bone2air and BWE

dccrn dpcrn seanet eben sounstream

Usage

There are two entry files for the current project:

• Entry file for training models: train.py
• Entry file for enhance noisy speech: enhancement.py

Training

Use train.py to train the model. It receives three command line parameters:

• -h, display help information
• -C, --config, specify the configuration file required for training
• -R, --resume, continue training from the checkpoint of the last saved model

Syntax: python train.py [-h] -C CONFIG [-R]

E.g.:

python train.py -C config/train/train.json
# The configuration file used to train the model is "config/train/train.json"
# Use all GPUs for training

python train.py -C config/train/train.json -R
# The configuration file used to train the model is "config/train/train.json"
# Use all GPUs to continue training from the last saved model checkpoint

CUDA_VISIBLE_DEVICES=1,2 python train.py -C config/train/train.json
# The configuration file used to train the model is "config/train/train.json"
# Use GPU No.1 and 2 for training

CUDA_VISIBLE_DEVICES=-1 python train.py -C config/train/train.json
# The configuration file used to train the model is "config/train/train.json"
# Use CPU for training

Supplement:

• Generally, the configuration files needed for training are placed in the config/train directory
• See the "Parameter Description" section for the parameters in the training configuration file
• The filename of the configuration file is the experiment name.

Enhancement

Use enhancement.py to enhance noisy speech, which receives the following parameters:

• -h, --help, display help information
• -C, --config, specify the model, the enhanced dataset, and custom args used to enhance the speech.
• -D, --device, enhance the GPU index used, -1 means use CPU
• -O, --output_dir, specify where to store the enhanced speech, you need to ensure that this directory exists in advance
• -M, --model_checkpoint_path, the path of the model checkpoint, the extension of the checkpoint file is .tar or .pth

Syntax: python enhancement.py [-h] -C CONFIG [-D DEVICE] -O OUTPUT_DIR -M MODEL_CHECKPOINT_PATH

E.g.:

python enhancement.py -C config/enhancement/unet_basic.json -D 0 -O enhanced -M /media/imucs/DataDisk/haoxiang/Experiment/Wave-U-Net-for-Speech-Enhancement/smooth_l1_loss/checkpoints/model_0020.pth
# The configuration file used to enhancement is "config/enhancement/unet_basic.json". Use this file to specify the model and dataset information required for enhancement
# Use GPU with index 0
# The output directory is "enhanced/", the directory needs to be created in advance
# Specify the path of the model checkpoint

python enhancement.py -C config/enhancement/unet_basic.json -D -1 -O enhanced -M /media/imucs/DataDisk/haoxiang/Experiment/Wave-U-Net-for-Speech-Enhancement/smooth_l1_loss/checkpoints/model_0020.tar
# Use CPU for enhancement

Supplement:

• Generally, the configuration files needed for enhancement are placed in the config/enhancement/ directory
• See the "Parameter Description" section for the parameters in the enhancement configuration file.

Visualization

All log information generated during training will be stored in the config["root_dir"]/<config_filename>/ directory. Assuming that the configuration file for training is config/train/sample_16384.json, the value of the root_dir parameter in sample_16384.json is /home/UNet/. Then, the logs generated during the current experimental training process will be stored In the /home/UNet/sample_16384/ directory. The directory will contain the following:

• logs/ directory: store Tensorboard related data, including loss curve, waveform file, speech file
• checkpoints/ directory: stores all checkpoints of the model, from which you can restart training or speech enhancement
• config.json file: backup of the training configuration file

During the training process, we can use tensorboard to start a static front-end server to visualize the log data in the relevant directory:

tensorboard --logdir config["root_dir"]/<config_filename>/

# You can use --port to specify the port of the tensorboard static server
tensorboard --logdir config["root_dir"]/<config_filename>/ --port <port>

# For example, the "root_dir" parameter in the configuration file is "/home/happy/Experiments", the configuration file name is "train_config.json", and the default port is modified to 6000. The following commands can be used:
tensorboard --logdir /home/happy/Experiments/train_config --port 6000

Directory description

During the training, multiple directories will be used, all with different purposes:

• Main directory: the directory where the current README.md is located, storing all source code
• Training directory: the config["root_dir"] directory in the training configuration file, which stores all experiment logs and model checkpoints of the current project
• Experiment directory: config["root_dir"]/<experiment name>/ directory, which stores the log information of a certain experiment

Parameter Description

Training

config/train/<config_filename>.json

The log information generated during the training process will be stored inconfig["root_dir"]/<config_filename>/.

