Music Source Separation; Train & Eval & Inference piplines and pretrained models we used for 2021 ISMIR MDX Challenge.

Overview

Open In Colab

Music Source Separation with Channel-wise Subband Phase Aware ResUnet (CWS-PResUNet)

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Introduction

This repo contains the pretrained Music Source Separation models I submitted to the 2021 ISMIR MSS Challenge. We only participate the Leaderboard A, so these models are solely trained on MUSDB18HQ.

You can use this repo to separate 'bass', 'drums', 'vocals', and 'other' tracks from a music mixture. Also we provides our vocals and other models' training pipline. You can train your own model easily.

As is shown in the following picture, in leaderboard A, we(ByteMSS) achieved the 2nd on Vocal score and 5th on average score. For bass and drums separation, we directly use the open-sourced demucs model. It's trained with only MUSDB18HQ data, thus is qualified for LeaderBoard A.

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1. Usage

1.1 Prepare running environment

First you need to clone this repo:

git clone https://github.com/haoheliu/CWS-ResUNet-MSS-Challenge-ISMIR-2021.git

Install the required packages

cd CWS-ResUNet-MSS-Challenge-ISMIR-2021 
pip3 install --upgrade virtualenv==16.7.9 # this version virtualenv support the --no-site-packages option
virtualenv --no-site-packages env_mss # create new environment
source env_mss/bin/activate # activate environment
pip3 install -r requirements.txt # install requirements

You'd better have wget and unzip command installed so that the scripts can automatically download pretrained models and unzip them.

1.2 Use pretrained model

To use the pretrained model to conduct music source separation. You can run the following demos. If it's the first time you run this program, it will automatically download the pretrained models.

python3 main -i <input-wav-file-path/folder> 
             -o <output-path-dir> 
             -s <sources-to-separate>  # vocals bass drums other (all four stems by default)
             --cuda  # if wanna use GPU, use this flag
             # --wiener  # if wanna use wiener filtering, use this flag. 
             # '--wiener' can take effect only when separation of all four tracks are done or you separate four tracks at the same time.
             
# <input-wav-file-path> is the .wav file to be separated or a folder containing all .wav mixtures.
# <output-path-dir> is the folder to store the separation results 
# python3 main.py -i <input-wav-file-path> -o <output-path-dir>
# Separate a single file to four sources
python3 main.py -i example/test/zeno_sign_stereo.wav -o example/results -s vocals bass drums other
# Separate all the files in a folder
python3 main.py -i example/test/ -o example/results
# Use GPU Acceleration
python3 main.py -i example/test/zeno_sign_stereo.wav -o example/results --cuda
# Separate all the files in a folder using GPU and wiener filtering post processing (may introduce new distortions, make the results even worse.)
python3 main.py -i example/test -o example/results --cuda # --wiener

Each pretrained model in this repo take us approximately two days on 8 V100 GPUs to train.

1.3 Train new models from scratch

1.3.1 How to train

For the training data:

  • If you havn't download musdb18hq, we will automatically download the dataset for you by running the following command.
  • If you have already download musdb18hq, you can put musdb18hq.zip or musdb18hq folder into the data folder and run init.sh to prepare this dataset.
source init.sh

Finally run either of these two commands to start training.

# For track 'vocals', we use a 4 subbands resunet to perform separation. 
# The input of model is mixture and its output is vocals waveform.
# Note: Batchsize is set to 16 by default. Check your hard ware configurations to avoid GPU OOM.
source models/resunet_conv8_vocals/run.sh

# For track 'other', we also use a 4 subbands resunet to perform separation.
# But for this track, we did a little modification.
# The input of model is mixture, and its output are bass, other and drums waveforms. (bass and drums are only used during training) 
# We calculate the losses for "bass","other", and "drums" these three sources together.
# Result shows that joint training is beneficial for 'other' track.
# Note: Batchsize is set to 16 by default. Check your hard ware configurations to avoid GPU OOM.
source models/resunet_joint_training_other/run.sh
  • By default, we use batchsize 8 and 8 gpus for vocal and batchsize 16 and 8 gpus for other. You can custum your own by modifying parameters in the above run.sh files.

  • Training logs will be presented in the mss_challenge_log folder. System will perform validations every two epoches.

Here we provide the result of a test run: 'source models/resunet_conv8_vocals/run.sh'.

