Speech Denoising Wavenet Save
A neural network for end-to-end speech denoising
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).
- Download here
- Extract to
data/NSDTSEA
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