tapeback 0.9.6

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

• Live transcription (opt-in): read the transcript while the meeting is still going — Whisper transcribes in the background every 60 seconds (set TAPEBACK_LIVE=true)
• 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: Anthropic, OpenAI, Groq, Gemini, DeepSeek, OpenRouter, Qwen (with automatic provider fallback)
• System tray: start/stop recording from the tray icon, no terminal needed
• CLI-first: tapeback start, Ctrl+C to stop, done

tapeback is modular — the base package handles recording and transcription. Speaker diarization and LLM summaries are optional and installed separately.

Installation

Arch Linux (AUR)

yay -S tapeback                              # recording + transcription
yay -S tapeback tapeback-tray                # + system tray icon
yay -S tapeback tapeback-llm                 # + LLM summaries
yay -S tapeback tapeback-diarize             # + speaker diarization
yay -S tapeback tapeback-tray tapeback-llm tapeback-diarize  # everything

All system dependencies (ffmpeg, PipeWire) are installed automatically.

Ubuntu / Debian (.deb)

Pre-built .deb packages are attached to every GitHub Release. Pick a version (see the releases page for the current one), download, install:

wget https://github.com/yastcher/tapeback/releases/download/v0.9.5/tapeback_0.9.5_amd64.deb
sudo apt install ./tapeback_0.9.5_amd64.deb

# Optional extras:
sudo apt install ./tapeback-tray_0.9.5_all.deb       # tray icon
sudo apt install ./tapeback-llm_0.9.5_all.deb        # LLM summaries
sudo apt install ./tapeback-diarize_0.9.5_all.deb    # speaker diarization

The base package bundles its own Python interpreter (from python-build-standalone) so it works on any modern Ubuntu or Debian regardless of the system Python version. Only system dependencies are ffmpeg and pulseaudio-utils (for parecord / pactl).

pip / uv

Install system dependencies first:

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

# Ubuntu / Debian
sudo apt install python3 pipx ffmpeg pulseaudio-utils

# Fedora
sudo dnf install python3 pipx ffmpeg pipewire-pulseaudio

Then install tapeback:

uv tool install tapeback                          # recording + transcription
uv tool install "tapeback[tray]"                  # + system tray icon
uv tool install "tapeback[llm]"                   # + LLM summaries
uv tool install "tapeback[diarize]"               # + speaker diarization
uv tool install "tapeback[tray,llm,diarize]"      # everything

pipx or Nix

# pipx
pipx install tapeback
pipx install "tapeback[tray,llm,diarize]"         # everything

# Nix
nix run github:yastcher/tapeback                  # basic
nix run github:yastcher/tapeback#tray             # + system tray icon
nix run github:yastcher/tapeback#llm              # + LLM summaries
nix run github:yastcher/tapeback#diarize          # + speaker diarization
nix run github:yastcher/tapeback#full             # everything

Quick start

tapeback start                     # start recording, Ctrl+C to stop

That's it. The transcript is saved to ~/tapeback/meetings/.

To save to your Obsidian vault instead:

mkdir -p ~/.config/tapeback
echo 'TAPEBACK_VAULT_PATH=~/Documents/obsidian/vault' > ~/.config/tapeback/.env

Tip: if you always meet in one language, pin it — auto-detection can misfire on a channel that starts silent (and even hallucinate). English terms inside another language still transcribe fine:

TAPEBACK_LANGUAGE=en tapeback start          # or add TAPEBACK_LANGUAGE=en to .env

System tray

Run without a terminal — right-click the tray icon to start/stop recording:

tapeback tray

Icon color shows the current state: gray = idle, red = recording, orange = processing.

To autostart on login, create ~/.config/autostart/tapeback-tray.desktop:

[Desktop Entry]
Name=tapeback
Exec=tapeback tray
Type=Application
X-GNOME-Autostart-enabled=true

tapeback's tray speaks the StatusNotifierItem D-Bus protocol directly — no pystray, no GTK, no XEmbed. KDE Plasma, Hyprland (with waybar), and Sway (with waybar/eww) display it out of the box on both X11 and Wayland.

