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4.4 KiB
clean_audio.sh — automatic speech cleaning (FFmpeg)
This script batch‑cleans noisy speech recordings with ffmpeg using simple, reliable filters tuned for ASR (e.g., faster‑whisper). By default it REQUIRES RNNoise (arnndn) and will try to auto‑discover or download a model. You can opt‑in to fallback filters with --allow-fallback.
Install
- Required: ffmpeg. Most distros:
sudo pacman -S ffmpegorsudo apt install ffmpeg. - Recommended: ffmpeg with
arnndnfilter and an RNNoise model file (e.g., from Mozilla RNNoise community models). The script will auto-detect common model locations or download one viaBash/get_rnnoise_model.sh. You can pass a specific model with-m /path/to/model.nn.
Make executable:
chmod +x Bash/clean_audio.sh
Quick start
- Single file, default ASR preset (16k mono, denoise, high‑pass, limiter):
Bash/clean_audio.sh path/to/file.wav
This produces path/to/file_clean.wav.
- Whole folder, 4 parallel jobs, output to
cleaned/:
Bash/clean_audio.sh path/to/folder -O cleaned -j 4
- Use an RNNoise model explicitly (if your ffmpeg has arnndn):
Bash/clean_audio.sh input.wav -m models/rnnoise_model.nn
If you omit -m, the script will look in common locations; if not found, it will attempt a download via Bash/get_rnnoise_model.sh.
Advanced options and compatibility:
-
The cleaner requires RNNoise by default. To allow non-ML fallback filters (afftdn), add
--allow-fallback. -
The script uses advanced filter settings when available (e.g., afftdn with
md). If your ffmpeg build lacks these options, it will error with guidance. Add--no-advanced(or--compat) to avoid such params. -
Podcast preset (adds dynamics and loudness leveling):
Bash/clean_audio.sh input.wav --preset podcast
Options
Usage: clean_audio.sh <input-file|input-dir> [options]
Options:
-O, --out-dir DIR Output directory (default: alongside input file).
-e, --ext EXT Output extension/container: wav|flac (default: wav).
-m, --model PATH RNNoise model file for arnndn; falls back to afftdn if unavailable.
--no-ml Do not use arnndn even if model is provided; use afftdn.
--preset NAME asr (default) | podcast | aggressive
-j, --jobs N Parallel jobs for directory mode (default: 1).
-f, --force Overwrite outputs if they exist.
-q, --quiet Reduce ffmpeg logging noise.
--lowpass FREQ Optional low-pass cutoff (e.g., 8000). Disabled by default.
--suffix SUF Suffix for output basename (default: _clean).
Designed for ASR (faster‑whisper)
Default output format is mono, 16 kHz, PCM 16‑bit WAV—ideal for most Whisper/faster‑whisper pipelines. You can feed the cleaned files directly into your transcription step.
If you prefer FLAC to save space without quality loss:
Bash/clean_audio.sh input.wav -e flac -O cleaned
Presets
- asr (default): light, ASR‑friendly cleanup; prevents clipping.
- podcast: adds gentle dynamics and approximate loudness normalization (single‑pass
loudnorm). - aggressive: heavier gate/dynamics; can suppress background more, but may slightly hurt ASR accuracy—use sparingly.
Tips
- If you see artifacts from RNNoise, try without a model (uses
afftdn), or add a low‑pass (e.g.,--lowpass 8000). - For extremely boomy bar recordings, raise high‑pass by editing
HIGHPASSin the script or add--lowpass. - If your ffmpeg lacks
arnndn, you can install a newer build or keep the fallback (afftdn works fine for many cases). - If your ffmpeg is missing features, you can use the helper:
chmod +x Bash/install_ffmpeg_with_arnndn.sh
Bash/install_ffmpeg_with_arnndn.sh
It will suggest distro options or build FFmpeg from source with --enable-librnnoise.
RNNoise model downloader helper:
```bash
chmod +x Bash/get_rnnoise_model.sh
Bash/get_rnnoise_model.sh --yes
```
This saves a model into `Bash/models/` which the cleaner will auto-discover.
Troubleshooting
- “arnndn not available”: Your ffmpeg wasn’t built with it. The script will use
afftdninstead. - Output sounds thin: lower the high‑pass (edit
HIGHPASS=80in script to60) or remove low‑pass. - Level too low/high: choose the
podcastpreset for auto leveling, or add your ownloudnormin post.
License
This helper script is provided under the repository’s LICENSE.