Speech Denoising with Deep Feature Losses (arXiv, sound examples)
This is a Tensorflow implementation of our Speech Denoising Convolutional Neural Network trained with Deep Feature Losses.
Contact: François Germain
- Citation
- Setup
- Denoising scripts
- Models
- Noisy data
- Deep feature loss
- Notes
- SoX installation instructions
If you use our code for research, please cite our paper: François G. Germain, Qifeng Chen, and Vladlen Koltun. Speech Denoising with Deep Feature Losses. arXiv:1806.10522. 2018.
The source code is published under the MIT license. See LICENSE for details. In general, you can use the code for any purpose with proper attribution. If you do something interesting with the code, we'll be happy to know. Feel free to contact us.
Required python libraries: Tensorflow with GPU support (>=1.4) + Scipy (>=1.1) + Numpy (>=1.14) + Tqdm (>=4.0.0). To install in your python distribution, run
pip install -r requirements.txt
Warning: Make sure your libraries (Cuda, Cudnn,...) are compatible with the tensorflow version you're using or the code will not run.
Required software (for resampling): SoX (Installation instructions)
Important note: At the moment, this algorithm requires using 32-bit floating-point audio files to perform correctly. You can use sox to convert your file. To convert audiofile.wav to 32-bit floating-point audio at 16kHz sampling rate, run:
sox audiofile.wav -r 16000 -b 32 -e float audiofile-float.wav
Tested in Ubuntu + Intel i7 CPU + Nvidia Titan X (Pascal) with Cuda (>=8.0) and CuDNN (>=5.0). CPU mode should also work with minor changes.
For a quick testing, you can download the default validation data by running:
./download_sedata_onlyval.sh
followed by:
python senet_infer.py
The denoised files will be stored in the folder dataset/valset_clean/, with the same name as the corresponding source files in dataset/valset_noisy/.
In order to run our algorithm with default parameters, you need to download the noisy dataset from Edinburgh DataShare (see below). The dataset can be automatically downloaded and pre-processed (i.e. resampled at 16kHz) by running the script
./download_sedata.sh
To download only the testing data, you can run the reduced script:
./download_sedata_onlyval.sh
If you want to use your own data for testing, you need to put all the .wav files in a single folder.
If you want to use your own data for training, you need to put your data in a single top folder. In that folder, you should have 4 individual folders:
- trainset_noisy/ (for the noisy speech training files),
- trainset_clean/ (for the ground truth clean speech training files),
- valset_noisy/ (for the noisy validation files), and
- valset_clean/ (for the noisy validation files).
The validation folders may be empty but they must exist. Matching files in the corresponding noisy and clean folders must have the same name.
The audio data must be sampled at 16kHz (you can resample your data using SoX - see download_data.sh for an example).
Once you've downloaded in the script download_data.sh, you can directly process the testing dataset by running
python senet_infer.py
The denoised files will be stored in the folder dataset/valset_noisy_denoised/, with the same name as the corresponding source files in dataset/valset_noisy/.
In our configuration, the algorithm allocates ~5GB of memory on the GPU for training. Running the code as is on GPUs with less memory may fail.
If you have custom testing data (formatted as described above) stored in a folder foldername/ and/or a custom denoising model with names se_model.ckpt.* stored in a folder model_folder/, you can test that model on that data by running:
python senet_infer.py -d folder_name -m model_folder
The denoised files will be stored in the folder folder_name_denoised/, with the same name as the corresponding source files.
Warning: At this time, when using a custom model, you must make sure that the system parameters in senet_infer.py match the ones used in the stored denoising model or the code won't run properly (if running at all).
Once you've downloaded in the script download_data.sh, you can directly train a model using the training dataset by running
python senet_train.py
The trained model will be stored in the root folder with the names se_model.ckpt.*.
In our configuration, the algorithm allocates ~5GB of memory on the GPU for training. Running the code as is on GPUs with less memory may fail.