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1.3.2 Use the model you trained

To use the the vocals and the other model you trained by your own. You need to modify the following two variables in the predictor.py to the path of your models.

41 ...
42  v_model_path = <path-to-your-vocals-model>
43  o_model_path = <path-to-your-other-model>
44 ...

1.4 Model Evaluation

Since the evaluation process is slow, we separate the evaluation process out as a single task. It's conducted on the validation results generated during training.

Steps:

  1. Locate the path of the validation result. After training, you will get a validation folder inside your loging directory (mss_challenge_log by default).

  2. Determine which kind of source you wanna evaluate (bass, vocals, others or drums). Make sure its results present in the validation folder.

  3. Run eval.sh with two arguments: the source type and the validation results folder (automatic generated after training in the logging folder).

For example:

# source eval.sh <source-type> <your-validation-results-folder-after-training> 

# evaluate vocal score
source eval.sh vocals mss_challenge_log/2021-08-11-subband_four_resunet_for_vocals-vocals/version_0/validations
# evaluate bass score
source eval.sh bass mss_challenge_log/2021-08-11-subband_four_resunet_for_vocals-vocals/version_0/validations
# evaluate drums score
source eval.sh drums mss_challenge_log/2021-08-11-subband_four_resunet_for_vocals-vocals/version_0/validations
# evaluate other score
source eval.sh other mss_challenge_log/2021-08-11-subband_four_resunet_for_vocals-vocals/version_0/validations

The system will save the overall score and the score for each song in the result folder.

For faster evalution, you can adjust the parameter MAX_THREAD insides the evaluator/eval.py to determine how many threads you gonna use. It's value should fit your computer resources. You can start with MAX_THREAD=3 and then try 6, 10 or 16.

2. todo

  • Open-source the training pipline (before 2021-08-20)
  • Write a report paper about my findings in this MSS Challenge (before 2021-08-31)

3. Reference

If you find our code useful for your research, please consider citing:

@inproceedings{Liu2020,
author={Haohe Liu and Lei Xie and Jian Wu and Geng Yang},
title={{Channel-Wise Subband Input for Better Voice and Accompaniment Separation on High Resolution Music}},
year=2020,
booktitle={Proc. Interspeech 2020},
pages={1241--1245},
doi={10.21437/Interspeech.2020-2555},
url={http://dx.doi.org/10.21437/Interspeech.2020-2555}
}.

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Comments
  • 分离任务遇到ImportError错误

    分离任务遇到ImportError错误

    环境:CentOS 8.2,Anaconda3-2021.05( Conda虚拟环境:Python3.8

    安装命令:pip3 install -r requirements.txt

    执行命令:python3 main.py -i example/test/xuemaojiao.wav -o example/results

    遇到错误为:

    Traceback (most recent call last):
      File "main.py", line 1, in <module>
        from predictor import SubbandResUNetPredictor
      File "/root/2021-ISMIR-MSS-Challenge-CWS-PResUNet/predictor.py", line 11, in <module>
        from demucs_predictor import DemucsPredictor
      File "/root/2021-ISMIR-MSS-Challenge-CWS-PResUNet/demucs_predictor.py", line 33, in <module>
        from demucs.utils import apply_model, load_model  # noqa
    ImportError: cannot import name 'apply_model' from 'demucs.utils' (/root/anaconda3/lib/python3.8/site-packages/demucs/utils.py)
    
    opened by acely 8
  • PROBLEMS USING CWS-PResUNet ON WINDOWS

    PROBLEMS USING CWS-PResUNet ON WINDOWS

    Hello, I am facing difficulties trying to use CWS-PResUNet on Windows 10, see below the logs of the problem I am having, if you can let me know where the problem is, I await your feedback, I will be very grateful thank you.

