erm — filler-word remover

erm

Local CLI that strips disfluencies (um, uh, er, erm, ah, hmm, mhm, mm, uh-huh, plus any-length elongations like ummmm / uhhhhh) from recordings of English speech.

It uses faster-whisper (running the large-v3 Whisper model by default — override with --model) for word-level timestamps, three audio-domain detectors that catch fillers Whisper hides, and ffmpeg for the cuts. Each splice is snapped to a local energy minimum and zero-crossing, optionally crossfaded with a length that scales with the cut size, and laid over a constant looped sample of the recording's own room tone so the noise floor stays uniform across edits.

Full docs at dougcalobrisi.github.io/erm (source in docs/): usage guides for getting good results — tuning & workflow, recipes, troubleshooting — plus maintainer-facing design docs on the detection passes, render pipeline, denoise/room-tone, and transcription.

Install

Requires Python 3.11+ and ffmpeg / ffprobe on PATH.

python3.13 -m venv .venv
source .venv/bin/activate
pip install -e '.[dev]'

Transcription device (GPU vs CPU)

Transcription runs on CPU by default and needs no extra setup. If you have an NVIDIA GPU, faster-whisper can use it — but only when the CUDA runtime libraries (libcublas, libcudnn) are installed. A machine with an NVIDIA GPU and driver but no CUDA runtime is the common case that produces:

RuntimeError: Library libcublas.so.12 is not found or cannot be loaded

erm handles this automatically: with the default --device auto, if the GPU can't be loaded it prints a warning and falls back to CPU, so transcription still completes. You have two ways to make it explicit:

• Force CPU (no warning, skips the GPU probe): erm input.wav --device cpu

• Enable the GPU by installing the CUDA wheels into the same environment:

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

  faster-whisper's CUDA backend needs CUDA 12 / cuDNN 9. See the faster-whisper GPU notes for details.

Usage

# Remove fillers; output and cut-list paths are auto-generated next to the input.
erm input.wav

# Specify output explicitly.
erm input.wav -o cleaned.wav

# Inspect what would be cut without rendering.
erm input.wav --dry-run

# Validate a rendered output against its source.
erm validate input.wav cleaned.wav --cuts cuts.json

# Pull just the cleaned AUDIO out of a video (default — no picture rendered).
erm talk.mp4            # -> talk-cleaned-*.wav

# Keep the PICTURE: render a synced video output (container inferred from input).
erm talk.mp4 --video    # -> talk-cleaned-*.mp4, audio + video in sync

When -o / --json are omitted, output paths are written next to the input as {stem}-cleaned-{YYYYMMDD-HHMMSS}.wav and {stem}-cuts-{YYYYMMDD-HHMMSS}.json.

Video

A video input works like any other recording, and by default erm emits the cleaned audio only (.wav) — the common "pull the audio out of this video" case, and identical to how erm has always behaved. Pass --video to render the picture too:

• The output container is inferred from the input (mp4→mp4, mov→mov…); an -o extension always overrides.
• Audio and video stay in sync by construction (within ~1 frame): both streams are rendered from the same edit timeline with the same frame-snapped crossfades, and the picture is conformed to the audio's exact length.
• --mode silence stream-copies the picture untouched (lossless, frame-exact). --mode remove re-encodes the spliced picture; --video-splice crossfade (default) dissolves each splice, --video-splice cut makes hard jump cuts.
• --min-gap-ms with --video plays the removed footage through the injected pause (muted) rather than freezing the frame.
• Audio is stored losslessly where the container allows (PCM in mov/mkv/avi); mp4 gets AAC 256k, webm gets Opus 160k.

See Modes and the video render & A/V sync design doc for the A/V-sync derivation.

Use inside AI coding agents

erm ships agent guidance so an AI assistant can install, run, and tune it for you.

Claude Code / Cowork (plugin):

/plugin marketplace add dougcalobrisi/erm
/plugin install erm@erm

This adds two skills: erm (install + clean a file, asks which mode/recipe fits) and erm-tune (diagnoses a bad result and maps the symptom to the right knob). Ask in plain language — "install erm", "clean up this podcast", "the splices sound smeared" — and the right skill triggers.

Other agents (Codex, Copilot, OpenCode, Cursor, Gemini CLI, pi.dev):