If you have custom training data (formatted as described above) stored in a folder foldername/ and/or a custom feature loss model with names loss_model.ckpt.* stored in a folder loss_folder/, you can train a speech denoising model on that data using that feature loss model by running:
python senet_train.py -d folder_name -l loss_folder -o out_folder
The trained model will be stored in folder out_folder/ (default is root folder) with the names se_model.ckpt.*.
Warning: At this time, when using a custom loss model, you must make sure that the system parameters in senet_train.py match the ones used in the stored loss model or the code won't run properly (if running at all).
The deep feature loss network graph and parameters are stored in the models/loss_model.ckpt.* files.
The denoising network graph and parameters are stored in the models/se_model.ckpt.* files. This model was trained following the procedure described in our associated paper. The current training script se_train.py is parameterized in such a way that an identical training procedure as in our associated paper would be performed on the specified training dataset.
The data used to train and test our system is available publicly on the Edinburgh DataShare website at https://datashare.is.ed.ac.uk/handle/10283/2791. Information on how the dataset is constructed can be found in Valentini-Botinhao et al., 2016. The dataset was used without alteration except for resampling at 16kHz.
We also provide scripts to (re-)train the loss model. As of know, using the two classification tasks described in our paper is hard-coded.
Our feature loss network is trained on the acoustic scene classification and domestic audio tagging tasks of the [DCASE 2016 Challenge(https://www.cs.tut.fi/sgn/arg/dcase2016/). Downloading and pre-processing (i.e., downsampling to 16kHz) the corresponding data can be done by running the script:
./download_lossdata.sh
Warning: The training script expects the data at the locations set in the downloading script.
Once the data is downloaded, you can (re-)train a deep feature loss model by running:
python lossnet_train.py
The loss model is stored in the root folder by default. A custom output directory for loss model can be specified as:
python lossnet_train.py -o out_folder
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Currently, the download scripts are only provided for UNIX-like systems (Linux & Mac OSX). If you plan on running our algorithm on Windows, please contact us and/or download and resample the data "by hand".
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Currently, dilation for 1-D layers is not properly implemented in the Tensorflow slim library we use. The functions signal_to_dilated and _dilated_to_signal in helper.py allows to transform a 1-D layer into an interlaced 2-D layer such that undilated convolution on the 2-D layer is equivalent to dilated convolution on the 1-D layer.
The latest version of SoX can be found on their SourceForge page at https://sourceforge.net/projects/sox/files/sox/ (Go to the folder corresponding to the latest version). Below are additional details regarding the installations for many common operating systems.
As of June 13, 2018, SoX can be installed from the Ubuntu repositories by running in a terminal:
sudo apt-get install sox
As of June 13, 2018, SoX can be installed from the Fedora repositories by running in a terminal:
sudo yum install sox
If you have Homebrew installed, just run in a terminal:
brew install sox
If you have Macports installed, just run in a terminal:
port install sox
You may need to run the command with root priviledges, in which case, run in a terminal:
sudo port install sox
SoX provides a pre-compiled executable for Mac OSX. You can download it at https://sourceforge.net/projects/sox/files/sox/14.4.2/sox-14.4.2-macosx.zip/download.
Then unzip the downloaded archive and move the extracted folder to your Applications folder.
The last step is to add that folder to your path. To do so, run in a terminal:
cd ~
echo "" >> .bash_profile
echo "# Adding SoX to path" >> .bash_profile
echo "export PATH=\$PATH:/Applications/sox-14.4.1" >> .bash_profile
source .bash_profile
Warning: The executable hasn't been updated since 2015 so consider using one of the two options above instead or compile from sources if the executable fails
Download sources from the terminal using:
wget https://sourceforge.net/projects/sox/files/sox/14.4.2/sox-14.4.2.tar.gz/download
Un-compress the archive:
tar -zxvf sox-14.4.2.tar.gz
Go into the folder with the extracted files:
cd sox-14.4.2
Compile and install SoX:
./configure
make -s
make install
Warning: Make sure there are no space in any of the folder name on the path of the source files or the building will fail.
Follow instructions provided here. If you need additional assistance, please contact us.