    (base) C:\Users\lucas\CWS>`` error

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    Collecting audioread>=2.0.0
      Using cached audioread-2.1.9.tar.gz (377 kB)
    Requirement already satisfied: numba>=0.43.0 in c:\programdata\anaconda3\lib\site-packages (from librosa->-r requirements.txt (line 14)) (0.53.1)
    Collecting pooch>=1.0
      Downloading pooch-1.5.1-py3-none-any.whl (57 kB)
         |████████████████████████████████| 57 kB 1.5 MB/s
    Requirement already satisfied: decorator>=3.0.0 in c:\programdata\anaconda3\lib\site-packages (from librosa->-r requirements.txt (line 14)) (5.0.6)
    Requirement already satisfied: llvmlite<0.37,>=0.36.0rc1 in c:\programdata\anaconda3\lib\site-packages (from numba>=0.43.0->librosa->-r requirements.txt (line 14)) (0.36.0)
    Requirement already satisfied: appdirs in c:\programdata\anaconda3\lib\site-packages (from pooch>=1.0->librosa->-r requirements.txt (line 14)) (1.4.4)
    Requirement already satisfied: threadpoolctl>=2.0.0 in c:\programdata\anaconda3\lib\site-packages (from scikit-learn!=0.19.0,>=0.14.0->librosa->-r requirements.txt (line 14)) (2.1.0)
    Requirement already satisfied: colorama>=0.3.4 in c:\programdata\anaconda3\lib\site-packages (from loguru->-r requirements.txt (line 4)) (0.4.4)
    Collecting win32-setctime>=1.0.0
      Using cached win32_setctime-1.0.3-py3-none-any.whl (3.5 kB)
    Requirement already satisfied: cycler>=0.10 in c:\programdata\anaconda3\lib\site-packages (from matplotlib->-r requirements.txt (line 16)) (0.10.0)
    Requirement already satisfied: kiwisolver>=1.0.1 in c:\programdata\anaconda3\lib\site-packages (from matplotlib->-r requirements.txt (line 16)) (1.3.1)
    Collecting stempeg>=0.2.3
      Using cached stempeg-0.2.3-py3-none-any.whl (963 kB)
    Collecting pyaml
      Downloading pyaml-21.8.3-py2.py3-none-any.whl (17 kB)
    Collecting ffmpeg-python>=0.2.0
      Using cached ffmpeg_python-0.2.0-py3-none-any.whl (25 kB)
    Collecting async-timeout<4.0,>=3.0
      Using cached async_timeout-3.0.1-py3-none-any.whl (8.2 kB)
    Requirement already satisfied: attrs>=17.3.0 in c:\programdata\anaconda3\lib\site-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.0.1->asteroid>=0.5.0->-r requirements.txt (line 11)) (20.3.0)
    Collecting yarl<2.0,>=1.0
      Downloading yarl-1.6.3-cp38-cp38-win_amd64.whl (125 kB)
         |████████████████████████████████| 125 kB 3.3 MB/s
    Collecting multidict<7.0,>=4.5
      Downloading multidict-5.1.0-cp38-cp38-win_amd64.whl (48 kB)
         |████████████████████████████████| 48 kB 3.2 MB/s
    Building wheels for collected packages: torch-stoi, aicrowd-api, demucs, diffq, julius, audioread, resampy, progressbar, typing, mir-eval, pesq, pystoi
      Building wheel for torch-stoi (setup.py) ... done
      Created wheel for torch-stoi: filename=torch_stoi-0.1.2-py3-none-any.whl size=6198 sha256=5631125bf34346dd6e26a5cccf9bedb54f60d1cfb2ca9a87f3ca8c52c6d47dd0
      Stored in directory: c:\users\lucas\appdata\local\pip\cache\wheels\55\96\76\4e46c2df4cfd5c6411d5d18bb46dd52552bdce0df460f94dc0
      Building wheel for aicrowd-api (setup.py) ... done
      Created wheel for aicrowd-api: filename=aicrowd_api-0.1.23-py2.py3-none-any.whl size=9074 sha256=88915cc65d57d1141ef8f616050d99e74cf7e52ea3c4ccbe0b48f9969d07a655
      Stored in directory: c:\users\lucas\appdata\local\pip\cache\wheels\3f\3c\8d\c3b51a33f18a288aa05dcbc1719914ba209d8024679a5cc7c6
      Building wheel for demucs (setup.py) ... done
      Created wheel for demucs: filename=demucs-2.0.3-py3-none-any.whl size=44124 sha256=60cf1145dc3b220654fc5315f3828a8aab8375a37a8e4d068db0c743b935da39
      Stored in directory: c:\users\lucas\appdata\local\pip\cache\wheels\05\5d\32\1b3f8e215f48f022fb1e61ce0278413ec70a7f79624cdd4f34
      Building wheel for diffq (setup.py) ... done
      Created wheel for diffq: filename=diffq-0.1.1-py3-none-any.whl size=18968 sha256=b05c11cc7927bc6071df4792b93da2e2f19c214e4b4370cf694477efc1fd4e3e
