├── src ├── conf │ ├── __init__.py │ ├── urmp.py │ ├── feature.py │ ├── inference.py │ ├── sample.py │ └── models.py ├── dataset │ ├── __init__.py │ └── urmp │ │ ├── urmp_test.py │ │ ├── urmp_generate_dataset.py │ │ ├── urmp_feature.py │ │ └── urmp_sample.py ├── utils │ ├── __init__.py │ ├── multiEpochsDataLoader.py │ ├── target_process.py │ ├── weiMidi.py │ └── utilities.py ├── __init__.py ├── models │ ├── model_factory.py │ └── layers.py ├── analyze │ ├── utilities.py │ └── draw_table.py └── inference │ ├── compute_measure.py │ └── utilities.py ├── scripts ├── draw.sh ├── generate_dataset.sh ├── generate_feature.sh ├── evaluate.sh ├── train-model.sh ├── synthesis.sh ├── evaluate-model.sh └── clean_packed_data.sh ├── songs └── road.mid ├── data └── urmp │ ├── Trombone │ └── train.lst │ ├── Clarinet │ └── train.lst │ ├── Viola │ └── train.lst │ ├── Cello │ └── train.lst │ ├── Saxophone │ └── train.lst │ ├── Flute │ └── train.lst │ ├── Trumpet │ └── train.lst │ ├── Violin │ └── train.lst │ ├── Vn_Fl_Tpt │ └── train-query.lst.bk │ └── testset │ ├── query.lst │ └── test.lst ├── imgs ├── model-fig-3.png └── model-fig-1-ab.png ├── .gitignore ├── run.sh ├── requirements.txt ├── LICENSE ├── synthesis.py ├── evaluate.py ├── evaluation ├── MSI-DIS │ ├── scores-190.json │ ├── scores-191.json │ ├── scores-198.json │ ├── scores-199.json │ ├── scores-192.json │ ├── scores-193.json │ ├── scores-194.json │ ├── scores-195.json │ ├── scores-196.json │ └── scores-197.json └── MSI │ ├── scores-192.json │ ├── scores-197.json │ ├── scores-190.json │ ├── scores-198.json │ ├── scores-193.json │ ├── scores-195.json │ ├── scores-199.json │ ├── scores-191.json │ ├── scores-194.json │ └── scores-196.json ├── README.md └── train.py /src/conf/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /src/dataset/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /src/utils/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /scripts/draw.sh: -------------------------------------------------------------------------------- 1 | python src/analyze/draw_table.py --evaluation_folder=evaluation 2 | -------------------------------------------------------------------------------- /scripts/generate_dataset.sh: -------------------------------------------------------------------------------- 1 | python src/dataset/urmp/urmp_generate_dataset.py --feature_dir=dataset/hdf5s/urmp --data_dir=data/urmp-rec 2 | -------------------------------------------------------------------------------- /scripts/generate_feature.sh: -------------------------------------------------------------------------------- 1 | python src/dataset/urmp/urmp_feature.py --dataset_dir=dataset/URMP/Dataset --feature_dir=dataset/hdf5s/urmp --process_num=8 2 | -------------------------------------------------------------------------------- /songs/road.mid: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Kikyo-16/A-unified-model-for-zero-shot-musical-source-separation-transcription-and-synthesis/HEAD/songs/road.mid -------------------------------------------------------------------------------- /data/urmp/Trombone/train.lst: -------------------------------------------------------------------------------- 1 | dataset/hdf5s/urmp/07_GString_tpt_tbn/AuSep_2_tbn_07_GString.h5 2 | dataset/hdf5s/urmp/15_Surprise_tpt_tpt_tbn/AuSep_3_tbn_15_Surprise.h5 -------------------------------------------------------------------------------- /imgs/model-fig-3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Kikyo-16/A-unified-model-for-zero-shot-musical-source-separation-transcription-and-synthesis/HEAD/imgs/model-fig-3.png -------------------------------------------------------------------------------- /scripts/evaluate.sh: -------------------------------------------------------------------------------- 1 | cuda_id=0 2 | evaluate_epoch=199 3 | 4 | # evaluate MSI-DIS model 5 | sh scripts/evaluate-model.sh $cuda_id MSI $evaluate_epoch save_model/MSI 8 6 | -------------------------------------------------------------------------------- /imgs/model-fig-1-ab.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Kikyo-16/A-unified-model-for-zero-shot-musical-source-separation-transcription-and-synthesis/HEAD/imgs/model-fig-1-ab.png -------------------------------------------------------------------------------- /scripts/train-model.sh: -------------------------------------------------------------------------------- 1 | cuda=$1 2 | model_name=$2 3 | model_folder=$3 4 | CUDA_VISIBLE_DEVICES=$cuda python train.py --model_name=$model_name --model_folder=$model_folder --epoch=200 5 | -------------------------------------------------------------------------------- /scripts/synthesis.sh: -------------------------------------------------------------------------------- 1 | CUDA_VISIBLE_DEVICES=0 python synthesis.py --model_name=MSI-DIS --model_path=save_model/wei_MSI-DIS/params_epoch-199.pkl \ 2 | --evaluation_folder=demo 3 | 4 | -------------------------------------------------------------------------------- /src/__init__.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | 4 | pkg_path = os.path.dirname(os.path.abspath(__file__)) 5 | sys.path.append(pkg_path) 6 | 7 | import conf, utils, dataset, models, inference 8 | 9 | 10 | -------------------------------------------------------------------------------- /src/conf/urmp.py: -------------------------------------------------------------------------------- 1 | TRAINING_FILE_LST_PATH = "data/urmp/INSTR_NAME/train.lst" 2 | SEEN_INSTRUMENTS=["Violin", 3 | "Cello", 4 | "Viola", 5 | "Flute", 6 | "Clarinet", 7 | "Saxophone", 8 | "Trumpet", 9 | "Trombone"] 10 | SEEN_INSTRUMENTS_NUM = len(SEEN_INSTRUMENTS) 11 | -------------------------------------------------------------------------------- /data/urmp/Clarinet/train.lst: -------------------------------------------------------------------------------- 1 | dataset/hdf5s/urmp/17_Nocturne_vn_fl_cl/AuSep_3_cl_17_Nocturne.h5 2 | dataset/hdf5s/urmp/14_Waltz_fl_fl_cl/AuSep_3_cl_14_Waltz.h5 3 | dataset/hdf5s/urmp/03_Dance_fl_cl/AuSep_2_cl_03_Dance.h5 4 | dataset/hdf5s/urmp/19_Pavane_cl_vn_vc/AuSep_1_cl_19_Pavane.h5 -------------------------------------------------------------------------------- /scripts/evaluate-model.sh: -------------------------------------------------------------------------------- 1 | cuda=$1 2 | model_name=$2 3 | epoch=$3 4 | model_folder=$4 5 | ps=$5 6 | CUDA_VISIBLE_DEVICES=$cuda python evaluate.py --model_name=$model_name --model_path=$model_folder/params_epoch-$epoch.pkl \ 7 | --evaluation_folder=evaluation --epoch=$epoch --ps=$ps 8 | -------------------------------------------------------------------------------- /src/conf/feature.py: -------------------------------------------------------------------------------- 1 | SAMPLE_RATE = 16000 2 | FRAMES_PER_SEC = 100 3 | 4 | WINDOW_SIZE = 2048 5 | HOP_SIZE = 160 6 | PAD_MODE = "reflect" 7 | WINDOW = "hann" 8 | CHANNELS_NUM = 1 9 | 10 | NOTES_NUM_EXCLUDE_SILENCE = 88 11 | NOTES_NUM = NOTES_NUM_EXCLUDE_SILENCE + 1 12 | BEGIN_NOTE = 21 13 | N_FFT = WINDOW_SIZE 14 | -------------------------------------------------------------------------------- /src/conf/inference.py: -------------------------------------------------------------------------------- 1 | INFERENCE_BATCH_SIZE = 12 2 | BATCH_FRAMES_NUM = 410 3 | N_ITER = 200 4 | PAD_FRAME = 5 5 | 6 | TEST_DATA_LST_PATH = "data/urmp/testset/test.lst" 7 | TEST_QUERY_LST_PATH = "data/urmp/testset/query.lst" 8 | 9 | 10 | SYN_DURATION = 10 11 | SYN_SONG_ONSET = 25 12 | 13 | MIX_ONSET = 35 14 | OCTAVE = 12 15 | -------------------------------------------------------------------------------- /data/urmp/Viola/train.lst: -------------------------------------------------------------------------------- 1 | dataset/hdf5s/urmp/44_K515_vn_vn_va_va_vc/AuSep_3_va_44_K515.h5 2 | dataset/hdf5s/urmp/44_K515_vn_vn_va_va_vc/AuSep_4_va_44_K515.h5 3 | dataset/hdf5s/urmp/24_Pirates_vn_vn_va_vc/AuSep_3_va_24_Pirates.h5 4 | dataset/hdf5s/urmp/13_Hark_vn_vn_va/AuSep_3_va_13_Hark.h5 5 | dataset/hdf5s/urmp/27_King_vn_vn_va_sax/AuSep_3_va_27_King.h5 -------------------------------------------------------------------------------- /src/conf/sample.py: -------------------------------------------------------------------------------- 1 | from conf.feature import * 2 | 3 | SOURCES_NUM_OF_MIXTURE = 2 4 | UP_BOUND = 2 5 | SHIFT_SEMITONE = 4 6 | POS_SHIFT_SEMITONE = int( 2 * SHIFT_SEMITONE + 1) 7 | DURATION = 3 8 | SAMPLE_DURATION = int(SAMPLE_RATE * DURATION) 9 | FRAME_DURATION = int(FRAMES_PER_SEC * DURATION) 10 | 11 | LEARNING_RATE = 5e-4 12 | DECAY = 100 13 | TRAINING_BATCH_SIZE = 12 14 | SAMPLES_NUM = 6240 15 | -------------------------------------------------------------------------------- /data/urmp/Cello/train.lst: -------------------------------------------------------------------------------- 1 | dataset/hdf5s/urmp/44_K515_vn_vn_va_va_vc/AuSep_5_vc_44_K515.h5 2 | dataset/hdf5s/urmp/24_Pirates_vn_vn_va_vc/AuSep_4_vc_24_Pirates.h5 3 | dataset/hdf5s/urmp/12_Spring_vn_vn_vc/AuSep_3_vc_12_Spring.h5 4 | dataset/hdf5s/urmp/01_Jupiter_vn_vc/AuSep_2_vc_01_Jupiter.h5 5 | dataset/hdf5s/urmp/19_Pavane_cl_vn_vc/AuSep_3_vc_19_Pavane.h5 6 | dataset/hdf5s/urmp/26_King_vn_vn_va_vc/AuSep_4_vc_26_King.h5 -------------------------------------------------------------------------------- /data/urmp/Saxophone/train.lst: -------------------------------------------------------------------------------- 1 | dataset/hdf5s/urmp/25_Pirates_vn_vn_va_sax/AuSep_4_sax_25_Pirates.h5 2 | dataset/hdf5s/urmp/16_Surprise_tpt_tpt_sax/AuSep_3_sax_16_Surprise.h5 3 | dataset/hdf5s/urmp/06_Entertainer_sax_sax/AuSep_1_sax_06_Entertainer.h5 4 | dataset/hdf5s/urmp/06_Entertainer_sax_sax/AuSep_2_sax_06_Entertainer.h5 5 | dataset/hdf5s/urmp/27_King_vn_vn_va_sax/AuSep_4_sax_27_King.h5 6 | dataset/hdf5s/urmp/10_March_tpt_sax/AuSep_2_sax_10_March.h5 -------------------------------------------------------------------------------- /data/urmp/Flute/train.lst: -------------------------------------------------------------------------------- 1 | dataset/hdf5s/urmp/17_Nocturne_vn_fl_cl/AuSep_2_fl_17_Nocturne.h5 2 | dataset/hdf5s/urmp/14_Waltz_fl_fl_cl/AuSep_1_fl_14_Waltz.h5 3 | dataset/hdf5s/urmp/14_Waltz_fl_fl_cl/AuSep_2_fl_14_Waltz.h5 4 | dataset/hdf5s/urmp/08_Spring_fl_vn/AuSep_1_fl_08_Spring.h5 5 | dataset/hdf5s/urmp/03_Dance_fl_cl/AuSep_1_fl_03_Dance.h5 6 | dataset/hdf5s/urmp/04_Allegro_fl_fl/AuSep_2_fl_04_Allegro.h5 7 | dataset/hdf5s/urmp/04_Allegro_fl_fl/AuSep_1_fl_04_Allegro.h5 -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__/ 2 | data/musicnet*/ 3 | data/musdb18/ 4 | data/maestro/ 5 | data/urmp-test/ 6 | data/urmp-rec/ 7 | output/ 8 | output/ 9 | reproduce_dataset/ 10 | results/ 11 | *.pyc 12 | src/analyze/analyze_experimental_results.py 13 | src/analyze/draw_3D_hQuery.py 14 | src/analyze/draw_dataset_distribution.py 15 | src/analyze/draw_epoch_curve.py 16 | src/analyze/draw_hQuery.py 17 | src/analyze/draw_instr_results.py 18 | src/analyze/draw_spec.py 19 | src/analyze/gradient_colors.py 20 | -------------------------------------------------------------------------------- /scripts/clean_packed_data.sh: -------------------------------------------------------------------------------- 1 | git rev-list --objects --all | grep demo/MSI > .packed_data.lst 2 | awk '{print $2}' .packed_data.lst > .packed_data.name 3 | rm .packed_data.lst 4 | for name in `cat .packed_data.name` 5 | do 6 | echo $name 7 | git filter-branch -f --index-filter "git rm -r --cached --ignore-unmatch $name" -- --all 8 | break 9 | done 10 | rm .packed_data.name 11 | #rm -Rf .git/refs/original 12 | #git reflog expire --expire=now --all 13 | #git gc --aggressive --prune=now 14 | # 15 | -------------------------------------------------------------------------------- /src/conf/models.py: -------------------------------------------------------------------------------- 1 | from conf.feature import * 2 | 3 | QUERY_DIM = 6 4 | 5 | MODEL_CONFIG = {"UNet": 6 | {"with_bn0" : True, 7 | "input_channels_num" : CHANNELS_NUM, 8 | "input_size" : WINDOW_SIZE // 2, 9 | "blocks_num" : 5, 10 | "condition_dim" : QUERY_DIM, 11 | "output_dim" : CHANNELS_NUM, 12 | }, 13 | "QueryNet": 14 | {"blocks_num" : 2, 15 | "input_size" : WINDOW_SIZE // 2, 16 | "input_channels_num" : CHANNELS_NUM, 17 | "pnum" : QUERY_DIM, 18 | }, 19 | "Transcriptor": 20 | {"blocks_num" : 2, 21 | "output_dim" : NOTES_NUM 22 | } 23 | } 24 | 25 | -------------------------------------------------------------------------------- /data/urmp/Trumpet/train.lst: -------------------------------------------------------------------------------- 1 | dataset/hdf5s/urmp/18_Nocturne_vn_fl_tpt/AuSep_3_tpt_18_Nocturne.h5 2 | dataset/hdf5s/urmp/07_GString_tpt_tbn/AuSep_1_tpt_07_GString.h5 3 | dataset/hdf5s/urmp/15_Surprise_tpt_tpt_tbn/AuSep_2_tpt_15_Surprise.h5 4 | dataset/hdf5s/urmp/15_Surprise_tpt_tpt_tbn/AuSep_1_tpt_15_Surprise.h5 5 | dataset/hdf5s/urmp/05_Entertainer_tpt_tpt/AuSep_2_tpt_05_Entertainer.h5 6 | dataset/hdf5s/urmp/05_Entertainer_tpt_tpt/AuSep_1_tpt_05_Entertainer.h5 7 | dataset/hdf5s/urmp/20_Pavane_tpt_vn_vc/AuSep_1_tpt_20_Pavane.h5 8 | dataset/hdf5s/urmp/10_March_tpt_sax/AuSep_1_tpt_10_March.h5 9 | dataset/hdf5s/urmp/09_Jesus_tpt_vn/AuSep_1_tpt_09_Jesus.h5 -------------------------------------------------------------------------------- /src/utils/multiEpochsDataLoader.py: -------------------------------------------------------------------------------- 1 | from torch.utils.data import DataLoader 2 | 3 | class _RepeatSampler(object): 4 | def __init__(self, sampler): 5 | self.sampler = sampler 6 | 7 | def __iter__(self): 8 | while True: 9 | yield from iter(self.sampler) 10 | 11 | class MultiEpochsDataLoader(DataLoader): 12 | def __init__(self, *args, **kwargs): 13 | super().__init__(*args, **kwargs) 14 | object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler)) 15 | self.iterator = super().__iter__() 16 | 17 | def __len__(self): 18 | return len(self.batch_sampler.sampler) 19 | 20 | def __iter__(self): 21 | for i in range(len(self)): 22 | yield next(self.iterator) 23 | -------------------------------------------------------------------------------- /run.sh: -------------------------------------------------------------------------------- 1 | # pack urmp dataset to hdf5 2 | #sh scripts/generate_feature.sh 3 | 4 | cuda_id=0 5 | 6 | # train transcription-only baseline 7 | #sh scripts/train-model.sh $cuda_id AMT save_model/AMT 8 | 9 | # train separation-only baseline 10 | #sh scripts/train-model.sh $cuda_id MSS save_model/MSS 11 | 12 | # train multi-task baseline 13 | #sh scripts/train-model.sh $cuda_id MSS-AMT save_model/MSS-AMT 14 | 15 | # train the proposed multi-task score-informed (MSI) model 16 | #sh scripts/train-model.sh $cuda_id MSI save_model/MSI 17 | 18 | # train the proposed multi-task score-informed with further disentanglement (MSI-DIS) model 19 | sh scripts/train-model.sh $cuda_id MSI-DIS save_model/MSI-DIS 20 | 21 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | appdirs==1.4.4 2 | audioread==2.1.9 3 | cached-property==1.5.2 4 | certifi==2021.5.30 5 | cffi==1.14.6 6 | charset-normalizer==2.0.4 7 | cycler==0.10.0 8 | dataclasses==0.8 9 | decorator==5.0.9 10 | future==0.18.2 11 | h5py==3.1.0 12 | idna==3.2 13 | joblib==1.0.1 14 | kiwisolver==1.3.1 15 | librosa==0.8.1 16 | llvmlite==0.36.0 17 | matplotlib==3.3.4 18 | mido==1.2.10 19 | mir-eval==0.6 20 | numba==0.53.1 21 | numpy==1.19.5 22 | packaging==21.0 23 | Pillow==8.3.1 24 | pooch==1.4.0 25 | prefetch-generator==1.0.1 26 | pycparser==2.20 27 | pyparsing==2.4.7 28 | python-dateutil==2.8.2 29 | requests==2.26.0 30 | resampy==0.2.2 31 | scikit-learn==0.24.2 32 | scipy==1.5.4 33 | six==1.16.0 34 | SoundFile==0.10.3.post1 35 | threadpoolctl==2.2.0 36 | torch==1.7.1+cu101 37 | torchaudio==0.7.2 38 | torchlibrosa==0.0.4 39 | torchvision==0.8.2+cu101 40 | typing-extensions==3.10.0.0 41 | urllib3==1.26.6 42 | -------------------------------------------------------------------------------- /data/urmp/Violin/train.lst: -------------------------------------------------------------------------------- 1 | dataset/hdf5s/urmp/44_K515_vn_vn_va_va_vc/AuSep_1_vn_44_K515.h5 2 | dataset/hdf5s/urmp/44_K515_vn_vn_va_va_vc/AuSep_2_vn_44_K515.h5 3 | dataset/hdf5s/urmp/24_Pirates_vn_vn_va_vc/AuSep_2_vn_24_Pirates.h5 4 | dataset/hdf5s/urmp/24_Pirates_vn_vn_va_vc/AuSep_1_vn_24_Pirates.h5 5 | dataset/hdf5s/urmp/17_Nocturne_vn_fl_cl/AuSep_1_vn_17_Nocturne.h5 6 | dataset/hdf5s/urmp/02_Sonata_vn_vn/AuSep_1_vn_02_Sonata.h5 7 | dataset/hdf5s/urmp/02_Sonata_vn_vn/AuSep_2_vn_02_Sonata.h5 8 | dataset/hdf5s/urmp/12_Spring_vn_vn_vc/AuSep_1_vn_12_Spring.h5 9 | dataset/hdf5s/urmp/08_Spring_fl_vn/AuSep_2_vn_08_Spring.h5 10 | dataset/hdf5s/urmp/13_Hark_vn_vn_va/AuSep_1_vn_13_Hark.h5 11 | dataset/hdf5s/urmp/13_Hark_vn_vn_va/AuSep_2_vn_13_Hark.h5 12 | dataset/hdf5s/urmp/01_Jupiter_vn_vc/AuSep_1_vn_01_Jupiter.h5 13 | dataset/hdf5s/urmp/19_Pavane_cl_vn_vc/AuSep_2_vn_19_Pavane.h5 14 | dataset/hdf5s/urmp/27_King_vn_vn_va_sax/AuSep_1_vn_27_King.h5 15 | dataset/hdf5s/urmp/27_King_vn_vn_va_sax/AuSep_2_vn_27_King.h5 16 | dataset/hdf5s/urmp/09_Jesus_tpt_vn/AuSep_2_vn_09_Jesus.h5 -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 Liwei Lin 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /synthesis.py: -------------------------------------------------------------------------------- 1 | import time 2 | import torch 3 | import torch.nn.functional as F 4 | import os 5 | import sys 6 | import numpy as np 7 | import torch.nn as nn 8 | import argparse 9 | 10 | import src 11 | from utils.utilities import (save_json, compute_time, print_dict, mkdir) 12 | from inference.inference import Inference 13 | from inference.compute_measure import (evaluate_transcription, evaluate_separation) 14 | 15 | 16 | if __name__=='__main__': 17 | 18 | parser = argparse.ArgumentParser(description='') 19 | parser.add_argument('--model_name', type=str, required=True, help='Model name in \ 20 | [`AMT` for trainscription-only baseline, \ 21 | `MSS` for separation-only baseline, \ 22 | `MSS-AMT` for multi-task baseline, \ 23 | `MSI` for the proposed multi-task score-informed model, \ 24 | `MSI-DIS` for the proposed multi-task score-informed with further disentanglement model].') 25 | parser.add_argument('--model_path', type=str, required=True, help='Model weights path.') 26 | parser.add_argument('--evaluation_folder', type=str, required=True, help='Directory to store evaluation results.') 