### Manual Python Dependencies Installation (Bash) Source: https://github.com/rikorose/deepfilternet/blob/main/README.md Instructions for manually installing Python dependencies and the libDF library for DeepFilterNet development. This includes installing PyTorch, maturin, and poetry, followed by installing the DeepFilterNet package with optional features. ```bash cd path/to/DeepFilterNet/ # cd into repository # Recommended: Install or activate a python env # Mandatory: Install cpu/cuda pytorch (>=1.8) dependency from pytorch.org, e.g.: pip install torch torchaudio -f https://download.pytorch.org/whl/cpu/torch_stable.html # Install build dependencies used to compile libdf and DeepFilterNet python wheels pip install maturin poetry # Install remaining DeepFilterNet python dependencies # *Option A:* Install DeepFilterNet python wheel globally within your environment. Do this if you want use # this repos as is, and don't want to develop within this repository. poetry -C DeepFilterNet install -E train -E eval ``` -------------------------------- ### Install DeepFilterNet Python Package (Bash) Source: https://github.com/rikorose/deepfilternet/blob/main/README.md Commands to install the DeepFilterNet Python package using pip. It includes installing PyTorch as a dependency and the main package, with an optional installation for training functionality. ```bash # Install cpu/cuda pytorch (>=1.9) dependency from pytorch.org, e.g.: pip install torch torchaudio -f https://download.pytorch.org/whl/cpu/torch_stable.html # Install DeepFilterNet pip install deepfilternet # Or install DeepFilterNet including data loading functionality for training (Linux only) pip install deepfilternet[train] ``` -------------------------------- ### Prepare Dataset for Training Source: https://github.com/rikorose/deepfilternet/blob/main/README.md Provides instructions and a Python snippet for preparing datasets in HDF5 format for DeepFilterNet training. It includes installing necessary libraries and running the `prepare_data.py` script. ```python # Install additional dependencies for dataset creation pip install h5py librosa soundfile # Go to DeepFilterNet python package cd path/to/DeepFilterNet/DeepFilterNet # Prepare text file (e.g. called training_set.txt) containing paths to .wav files # # usage: prepare_data.py [-h] [--num_workers NUM_WORKERS] [--max_freq MAX_FREQ] [--sr SR] [--dtype DTYPE] # [--codec CODEC] [--mono] [--compression COMPRESSION] # type audio_files hdf5_db # # where: # type: One of `speech`, `noise`, `rir` # audio_files: Text file containing paths to audio files to include in the dataset ``` -------------------------------- ### Install DeepFilterNet Dependencies (Shell) Source: https://github.com/rikorose/deepfilternet/blob/main/DeepFilterNet/README.md Installs the necessary dependencies for training and evaluation within the DeepFilterNet repository. It uses Poetry for package management and sets the Python path to include the DeepFilterNet package. ```bash poetry -C DeepFilterNet install -E train -E eval --no-root export PYTHONPATH=$PWD/DeepFilterNet ``` -------------------------------- ### Install libdfdata Python Package (Shell) Source: https://github.com/rikorose/deepfilternet/blob/main/DeepFilterNet/README.md Optional step to install the `libdfdata` Python package, which provides dataset and dataloading functionality for training. It requires HDF5 headers as a build dependency. An alternative command demonstrates building with static HDF5 linking. ```bash # Required build dependency: HDF5 headers (e.g. ubuntu: libhdf5-dev) maturin develop --release -m pyDF-data/Cargo.toml # If you have troubles with hdf5 you may try to build and link hdf5 statically: maturin develop --release --features hdf5-static -m pyDF-data/Cargo.toml ``` -------------------------------- ### Install DeepFilterNet Dependencies and Build Packages Source: https://github.com/rikorose/deepfilternet/blob/main/README.md Installs project dependencies for training and evaluation, and builds the 'libdf' and 'libdfdata' Python packages using maturin. It also shows how to handle HDF5 linking for 'libdfdata'. ```bash poetry -C DeepFilterNet install -E train -E eval --no-root export PYTHONPATH=$PWD/DeepFilterNet # And set the python path correctly # Build and install libdf python package required for enhance.py maturin develop --release -m pyDF/Cargo.toml # *Optional*: Install libdfdata python package with dataset and dataloading functionality for training # Required build dependency: HDF5 headers (e.g. ubuntu: libhdf5-dev) maturin develop --release -m pyDF-data/Cargo.toml # If you have troubles with hdf5 you may try to build and link hdf5 statically: maturin develop --release --features hdf5-static -m pyDF-data/Cargo.toml ``` -------------------------------- ### Enhance Audio with DeepFilterNet Python (Bash) Source: https://github.com/rikorose/deepfilternet/blob/main/README.md Example command to enhance noisy audio files using the installed DeepFilterNet Python package. The command takes the path to the noisy audio file as input and can optionally specify an output directory. ```bash # Specify an output directory with --output-dir [OUTPUT_DIR] deepFilter path/to/noisy_audio.wav ``` -------------------------------- ### Build and Install libdf Python Package (Shell) Source: https://github.com/rikorose/deepfilternet/blob/main/DeepFilterNet/README.md Builds and installs the `libdf` Python package using Maturin, which is required for the `enhance.py` script. This command compiles the Rust code into a Python extension. ```bash maturin develop --release -m pyDF/Cargo.toml ``` -------------------------------- ### DeepFilterNet Dataset Configuration Example Source: https://github.com/rikorose/deepfilternet/blob/main/README.md A JSON configuration file specifying the datasets for training, validation, and testing. Each entry includes the HDF5 dataset filename and a sampling factor, which can be used for over/under-sampling specific datasets. ```json { "train": [ [ "TRAIN_SET_SPEECH.hdf5", 1.0 ], [ "TRAIN_SET_NOISE.hdf5", 1.0 ], [ "TRAIN_SET_RIR.hdf5", 1.0 ] ], "valid": [ [ "VALID_SET_SPEECH.hdf5", 1.0 ], [ "VALID_SET_NOISE.hdf5", 1.0 ], [ "VALID_SET_RIR.hdf5", 1.0 ] ], "test": [ [ "TEST_SET_SPEECH.hdf5", 1.0 ], [ "TEST_SET_NOISE.hdf5", 1.0 ], [ "TEST_SET_RIR.hdf5", 1.0 ] ] } ``` -------------------------------- ### DeepFilterNet Command-Line Audio Enhancement (Shell) Source: https://github.com/rikorose/deepfilternet/blob/main/DeepFilterNet/README.md Demonstrates how to enhance noisy audio files using the DeepFilterNet command-line interface. It shows the help message and provides examples for enhancing audio with the original DeepFilterNet and DeepFilterNet2 models. ```bash $ python DeepFilterNet/df/enhance.py --help usage: enhance.py [-h] [--model-base-dir MODEL_BASE_DIR] [--pf] [--output-dir OUTPUT_DIR] [--log-level LOG_LEVEL] [--compensate-delay] noisy_audio_files [noisy_audio_files ...] positional arguments: noisy_audio_files List of noise files to mix with the clean speech file. optional arguments: -h, --help show this help message and exit --model-base-dir MODEL_BASE_DIR, -m MODEL_BASE_DIR Model directory containing checkpoints and config. To load a pretrained model, you may just provide the model name, e.g. `DeepFilterNet`. By default, the pretrained DeepFilterNet2 model is loaded. --pf Post-filter that slightly over-attenuates very noisy sections. --output-dir OUTPUT_DIR, -o OUTPUT_DIR Directory in which the enhanced audio files will be stored. --log-level LOG_LEVEL Logger verbosity. Can be one of (debug, info, error, none) --compensate-delay, -D Add some paddig to compensate the delay introduced by the real-time STFT/ISTFT implementation. # Enhance audio with original DeepFilterNet python DeepFilterNet/df/enhance.py -m DeepFilterNet path/to/noisy_audio.wav # Enhance audio with DeepFilterNet2 python DeepFilterNet/df/enhance.py -m DeepFilterNet2 path/to/noisy_audio.wav ``` -------------------------------- ### Run Pipewire Filter-Chain with Debug Logging Source: https://github.com/rikorose/deepfilternet/blob/main/ladspa/README.md This snippet demonstrates how to run a Pipewire filter-chain, specifically for DeepFilterNet, while enabling debug logging. This is useful for troubleshooting issues