      Stored in directory: c:\users\lucas\appdata\local\pip\cache\wheels\a8\7d\03\1dd37526a1604522a917a81b0b9bae38d40ce11a74c3c95186
      Building wheel for julius (setup.py) ... done
      Created wheel for julius: filename=julius-0.2.5-py3-none-any.whl size=20813 sha256=bd8e409152b810e387d067d29fa6ca4a0067c3fef7af573c9c80baf267491dfe
      Stored in directory: c:\users\lucas\appdata\local\pip\cache\wheels\6d\ff\66\088e6c688cb47c6e2afe6559b7a7ddcffbf53ccaae92b90bf4
      Building wheel for audioread (setup.py) ... done
      Created wheel for audioread: filename=audioread-2.1.9-py3-none-any.whl size=23141 sha256=e5c8ad0b0ae24c7961cba245bda2dec2ce99e2c89c26086a21052bd9aa71c162
      Stored in directory: c:\users\lucas\appdata\local\pip\cache\wheels\49\5a\e4\df590783499a992a88de6c0898991d1167453a3196d0d1eeb7
      Building wheel for resampy (setup.py) ... done
      Created wheel for resampy: filename=resampy-0.2.2-py3-none-any.whl size=320718 sha256=9fe4056854b7b2b78d5d43c95a4ac3aa4c720d51211b076ce22f069ca8d9060d
      Stored in directory: c:\users\lucas\appdata\local\pip\cache\wheels\6f\d1\5d\f13da53b1dcbc2624ff548456c9ffb526c914f53c12c318bb4
      Building wheel for progressbar (setup.py) ... done
      Created wheel for progressbar: filename=progressbar-2.5-py3-none-any.whl size=12075 sha256=12fb0f8bfb2f7fdf35adfdcb282b58129be1d9d5afd225ec6e802ce7bf966f7d
      Stored in directory: c:\users\lucas\appdata\local\pip\cache\wheels\2c\67\ed\d84123843c937d7e7f5ba88a270d11036473144143355e2747
      Building wheel for typing (setup.py) ... done
      Created wheel for typing: filename=typing-3.7.4.3-py3-none-any.whl size=26308 sha256=39ae383c669e6592a133ddf74f66c27cd510de6b6ac702f1d230776ab4c519b7
      Stored in directory: c:\users\lucas\appdata\local\pip\cache\wheels\5e\5d\01\3083e091b57809dad979ea543def62d9d878950e3e74f0c930
      Building wheel for mir-eval (setup.py) ... done
      Created wheel for mir-eval: filename=mir_eval-0.6-py3-none-any.whl size=96514 sha256=1376f8f0e69d284d5e046c2284a80c38efbf6d9d661c982931ddf12b3ab1b82f
      Stored in directory: c:\users\lucas\appdata\local\pip\cache\wheels\1c\47\0b\416b95d5fceba56809699852c33ae5291ffd2f0e73181ffd6c
      Building wheel for pesq (setup.py) ... error
      ERROR: Command errored out with exit status 1:
       command: 'C:\ProgramData\Anaconda3\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\lucas\\AppData\\Local\\Temp\\pip-install-n_qyux9y\\pesq_ed38c459ee364f729530065fe03e90dc\\setup.py'"'"'; __file__='"'"'C:\\Users\\lucas\\AppData\\Local\\Temp\\pip-install-n_qyux9y\\pesq_ed38c459ee364f729530065fe03e90dc\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\lucas\AppData\Local\Temp\pip-wheel-k9wuzi4j'
           cwd: C:\Users\lucas\AppData\Local\Temp\pip-install-n_qyux9y\pesq_ed38c459ee364f729530065fe03e90dc\
      Complete output (22 lines):
      running bdist_wheel
      running build
      running build_py
      creating build
      creating build\lib.win-amd64-3.8
      creating build\lib.win-amd64-3.8\pesq
      copying pesq\__init__.py -> build\lib.win-amd64-3.8\pesq
      copying pesq\cypesq.pyx -> build\lib.win-amd64-3.8\pesq
      copying pesq\dsp.h -> build\lib.win-amd64-3.8\pesq
      copying pesq\pesq.h -> build\lib.win-amd64-3.8\pesq
      copying pesq\pesqio.h -> build\lib.win-amd64-3.8\pesq
      copying pesq\pesqmain.h -> build\lib.win-amd64-3.8\pesq
      copying pesq\pesqpar.h -> build\lib.win-amd64-3.8\pesq
      copying pesq\dsp.c -> build\lib.win-amd64-3.8\pesq
      copying pesq\pesqdsp.c -> build\lib.win-amd64-3.8\pesq
      copying pesq\pesqmod.c -> build\lib.win-amd64-3.8\pesq
      running build_ext
      cythoning pesq/cypesq.pyx to pesq\cypesq.c
      C:\ProgramData\Anaconda3\lib\site-packages\Cython\Compiler\Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: C:\Users\lucas\AppData\Local\Temp\pip-install-n_qyux9y\pesq_ed38c459ee364f729530065fe03e90dc\pesq\cypesq.pyx
        tree = Parsing.p_module(s, pxd, full_module_name)
      building 'cypesq' extension
      error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/
      ----------------------------------------
      ERROR: Failed building wheel for pesq
      Running setup.py clean for pesq
      Building wheel for pystoi (setup.py) ... done
      Created wheel for pystoi: filename=pystoi-0.3.3-py2.py3-none-any.whl size=7781 sha256=ab6a0da4d3b71a56a2d6fa38302a42952458aa93800d6047716b2e3cb3833adf
      Stored in directory: c:\users\lucas\appdata\local\pip\cache\wheels\62\35\75\c07f0861a60fb8aacf44fdd5c8c214a224a6c9edb4a4e1402f
    Successfully built torch-stoi aicrowd-api demucs diffq julius audioread resampy progressbar typing mir-eval pystoi
    Failed to build pesq
    Installing collected packages: pyasn1, rsa, pyasn1-modules, oauthlib, multidict, cachetools, yarl, requests-oauthlib, google-auth, async-timeout, torch, tensorboard-plugin-wit, tensorboard-data-server, protobuf, markdown, jmespath, grpcio, google-auth-oauthlib, fsspec, aiohttp, absl-py, torchmetrics, torchaudio, tensorboard, smmap, pytorch-ranger, pystoi, pyDeprecate, pesq, mir-eval, ffmpeg-python, einops, cached-property, botocore, win32-setctime, torch-stoi, torch-optimizer, stempeg, SoundFile, s3transfer, resampy, redis, pytorch-lightning, pyaml, pooch, pb-bss-eval, lameenc, julius, humanfriendly, huggingface-hub, gitdb, diffq, audioread, asteroid-filterbanks, typing, torchvision, torchlibrosa, tensorboardX, setuptools-scm, pynvml, progressbar, openunmix, norbert, musdb, loguru, librosa, GitPython, demucs, coloredlogs, boto3, asteroid, aicrowd-api
      Attempting uninstall: fsspec
        Found existing installation: fsspec 0.9.0
        Uninstalling fsspec-0.9.0:
          Successfully uninstalled fsspec-0.9.0
        Running setup.py install for pesq ... error
        ERROR: Command errored out with exit status 1:
         command: 'C:\ProgramData\Anaconda3\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\lucas\\AppData\\Local\\Temp\\pip-install-n_qyux9y\\pesq_ed38c459ee364f729530065fe03e90dc\\setup.py'"'"'; __file__='"'"'C:\\Users\\lucas\\AppData\\Local\\Temp\\pip-install-n_qyux9y\\pesq_ed38c459ee364f729530065fe03e90dc\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\lucas\AppData\Local\Temp\pip-record-v7qod0sz\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\ProgramData\Anaconda3\Include\pesq'
             cwd: C:\Users\lucas\AppData\Local\Temp\pip-install-n_qyux9y\pesq_ed38c459ee364f729530065fe03e90dc\
        Complete output (20 lines):
        running install
        running build
        running build_py
        creating build
        creating build\lib.win-amd64-3.8
        creating build\lib.win-amd64-3.8\pesq
        copying pesq\__init__.py -> build\lib.win-amd64-3.8\pesq
        copying pesq\cypesq.pyx -> build\lib.win-amd64-3.8\pesq
        copying pesq\dsp.h -> build\lib.win-amd64-3.8\pesq
        copying pesq\pesq.h -> build\lib.win-amd64-3.8\pesq
        copying pesq\pesqio.h -> build\lib.win-amd64-3.8\pesq
        copying pesq\pesqmain.h -> build\lib.win-amd64-3.8\pesq
        copying pesq\pesqpar.h -> build\lib.win-amd64-3.8\pesq
        copying pesq\dsp.c -> build\lib.win-amd64-3.8\pesq
        copying pesq\pesqdsp.c -> build\lib.win-amd64-3.8\pesq
        copying pesq\pesqmod.c -> build\lib.win-amd64-3.8\pesq
        running build_ext
        skipping 'pesq\cypesq.c' Cython extension (up-to-date)
        building 'cypesq' extension
        error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/
        ----------------------------------------
    ERROR: Command errored out with exit status 1: 'C:\ProgramData\Anaconda3\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\lucas\\AppData\\Local\\Temp\\pip-install-n_qyux9y\\pesq_ed38c459ee364f729530065fe03e90dc\\setup.py'"'"'; __file__='"'"'C:\\Users\\lucas\\AppData\\Local\\Temp\\pip-install-n_qyux9y\\pesq_ed38c459ee364f729530065fe03e90dc\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\lucas\AppData\Local\Temp\pip-record-v7qod0sz\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\ProgramData\Anaconda3\Include\pesq' Check the logs for full command output.
    
