DeepFilterNet
v0.5.6
使用深層過濾的全頻段音頻（48kHz）的低複雜性語音增強框架（48kHz）。
對於PipeWire集成為虛擬噪聲抑制麥克風，在這裡查看。
運行演示（僅Linux）使用：
cargo +nightly run -p df-demo --features ui --bin df-demo --release新的DeepFilternet演示： DeepFilternet：感知動機的實時演講增強
新的多幀過濾紙：助聽器的深層多幀過濾
實時版本和LADSPA插件
deep-filter audio-file.wavDeepFilternet2紙： DeepFilternet2：靠近嵌入式設備的全帶音頻的實時語音增強
原始DeepFilternet紙： DeepFilternet：基於Deep Feltering的全頻段音頻的低複雜性語音增強框架
從發布頁面下載預編譯的深濾器二進製文件。您可以使用deep-filter來抑制噪聲.wav音頻文件中的噪聲。當前，僅支持具有48kHz的採樣率的WAV文件。
USAGE:
deep-filter [OPTIONS] [FILES]...
ARGS:
< FILES > ...
OPTIONS:
-D, --compensate-delay
Compensate delay of STFT and model lookahead
-h, --help
Print help information
-m, --model < MODEL >
Path to model tar.gz. Defaults to DeepFilterNet2.
-o, --out-dir < OUT_DIR >
[default: out]
--pf
Enable postfilter
-v, --verbose
Logging verbosity
-V, --version
Print version information如果您想將Pytorch後端使用用於GPU處理，請參見下面的Python使用情況。
該框架支持Linux，MacOS和Windows。訓練僅在Linux下進行測試。該框架的結構如下：
libDF包含用於數據加載和增強的RUST代碼。DeepFilterNet包含DeepFilternet代碼訓練，評估和可視化以及預驗證的模型權重。pyDF包含Libdf STFT/ISTFT處理環的Python包裝器。pyDF-data包含LIBDF數據集功能的Python包裝器，並提供了Pytorch數據加載程序。ladspa包含一個LADSPA插件，用於實時抑制噪聲。models包含用於在DeepFilternet（Python）或Libdf/deep-Filter（Rust）中使用的預測模型通過PIP安裝DeepFilternet Python輪：
# 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]使用DeepFilternet運行來增強嘈雜的音頻文件
# Specify an output directory with --output-dir [OUTPUT_DIR]
deepFilter path/to/noisy_audio.wav通過Rusup安裝貨物。建議使用conda或virtualenv 。請閱讀評論，僅執行所需的命令。
python依賴和libdf的安裝：
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
# *Option B:* If you want to develop within this repo, install only dependencies and work with the repository version
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使用DeepFilternet運行來增強嘈雜的音頻文件
$ 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 from df import enhance , init_df
model , df_state , _ = init_df () # Load default model
enhanced_audio = enhance ( model , df_state , noisy_audio )請參閱此處的完整示例。
入口點為DeepFilterNet/df/train.py 。它期望包含HDF5數據集的數據目錄以及數據集配置JSON文件。
因此，您首先需要以HDF5格式創建數據集。每個數據集通常僅具有訓練，驗證或測試集的噪聲，語音或RIR。
# 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
# hdf5_db: Output HDF5 dataset.
python df / scripts / prepare_data . py - - sr 48000 speech training_set . txt TRAIN_SET_SPEECH . hdf5所有數據集應在一個數據集文件夾中用於火車腳本。
數據集配置文件應包含3個條目：“火車”，“有效”，“測試”。其中每個包含數據集列表（例如語音，噪聲和RIR數據集）。您可以使用多個語音或噪聲數據集。可選地，可以指定可用於超過/不足數據集的抽樣因子。假設您有一個具有瞬態噪聲的特定數據集，並希望通過過採樣來增加非平穩噪聲的數量。在大多數情況下，您想將此因素設置為1。
dataset.cfg
{
"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
]
]
}最後，啟動培訓腳本。如果不存在用於日誌記錄，一些音頻樣本，模型檢查點和配置，則培訓腳本可能會創建模型base_dir 。如果找不到配置文件，它將創建默認配置。有關配置文件，請參見DeepFilternet/Pretraining_models/deepfilternet。
# usage: train.py [-h] [--debug] data_config_file data_dir base_dir
python df / train . py path / to / dataset . cfg path / to / data_dir / path / to / base_dir / 為了複製任何指標，我們建議通過pip install deepfilternet使用Python實現。
如果您使用此框架，請引用： DeepFilternet：基於深層過濾的全樂隊音頻的低複雜性語音增強框架
@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 }
}如果您使用DeepFilternet2模型，請引用： DeepFilternEt2：在嵌入式設備上進行實時演講，以進行全頻段音頻
@inproceedings { schroeter2022deepfilternet2 ,
title = { {DeepFilterNet2}: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio } ,
author = { Schröter, Hendrik and Escalante-B., Alberto N. and Rosenkranz, Tobias and Maier, Andreas } ,
booktitle = { 17th International Workshop on Acoustic Signal Enhancement (IWAENC 2022) } ,
year = { 2022 } ,
}如果您使用DeepFilternet3模型，請引用： DeepFilternet：感知動機的實時演講增強
@inproceedings { schroeter2023deepfilternet3 ,
title = { {DeepFilterNet}: Perceptually Motivated Real-Time Speech Enhancement } ,
author = { Schröter, Hendrik and Rosenkranz, Tobias and Escalante-B., Alberto N. and Maier, Andreas } ,
booktitle = { INTERSPEECH } ,
year = { 2023 } ,
}如果使用多幀波束形成算法。請引用助聽器的深層多幀過濾
@inproceedings { schroeter2023deep_mf ,
title = { Deep Multi-Frame Filtering for Hearing Aids } ,
author = { Schröter, Hendrik and Rosenkranz, Tobias and Escalante-B., Alberto N. and Maier, Andreas } ,
booktitle = { INTERSPEECH } ,
year = { 2023 } ,
}DeepFilternet是免費的開源！該存儲庫中的所有代碼均在兩個下進行雙許可：
可以選擇。這意味著您可以選擇自己喜歡的許可證！
除非您另有明確說明，否則任何有意提交的捐款（如Apache-2.0許可證中定義）應為雙重許可，如上所述，沒有任何其他條款或條件。