27 | parser.add_argument('--epoch', type=str, required=True, help='Epoch.') 28 | 29 | 30 | args = parser.parse_args() 31 | 32 | model_name = args.model_name 33 | model_path = args.model_path 34 | output_dir = args.evaluation_folder 35 | epoch = args.epoch 36 | 37 | evaluation_dir = f"{output_dir}/{model_name}" 38 | 39 | path = "songs/road.mid" 40 | 41 | inference = Inference(model_name, model_path, evaluation_dir, epoch) 42 | inference.synthesis(path, 0) 43 | 44 | 45 | -------------------------------------------------------------------------------- /data/urmp/Vn_Fl_Tpt/train-query.lst.bk: -------------------------------------------------------------------------------- 1 | Oboe,Bassoon dataset/hdf5s/urmp/41_Miserere_fl_fl_ob_sax_bn/AuSep_3_ob_41_Miserere.h5,dataset/hdf5s/urmp/41_Miserere_fl_fl_ob_sax_bn/AuSep_5_bn_41_Miserere.h5 2 | Oboe,Bassoon dataset/hdf5s/urmp/28_Fugue_fl_ob_cl_bn/AuSep_2_ob_28_Fugue.h5,dataset/hdf5s/urmp/28_Fugue_fl_ob_cl_bn/AuSep_4_bn_28_Fugue.h5 3 | Tuba,Horn dataset/hdf5s/urmp/42_Arioso_tpt_tpt_hn_tbn_tba/AuSep_5_tba_42_Arioso.h5,dataset/hdf5s/urmp/42_Arioso_tpt_tpt_hn_tbn_tba/AuSep_3_hn_42_Arioso.h5 4 | Tuba,Horn dataset/hdf5s/urmp/43_Chorale_tpt_tpt_hn_tbn_tba/AuSep_5_tba_43_Chorale.h5,dataset/hdf5s/urmp/43_Chorale_tpt_tpt_hn_tbn_tba/AuSep_3_hn_43_Chorale.h5 5 | Violin,Clarinet dataset/hdf5s/urmp/32_Fugue_vn_vn_va_vc/AuSep_2_vn_32_Fugue.h5,dataset/hdf5s/urmp/28_Fugue_fl_ob_cl_bn/AuSep_3_cl_28_Fugue.h5 6 | Saxophone,Flute dataset/hdf5s/urmp/23_Rejouissance_cl_sax_tba/AuSep_2_sax_23_Rejouissance.h5,dataset/hdf5s/urmp/37_Rondeau_fl_vn_va_cl/AuSep_1_fl_37_Rondeau.h5 7 | Viola,Cello dataset/hdf5s/urmp/32_Fugue_vn_vn_va_vc/AuSep_3_va_32_Fugue.h5,dataset/hdf5s/urmp/32_Fugue_vn_vn_va_vc/AuSep_4_vc_32_Fugue.h5 8 | Trumpet,Trombone dataset/hdf5s/urmp/18_Nocturne_vn_fl_tpt/AuSep_3_tpt_18_Nocturne.h5,dataset/hdf5s/urmp/21_Rejouissance_cl_tbn_tba/AuSep_2_tbn_21_Rejouissance.h5 9 | Violin,Double_bass dataset/hdf5s/urmp/32_Fugue_vn_vn_va_vc/AuSep_2_vn_32_Fugue.h5,dataset/hdf5s/urmp/38_Jerusalem_vn_vn_va_vc_db/AuSep_5_db_38_Jerusalem.h5 10 | Trombone,Tuba dataset/hdf5s/urmp/34_Fugue_tpt_tpt_hn_tbn/AuSep_4_tbn_34_Fugue.h5,dataset/hdf5s/urmp/43_Chorale_tpt_tpt_hn_tbn_tba/AuSep_5_tba_43_Chorale.h5 11 | Trumpet,Horn dataset/hdf5s/urmp/18_Nocturne_vn_fl_tpt/AuSep_3_tpt_18_Nocturne.h5,dataset/hdf5s/urmp/43_Chorale_tpt_tpt_hn_tbn_tba/AuSep_3_hn_43_Chorale.h5 12 | Flute,Oboe dataset/hdf5s/urmp/37_Rondeau_fl_vn_va_cl/AuSep_1_fl_37_Rondeau.h5,dataset/hdf5s/urmp/41_Miserere_fl_fl_ob_sax_bn/AuSep_3_ob_41_Miserere.h5 13 | Clarinet,Bassoon dataset/hdf5s/urmp/37_Rondeau_fl_vn_va_cl/AuSep_4_cl_37_Rondeau.h5,dataset/hdf5s/urmp/41_Miserere_fl_fl_ob_sax_bn/AuSep_5_bn_41_Miserere.h5 14 | -------------------------------------------------------------------------------- /src/models/model_factory.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import torch.nn as nn 4 | import torch.nn.functional as F 5 | import sys 6 | import numpy as np 7 | import time 8 | import h5py 9 | from torchlibrosa.stft import STFT, ISTFT, magphase 10 | 11 | from utils.utilities import (read_lst, read_config) 12 | from models.models import (AMTBaseline, MSSBaseline, MultiTaskBaseline, DisentanglementModel) 13 | 14 | from conf.feature import * 15 | 16 | et = 1e-8 17 | 18 | class ModelFactory(nn.Module): 19 | 20 | def __init__(self, model_name): 21 | super(ModelFactory, self).__init__() 22 | 23 | self.stft = STFT(n_fft=WINDOW_SIZE, hop_length=HOP_SIZE, 24 | win_length=WINDOW_SIZE, window=WINDOW, center=True, 25 | pad_mode=PAD_MODE, freeze_parameters=True) 26 | 27 | self.istft = ISTFT(n_fft=WINDOW_SIZE, hop_length=HOP_SIZE, 28 | win_length=WINDOW_SIZE, window=WINDOW, center=True, 29 | pad_mode=PAD_MODE, freeze_parameters=True) 30 | 31 | 32 | if model_name in ['AMT', 'AMTBaseline']: 33 | network = AMTBaseline() 34 | elif model_name in ['MSS', 'MSSBaseline']: 35 | network = MSSBaseline() 36 | elif model_name in ['MSS-AMT', 'MultiTaskBaseline']: 37 | network = MultiTaskBaseline() 38 | elif model_name in ['MSI', 'MSI-DIS', 'DisentanglementModel']: 39 | network = DisentanglementModel() 40 | 41 | self.network = network 42 | 43 | def wav2spec(self, input): 44 | channels_num = input.shape[-2] 45 | 46 | def spectrogram(input): 47 | (real, imag) = self.stft(input) 48 | spec = (real ** 2 + imag ** 2) ** 0.5 49 | return spec 50 | 51 | spec_list = [] 52 | 53 | for channel in range(channels_num): 54 | spec = spectrogram(input[:, channel, :]) 55 | spec_list.append(spec) 56 | 57 | spec = torch.cat(spec_list, 1)[:, :, :, :-1] 58 | return spec 59 | 60 | def forward(self, input, mode=None): 61 | if mode == "wav2spec": 62 | spec = self.wav2spec(input) 63 | return spec 64 | return self.network(input) if mode is None else self.network(input, mode) 65 | 66 | 67 | if __name__ == '__main__': 68 | model_name = 'MSI-DIS' 69 | model = ModelFactory(model_name) 70 | 71 | 72 | -------------------------------------------------------------------------------- /src/utils/target_process.py: -------------------------------------------------------------------------------- 1 | import os 2 | import h5py 3 | import numpy as np 4 | import random 5 | 6 | 7 | class TargetProcessor(object): 8 | '''target process''' 9 | 10 | def __init__(self, segment_seconds, frames_per_second, begin_note, classes_num): 11 | self.segment_seconds = segment_seconds 12 | self.frames_per_second = frames_per_second 13 | self.begin_note = begin_note 14 | self.classes_num = classes_num 15 | self.max_piano_note = self.classes_num - 1 16 | 17 | def process(self, start_time, midi_events_time): 18 | 19 | for midi_events_time_st, events in enumerate(midi_events_time): 20 | if events[1] >= start_time: 21 | break 22 | 23 | frames_per_second = self.frames_per_second 24 | segment_seconds = self.segment_seconds 25 | begin_note = self.begin_note 26 | segment_frames = segment_seconds * frames_per_second 27 | classes_num = self.classes_num 28 | end_time = start_time + segment_seconds 29 | #mask_segments = [] 30 | frame_roll = np.ones([int(segment_frames) + 1]) * classes_num 31 | onset_offset = np.zeros([int(segment_frames) + 1]) 32 | #frame_roll_mask = np.ones([int(segment_frames) + 1]) * classes_num 33 | for i in range(midi_events_time_st, midi_events_time.shape[0]): 34 | st = midi_events_time[i][0] 35 | ed = midi_events_time[i][1] 36 | with_onset = True 37 | with_offset = True 38 | 39 | assert ed >= st 40 | 41 | if st > end_time: 42 | break 43 | if st < start_time: 44 | st = start_time 45 | with_onset = False 46 | 47 | if ed > end_time: 48 | ed = end_time 49 | with_offset = False 50 | 51 | 52 | note = int(midi_events_time[i][2]) 53 | 54 | st = int((st - start_time)* frames_per_second) 55 | ed = int((ed - start_time)* frames_per_second) 56 | if ed <= st: 57 | ed = st + 1 58 | frame_roll[st : ed] = np.clip(note, 0, classes_num - 1) 59 | duration = ed - st 60 | 61 | if with_onset: 62 | onset_offset[st] = 1 63 | if with_offset: 64 | onset_offset[ed - 1] = 2 65 | 66 | target_dict = {} 67 | target_dict['frame_roll'] = frame_roll 68 | target_dict['onset_offset'] = onset_offset 69 | return target_dict 70 | -------------------------------------------------------------------------------- /evaluate.py: -------------------------------------------------------------------------------- 1 | import time 2 | import torch 3 | import torch.nn.functional as F 4 | import os 5 | import sys 6 | import argparse 7 | import numpy as np 8 | import torch.nn as nn 9 | 10 | import src 11 | from utils.utilities import (save_json, compute_time, print_dict, mkdir) 12 | from inference.inference import Inference 13 | from inference.compute_measure import (evaluate_transcription, evaluate_separation) 14 | 15 | if __name__=='__main__': 16 | 17 | parser = argparse.ArgumentParser(description='') 18 | parser.add_argument('--model_name', type=str, required=True, help='Model name in \ 19 | [`AMT` for trainscription-only baseline, \ 20 | `MSS` for separation-only baseline, \ 21 | `MSS-AMT` for multi-task baseline, \ 22 | `MSI` for the proposed multi-task score-informed model, \ 23 | `MSI-DIS` for the proposed multi-task score-informed with further disentanglement model].') 24 | parser.add_argument('--model_path', type=str, required=True, help='Model weights path.') 25 | parser.add_argument('--evaluation_folder', type=str, required=True, help='Directory to store evaluation results.') 26 | parser.add_argument('--epoch', type=str, required=True, help='Epoch.') 27 | parser.add_argument('--ps', type=int, required=True, help='Processes number.') 28 | 29 | args = parser.parse_args() 30 | 31 | model_name = args.model_name 32 | model_path = args.model_path 33 | output_dir = args.evaluation_folder 34 | epoch = args.epoch 35 | processes_num = args.ps 36 | 37 | evaluation_dir = f"{output_dir}/{model_name}" 38 | 39 | inference = Inference(model_name, model_path, evaluation_dir, epoch) 40 | preds = inference.inference() 41 | 42 | scores = {} 43 | for i, mode in enumerate(preds): 44 | scores[mode] = {} 45 | pred = preds[mode] 46 | if mode in ["AMT", "MSS-AMT", "MSI", "MSI-DIS"]: 47 | scores[mode]["transcription"] = evaluate_transcription(pred, processes_num=processes_num) 48 | if mode in ["MSS", "MSS-AMT", "MSI", "MSI-S", "MSI-MSI", "MSI-DIS", "MSI-DIS-S"]: 49 | scores[mode]["separation"] = evaluate_separation(pred, processes_num=processes_num) 50 | save_json(inference.score_path, scores) 51 | 52 | -------------------------------------------------------------------------------- /src/analyze/utilities.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import time 4 | 5 | sys.path.insert(1, os.path.join(sys.path[0], '../..')) 6 | 7 | import src 8 | from utils.utilities import (load_json) 9 | import numpy as np 10 | 11 | 12 | BOUND = 1 13 | 14 | INSTRUMENTS = {"seen": "Violin,Cello,Viola,Flute,Clarinet,Saxophone,Trumpet,Trombone", 15 | "unseen": "Horn,Tuba,Double_Bass,Bassoon,Oboe"} 16 | 17 | skip_instrs = [] 18 | 19 | for instr in INSTRUMENTS: 20 | INSTRUMENTS[instr] = INSTRUMENTS[instr].split(',') 21 | 22 | def seen(instr): 23 | for seen in INSTRUMENTS: 24 | if instr in INSTRUMENTS[seen]: 25 | return seen 26 | 27 | def ave_val(x): 28 | return np.mean(x) 29 | 30 | def compute_results(json_data): 31 | scores = {} 32 | for mode in json_data: 33 | scores[mode] = {} 34 | data_per_mode = json_data[mode] 35 | for sheet_name in data_per_mode: 36 | results = {"seen-seen": [], "seen-unseen" : [], "unseen-unseen" : [], "seen" : [], "unseen" : [], 'all' : []} 37 | sheet_data = data_per_mode[sheet_name] 38 | for row in sheet_data: 39 | pairs = [] 40 | tags = [] 41 | for j, instr in enumerate(row): 42 | if instr in skip_instrs: 43 | break 44 | if instr not in results: 45 | results[instr] = [] 46 | results[instr].append(float(row[instr])) 47 | seen_tag = seen(instr) 48 | pairs.append(float(row[instr])) 49 | tags.append(seen_tag) 50 | results[seen_tag].append(float(row[instr])) 51 | results["all"].append(float(row[instr])) 52 | 53 | if len(tags) < 2: 54 | continue 55 | seen_tag = '-'.join(tags) 56 | seen_tag = "seen-unseen" if seen_tag == "unseen-seen" else seen_tag 57 | 58 | results[seen_tag] += pairs 59 | 60 | for seen_tag in results: 61 | results[seen_tag] = ave_val(results[seen_tag]) 62 | 63 | scores[mode][sheet_name] = results 64 | 65 | return scores 66 | 67 | def get_json_data(score_path): 68 | #score_path = f"evaluation/demo/{model_name}/scores-{epoch}.json" 69 | json_data = load_json(score_path) 70 | return compute_results(json_data) 71 | 72 | def get_results(scores): 73 | return [scores["seen"], scores["unseen"], scores["all"]] 74 | 75 | 76 | def example(model_name, sheet_name, epoch): 77 | score_path = f"evaluation/{model_name}/scores-{epoch}.json" 78 | json_data = load_json(score_path) 79 | scores = compute_results(json_data) 80 | print(scores[model_name][sheet_name]["seen"], scores[model_name][sheet_name]["unseen"], scores[model_name][sheet_name]["all"]) 81 | 82 | if __name__=="__main__": 83 | model_name = "MSS" 84 | sheet_name = "separation" 85 | 86 | #model_name = "AMT" 87 | #sheet_name = "transcription" 88 | 89 | for i in range(80): 90 | epoch = i 91 | example(model_name, sheet_name, epoch) 92 | 93 | 94 | -------------------------------------------------------------------------------- /src/analyze/draw_table.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import os 3 | import sys 4 | import argparse 5 | 6 | sys.path.insert(1, os.path.join(sys.path[0], '../..')) 7 | 8 | import src 9 | from analyze.utilities import (get_json_data, get_results) 10 | from utils.utilities import (mkdir) 11 | 12 | skip = ["MSI-S", "MSI-DIS-S", "DMSI-S"] 13 | 14 | 15 | def get_data_with_last_10_epochs(folder, model_name): 16 | folder = os.path.join(folder, f"{model_name}") 17 | if not os.path.exists(folder): 18 | return {} 19 | 20 | files = os.listdir(folder) 21 | 22 | data = {"transcription" : {}, "separation" : {}} 23 | for sheet_name in data: 24 | data[sheet_name] = {"seen" : {}, "unseen" : {}, "all" : {}} 25 | 26 | for f in files: 27 | if not str.startswith(f, "score"): 28 | continue 29 | 30 | epoch = int(f.split(".")[0].split("-")[1]) 31 | 32 | assert epoch >= 190 and epoch < 200 33 | 34 | path = os.path.join(folder, f) 35 | json_data = get_json_data(path) 36 | 37 | for mode in json_data: 38 | for sheet_name in json_data[mode]: 39 | sheet_data = json_data[mode][sheet_name] 40 | for i, tag in enumerate(data[sheet_name]): 41 | if mode not in data[sheet_name][tag]: 42 | data[sheet_name][tag][mode] = [] 43 | data[sheet_name][tag][mode].append([epoch, sheet_data[tag]]) 44 | 45 | def cmp(item): 46 | return item[0] 47 | 48 | for sheet_name in data: 49 | for tag in data[sheet_name]: 50 | for mode in data[sheet_name][tag]: 51 | data[sheet_name][tag][mode].sort(key=cmp) 52 | 53 | return data 54 | 55 | def draw_table(data): 56 | 57 | for sheet_name in data: 58 | sheet_data = data[sheet_name] 59 | for i, tag in enumerate(sheet_data): 60 | tag_data = sheet_data[tag] 61 | for mode in tag_data: 62 | if mode in skip: 63 | continue 64 | results = [c[1] for c in tag_data[mode]] 65 | assert len(results) == 10 66 | mu = np.mean(results) 67 | pstd = np.sqrt(((results-mu) * (results-mu)).sum()) 68 | interv = pstd * 1.96 / 10 69 | mu = round(mu, 2) 70 | interv = round(interv, 2) 71 | print(mode, f"&${mu}\pm{interv}$") 72 | 73 | def get_data(evaluation_folder): 74 | model_names = ["MSS", "AMT", "MSS-AMT", "MSI", "MSI-DIS"] 75 | results = {} 76 | for model_name in model_names: 77 | data = get_data_with_last_10_epochs(evaluation_folder, model_name) 78 | for sheet_name in data: 79 | if sheet_name not in results: 80 | results[sheet_name] = {} 81 | for seen_tag in data[sheet_name]: 82 | if seen_tag not in results[sheet_name]: 83 | results[sheet_name][seen_tag] = {} 84 | for mode in data[sheet_name][seen_tag]: 85 | results[sheet_name][seen_tag][mode] = data[sheet_name][seen_tag][mode] 86 | return results 87 | 88 | 89 | if __name__=="__main__": 90 | parser = argparse.ArgumentParser(description='') 91 | parser.add_argument('--evaluation_folder', type=str, required=True, help='Directory to store evaluation results.') 92 | 93 | args = parser.parse_args() 94 | 95 | evaluation_folder = args.evaluation_folder 96 | data = get_data(evaluation_folder) 97 | draw_table(data) 98 | -------------------------------------------------------------------------------- /src/dataset/urmp/urmp_test.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import librosa 3 | import sys 4 | import os 5 | import h5py 6 | 7 | from utils.utilities import (parse_frameroll2annotation, read_lst, read_config, write_lst, mkdir, int16_to_float32) 8 | from conf.feature import * 9 | from conf.inference import * 10 | 11 | class UrmpTest(object): 12 | def __init__(self): 13 | file_lst = read_lst(TEST_DATA_LST_PATH) 14 | query_lst = read_lst(TEST_QUERY_LST_PATH) 15 | data_path = [] 16 | for i, f in enumerate(file_lst): 17 | fs = f.split('\t') 18 | qs = query_lst[i].split('\t') 19 | instruments = fs[0].split(',') 20 | files = fs[1].split(',') 21 | query = qs[1].split(',') 22 | sample_name = str.replace(files[0].split('_')[-2], '.' ,'') 23 | sample = {} 24 | for j, instr in enumerate(instruments): 25 | sample[instr] = {} 26 | sample[instr]['ref'] = files[j] 27 | sample[instr]['query'] = query[j] 28 | 29 | data_path.append({'sample_name' : sample_name, 'instrs' : sample}) 30 | 31 | self.data_path = data_path 32 | 33 | 34 | def vad(self, x, frame_roll, frames_per_second=FRAMES_PER_SEC, sample_rate=SAMPLE_RATE, notes_num=NOTES_NUM_EXCLUDE_SILENCE): 35 | 36 | frames_per_sample = frames_per_second * 1. / sample_rate 37 | 38 | if len(x.shape) == 2: 39 | y = x[0] 40 | else: 41 | y = x 42 | output = np.zeros_like(y) 43 | frame_roll_len = int(y.shape[-1] / sample_rate * frames_per_second + 1) 44 | frame_roll = frame_roll[ : frame_roll_len] 45 | new_frame_roll = np.zeros_like(frame_roll) + notes_num 46 | split_index = librosa.effects.split(y, top_db=18) 47 | st = 0 48 | ed = 0 49 | for index in split_index: 50 | ed = st + index[1] - index[0] 51 | output[st : ed] = y[index[0] : index[1]] 52 | offset = (index[1] - index[0]) * frames_per_sample 53 | ori_st = int(st * frames_per_sample) 54 | ori_ed = int(st * frames_per_sample + offset) 55 | obj_st = int(index[0] * frames_per_sample) 56 | obj_ed = int(index[0] * frames_per_sample + offset) 57 | offset = (ori_ed - ori_st) if ori_ed - ori_st < ed - st else obj_ed - obj_st 58 | new_frame_roll[ori_st : ori_st + offset] = frame_roll[obj_st : obj_st + offset] 59 | st = ed 60 | output = output[:ed] 61 | new_frame_roll = new_frame_roll[: int(ed * frames_per_sample)] 62 | 63 | if len(x.shape) == 2: 64 | output = output[None, :] 65 | return output, new_frame_roll 66 | 67 | 68 | def test_samples(self): 69 | for data in self.data_path: 70 | sample_name = data['sample_name'] 71 | sample = data['instrs'] 72 | samples = [] 73 | mix = [] 74 | for instr in sample: 75 | ref = sample[instr]['ref'] 76 | queries = sample[instr]['query'].split(' ') 77 | query = [] 78 | tr_query = [] 79 | for q in queries: 80 | with h5py.File(q, 'r') as hf: 81 | waveform = int16_to_float32(hf['waveform'][:])[None, :] 82 | frame_roll = hf['frame_roll'][:].astype(np.int) 83 | 84 | waveform, frame_roll = self.vad(waveform, frame_roll) 85 | query.append(waveform) 86 | tr_query.append(parse_frameroll2annotation(frame_roll)) 87 | 88 | with h5py.File(ref, 'r') as hf: 89 | wav_ref = int16_to_float32(hf['waveform'][:])[None, :] 90 | tr_ref = hf['note_annotations_txt'][0].decode() 91 | frame_roll = hf['frame_roll'][:].astype(np.int) 92 | 93 | samples.append([instr, wav_ref, tr_ref, frame_roll, query, tr_query]) 94 | 95 | ref_len = samples[0][1].shape[-1] 96 | for i, ref in enumerate(samples): 97 | ref_len = ref[1].shape[-1] if ref_len > ref[1].shape[-1] else ref_len 98 | 99 | samples = [[s[0], s[1][:, :ref_len]] + s[2:] for s in samples] 100 | mix = [s[1] for s in samples] 101 | mix = np.stack(mix, 0) 102 | test_sample = {'mix' : mix, 'sample_name': sample_name, 'instrs' : samples} 103 | yield test_sample 104 | 105 | -------------------------------------------------------------------------------- /data/urmp/testset/query.lst: -------------------------------------------------------------------------------- 1 | Violin,Saxophone dataset/hdf5s/urmp/35_Rondeau_vn_vn_va_db/AuSep_2_vn_35_Rondeau.h5,dataset/hdf5s/urmp/30_Fugue_fl_fl_ob_sax/AuSep_4_sax_30_Fugue.h5 2 | Violin,Double_Bass dataset/hdf5s/urmp/35_Rondeau_vn_vn_va_db/AuSep_2_vn_35_Rondeau.h5,dataset/hdf5s/urmp/35_Rondeau_vn_vn_va_db/AuSep_4_db_35_Rondeau.h5 3 | Cello,Viola dataset/hdf5s/urmp/11_Maria_ob_vc/AuSep_2_vc_11_Maria.h5,dataset/hdf5s/urmp/32_Fugue_vn_vn_va_vc/AuSep_3_va_32_Fugue.h5 4 | Violin,Double_Bass dataset/hdf5s/urmp/39_Jerusalem_vn_vn_va_sax_db/AuSep_1_vn_39_Jerusalem.h5,dataset/hdf5s/urmp/39_Jerusalem_vn_vn_va_sax_db/AuSep_5_db_39_Jerusalem.h5 5 | Violin,Cello dataset/hdf5s/urmp/39_Jerusalem_vn_vn_va_sax_db/AuSep_1_vn_39_Jerusalem.h5,dataset/hdf5s/urmp/11_Maria_ob_vc/AuSep_2_vc_11_Maria.h5 6 | Viola,Clarinet dataset/hdf5s/urmp/39_Jerusalem_vn_vn_va_sax_db/AuSep_3_va_39_Jerusalem.h5,dataset/hdf5s/urmp/23_Rejouissance_cl_sax_tba/AuSep_1_cl_23_Rejouissance.h5 7 | Flute,Violin dataset/hdf5s/urmp/30_Fugue_fl_fl_ob_sax/AuSep_1_fl_30_Fugue.h5,dataset/hdf5s/urmp/39_Jerusalem_vn_vn_va_sax_db/AuSep_1_vn_39_Jerusalem.h5 8 | Saxophone,Clarinet dataset/hdf5s/urmp/41_Miserere_fl_fl_ob_sax_bn/AuSep_4_sax_41_Miserere.h5,dataset/hdf5s/urmp/40_Miserere_fl_fl_ob_cl_bn/AuSep_4_cl_40_Miserere.h5 9 | Saxophone,Tuba dataset/hdf5s/urmp/39_Jerusalem_vn_vn_va_sax_db/AuSep_4_sax_39_Jerusalem.h5,dataset/hdf5s/urmp/43_Chorale_tpt_tpt_hn_tbn_tba/AuSep_5_tba_43_Chorale.h5 10 | Trombone,Saxophone dataset/hdf5s/urmp/43_Chorale_tpt_tpt_hn_tbn_tba/AuSep_4_tbn_43_Chorale.h5,dataset/hdf5s/urmp/39_Jerusalem_vn_vn_va_sax_db/AuSep_4_sax_39_Jerusalem.h5 11 | Oboe,Saxophone dataset/hdf5s/urmp/11_Maria_ob_vc/AuSep_1_ob_11_Maria.h5,dataset/hdf5s/urmp/22_Rejouissance_sax_tbn_tba/AuSep_1_sax_22_Rejouissance.h5 12 | Oboe,Viola dataset/hdf5s/urmp/11_Maria_ob_vc/AuSep_1_ob_11_Maria.h5,dataset/hdf5s/urmp/35_Rondeau_vn_vn_va_db/AuSep_3_va_35_Rondeau.h5 13 | Flute,Cello dataset/hdf5s/urmp/37_Rondeau_fl_vn_va_cl/AuSep_1_fl_37_Rondeau.h5,dataset/hdf5s/urmp/36_Rondeau_vn_vn_va_vc/AuSep_4_vc_36_Rondeau.h5 14 | Flute,Trombone dataset/hdf5s/urmp/40_Miserere_fl_fl_ob_cl_bn/AuSep_1_fl_40_Miserere.h5,dataset/hdf5s/urmp/43_Chorale_tpt_tpt_hn_tbn_tba/AuSep_4_tbn_43_Chorale.h5 15 | Clarinet,Horn dataset/hdf5s/urmp/37_Rondeau_fl_vn_va_cl/AuSep_4_cl_37_Rondeau.h5,dataset/hdf5s/urmp/31_Slavonic_tpt_tpt_hn_tbn/AuSep_3_hn_31_Slavonic.h5 16 | Clarinet,Bassoon dataset/hdf5s/urmp/40_Miserere_fl_fl_ob_cl_bn/AuSep_4_cl_40_Miserere.h5,dataset/hdf5s/urmp/40_Miserere_fl_fl_ob_cl_bn/AuSep_5_bn_40_Miserere.h5 17 | Trumpet,Violin dataset/hdf5s/urmp/42_Arioso_tpt_tpt_hn_tbn_tba/AuSep_2_tpt_42_Arioso.h5,dataset/hdf5s/urmp/35_Rondeau_vn_vn_va_db/AuSep_2_vn_35_Rondeau.h5 18 | Trumpet,Violin dataset/hdf5s/urmp/43_Chorale_tpt_tpt_hn_tbn_tba/AuSep_1_tpt_43_Chorale.h5,dataset/hdf5s/urmp/35_Rondeau_vn_vn_va_db/AuSep_2_vn_35_Rondeau.h5 19 | Flute,Clarinet dataset/hdf5s/urmp/37_Rondeau_fl_vn_va_cl/AuSep_1_fl_37_Rondeau.h5,dataset/hdf5s/urmp/29_Fugue_fl_fl_ob_cl/AuSep_4_cl_29_Fugue.h5 20 | Flute,Saxophone dataset/hdf5s/urmp/37_Rondeau_fl_vn_va_cl/AuSep_1_fl_37_Rondeau.h5,dataset/hdf5s/urmp/30_Fugue_fl_fl_ob_sax/AuSep_4_sax_30_Fugue.h5 21 | Bassoon,Oboe dataset/hdf5s/urmp/28_Fugue_fl_ob_cl_bn/AuSep_4_bn_28_Fugue.h5,dataset/hdf5s/urmp/30_Fugue_fl_fl_ob_sax/AuSep_3_ob_30_Fugue.h5 22 | Trumpet,Trombone dataset/hdf5s/urmp/42_Arioso_tpt_tpt_hn_tbn_tba/AuSep_2_tpt_42_Arioso.h5,dataset/hdf5s/urmp/33_Elise_tpt_tpt_hn_tbn/AuSep_4_tbn_33_Elise.h5 23 | Trumpet,Horn dataset/hdf5s/urmp/31_Slavonic_tpt_tpt_hn_tbn/AuSep_2_tpt_31_Slavonic.h5,dataset/hdf5s/urmp/34_Fugue_tpt_tpt_hn_tbn/AuSep_3_hn_34_Fugue.h5 24 | Tuba,Trumpet dataset/hdf5s/urmp/42_Arioso_tpt_tpt_hn_tbn_tba/AuSep_5_tba_42_Arioso.h5,dataset/hdf5s/urmp/42_Arioso_tpt_tpt_hn_tbn_tba/AuSep_2_tpt_42_Arioso.h5 25 | Trumpet,Trombone dataset/hdf5s/urmp/43_Chorale_tpt_tpt_hn_tbn_tba/AuSep_1_tpt_43_Chorale.h5,dataset/hdf5s/urmp/21_Rejouissance_cl_tbn_tba/AuSep_2_tbn_21_Rejouissance.h5 26 | Trumpet,Horn dataset/hdf5s/urmp/31_Slavonic_tpt_tpt_hn_tbn/AuSep_2_tpt_31_Slavonic.h5,dataset/hdf5s/urmp/42_Arioso_tpt_tpt_hn_tbn_tba/AuSep_3_hn_42_Arioso.h5 27 | Cello,Oboe dataset/hdf5s/urmp/36_Rondeau_vn_vn_va_vc/AuSep_4_vc_36_Rondeau.h5,dataset/hdf5s/urmp/30_Fugue_fl_fl_ob_sax/AuSep_3_ob_30_Fugue.h5 28 | Trumpet,Tuba dataset/hdf5s/urmp/33_Elise_tpt_tpt_hn_tbn/AuSep_2_tpt_33_Elise.h5,dataset/hdf5s/urmp/43_Chorale_tpt_tpt_hn_tbn_tba/AuSep_5_tba_43_Chorale.h5 29 | Trumpet,Trombone dataset/hdf5s/urmp/34_Fugue_tpt_tpt_hn_tbn/AuSep_1_tpt_34_Fugue.h5,dataset/hdf5s/urmp/33_Elise_tpt_tpt_hn_tbn/AuSep_4_tbn_33_Elise.h5 30 | Horn,Trumpet dataset/hdf5s/urmp/33_Elise_tpt_tpt_hn_tbn/AuSep_3_hn_33_Elise.h5,dataset/hdf5s/urmp/43_Chorale_tpt_tpt_hn_tbn_tba/AuSep_1_tpt_43_Chorale.h5 31 | Trumpet,Horn dataset/hdf5s/urmp/42_Arioso_tpt_tpt_hn_tbn_tba/AuSep_2_tpt_42_Arioso.h5,dataset/hdf5s/urmp/33_Elise_tpt_tpt_hn_tbn/AuSep_3_hn_33_Elise.h5 32 | Trumpet,Trombone dataset/hdf5s/urmp/33_Elise_tpt_tpt_hn_tbn/AuSep_2_tpt_33_Elise.h5,dataset/hdf5s/urmp/42_Arioso_tpt_tpt_hn_tbn_tba/AuSep_4_tbn_42_Arioso.h5 -------------------------------------------------------------------------------- /data/urmp/testset/test.lst: -------------------------------------------------------------------------------- 1 | Violin,Saxophone dataset/hdf5s/urmp/39_Jerusalem_vn_vn_va_sax_db/AuSep_1_vn_39_Jerusalem.h5,dataset/hdf5s/urmp/39_Jerusalem_vn_vn_va_sax_db/AuSep_4_sax_39_Jerusalem.h5 2 | Violin,Double_Bass dataset/hdf5s/urmp/39_Jerusalem_vn_vn_va_sax_db/AuSep_2_vn_39_Jerusalem.h5,dataset/hdf5s/urmp/39_Jerusalem_vn_vn_va_sax_db/AuSep_5_db_39_Jerusalem.h5 3 | Cello,Viola dataset/hdf5s/urmp/38_Jerusalem_vn_vn_va_vc_db/AuSep_4_vc_38_Jerusalem.h5,dataset/hdf5s/urmp/39_Jerusalem_vn_vn_va_sax_db/AuSep_3_va_39_Jerusalem.h5 4 | Violin,Double_Bass dataset/hdf5s/urmp/35_Rondeau_vn_vn_va_db/AuSep_2_vn_35_Rondeau.h5,dataset/hdf5s/urmp/35_Rondeau_vn_vn_va_db/AuSep_4_db_35_Rondeau.h5 5 | Violin,Cello dataset/hdf5s/urmp/35_Rondeau_vn_vn_va_db/AuSep_1_vn_35_Rondeau.h5,dataset/hdf5s/urmp/36_Rondeau_vn_vn_va_vc/AuSep_4_vc_36_Rondeau.h5 6 | Viola,Clarinet dataset/hdf5s/urmp/35_Rondeau_vn_vn_va_db/AuSep_3_va_35_Rondeau.h5,dataset/hdf5s/urmp/37_Rondeau_fl_vn_va_cl/AuSep_4_cl_37_Rondeau.h5 7 | Flute,Violin dataset/hdf5s/urmp/37_Rondeau_fl_vn_va_cl/AuSep_1_fl_37_Rondeau.h5,dataset/hdf5s/urmp/35_Rondeau_vn_vn_va_db/AuSep_2_vn_35_Rondeau.h5 8 | Saxophone,Clarinet dataset/hdf5s/urmp/22_Rejouissance_sax_tbn_tba/AuSep_1_sax_22_Rejouissance.h5,dataset/hdf5s/urmp/23_Rejouissance_cl_sax_tba/AuSep_1_cl_23_Rejouissance.h5 9 | Saxophone,Tuba dataset/hdf5s/urmp/23_Rejouissance_cl_sax_tba/AuSep_2_sax_23_Rejouissance.h5,dataset/hdf5s/urmp/23_Rejouissance_cl_sax_tba/AuSep_3_tba_23_Rejouissance.h5 10 | Trombone,Saxophone dataset/hdf5s/urmp/21_Rejouissance_cl_tbn_tba/AuSep_2_tbn_21_Rejouissance.h5,dataset/hdf5s/urmp/22_Rejouissance_sax_tbn_tba/AuSep_1_sax_22_Rejouissance.h5 11 | Oboe,Saxophone dataset/hdf5s/urmp/30_Fugue_fl_fl_ob_sax/AuSep_3_ob_30_Fugue.h5,dataset/hdf5s/urmp/30_Fugue_fl_fl_ob_sax/AuSep_4_sax_30_Fugue.h5 12 | Oboe,Viola dataset/hdf5s/urmp/28_Fugue_fl_ob_cl_bn/AuSep_2_ob_28_Fugue.h5,dataset/hdf5s/urmp/32_Fugue_vn_vn_va_vc/AuSep_3_va_32_Fugue.h5 13 | Flute,Cello dataset/hdf5s/urmp/30_Fugue_fl_fl_ob_sax/AuSep_1_fl_30_Fugue.h5,dataset/hdf5s/urmp/32_Fugue_vn_vn_va_vc/AuSep_4_vc_32_Fugue.h5 14 | Flute,Trombone dataset/hdf5s/urmp/30_Fugue_fl_fl_ob_sax/AuSep_2_fl_30_Fugue.h5,dataset/hdf5s/urmp/34_Fugue_tpt_tpt_hn_tbn/AuSep_4_tbn_34_Fugue.h5 15 | Clarinet,Horn dataset/hdf5s/urmp/29_Fugue_fl_fl_ob_cl/AuSep_4_cl_29_Fugue.h5,dataset/hdf5s/urmp/34_Fugue_tpt_tpt_hn_tbn/AuSep_3_hn_34_Fugue.h5 16 | Clarinet,Bassoon dataset/hdf5s/urmp/28_Fugue_fl_ob_cl_bn/AuSep_3_cl_28_Fugue.h5,dataset/hdf5s/urmp/28_Fugue_fl_ob_cl_bn/AuSep_4_bn_28_Fugue.h5 17 | Trumpet,Violin dataset/hdf5s/urmp/34_Fugue_tpt_tpt_hn_tbn/AuSep_1_tpt_34_Fugue.h5,dataset/hdf5s/urmp/32_Fugue_vn_vn_va_vc/AuSep_1_vn_32_Fugue.h5 18 | Trumpet,Violin dataset/hdf5s/urmp/34_Fugue_tpt_tpt_hn_tbn/AuSep_2_tpt_34_Fugue.h5,dataset/hdf5s/urmp/32_Fugue_vn_vn_va_vc/AuSep_2_vn_32_Fugue.h5 19 | Flute,Clarinet dataset/hdf5s/urmp/40_Miserere_fl_fl_ob_cl_bn/AuSep_1_fl_40_Miserere.h5,dataset/hdf5s/urmp/40_Miserere_fl_fl_ob_cl_bn/AuSep_4_cl_40_Miserere.h5 20 | Flute,Saxophone dataset/hdf5s/urmp/40_Miserere_fl_fl_ob_cl_bn/AuSep_2_fl_40_Miserere.h5,dataset/hdf5s/urmp/41_Miserere_fl_fl_ob_sax_bn/AuSep_4_sax_41_Miserere.h5 21 | Bassoon,Oboe dataset/hdf5s/urmp/40_Miserere_fl_fl_ob_cl_bn/AuSep_5_bn_40_Miserere.h5,dataset/hdf5s/urmp/40_Miserere_fl_fl_ob_cl_bn/AuSep_3_ob_40_Miserere.h5 22 | Trumpet,Trombone dataset/hdf5s/urmp/43_Chorale_tpt_tpt_hn_tbn_tba/AuSep_1_tpt_43_Chorale.h5,dataset/hdf5s/urmp/43_Chorale_tpt_tpt_hn_tbn_tba/AuSep_4_tbn_43_Chorale.h5 23 | Trumpet,Horn dataset/hdf5s/urmp/43_Chorale_tpt_tpt_hn_tbn_tba/AuSep_2_tpt_43_Chorale.h5,dataset/hdf5s/urmp/43_Chorale_tpt_tpt_hn_tbn_tba/AuSep_3_hn_43_Chorale.h5 24 | Tuba,Trumpet dataset/hdf5s/urmp/43_Chorale_tpt_tpt_hn_tbn_tba/AuSep_5_tba_43_Chorale.h5,dataset/hdf5s/urmp/43_Chorale_tpt_tpt_hn_tbn_tba/AuSep_1_tpt_43_Chorale.h5 25 | Trumpet,Trombone dataset/hdf5s/urmp/33_Elise_tpt_tpt_hn_tbn/AuSep_2_tpt_33_Elise.h5,dataset/hdf5s/urmp/33_Elise_tpt_tpt_hn_tbn/AuSep_4_tbn_33_Elise.h5 26 | Trumpet,Horn dataset/hdf5s/urmp/33_Elise_tpt_tpt_hn_tbn/AuSep_1_tpt_33_Elise.h5,dataset/hdf5s/urmp/33_Elise_tpt_tpt_hn_tbn/AuSep_3_hn_33_Elise.h5 27 | Cello,Oboe dataset/hdf5s/urmp/11_Maria_ob_vc/AuSep_2_vc_11_Maria.h5,dataset/hdf5s/urmp/11_Maria_ob_vc/AuSep_1_ob_11_Maria.h5 28 | Trumpet,Tuba dataset/hdf5s/urmp/42_Arioso_tpt_tpt_hn_tbn_tba/AuSep_2_tpt_42_Arioso.h5,dataset/hdf5s/urmp/42_Arioso_tpt_tpt_hn_tbn_tba/AuSep_5_tba_42_Arioso.h5 29 | Trumpet,Trombone dataset/hdf5s/urmp/42_Arioso_tpt_tpt_hn_tbn_tba/AuSep_1_tpt_42_Arioso.h5,dataset/hdf5s/urmp/42_Arioso_tpt_tpt_hn_tbn_tba/AuSep_4_tbn_42_Arioso.h5 30 | Horn,Trumpet dataset/hdf5s/urmp/42_Arioso_tpt_tpt_hn_tbn_tba/AuSep_3_hn_42_Arioso.h5,dataset/hdf5s/urmp/42_Arioso_tpt_tpt_hn_tbn_tba/AuSep_2_tpt_42_Arioso.h5 31 | Trumpet,Horn dataset/hdf5s/urmp/31_Slavonic_tpt_tpt_hn_tbn/AuSep_2_tpt_31_Slavonic.h5,dataset/hdf5s/urmp/31_Slavonic_tpt_tpt_hn_tbn/AuSep_3_hn_31_Slavonic.h5 32 | Trumpet,Trombone dataset/hdf5s/urmp/31_Slavonic_tpt_tpt_hn_tbn/AuSep_1_tpt_31_Slavonic.h5,dataset/hdf5s/urmp/31_Slavonic_tpt_tpt_hn_tbn/AuSep_4_tbn_31_Slavonic.h5 -------------------------------------------------------------------------------- /src/utils/weiMidi.py: -------------------------------------------------------------------------------- 1 | from mido import MidiFile 2 | import numpy as np 3 | 4 | MAX_TICKS = 1000007 5 | FRAMES_PER_SECOND = 100 6 | NOTES_NUM = 88 7 | BEGIN_NOTE = 21 8 | GRAIN_SEC = 0.03 9 | GRAIN_FRAME = FRAMES_PER_SECOND * GRAIN_SEC 10 | 11 | 12 | def devide(msg): 13 | return str(msg).split(' ') 14 | 15 | def calculate_second(tempo, ticks_per_beat, onset_ticks, ticks): 16 | second = 0 17 | for i in range(ticks): 18 | microseconds_per_beat = tempo[onset_ticks + ticks] 19 | beats_per_second = 1e6 / microseconds_per_beat 20 | ticks_per_second = ticks_per_beat * beats_per_second 21 | second += 1. / ticks_per_second 22 | return second 23 | 24 | def read_midi(midi_path): 25 | 26 | midi_file = MidiFile(midi_path) 27 | ticks_per_beat = midi_file.ticks_per_beat 28 | 29 | #meta = {"key_signature": None, "tempo": [0, 0] } 30 | 31 | check = {} 32 | cur = 0 33 | pre_tempo = -1 34 | tempo_record = np.zeros([MAX_TICKS]) 35 | 36 | for msg in midi_file.tracks[0]: 37 | detailed_msg = devide(msg) 38 | #if msg.type == "key_signature": 39 | # meta["key_signature"] = msg.key 40 | if msg.type == "set_tempo": 41 | tempo_record[cur : cur + msg.time] = pre_tempo 42 | pre_tempo = msg.tempo 43 | cur += msg.time 44 | 45 | tempo_record[cur:] = pre_tempo 46 | 47 | tracks = [] 48 | 49 | ticks = 0 50 | time_in_seconds = [] 51 | 52 | for i, tr in enumerate(midi_file.tracks[1:]): 53 | track = [] 54 | seconds = [] 55 | second = 0. 56 | ticks = 0 57 | for msg in tr: 58 | track.append(str(msg)) 59 | second += calculate_second(tempo_record, ticks_per_beat, ticks, msg.time) 60 | ticks += msg.time 61 | seconds.append(second) 62 | 63 | tracks.append(track) 64 | time_in_seconds.append(seconds) 65 | 66 | midiTracks = [] 67 | for i, tr in enumerate(tracks): 68 | midiTrack = WeiMidiTrack(tr, time_in_seconds[i]) 69 | midiTracks.append(midiTrack) 70 | 71 | return midiTracks 72 | 73 | def frame(second): 74 | return int(second * FRAMES_PER_SECOND) 75 | 76 | def c2note(msg): 77 | return int(msg.split("=")[-1]) 78 | 79 | def c2velocity(msg): 80 | return int(msg.split("=")[-1]) 81 | 82 | def convert2frameRoll(tracks, seconds): 83 | onset_note = -1 84 | onset = 0 85 | frameRoll = np.zeros([NOTES_NUM + 1, frame(seconds[-1]) + 1]) 86 | frameRoll_pairs = [] 87 | buffer_notes = {} 88 | for i, tr in enumerate(tracks): 89 | detailed_tr = devide(tr) 90 | tag = detailed_tr[0] 91 | if tag not in ["note_on", "note_off"]: 92 | continue 93 | velocity = c2velocity(detailed_tr[3]) 94 | current_frame = frame(seconds[i]) 95 | note = c2note(detailed_tr[2]) 96 | if note >= NOTES_NUM or note < 0: 97 | continue 98 | 99 | if tag == "note_on" and velocity > 0: 100 | buffer_notes[note] = current_frame 101 | 102 | elif note in buffer_notes and buffer_notes[note] > 0: 103 | onset = buffer_notes[note] 104 | frameRoll[note - BEGIN_NOTE, onset : current_frame] = 1 105 | frameRoll_pairs.append([note - BEGIN_NOTE, onset, current_frame]) 106 | buffer_notes[note] = -1 107 | #onset = current_frame 108 | #onset_note = c2note(detailed_tr[2]) - BEGIN_NOTE if tag == "note_on" else -1 109 | 110 | #if onset_note > 0: 111 | # frameRoll[onset_note, onset:] = 1 112 | 113 | for i in range(frameRoll.shape[-1]): 114 | if frameRoll[:, i].sum() < 1: 115 | frameRoll[NOTES_NUM, i] =1 116 | 117 | return frameRoll, frameRoll_pairs 118 | 119 | def checkMono(frameRoll): 120 | cnt = 0 121 | for i in range(frameRoll.shape[-1]): 122 | if frameRoll[:, i].sum() > 1: 123 | cnt += 1 124 | else: 125 | cnt = 0 126 | if cnt > GRAIN_FRAME: 127 | return False 128 | return True 129 | 130 | 131 | class WeiMidiTrack(object): 132 | def __init__(self, midi_events, seconds): 133 | self.frameRoll, self.frameRollPair = convert2frameRoll(midi_events, seconds) 134 | self.isMono = checkMono(self.frameRoll) 135 | #print(self.isMono) 136 | 137 | def monoFrameRoll(self): 138 | assert self.isMono 139 | frameRoll = np.argmax(self.frameRoll, 0) 140 | return frameRoll 141 | 142 | class WeiMidi(object): 143 | def __init__(self, path): 144 | self.midi_path = path 145 | self.midi_tracks = read_midi(path) 146 | self.maxSec = self.get_maxSec() 147 | 148 | def get_maxSec(self): 149 | maxSec = 0 150 | for i in range(self.tracks_num()): 151 | if len(self.frameRoll_pair(i)) > 0: 152 | if len(self.frameRoll_pair(i)[-1]) > 0: 153 | sec = self.frameRoll_pair(i)[-1][-1] 154 | maxSec = maxSec if maxSec > sec else sec 155 | return maxSec 156 | 157 | def is_mono(self, n): 158 | return self.midi_tracks[n].isMono 159 | 160 | def tracks_num(self): 161 | return len(self.midi_tracks) 162 | 163 | def frameRoll_pair(self, n): 164 | return self.midi_tracks[n].frameRollPair 165 | 166 | def __getitem__(self, n): 167 | assert isinstance(n, int) 168 | return self.midi_tracks[n].monoFrameRoll() 169 | 170 | def test(): 171 | path = 'data/midi/20210409033250183-9212.mid' 172 | song = WeiMidi(path) 173 | #for i in range(song.tracks_num()): 174 | #a = song[i] 175 | #if a.shape[0] * 88 == a.sum(): 176 | 177 | if __name__ == '__main__': 178 | test() 179 | -------------------------------------------------------------------------------- /src/inference/compute_measure.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import shutil 4 | import librosa 5 | import mir_eval 6 | import numpy as np 7 | from sklearn import metrics 8 | import multiprocessing 9 | from multiprocessing import Manager 10 | 11 | from utils.utilities import (get_process_groups, read_lst, read_config, write_lst, mkdir, int16_to_float32) 12 | from conf.feature import * 13 | 14 | 15 | def load_audio_pair(est_path, ref_path, sample_rate=SAMPLE_RATE): 16 | max_len = -1 17 | ests = [] 18 | for path in est_path: 19 | est, _ = librosa.load(path, sr=sample_rate, mono=True) 20 | ests.append(est) 21 | if est.shape[-1] > max_len: 22 | max_len = est.shape[-1] 23 | 24 | refs = [] 25 | for path in ref_path: 26 | ref, _ = librosa.load(path, sr=sample_rate, mono=True) 27 | refs.append(ref) 28 | 29 | ref = np.zeros([len(refs), max_len]) 30 | for i in range(len(refs)): 31 | ref[i, : refs[i].shape[-1]] = refs[i] 32 | 33 | est = np.zeros([len(refs), max_len]) 34 | for i in range(len(refs)): 35 | est[i, : ests[i].shape[-1]] = ests[i] 36 | return est, ref 37 | 38 | def frame_roll_from_path(path, max_frame=-1, frames_per_second=100, notes_num=88): 39 | segments = read_lst(path) 40 | segments = [seg.rstrip().split('\t') for seg in segments] 41 | if max_frame == -1: 42 | max_frame = int(float(segments[-1][1]) * frames_per_second + 1) 43 | frame_roll = np.zeros([max_frame, notes_num + 1]) 44 | frame_roll[:, notes_num] = 1 45 | for seg in segments: 46 | st = int(float(seg[0]) * frames_per_second) 47 | ed = int(float(seg[1]) * frames_per_second + 1) 48 | if st >= max_frame: 49 | break 50 | if ed > max_frame: 51 | ed = max_frame 52 | frame_roll[st : ed, int(float(seg[2]))] = 1 53 | frame_roll[st : ed, notes_num] = 0 54 | if ed == max_frame: 55 | break 56 | return frame_roll, max_frame 57 | 58 | 59 | def measure_for_transcription(est_path, ref_path, mode='frame'): 60 | if mode == "onset": 61 | est_intervals, est_pitches = mir_eval.io.load_valued_intervals(est_path) 62 | ref_intervals, ref_pitches = mir_eval.io.load_valued_intervals(ref_path) 63 | precision, recall, f_measure, _ = mir_eval.transcription.precision_recall_f1_overlap( 64 | ref_intervals, ref_pitches, est_intervals, est_pitches) 65 | else: 66 | ref_frame_roll, max_frame = frame_roll_from_path(ref_path) 67 | est_frame_roll, _ = frame_roll_from_path(est_path, max_frame) 68 | pre = metrics.average_precision_score(ref_frame_roll, est_frame_roll, average='micro') 69 | precision = recall = f_measure = pre 70 | 71 | return precision, recall, f_measure 72 | 73 | 74 | def measure_for_separation(est_path, ref_path, sample_rate=SAMPLE_RATE): 75 | 76 | if type(est_path) is str: 77 | est, ref = load_audio_pair(est_path, ref_path, sample_rate) 78 | else: 79 | est = est_path 80 | ref = ref_path 81 | (sdr, sir, sar, perm) = mir_eval.separation.bss_eval_sources(ref, est, compute_permutation=True) 82 | 83 | return sdr, sir, sar 84 | 85 | 86 | def evaluate_transcription(samples, processes_num=1): 87 | return multi_process_evaluation(samples, processes_num, "transcription") 88 | 89 | def evaluate_separation(samples, processes_num=1): 90 | return multi_process_evaluation(samples, processes_num, "separation") 91 | 92 | def multi_process_evaluation(samples, processes_num=1, mode="separation"): 93 | 94 | def process_unit(n): 95 | sample = samples[n] 96 | sample_score = {} 97 | for instr in sample: 98 | pairs = sample[instr][mode] 99 | for pair in pairs: 100 | if mode == "separation": 101 | est, _ = librosa.load(pair[0], sr=SAMPLE_RATE, mono=True) 102 | ref, _ = librosa.load(pair[1], sr=SAMPLE_RATE, mono=True) 103 | sdr, sir, sar = measure_for_separation(est, ref) 104 | sample_score[instr] = sdr[0] 105 | else: 106 | f1, pre, recall = measure_for_transcription(pair[0], pair[1]) 107 | sample_score[instr] = f1 108 | return sample_score 109 | 110 | def process_group(sample_scores, st, ed, total_num, pid): 111 | print(f"process {pid + 1} starts") 112 | for n in range(st, ed): 113 | sample_score = process_unit(n) 114 | print(f"process {pid + 1} : {n + 1}/{total_num} done.") 115 | sample_scores[n] = sample_score 116 | print(f"process {pid + 1} ends") 117 | return sample_scores 118 | 119 | samples_num = len(samples) 120 | sample_scores = range(len(samples)) 121 | if processes_num < 2: 122 | sample_scores = list(sample_scores) 123 | sample_scores = process_group(sample_scores, 0, samples_num, samples_num, 0) 124 | 125 | else: 126 | with Manager() as manager: 127 | return_list = manager.list(sample_scores) 128 | groups = get_process_groups(samples_num, processes_num) 129 | ps = [] 130 | for pid, (st, ed) in enumerate(groups): 131 | p = multiprocessing.Process(target = process_group, args = (return_list, st, ed, samples_num, pid)) 132 | p.start() 133 | ps.append(p) 134 | for p in ps: 135 | p.join() 136 | sample_scores = list(return_list) 137 | return sample_scores 138 | 139 | 140 | 141 | if __name__=='__main__': 142 | est_path = ['evaluation/separation/test/AuSep_1_vn_27_King_est_1_.wav', 'evaluation/separation/test/AuSep_2_fl_30_Fugue_est_1_.wav'] 143 | ref_path = ['evaluation/separation/test/AuSep_1_vn_27_King_ref_1_.wav', 'evaluation/separation/test/AuSep_2_fl_30_Fugue_ref_1_.wav'] 144 | sdr, sir, sar = separation_evaluation(est_path, ref_path) 145 | print(sdr) 146 | 147 | -------------------------------------------------------------------------------- /src/inference/utilities.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import torch.nn as nn 4 | import torch.nn.functional as F 5 | import torchaudio 6 | import torchaudio.functional as AF 7 | 8 | import librosa 9 | import math 10 | 11 | import sys 12 | import numpy as np 13 | import time 14 | 15 | 16 | from conf.feature import * 17 | from conf.inference import * 18 | 19 | def align(a, b, dim): 20 | return a.transpose(0, dim)[:b.shape[dim]].transpose(0, dim) 21 | 22 | def get_fft_window(): 23 | fft_window = librosa.filters.get_window(WINDOW, WINDOW_SIZE, fftbins=True) 24 | fft_window = librosa.util.pad_center(fft_window, N_FFT) 25 | return torch.from_numpy(fft_window) 26 | 27 | FFT_WINDOW = get_fft_window() 28 | 29 | 30 | def onehot_tensor(x, dim=0, classes_num=NOTES_NUM): 31 | x = x.unsqueeze(dim) 32 | shape = list(x.shape) 33 | shape[dim] = classes_num 34 | y = torch.zeros(shape).to(x.device).scatter_(dim, x, 1) 35 | return y 36 | 37 | 38 | def spec2wav(x, cos, sin, wav_len, syn_phase=0, device="cuda"): 39 | 40 | #''' 41 | # args : channels * frames * n_fft 42 | #''' 43 | 44 | x = F.pad(x, (0, 1), "constant", 0) 45 | fft_window = FFT_WINDOW.cuda() if device == "cuda" else FFT_WINDOW 46 | 47 | if syn_phase == 1: 48 | print("here") 49 | wav_len = int((x.shape[-2] - 1)/ FRAMES_PER_SEC * SAMPLE_RATE) 50 | wav = AF.griffinlim(x.transpose(1, 2), 51 | window=fft_window, 52 | n_fft=N_FFT, 53 | hop_length=HOP_SIZE, 54 | win_length=WINDOW_SIZE, 55 | power=1, 56 | normalized=False, 57 | length=wav_len, 58 | n_iter=N_ITER, 59 | momentum=0, 60 | rand_init=False) 61 | elif syn_phase == 2: 62 | itersNum = 100 63 | for i in range(itersNum): 64 | spec = torch.stack([x * cos, x * sin], -1).transpose(1, 2) 65 | wav = torch.istft(spec, 66 | n_fft=N_FFT, 67 | hop_length=HOP_SIZE, 68 | win_length=WINDOW_SIZE, 69 | window=fft_window, 70 | center=True, 71 | normalized=False, 72 | onesided=None, 73 | length=wav_len, 74 | return_complex=False) 75 | if i < itersNum - 1: 76 | _, cos, sin = wav2spec(wav) 77 | 78 | 79 | 80 | elif syn_phase == 0: 81 | spec = torch.stack([x * cos, x * sin], -1).transpose(1, 2) 82 | wav = torch.istft(spec, 83 | n_fft=N_FFT, 84 | hop_length=HOP_SIZE, 85 | win_length=WINDOW_SIZE, 86 | window=fft_window, 87 | center=True, 88 | normalized=False, 89 | onesided=None, 90 | length=wav_len, 91 | return_complex=False) 92 | return wav 93 | 94 | def wav2spec(x, device="cuda"): 95 | ''' 96 | return channel * frames * n_fft 97 | ''' 98 | 99 | fft_window = FFT_WINDOW.cuda() if device == "cuda" else FFT_WINDOW 100 | 101 | spec = torch.stft(x, 102 | N_FFT, 103 | hop_length=HOP_SIZE, 104 | win_length=WINDOW_SIZE, 105 | window=fft_window, 106 | center=True, 107 | pad_mode='reflect', 108 | normalized=False, 109 | onesided=None, 110 | return_complex=False) 111 | spec = spec.transpose(1, 2) 112 | real = spec[:, :, :, 0] 113 | imag = spec[:, :, :, 1] 114 | mag = (real ** 2 + imag ** 2) ** 0.5 115 | cos = real / torch.clamp(mag, 1e-10, np.inf) 116 | sin = imag / torch.clamp(mag, 1e-10, np.inf) 117 | return mag[:, :, :-1], cos, sin 118 | 119 | def save_audio(wav, path): 120 | torchaudio.save(path, wav.float().cpu(), SAMPLE_RATE) 121 | 122 | 123 | def devide_into_batches(x, pad_value=0, overlap_edge=PAD_FRAME, duration_axis=-1): 124 | 125 | x = x.unsqueeze(0).unsqueeze(-1) 126 | duration_axis = duration_axis - 1 if duration_axis < 0 else duration_axis + 1 127 | x = x.transpose(duration_axis, -1) 128 | 129 | frames_num = x.shape[-1] 130 | 131 | batch_frames_num_non_padding = BATCH_FRAMES_NUM - overlap_edge * 2 132 | segments_num = frames_num // batch_frames_num_non_padding 133 | 134 | if pad_value == -1: 135 | x = x[ :segments_num * batch_frames_num_non_padding] 136 | elif segments_num * batch_frames_num_non_padding < frames_num: 137 | x = F.pad(x, (0, int((segments_num + 1) * batch_frames_num_non_padding) - frames_num), 'constant', value=pad_value) 138 | segments_num += 1 139 | 140 | x = F.pad(x, (overlap_edge, overlap_edge), 'constant', value=pad_value) 141 | 142 | x = x.transpose(-1, 0) 143 | samples = [] 144 | for i in range(segments_num): 145 | st = i * batch_frames_num_non_padding 146 | ed = st + BATCH_FRAMES_NUM 147 | sample = x[st : ed].transpose(0, duration_axis).squeeze(0).squeeze(-1) 148 | samples.append(sample) 149 | 150 | batches = [] 151 | samples_num = len(samples) 152 | batches_num = (samples_num + INFERENCE_BATCH_SIZE - 1) // INFERENCE_BATCH_SIZE 153 | for i in range(batches_num): 154 | st = i * INFERENCE_BATCH_SIZE 155 | ed = st + INFERENCE_BATCH_SIZE 156 | ed = samples_num if ed > samples_num else ed 157 | batches.append(torch.stack(samples[st : ed], 0)) 158 | return batches 159 | 160 | 161 | def merge_batches(x, overlap_edge=PAD_FRAME, duration_axis=-1): 162 | if duration_axis >= 0: 163 | duration_axis += 1 164 | x = x.unsqueeze(0).transpose(0, duration_axis) 165 | if duration_axis >= 0: 166 | duration_axis -= 1 167 | x = x[overlap_edge : -overlap_edge].transpose(0, 1).flatten(0, 1).transpose(0, duration_axis).squeeze(0) 168 | return x 169 | 170 | 171 | def merge_from_list(x, index=0): 172 | results = [] 173 | for unit in x: 174 | if type(unit) in [tuple, list]: 175 | results.append(unit[index]) 176 | else: 177 | results.append(unit) 178 | return torch.cat(results, 0) 179 | 180 | -------------------------------------------------------------------------------- /evaluation/MSI-DIS/scores-190.json: -------------------------------------------------------------------------------- 1 | {"MSI-DIS": {"transcription": [{"Violin": 0.9155356872822401, "Saxophone": 0.4922168581534541}, {"Violin": 0.906904571797, "Double_Bass": 0.7330175196626085}, {"Cello": 0.7716511678000424, "Viola": 0.6434034747048931}, {"Violin": 0.9200418795170666, "Double_Bass": 0.8179615908098763}, {"Violin": 