deepfilter-multimedia 0.1.0

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 (when published)

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.

Supported Formats

Video

MP4, MKV, AVI, MOV, WebM, FLV, WMV, M4V

Audio

WAV, MP3, FLAC, OGG, M4A, AAC, WMA

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