Skip to content

Latest commit

 

History

History
115 lines (78 loc) · 4.72 KB

getting_started.md

File metadata and controls

115 lines (78 loc) · 4.72 KB

Getting Started

Data

Clean Speech and Noise

The reported performance of the FullSubNet in the paper is on the Deep Noise Suppression Challenge - INTERSPEECH 2020 (DNS-INTERSPEECH-2020). It consists of 65000 speech clips (the 30s per clip) and 65000 noise clips (10s per clip). You can download this dataset from https://github.com/microsoft/DNS-Challenge.git.

This Git repository contains the DNS Challenge dataset (INTERSPEECH 2020) and the newer DNS Challenge dataset (ICASSP 2021). The default branch of the Git repository is the ICASSP 2021 Dataset. Please check out the default branch to the interspeech2020 branch if you would like to obtain the results reported in the paper.

Room Impulse Responses

As mentioned in the paper, the room impulse responses (RIRs) come from the Multichannel Impulse Response Databaset and the Reverb Challenge dataset. For your convenience, we have separated and organized these RIRs. You can download them from the [Releases page (https://github.com/haoxiangsnr/FullSubNet/releases).

Usage

Training

First, we need to enter a directory named after the dataset, such as dns_interspeech_2020. Then, we could call the default training configuration:

# enter a directory named after the dataset, such as dns_interspeech_2020
cd FullSubNet/recipes/dns_interspeech_2020

# Use a default config and two GPUs to train the FullSubNet model
CUDA_VISIBLE_DEVICES=0,1
torchrun --standalone --nnodes=1 --nproc_per_node=2 train.py -C fullsubnet/train.toml

# Use default config and one GPU to train the Fullband baseline model
CUDA_VISIBLE_DEVICES=0
torchrun --standalone --nnodes=1 --nproc_per_node=1 train.py -C fullband_baseline/train.toml

# Resume the experiment using "-R" parameter
CUDA_VISIBLE_DEVICES=0,1
torchrun --standalone --nnodes=1 --nproc_per_node=2 train.py -C fullband_baseline/train.toml -R

See more details in FullSubNet/recipes/dns_interspeech_2020/train.py and FullSubNet/recipes/dns_interspeech_2020/**/train.toml.

Logs and Visualization

The logs during the training will be stored, and we can visualize it using TensorBoard. Assuming that:

  • The file path of the training configuration is FullSubNet/recipes/dns_interspeech_2020/fullsubnet/train.toml
  • In the training configuration, the key save_dir has a value "~/Experiments/FullSubNet"

Then, the log information will be stored in the ~/Experiments/FullSubNet/train directory. This directory contains the following:

  • logs/ directory: store the TensorBoard related data, including loss curves, audio files, and spectrogram figures.
  • checkpoints/ directory: stores all checkpoints during training, from which you can resume the training or start an inference.
  • *.toml file: the backup of the current training configuration.

In the logs/ directory, use the following command to visualize loss curves, spectrogram figures, and audio files during the training and the validation.

tensorboard --logdir ~/Experiments/FullSubNet/train

# specify a port 45454
tensorboard --logdir ~/Experiments/FullSubNet/train --port 45454

Inference

After training, you can enhance noisy speech. Take the FullSubNet as an example:

  1. Checking the noisy speech directory path and the sample rate in FullSubNet/recipes/dns_interspeech_2020/fullsubnet/inference.toml.
[dataset.args]
dataset_dir_list = [
    "/path/to/your/dataset_1/",
    "/path/to/your/dataset_2/",
    "others..."
]
sr = 16000
  1. Switch to FullSubNet/recipes/dns_interspeech_2020 directory and start inference:
cd FullSubNet/recipes/dns_interspeech_2020

# One GPU is used by default
python inference.py \
  -C fullsubnet/inference.toml \
  -M /path/to/your/checkpoint_dir/best_model.tar \
  -O /path/to/your/enhancement/dir

Applying a Pre-trained Model

Or, in the inference stage, you can use a pre-trained model downloaded from the Releases Page:

  1. As mentioned above, you need to check the noisy speech directory path and the sample rate in FullSubNet/recipes/dns_interspeech_2020/fullsubnet/inference.toml are correct.
  2. Change the "-M" parameter to the path of the pre-trained model downloaded from the Releases Page.

Check more details of inference parameters in FullSubNet/recipes/dns_interspeech_2020/fullsubnet/inference.toml.

Metrics

Calculating metrics (SI_SDR, STOI, WB_PESQ, NB_PESQ, etc.) using the following command lines:

# Switch path
cd FullSubNet

# DNS-INTERSPEECH-2020
python tools/calculate_metrics.py \
  -R /path/to/reference/ \
  -E /path/to/enhancement/ \
  -M SI_SDR,STOI,WB_PESQ,NB_PESQ \
  -S DNS_1