GNOME

GNOME Shell does not display SNI items natively. Install the AppIndicator Support extension (one-time setup, also needed by Slack, Dropbox, etc.):

# Ubuntu / Debian
sudo apt install gnome-shell-extension-appindicator
# Fedora
sudo dnf install gnome-shell-extension-appindicator

Open the Extensions app, enable Ubuntu AppIndicators (or AppIndicator and KStatusNotifierItem Support), log out + back in. tapeback tray prints this hint to stderr on startup if it detects an affected session. See issue #3 for background.

Speaker diarization

Speaker diarization identifies who said what in the recording. Without it, tapeback uses stereo channels: your mic is labeled "You", everything else is labeled "Other".

To enable diarization, you need 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. Create token at huggingface.co/settings/tokens
5. Add to ~/.config/tapeback/.env:

TAPEBACK_HF_TOKEN=hf_your_token_here

First run downloads the pyannote model (~1 GB). An NVIDIA GPU is strongly recommended — diarization on CPU is very slow.

LLM summarization

After transcription, tapeback can add a brief summary, action items, and key decisions using an LLM.

Set an API key for at least one provider in ~/.config/tapeback/.env:

TAPEBACK_LLM_PROVIDER=gemini
GEMINI_API_KEY=...

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 start [NAME]              Start recording (Ctrl+C to stop)
tapeback stop                      Stop recording from another terminal
tapeback tray                      System tray icon
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

tapeback start --no-live           # one-shot override: skip live transcription even if TAPEBACK_LIVE=true
tapeback start --no-diarize        # skip speaker identification
tapeback start --no-summarize      # skip LLM summary
tapeback process meeting.mp3 --name "weekly-standup"
tapeback summarize notes.md --provider gemini --model gemini-2.5-pro

Output format

Stereo recordings produce two transcript sections:

• Transcript — raw Whisper output with channel-based labels (You / Other)
• Diarized Transcript — speaker-identified output (You / Speaker 1 / Speaker 2 / ...)

Words where Whisper is uncertain (probability < 0.5) are shown in italics.

---
date: 2026-03-23
time: "14:30"
duration: "01:23:45"
language: en
audio: "[[attachments/audio/2026-03-23_14-30-00.wav]]"
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

**Duration:** 1h 23m 45s | **Language:** en

---

## Transcript

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

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

---

## Diarized Transcript

[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 1:** Can we move on to the frontend?

Configuration reference

All settings via environment variables (prefix TAPEBACK_) or ~/.config/tapeback/.env file.

Core

Variable	Default	Description
TAPEBACK_VAULT_PATH	~/tapeback	Path to output directory (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	auto	Language code (auto for auto-detection, or en, ru, fr, etc.)
TAPEBACK_DEVICE	cuda	cuda or cpu
TAPEBACK_COMPUTE_TYPE	auto	auto, float16, int8, or float32 (auto → float16 on CUDA, int8 on CPU; pin int8 if your GPU is memory-tight)
TAPEBACK_BEAM_SIZE	4	Whisper beam search width (lower = faster, slightly less accurate)
TAPEBACK_TEMPERATURE	[0.0, 0.2, 0.4, 0.6, 0.8, 1.0]	Temperature fallback ladder. The high steps break Whisper out of hallucination loops on noisy audio — don't shorten unless your input is clean (shortening can cause repeat loops: slower and worse)
TAPEBACK_BATCH_SIZE	0	Batched inference (faster-whisper BatchedInferencePipeline) — processes VAD segments in parallel, several× faster on GPU. 0 = off; try 8. On small GPUs (≤4 GB) it may OOM even at 4 — use 2, pair with TAPEBACK_COMPUTE_TYPE=int8, or keep 0. OOM falls back to CPU automatically
TAPEBACK_CHUNK_LENGTH	7	Max VAD chunk (seconds) before splitting for Whisper; prevents lost speech after long pauses
TAPEBACK_NO_SPEECH_THRESHOLD	0.4	Whisper silence-rejection threshold (lower = more aggressive; suppresses training-data hallucinations on pauses)
TAPEBACK_LANGUAGE_DETECTION_SEGMENTS	1	Segments probed before deciding the language; raise (e.g. 4) if a channel that starts silent gets the wrong language
TAPEBACK_MULTILINGUAL	false	Per-segment language detection for mixed-language recordings (code-switching). Less stable than a fixed TAPEBACK_LANGUAGE
TAPEBACK_HALLUCINATION_SILENCE_THRESHOLD	(off)	Seconds; skip silent gaps when a hallucination is detected. ⚠ Triggers per-segment re-processing — can be much slower on pause-heavy channels (e.g. the mic). Leave off unless it measurably helps your audio
TAPEBACK_PAUSE_THRESHOLD	1.0	Seconds; split segments on silence gaps >= this
TAPEBACK_GATE_MIC_SILENCE	true	Silence the mic channel where you're only listening (mic quiet / monitor dominant) before transcription, so Whisper doesn't loop on the pauses. Dual-channel pipeline only

Live transcription

Variable	Default	Description
TAPEBACK_LIVE	false	Enable live transcription during recording (opt-in; competes with the post-recording pipeline for GPU memory on small cards)
TAPEBACK_LIVE_INTERVAL	60	Seconds between transcription cycles
TAPEBACK_LIVE_OVERLAP	2.0	Seconds of overlap between chunks
TAPEBACK_LIVE_MIN_CHUNK	5.0	Minimum new audio (seconds) to trigger transcription

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
TAPEBACK_HF_TOKEN	(empty)	HuggingFace token (setup)
TAPEBACK_MAX_SPEAKERS	(auto)	Maximum number of speakers
TAPEBACK_SPECTRAL_MERGE_THRESHOLD	0.96	Spectral speaker merging (0 = off; lower merges more aggressively)

LLM summarization

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

Troubleshooting

GPU transcription falls back to CPU on CUDA 13 systems

If the log shows Warning: CUDA runtime error, falling back to CPU: Library libcublas.so.12 is not found, your system has CUDA 13 (e.g. recent Arch) but faster-whisper's ctranslate2 backend is built against CUDA 12 and can't find libcublas.so.12 / libcudnn.so.9. Diarization (PyTorch) still uses the GPU, but transcription drops to slow CPU.

Install the CUDA 12 runtime libraries — tapeback preloads them automatically:

uv pip install nvidia-cublas-cu12 nvidia-cudnn-cu12

They install alongside the CUDA 13 libraries without conflict. No LD_LIBRARY_PATH needed: when TAPEBACK_DEVICE=cuda (the default), tapeback finds and preloads them on startup. If they're not installed, transcription falls back to CPU with the message above.

Wrong language or repeated/garbled text on the "You" channel

If the note's language: is wrong (e.g. ja for an English meeting) or the You channel is full of repeats (Do you hear me? Do you hear me?…) or foreign script, the mic channel started with silence while you were listening, and Whisper guessed the language from that silence and hallucinated.

Fixes, in order of reliability:

• Pin the language if you know it: TAPEBACK_LANGUAGE=ru (or en, …). English terms inside another language still transcribe fine.
• Probe more speech before deciding: TAPEBACK_LANGUAGE_DETECTION_SEGMENTS=4.
• Mixed-language meetings (real code-switching): TAPEBACK_MULTILINGUAL=true.
• Suppress silence hallucinations: TAPEBACK_HALLUCINATION_SILENCE_THRESHOLD=2.0.

Uninstall

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

# pip / uv
uv tool uninstall tapeback

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

Roadmap

• Speaker profiles: learn and remember recurring speakers across meetings
• Multi-language meetings: detect and handle language switches mid-meeting
• Windows support: WASAPI loopback capture

Support

If you find tapeback useful, consider a small donation:

USDT (TRC-20)	ADA (Cardano)
TAECw9FebnoSN2n3H2Fk9Bv5aA8fwpCuBB	addr1q9tqg2g8wxpxawsrvea84lms3ampuda0ygzawuxq77sxwr48mxj2vq2rzd4nsmhpdhy6lftp30tz78tetzr29mtvkqmsskrmp7

Links

• Changelog
• Deploy
• CI/CD

License

Apache-2.0. See LICENSE.