    
    **(base) C:\Users\lucas\CWS>python main.py -i example/test/xuemaojiao.wav -o example/results -s vocals bass drums other**
    Loading demucs model...
    Downloading: "https://dl.fbaipublicfiles.com/demucs/v3.0/demucs-e07c671f.th" to ./utils/demucs_checkpoints\checkpoints\demucs-e07c671f.th
    100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 0.99G/0.99G [06:46<00:00, 2.61MB/s]
    Downloading the weight of model for the vocal track
    wget https://zenodo.org/record/5175846/files/epoch%3D49-val_loss%3D0.0902_trimed.ckpt?download=1 -O models/resunet_conv8_vocals/checkpoints/vocals/epoch=49-val_loss=0.0902_trimed.ckpt
    --2021-08-27 19:50:10--  https://zenodo.org/record/5175846/files/epoch%3D49-val_loss%3D0.0902_trimed.ckpt?download=1
    Resolving zenodo.org (zenodo.org)... 137.138.76.77
    Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.
    HTTP request sent, awaiting response... 200 OK
    Length: 904040650 (862M) [application/octet-stream]
    Saving to: 'models/resunet_conv8_vocals/checkpoints/vocals/epoch=49-val_loss=0.0902_trimed.ckpt'
    
    =49-val_loss=0.0902_trimed.ckpt                        4%[====>                                                         49-val_loss=0.0902_trimed.ckpt                         4%[====>                                                         9-val_loss=0.0902_trimed.ckpt                          4%[====>                                                         -val_loss=0.0902_trimed.ckpt                           4%[====>                                                         val_loss=0.0902_trimed.ckpt                            5%[====>                                                         al_loss=0.0902_trimed.ckpt       2_trimed.ckpt                                          5%[=====>                                                                                                                ]  50,59M  2,51MB/s    eta 5m 3_trimed.ckpt                                           5%[=====>                                                        trimed.ckpt                                            5%[=====>                                                        rimed.ckpt                                             5%[======>                                                       imed.ckpt                                              6%[======>                                                       med.ckpt                                               6%[======>                                                       ed.ckpt                                                6%[======>                                                       d.ckpt                                                 6%[======>                                                       .ckpt                                                  6%[======>                                                       ckpt                                                   6%[======>                                                       kpt                                                    6%[======>                                                       pt                                                     6%[======>                                                       t                                                      6%[======>                                                                                                              6%[======>                                                                                                          m   6%[======>                                                                   models/resunet_conv8_vocals/checkpoints/vocals/epoch 100%[=====================================================================================================================>] 862,16M  2,57MB/s    in 5m 42s
    
    2021-08-27 19:55:55 (2,52 MB/s) - 'models/resunet_conv8_vocals/checkpoints/vocals/epoch=49-val_loss=0.0902_trimed.ckpt' saved [904040650/904040650]
    