with the audio filter setup. ```bash RUST_LOG=DEBUG pipewire -c filter-chain.conf ``` -------------------------------- ### Enhance Audio Files from Command Line Source: https://github.com/rikorose/deepfilternet/blob/main/README.md Demonstrates how to use the `enhance.py` script to enhance noisy audio files using DeepFilterNet models. It shows the script's help message and examples for using different DeepFilterNet versions. ```bash python DeepFilterNet/df/enhance.py --help # Enhance audio with original DeepFilterNet python DeepFilterNet/df/enhance.py -m DeepFilterNet path/to/noisy_audio.wav # Enhance audio with DeepFilterNet2 python DeepFilterNet/df/enhance.py -m DeepFilterNet2 path/to/noisy_audio.wav ``` -------------------------------- ### DeepFilterNet Training Script Usage Source: https://github.com/rikorose/deepfilternet/blob/main/README.md This command initiates the training process for DeepFilterNet models. It requires a dataset configuration file, the path to the dataset directory, and the base directory for logging and model checkpoints. A default configuration will be created if none is found. ```bash python df/train.py path/to/dataset.cfg path/to/data_dir/ path/to/base_dir/ ``` -------------------------------- ### Run Pipewire Filter-Chain Source: https://github.com/rikorose/deepfilternet/blob/main/ladspa/README.md This snippet shows the command to run a Pipewire filter-chain after the configuration file has been set up. This is the standard way to activate the DeepFilterNet audio processing. ```bash pipewire -c filter-chain.conf ``` -------------------------------- ### Run DeepFilterNet Demo (Bash) Source: https://github.com/rikorose/deepfilternet/blob/main/README.md Command to execute the DeepFilterNet demo application on Linux systems. This command utilizes Cargo, the Rust package manager, to build and run the demo with UI features enabled. ```bash cargo +nightly run -p df-demo --features ui --bin df-demo --release ``` -------------------------------- ### Deep-filter Command-Line Usage (Bash) Source: https://github.com/rikorose/deepfilternet/blob/main/README.md Shows the command-line interface for the 'deep-filter' executable, used for suppressing noise in WAV audio files. It outlines available options for model selection, output directory, and feature toggles. ```bash USAGE: deep-filter [OPTIONS] [FILES]... ARGS: ... OPTIONS: -D, --compensate-delay Compensate delay of STFT and model lookahead -h, --help Print help information -m, --model Path to model tar.gz. Defaults to DeepFilterNet2. -o, --out-dir [default: out] --pf Enable postfilter -v, --verbose Logging verbosity -V, --version Print version information ``` -------------------------------- ### Prepare HDF5 Dataset for DeepFilterNet Source: https://github.com/rikorose/deepfilternet/blob/main/README.md This script prepares audio datasets in HDF5 format for training DeepFilterNet models. It takes the sampling rate, a text file listing audio files, and an output HDF5 filename as input. Ensure all datasets are in a single folder for the training script. ```bash python df/scripts/prepare_data.py --sr 48000 speech training_set.txt TRAIN_SET_SPEECH.hdf5 ``` -------------------------------- ### Build DeepFilterNet LADSPA Plugin with Cargo Source: https://github.com/rikorose/deepfilternet/blob/main/ladspa/README.md This snippet shows how to build the DeepFilterNet LADSPA plugin using Cargo, the Rust build system and package manager. It compiles the plugin in release mode and indicates where the compiled library will be located. ```bash cargo build --release -p deep-filter-ladspa ls target/release/libdeep_filter_ladspa* ``` -------------------------------- ### Enhance Audio Within Python Script Source: https://github.com/rikorose/deepfilternet/blob/main/README.md Shows how to integrate DeepFilterNet's audio enhancement capabilities directly into a Python script using the `init_df` and `enhance` functions. ```python from df import enhance, init_df model, df_state, _ = init_df() # Load default model enhanced_audio = enhance(model, df_state, noisy_audio) ``` === COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.