tapeback stop                      Stop recording from another terminal
tapeback process <FILE> [--name N] Transcribe an existing audio file
tapeback summarize <FILE>          Add LLM summary to transcript
tapeback status                    Show recording status and settings

Common options

tapeback start --no-diarize        # Skip speaker identification
tapeback start --no-summarize      # Skip LLM summary
tapeback process file.mp3 --name "weekly-standup"
tapeback summarize file.md --provider gemini --model gemini-2.5-pro

Output format

---
date: 2026-03-23
time: "14:30"
duration: "01:23:45"
language: en
tags:
  - meeting
  - transcript
---

## Summary

Brief overview of the meeting.

### Action Items

- [ ] **You:** Send the report by Friday
- [ ] **Speaker 1:** Review the PR

### Key Decisions

- Use PostgreSQL instead of MongoDB

---

# Meeting 2026-03-23 14:30

![[attachments/audio/2026-03-23_14-30-00.wav]]

[00:00:01] **You:** Hello, let's start with the backend changes.

[00:01:23] **Speaker 1:** Sure, I have the slides ready.

[00:02:45] **Speaker 2:** Can we start with the backend changes?

Architecture

src/tapeback/
  cli.py          Click CLI (start, stop, process, summarize, status)
  recorder.py     PulseAudio recording via parecord
  audio.py        ffmpeg audio processing (split channels, normalize, convert)
  transcriber.py  faster-whisper transcription
  diarizer.py     pyannote speaker diarization + spectral speaker merging
  formatter.py    Markdown generation (pure formatting, no I/O)
  vault.py        Obsidian vault file I/O
  summarizer.py   LLM summarization with multi-provider fallback
  models.py       Domain objects (Segment, Word, DiarizationSegment, Summary)
  settings.py     pydantic-settings configuration

Development

git clone https://github.com/yastcher/tapeback
cd tapeback
uv sync --group dev

uv run ruff check       # lint
uv run ruff format      # format
uv run ty check         # type check
uv run pytest           # test (coverage >= 85%)

Releasing

scripts/release.sh 0.9.0          # bump version in pyproject.toml + PKGBUILDs
# commit, tag, push → CI publishes to PyPI
scripts/aur-publish.sh 0.9.0      # update all 3 AUR packages

Roadmap

• Custom diarization model: train a speaker embedding model on meeting audio to replace the generic pyannote pipeline
• Windows client: WASAPI loopback capture
• Real-time transcription: live streaming with partial results
• Web dashboard: browser UI for reviewing and searching meeting history
• Speaker profiles: learn and remember recurring speakers across meetings
• Multi-language meetings: detect and handle language switches mid-meeting

License

Apache-2.0. See LICENSE.