deepfilter-multimedia 0.1.1

Noise reduction for audio and video files using DeepFilterNet

DeepFilter Multimedia

Remove noise from audio and video files using DeepFilterNet. This CLI tool (dfm) provides a simple interface for applying state-of-the-art deep learning-based noise reduction to multimedia files.

Features

• Audio Support: WAV, MP3, FLAC, OGG, M4A, AAC, WMA
• Video Support: MP4, MKV, AVI, MOV, WebM, FLV, WMV, M4V
• Easy CLI: Simple command-line interface (dfm)
• Auto-detection: Automatically detects file type
• Batch Processing: Process multiple files at once

Installation

From PyPI

pip install deepfilter-multimedia

From Source

# Clone the repository
git clone https://github.com/svemyh/deepfilter-multimedia.git
cd deepfilter-multimedia

# Install dependencies
pip install -e .

Requirements

• Python 3.8+
• PyTorch 1.9+
• FFmpeg (for video processing)
• DeepFilterNet

Install FFmpeg:

# Ubuntu/Debian
sudo apt install ffmpeg

# macOS
brew install ffmpeg

# Windows
# Download from https://ffmpeg.org/download.html

Usage

Basic Usage

Process a single audio or video file:

dfm input.mp4

The enhanced file will be saved to output/input_enhanced.mp4.

Specify Output Path

dfm input.mp4 -o output/clean.mp4

Process Multiple Files

dfm video1.mp4 video2.mkv audio1.wav

All files will be saved to the output/ directory.

Quiet Mode

Disable progress messages:

dfm input.mp4 -q

Help

dfm --help

Examples

Clean noisy interview recording

dfm noisy_interview.mp4 -o clean_interview.mp4

Process podcast audio

dfm podcast_episode.mp3

Batch process multiple videos

dfm video1.mkv video2.mp4 video3.avi

Use as Python module

from deepfilter_multimedia.core import process_file

# Process a file
output_path = process_file("noisy_video.mp4", "clean_video.mp4")
print(f"Enhanced video saved to: {output_path}")

How It Works

1. For Videos:

   • Extracts audio track (48kHz, stereo)
   • Applies DeepFilterNet noise reduction
   • Reassembles video with enhanced audio
   • Keeps original video quality

2. For Audio:

   • Loads audio file
   • Applies DeepFilterNet noise reduction
   • Saves enhanced audio

Important Notes

Sample Rate: DeepFilterNet is optimized for 48kHz audio. Input files at other sample rates (16kHz, 44.1kHz, etc.) will be automatically resampled to 48kHz before processing. Output files are saved at 48kHz.

• Quality: The model works best with 48kHz audio as it was trained on this sample rate
• Upsampling: Files below 48kHz (e.g., 16kHz phone recordings) will be upsampled - results may vary
• Downsampling: Files above 48kHz (e.g., 96kHz studio recordings) will be downsampled - some high-frequency information may be lost

Model Download: On first run, DeepFilterNet will download the pretrained model (~50MB). This may take a few moments.

Attribution

This project is based on DeepFilterNet by Hendrik Schröter et al.

If you use this tool, please cite the original DeepFilterNet papers:

@inproceedings{schroeter2022deepfilternet,
  title={{DeepFilterNet}: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filtering},
  author={Schröter, Hendrik and Escalante-B., Alberto N. and Rosenkranz, Tobias and Maier, Andreas},
  booktitle={ICASSP 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2022},
  organization={IEEE}
}

License

This project is licensed under the MIT License - see the LICENSE file for details.

DeepFilterNet is dual-licensed under MIT and Apache 2.0 licenses.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Links

• DeepFilterNet Repository
• DeepFilterNet Paper
• Report Issues
• Changelog

Troubleshooting

Sample Rate Issues

Q: My audio is 44.1kHz (CD quality). Will it work?
A: Yes! It will be automatically resampled to 48kHz. Quality should be excellent since you're only changing sample rate slightly.

Q: My recording is 16kHz (phone/voice). Will noise reduction work?
A: Yes, but results may vary. The model is trained on 48kHz, so upsampling from 16kHz may not capture all the detail the model expects. Try it and see - many users report good results even with lower sample rates.

Q: Why is my output always 48kHz?
A: DeepFilterNet is specifically trained on 48kHz audio and cannot operate at other sample rates. This is a fundamental limitation of the model architecture.

FFmpeg Issues

FFmpeg not found:
Make sure FFmpeg is installed and available in your PATH:

ffmpeg -version

CUDA/GPU Support

For GPU acceleration, install PyTorch with CUDA support:

pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu118