fixed inconsistent ddp entrance and update pytorch version

README.md

@@ -1,13 +1,13 @@
 # FullSubNet
 
-![Platform](https://img.shields.io/badge/Platform-macos%20%7C%20linux-lightgrey)
+![Platform](https://img.shields.io/badge/Platform-linux-lightgrey)
 ![Python version](https://img.shields.io/badge/Python-%3E%3D3.8.0-orange)
-![Pytorch Version](https://img.shields.io/badge/PyTorch-%3E%3D1.7-brightgreen)
+![Pytorch Version](https://img.shields.io/badge/PyTorch-%3E%3D1.10-brightgreen)
 ![GitHub repo size](https://img.shields.io/github/repo-size/haoxiangsnr/FullSubNet)
 
 This Git repository for the official PyTorch implementation
 of ["FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement"](https://arxiv.org/abs/2010.15508), accepted
-to ICASSP 2021. 
+to ICASSP 2021.
 
 :bulb:[[Demo\]](https://www.haoxiangsnr.com/demo/fullsubnet/) | :page_facing_up:[[PDF\]](https://arxiv.org/abs/2010.15508) | :floppy_disk:[[Model Checkpoint\]](https://github.com/haoxiangsnr/FullSubNet/releases)
 

docs/getting_started.md

@@ -16,23 +16,23 @@ Git repository is the ICASSP 2021 Dataset. You need to check out the default bra
 
 ### Training
 
-First, we need to enter a directory named after the dataset, such as `dns_interspeech_2020`. Then, we can call the default training configuration:
+First, we need to enter a directory named after the dataset, such as `dns_interspeech_2020`. Then, we could call the default training configuration:
 
 ```shell
 # 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_VISIABLE_DEVICES=0,1
-python -m torch.distributed.run --standalone --nnodes=1 --nproc_per_node=2 train.py -C fullsubnet/train.toml
+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_VISIABLE_DEVICES=0
-python -m torch.distributed.run --standalone --nnodes=1 --nproc_per_node=1 train.py -C fullband_baseline/train.toml
+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_VISIABLE_DEVICES=0,1
-python -m torch.distributed.run --standalone --nnodes=1 --nproc_per_node=2 train.py -C fullband_baseline/train.toml -R
+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`.
@@ -101,7 +101,7 @@ Check more details of inference parameters in `FullSubNet/recipes/dns_interspeec
 Calculating metrics (SI_SDR, STOI, WB_PESQ, NB_PESQ, etc.) using the following command lines:
 
 ```shell
-# Switching path
+# Switch path
 cd FullSubNet
 
 # DNS-INTERSPEECH-2020

docs/prerequisites.md

@@ -1,6 +1,6 @@
 # Prerequisites
 
-- Linux or macOS
+- Linux-based system
 - Anaconda or Miniconda
 - NVIDIA GPU + CUDA CuDNN (CPU is **not** be supported)
 
@@ -9,7 +9,7 @@ The advantage of using conda instead of pip is that conda will ensure that you h
 
 ## Clone
 
-Firstly, you need to clone this repository:
+Firstly, clone this repository:
 
 ```shell
 git clone https://github.com/haoxiangsnr/FullSubNet
@@ -22,19 +22,18 @@ Install Anaconda or Miniconda, and then install conda and pip packages:
 
 ```shell
 # create a conda environment
-conda create --name FullSubNet python=3.8
+conda create --name FullSubNet python=3
 conda activate FullSubNet
 
 # install conda packages
-# ensure python=3.8, cudatoolkit=10.2, pytorch=1.7.1, torchaudio=0.7
-conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
+# ensure python=3.x, pytorch=1.10.x, torchaudio=0.10
+conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
 conda install tensorboard joblib matplotlib
 
 # install pip packages
-# ensure librosa=0.8
 pip install Cython
 pip install librosa pesq pypesq pystoi tqdm toml mir_eval torch_complex rich
 
-# (Optional) if you have "mp3" format audio in your dataset, you need to install ffmpeg.
+# (Optional) if there are "mp3" format audio files in your dataset, you need to install ffmpeg.
 conda install -c conda-forge ffmpeg
 ```