A Wavenet For Speech Denoising

A neural network for end-to-end speech denoising, as described in: "A Wavenet For Speech Denoising"

Listen to denoised samples under varying noise conditions and SNRs here

Installation

It is recommended to use a virtual environment

git clone https://github.com/drethage/speech-denoising-wavenet.git

pip install -r requirements.txt

Install pygpu

Currently the project requires Keras 1.2 and Theano 0.9.0, the large dilations present in the architecture are not supported by the current version of Tensorflow (1.2.0)

Usage

A pre-trained model (best-performing model described in the paper) can be found in sessions/001/models and is ready to be used out-of-the-box. The parameterization of this model is specified in sessions/001/config.json

Download the dataset as described below

Denoising:

Example: THEANO_FLAGS=optimizer=fast_compile,device=gpu python main.py --mode inference --config sessions/001/config.json --noisy_input_path data/NSDTSEA/noisy_testset_wav --clean_input_path data/NSDTSEA/clean_testset_wav

Speedup

To achieve faster denoising, one can increase the target-field length by use of the optional --target_field_length argument. This defines the amount of samples that are denoised in a single forward propagation, saving redundant calculations. In the following example, it is increased 10x that of when the model was trained, the batch_size is reduced to 4.

Faster Example: THEANO_FLAGS=device=gpu python main.py --mode inference --target_field_length 16001 --batch_size 4 --config sessions/001/config.json --noisy_input_path data/NSDTSEA/noisy_testset_wav --clean_input_path data/NSDTSEA/clean_testset_wav

Training:

THEANO_FLAGS=device=gpu python main.py --mode training --config config.json

Configuration

A detailed description of all configurable parameters can be found in config.md

Optional command-line arguments:

Argument

Valid Inputs

Default

Description

mode

[training, inference]

training

config

string

config.json

Path to JSON-formatted config file

print_model_summary

bool

False

Prints verbose summary of the model

load_checkpoint

string

None

Path to hdf5 file containing a snapshot of model weights

Additional arguments during inference:

Argument

Valid Inputs

Default

Description

one_shot

bool

False

Denoises each audio file in a single forward propagation

target_field_length

int

as defined in config.json

Overrides parameter in config.json for denoising with different target-field lengths than used in training

batch_size

int

as defined in config.json

# of samples per batch

condition_value

int

1

Corresponds to speaker identity

clean_input_path

string

None

If supplied, SNRs of denoised samples are computed

Dataset

The "Noisy speech database for training speech enhancement algorithms and TTS models" (NSDTSEA) is used for training the model. It is provided by the University of Edinburgh, School of Informatics, Centre for Speech Technology Research (CSTR).

Extract to data/NSDTSEA