{
    "seed": 0, // Random seeds to ensure experiment repeatability
    "description": "...",  // Experiment description, will be displayed in Tensorboard later
    "root_dir": "~/Experiments/Wave-U-Net", // Directory for storing experiment results
    "cudnn_deterministic": false,
    "trainer": { // For training process
        "module": "trainer.trainer", // Which trainer
        "main": "Trainer", // The concrete class of the trainer model
        "epochs": 1200, // Upper limit of training
        "save_checkpoint_interval": 10, // Save model breakpoint interval
        "validation":{
        "interval": 10, // validation interval
         "find_max": true, // When find_max is true, if the calculated metric is the known maximum value, it will cache another copy of the current round of model checkpoint.
        "custon": {
            "visualize_audio_limit": 20, // The interval of visual audio during validation. The reason for setting this parameter is that visual audio is slow
            "visualize_waveform_limit": 20, // The interval of the visualization waveform during validation. The reason for setting this parameter is because the visualization waveform is slow
            "visualize_spectrogram_limit": 20, // Verify the interval of the visualization spectrogram. This parameter is set because the visualization spectrum is slow
            "sample_length": 16384 // See train dataset
            } 
        }
    },
    "model": {
        "module": "model.unet_basic", // Model files used for training
        "main": "Model", // Concrete class of training model
        "args": {} // Parameters passed to the model class
    },
    "loss_function": {
        "module": "model.loss", // Model file of loss function
        "main": "mse_loss", // Concrete class of loss function
        "args": {} // Parameters passed to the model class
    },
    "optimizer": {
        "lr": 0.001,
        "beta1": 0.9,
        "beat2": 0.009
    },
    "train_dataset": {
        "module": "dataset.waveform_dataset", // Store the training set model file
        "main": "Dataset", // Concrete class of training dataset
        "args": { // The parameters passed to the training set class, see the specific training set class for details
            "dataset": "~/Datasets/SEGAN_Dataset/train_dataset.txt",
            "limit": null,
            "offset": 0,
            "sample_length": 16384,
            "mode":"train"
        }
    },
    "validation_dataset": {
        "module": "dataset.waveform_dataset",
        "main": "Dataset",
        "args": {
            "dataset": "~/Datasets/SEGAN_Dataset/test_dataset.txt",
            "limit": 400,
            "offset": 0,
            "mode":"validation"
        }
    },
    "train_dataloader": {
        "batch_size": 120,
        "num_workers": 40, // How many threads to start to preprocess the data
        "shuffle": true,
        "pin_memory":true
    }
}

Enhancement

config/enhancement/*.json

{
    "model": {
        "module": "model.unet_basic",  // Store the model file
        "main": "UNet",  // The specific model class in the file
        "args": {}  // Parameters passed to the model class
    },
    "dataset": {
        "module": "dataset.waveform_dataset_enhancement",  // Store the enhancement dataset file
        "main": "WaveformDataset",  // Concrete class of enhacnement dataset
        "args": {  // The parameters passed to the dataset class, see the specific enhancement dataset class for details
            "dataset": "/home/imucs/tmp/UNet_and_Inpainting/data.txt",
            "limit": 400,
            "offset": 0,
            "sample_length": 16384
        }
    },
    "custom": {
        "sample_length": 16384
    }
}

During the enhancement, only the path of the noisy speech can be listed in the *.txt file, similar to this:

# enhancement_*.txt

/home/imucs/tmp/UNet_and_Inpainting/0001_babble_-7dB_Clean.wav
/home/imucs/tmp/UNet_and_Inpainting/0001_babble_-7dB_Enhanced_Inpainting_200.wav
/home/imucs/tmp/UNet_and_Inpainting/0001_babble_-7dB_Enhanced_Inpainting_270.wav
/home/imucs/tmp/UNet_and_Inpainting/0001_babble_-7dB_Enhanced_UNet.wav
/home/imucs/tmp/UNet_and_Inpainting/0001_babble_-7dB_Mixture.wav

TODO

• Use full-length speech for validation
• Enhancement script