denoise-audio 0.1.1

Denoise WAV audio using RNNoise, DeepFilterNet, and FacebookResearch Denoiser.

denoise-audio

A CLI tool for denoising WAV audio with 3 leading backends:

• rnnoise (fast, CPU-only, low-latency)
• deepfilternet (high-quality full-band denoising)
• fbdenoiser (FacebookResearch Denoiser / causal Demucs; strong enhancement)

Inputs/outputs are WAV files. If your audio is not WAV, convert it first.

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Install with pip (PyPI)

Use this if you want to install and use the tool without cloning the repo.

Install

pip install denoise-audio

CLI usage (pip)

Global help:

python -m denoise --help

List available backends:

python -m denoise --list-models

Model-specific help:

python -m denoise --model rnnoise --help
python -m denoise --model deepfilternet --help
python -m denoise --model fbdenoiser --help

Basic command (all models):

python -m denoise --model <model> --input <in.wav> --output <out.wav>

• <model> is one of: rnnoise, deepfilternet, fbdenoiser
• --input and --output must be WAV files

Recommended environment (Python 3.10)

A fresh virtual environment with Python 3.10 is recommended to avoid dependency conflicts.

Troubleshooting: wheel / packaging conflicts

Some environments may already have a newer wheel installed that requires packaging>=24, while deepfilternet requires packaging<24.

If you see an error mentioning a wheel/packaging conflict, use a clean venv (recommended) or pin compatible versions:

python -m pip install --upgrade "packaging>=23,<24" "wheel<0.46"
python -m pip install --upgrade --force-reinstall denoise-audio

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Python usage (import)

You can also use this package directly in your Python code after installing with pip install denoise-audio.

Quick sanity check

python -c "import denoise; print(denoise.__version__)"

List available models/backends

from denoise import available_models, backend_help

print(available_models())
print(backend_help())

Inspect supported keyword arguments for each model

from denoise import model_kwargs_help

print(model_kwargs_help("rnnoise"))
print(model_kwargs_help("deepfilternet"))
print(model_kwargs_help("fbdenoiser"))

Run denoising from a Python file

Create run_denoise.py:

from denoise import denoise_file

IN_WAV = "input.wav"
OUT_WAV = "output.wav"

# Choose one: rnnoise | deepfilternet | fbdenoiser
MODEL = "rnnoise"

# Model-specific kwargs (examples below)
kwargs = {}

# Example: RNNoise
# kwargs = {"rnnoise_sample_rate": 48000}

# Example: DeepFilterNet
# kwargs = {"df_model": "DeepFilterNet3", "df_pf": True, "df_compensate_delay": True}

# Example: FacebookResearch Denoiser
# kwargs = {"fb_model": "dns64", "fb_device": "cpu", "fb_dry": 1.0}

for _ in denoise_file(IN_WAV, OUT_WAV, model=MODEL, **kwargs):
    pass

print(f"Wrote: {OUT_WAV}")

Run it:

python run_denoise.py

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Install from GitHub (uv)

Install uv using Astral’s standalone installer:

https://docs.astral.sh/uv/getting-started/installation/#standalone-installer

Verify:

uv --version

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Install dependencies

git clone https://github.com/Surya-Rayala/denoise-audio.git
cd denoise-audio
uv sync

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CLI usage

Run everything with uv run to ensure you’re using the project environment:

Global help

uv run python -m denoise --help

List available backends

uv run python -m denoise --list-models

Model-specific help

Pass --model <name> and --help to see only that backend’s options:

uv run python -m denoise --model rnnoise --help
uv run python -m denoise --model deepfilternet --help
uv run python -m denoise --model fbdenoiser --help

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Basic command (all models)

uv run python -m denoise --model <model> --input <in.wav> --output <out.wav>

• <model> is one of: rnnoise, deepfilternet, fbdenoiser
• --input and --output must be WAV files

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Models

RNNoise (--model rnnoise)

Basic command

uv run python -m denoise --model rnnoise --input <in.wav> --output <out.wav>

Arguments

• --rnnoise-sample-rate <int>: Force the RNNoise wrapper sample rate. If omitted, the tool infers the sample rate from the input WAV (recommended).

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DeepFilterNet (--model deepfilternet)

Basic command

uv run python -m denoise --model deepfilternet --input <in.wav> --output <out.wav>

Arguments

• --df-model <name>: Select which pretrained DeepFilterNet model to load. Common options: DeepFilterNet, DeepFilterNet2, DeepFilterNet3.
  • To see available models, visit: https://github.com/Rikorose/DeepFilterNet/tree/main/models
• --df-pf: Enable the post-filter (can reduce residual noise; may sound more aggressive in very noisy sections).
• --df-compensate-delay: Add padding to compensate processing delay (useful when you need better alignment with the original audio).

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FacebookResearch Denoiser (--model fbdenoiser)

Basic command

uv run python -m denoise --model fbdenoiser --input <in.wav> --output <out.wav>

Arguments

• --fb-model <name>: Choose the pretrained model:
  • dns48: pre-trained real time H=48 model trained on DNS
  • dns64: pre-trained real time H=64 model trained on DNS
  • master64: pre-trained real time H=64 model trained on DNS and Valentini
• --fb-device <device>: Inference device, e.g. cpu or cuda. If omitted, it automatically uses CUDA if available, otherwise CPU.
• --fb-dry <float>: Dry/wet mix (0.0 = original input only, 1.0 = denoised output only). Values outside [0.0, 1.0] are clamped.
• --fb-streaming: Enable streaming mode (flag is accepted by the CLI).
• --fb-batch-size <int>: Batch size (flag is accepted by the CLI).
• --fb-num-workers <int>: Number of workers (flag is accepted by the CLI).
• --fb-verbose: Enable verbose logging (flag is accepted by the CLI).

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Updating from Git

If you’re using the repo locally with uv sync + uv run, updating to new code from Git is:

cd denoise-audio
git pull
uv sync

If you have local changes and git pull refuses

Use one of these (pick what you intend):

• Keep your local changes and rebase on top:

git pull --rebase
uv sync

• Discard local changes and reset to remote (⚠️ destructive):

git fetch origin
git reset --hard origin/main
uv sync

License

This project's source code is licensed under the MIT License.

Note on Dependencies: This tool relies on the denoiser library (Facebook Research), which is licensed under CC-BY-NC 4.0 (Non-Commercial). Consequently, this tool as a whole is suitable for research and personal use only, unless you obtain a commercial license for the denoiser dependency.