    Downloading the weight of model for the other track
    wget https://zenodo.org/record/5175846/files/epoch%3D33-val_loss%3D0.4293_trimed.ckpt?download=1 -O models/resunet_joint_training_other/checkpoints_nov/other/epoch=33-val_loss=0.4293_trimed.ckpt
    --2021-08-27 19:55:55--  https://zenodo.org/record/5175846/files/epoch%3D33-val_loss%3D0.4293_trimed.ckpt?download=1
    Resolving zenodo.org (zenodo.org)... 137.138.76.77
    Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.
    HTTP request sent, awaiting response... 200 OK
    Length: 425901062 (406M) [application/octet-stream]
    Saving to: 'models/resunet_joint_training_other/checkpoints_nov/other/epoch=33-val_loss=0.4293_trimed.ckpt'
    
    models/resunet_joint_training_other/checkpoints_nov/ 100%[=====================================================================================================================>] 406,17M  2,72MB/s    in 2m 42s
    
    2021-08-27 19:58:38 (2,51 MB/s) - 'models/resunet_joint_training_other/checkpoints_nov/other/epoch=33-val_loss=0.4293_trimed.ckpt' saved [425901062/425901062]
    
    Loading vocal model...
    Traceback (most recent call last):
      File "main.py", line 22, in <module>
        submission.prediction_setup()
      File "C:\Users\lucas\CWS\predictor.py", line 72, in prediction_setup
        self.v_model = self.reload(v_model_path, Conv8Res(channels=2, target="vocals"), nsrc=2)
      File "C:\Users\lucas\CWS\predictor.py", line 81, in reload
        model = model.load_from_checkpoint(pth) if (len(pth) != 0) else model
      File "C:\ProgramData\Anaconda3\lib\site-packages\pytorch_lightning\core\saving.py", line 153, in load_from_checkpoint
        model = cls._load_model_state(checkpoint, strict=strict, **kwargs)
      File "C:\ProgramData\Anaconda3\lib\site-packages\pytorch_lightning\core\saving.py", line 201, in _load_model_state
        keys = model.load_state_dict(checkpoint["state_dict"], strict=strict)
      File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 1406, in load_state_dict
        raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
    RuntimeError: Error(s) in loading state_dict for UNetResComplex_100Mb:
            Missing key(s) in state_dict: "wav_spec_loss.f_helper.istft.ola_window", "f_helper.istft.ola_window".
            Unexpected key(s) in state_dict: "wav_spec_loss.f_helper.istft.reverse.weight", "wav_spec_loss.f_helper.istft.overlap_add.weight", "f_helper.istft.reverse.weight", "f_helper.istft.overlap_add.weight".
    opened by lucasdobr15 5
  • Equally Divided Subband and Complex Spectrogram

    Equally Divided Subband and Complex Spectrogram

    Hi, I've read your paper and have a few questions,

    1. As lower frequency contains more information, will the result be better if the subband is not equally divided? Perhaps log-scale? (and maybe add another transformation so that they have the same shape to concatenate.) I'd like to try it, yet I'm not sure how the filters (models/filters/*.mat) are generated.
    2. If I understand it correctly, the U-Net cannot see the phase information, https://github.com/haoheliu/2021-ISMIR-MSS-Challenge-CWS-PResUNet/blob/2f84db8c1455cea473eb4d72bc3779e0e37ea660/models/resunet_conv1_vocals/model.py#L195 since only sp is forwarded to U-Net.
      I've tried adding phase on other channels, so that the input to the U-Net will be (batch, channel*2, time, frequency), and the rest of the code is the same. But the result is worse. Do you have any thoughts on this?

    Thanks a million!

    opened by sophia1488 3
  • Thanks very much!!!!

    Thanks very much!!!!

    I have not tried it yet, but I have been looking for software for 3 months to separate the noise of keystrokes from my music, I recorded it on a dictaphone in nature, and this noise of impacts terribly cuts my ears, if really your software will be able to help me (I will try later when there is a stable internet for downloading repo), I will be grateful to you for a thousand years!

    opened by kingtelepuz5 1
Owner
Lau
Lau
Woosung Choi 63 Nov 14, 2022
Music source separation is a task to separate audio recordings into individual sources

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audioLIME: Listenable Explanations Using Source Separation

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Audio Source Separation is the process of separating a mixture into isolated sounds from individual sources

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