0.855165271815949, "Cello": 0.8337352880755539}, {"Viola": 0.6149773318368282, "Clarinet": 0.7710395391594762}, {"Flute": 0.7083241588874832, "Violin": 0.887958054553282}, {"Saxophone": 0.6605534252500374, "Clarinet": 0.6594286094936119}, {"Saxophone": 0.5860077041512487, "Tuba": 0.5673013015675976}, {"Trombone": 0.7877233204170254, "Saxophone": 0.03781229740252132}, {"Oboe": 0.18800959366764133, "Saxophone": 0.44948093132197087}, {"Oboe": 0.4796317221126738, "Viola": 0.8448553667172828}, {"Flute": 0.8280862424486122, "Cello": 0.8930023244670272}, {"Flute": 0.8267930141461336, "Trombone": 0.8788783567444686}, {"Clarinet": 0.7461117325660884, "Horn": 0.7032581753974934}, {"Clarinet": 0.6563554459912304, "Bassoon": 0.2353563387884311}, {"Trumpet": 0.8619435148322085, "Violin": 0.724248884975393}, {"Trumpet": 0.46115154108952194, "Violin": 0.7666968607472413}, {"Flute": 0.8527652724900775, "Clarinet": 0.7393480261360227}, {"Flute": 0.6586441061688577, "Saxophone": 0.2491150773942218}, {"Bassoon": 0.46117680233655867, "Oboe": 0.3668244893427309}, {"Trumpet": 0.6322599603098203, "Trombone": 0.8572844451707077}, {"Trumpet": 0.4909331593806311, "Horn": 0.8389384407916984}, {"Tuba": 0.5497697590729947, "Trumpet": 0.855529304200216}, {"Trumpet": 0.47877785625509706, "Trombone": 0.6518660545606864}, {"Trumpet": 0.8618582497239909, "Horn": 0.8798279812510436}, {"Cello": 0.5947627192421981, "Oboe": 0.409469028065668}, {"Trumpet": 0.9389893409351974, "Tuba": 0.8680561027336061}, {"Trumpet": 0.798509790637697, "Trombone": 0.798237047904076}, {"Horn": 0.7659901411178002, "Trumpet": 0.48924055691281143}, {"Trumpet": 0.7763481761537584, "Horn": 0.7618845583301896}, {"Trumpet": 0.6581694910442168, "Trombone": 0.7394180175342637}], "separation": [{"Violin": 6.541116437371974, "Saxophone": 1.8881928954020841}, {"Violin": 9.085971834725244, "Double_Bass": 6.939945149867201}, {"Cello": 7.948946571611801, "Viola": 2.9854057021232103}, {"Violin": 8.027842855421742, "Double_Bass": 7.623472124412347}, {"Violin": 9.628268860563933, "Cello": 10.901597768940217}, {"Viola": 1.7124445277319533, "Clarinet": 4.8651812471553075}, {"Flute": 8.468505210426615, "Violin": 5.89170621585735}, {"Saxophone": -1.7425961502063514, "Clarinet": 1.789691326687349}, {"Saxophone": 5.058587893150619, "Tuba": 4.732612885768726}, {"Trombone": 6.439471796882162, "Saxophone": -12.734088587651616}, {"Oboe": -7.105028712955862, "Saxophone": -1.4624145596087077}, {"Oboe": 0.6622300530031615, "Viola": 6.4944053285021}, {"Flute": 8.588378412276096, "Cello": 11.381660139740637}, {"Flute": 11.285043361386428, "Trombone": 6.808830188676809}, {"Clarinet": 6.456273989937547, "Horn": 4.743802232015218}, {"Clarinet": 5.244730530716417, "Bassoon": -10.493317188874995}, {"Trumpet": 4.340486727770855, "Violin": 5.290570254455115}, {"Trumpet": 0.6531070386405622, "Violin": 4.709271765210785}, {"Flute": 12.234576327549737, "Clarinet": 8.912410369226688}, {"Flute": 8.201778642251398, "Saxophone": -5.0961533288595575}, {"Bassoon": 5.596215024044069, "Oboe": -0.9849432318223452}, {"Trumpet": 4.45269382716973, "Trombone": 6.427959235523154}, {"Trumpet": 0.20277078878847551, "Horn": 9.49491145739134}, {"Tuba": 8.066828307649828, "Trumpet": 9.994846913719279}, {"Trumpet": 0.7814876576097167, "Trombone": 2.794755491462097}, {"Trumpet": 8.926938201028934, "Horn": 8.991136171921566}, {"Cello": 7.020040020491056, "Oboe": 6.0812640429824985}, {"Trumpet": 8.88255234062147, "Tuba": 8.051688364863747}, {"Trumpet": 4.2050602035974585, "Trombone": 4.82251196830306}, {"Horn": 5.624537966554493, "Trumpet": -2.830977900011998}, {"Trumpet": 5.897005864337276, "Horn": 8.267664444168025}, {"Trumpet": 6.30121119732959, "Trombone": 3.751358755393699}]}, "MSI-DIS-S": {"separation": [{"Violin": 6.613001284988619, "Saxophone": 6.908647788563865}, {"Violin": 9.889224519215695, "Double_Bass": 5.908313175088509}, {"Cello": 7.950142830194029, "Viola": 5.165176589502689}, {"Violin": 8.197718446853044, "Double_Bass": 7.135280756021612}, {"Violin": 9.412095105067737, "Cello": 10.62875802088553}, {"Viola": 3.415010862232417, "Clarinet": 7.018410524186724}, {"Flute": 8.771259771677965, "Violin": 8.679388561407553}, {"Saxophone": -0.8469234611064538, "Clarinet": 3.156610247604527}, {"Saxophone": 10.107492749043523, "Tuba": 5.391805023134176}, {"Trombone": 6.344937936708229, "Saxophone": 7.449428411196601}, {"Oboe": 4.354182781300693, "Saxophone": 4.535728677183428}, {"Oboe": 3.3279251141151307, "Viola": 7.941159170648245}, {"Flute": 7.517535391227003, "Cello": 10.472847415042636}, {"Flute": 10.774473481643323, "Trombone": 7.022919250744013}, {"Clarinet": 8.423195318092167, "Horn": 10.013757977040878}, {"Clarinet": 10.330679457956993, "Bassoon": 4.844246401602945}, {"Trumpet": 5.439569589818225, "Violin": 5.5061771445010965}, {"Trumpet": 3.1963841212511213, "Violin": 5.055929487124545}, {"Flute": 14.526501153758142, "Clarinet": 12.725290612709902}, {"Flute": 10.088511882368177, "Saxophone": 6.786088697778048}, {"Bassoon": 10.047690078508866, "Oboe": -2.669156077895666}, {"Trumpet": 4.376574626491411, "Trombone": 5.963778598996368}, {"Trumpet": 10.521624108965868, "Horn": 11.668561666943846}, {"Tuba": 6.828793099384825, "Trumpet": 9.923279545351896}, {"Trumpet": 3.636903746109033, "Trombone": 6.012195640826754}, {"Trumpet": 9.820882133987544, "Horn": 10.475290472025538}, {"Cello": 6.770775799798311, "Oboe": 7.108617519921708}, {"Trumpet": 8.935373367206758, "Tuba": 8.200000625712908}, {"Trumpet": 5.243726242520109, "Trombone": 6.64422064218101}, {"Horn": 8.085503508412174, "Trumpet": 3.5976478306216753}, {"Trumpet": 8.737509266782887, "Horn": 10.902989949246876}, {"Trumpet": 5.653195910534603, "Trombone": 3.8592234846691973}]}} -------------------------------------------------------------------------------- /evaluation/MSI-DIS/scores-191.json: -------------------------------------------------------------------------------- 1 | {"MSI-DIS": {"transcription": [{"Violin": 0.9152028513501567, "Saxophone": 0.5281816750237217}, {"Violin": 0.8894809825456819, "Double_Bass": 0.7564888710487585}, {"Cello": 0.7955522662816865, "Viola": 0.589311791845628}, {"Violin": 0.9240299544268021, "Double_Bass": 0.8178991333418093}, {"Violin": 0.8556467102425984, "Cello": 0.8420676657261306}, {"Viola": 0.6177582103382321, "Clarinet": 0.7477714226716111}, {"Flute": 0.7147039244734346, "Violin": 0.8772163576984613}, {"Saxophone": 0.6055464920242927, "Clarinet": 0.6087610358509389}, {"Saxophone": 0.6233418086930349, "Tuba": 0.508229096911424}, {"Trombone": 0.7511127309593514, "Saxophone": 0.05001789349254738}, {"Oboe": 0.1642970139611122, "Saxophone": 0.42103724990527547}, {"Oboe": 0.46962586287749836, "Viola": 0.829122827556487}, {"Flute": 0.8133807880925873, "Cello": 0.9023525384577459}, {"Flute": 0.8009353684165248, "Trombone": 0.8675524570849545}, {"Clarinet": 0.7944425367678136, "Horn": 0.6762319968246755}, {"Clarinet": 0.5851073148392318, "Bassoon": 0.29180740150863377}, {"Trumpet": 0.8792550555115606, "Violin": 0.7914435302799414}, {"Trumpet": 0.709345011107144, "Violin": 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{"MSI": {"transcription": [{"Violin": 0.9274906863337553, "Saxophone": 0.5043368474157827}, {"Violin": 0.914386969149301, "Double_Bass": 0.7730874989655719}, {"Cello": 0.7739149409285708, "Viola": 0.6573918072270389}, {"Violin": 0.9341836426263121, "Double_Bass": 0.8482583407528802}, {"Violin": 0.8420241341423795, "Cello": 0.8469197040403452}, {"Viola": 0.5588747697109471, "Clarinet": 0.7887064087570265}, {"Flute": 0.7256794977633976, "Violin": 0.8846825615282289}, {"Saxophone": 0.41758428008822285, "Clarinet": 0.4745905919360384}, {"Saxophone": 0.5008825092141026, "Tuba": 0.6009273497542329}, {"Trombone": 0.8237454859698395, "Saxophone": 0.037795896748970326}, {"Oboe": 0.17629782871849398, "Saxophone": 0.5489591573760875}, {"Oboe": 0.4470377803679896, "Viola": 0.8775972488727447}, {"Flute": 0.8245298887051747, "Cello": 0.9098069836136474}, {"Flute": 0.8085748162858232, "Trombone": 0.887140237057048}, {"Clarinet": 0.8218144367199967, "Horn": 0.7584396533479583}, {"Clarinet": 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0.5625548825248867, "Trombone": 0.6839988061598271}], "separation": [{"Violin": 8.002384350102595, "Saxophone": 5.451628907209875}, {"Violin": 12.104245950152052, "Double_Bass": 7.448966351944486}, {"Cello": 6.6859480871167465, "Viola": 5.200347895991829}, {"Violin": 11.102478884432239, "Double_Bass": 10.032329776858885}, {"Violin": 11.889955175893839, "Cello": 14.180611149691524}, {"Viola": 3.7982159793782424, "Clarinet": 4.7174038599921895}, {"Flute": 11.069693917056645, "Violin": 7.364196869323203}, {"Saxophone": -3.164941206815505, "Clarinet": -2.0062585695287103}, {"Saxophone": 2.6221343692888506, "Tuba": 6.83415768262533}, {"Trombone": 9.558411751715411, "Saxophone": -14.789737378334515}, {"Oboe": -8.675431616942674, "Saxophone": 1.668428640979334}, {"Oboe": 1.0431281882152108, "Viola": 9.705655649674934}, {"Flute": 10.654960335024237, "Cello": 15.046428454678441}, {"Flute": 12.521223809661198, "Trombone": 10.224332377154543}, {"Clarinet": 7.474110284591646, "Horn": 6.721126014335286}, {"Clarinet": 7.219275456942902, "Bassoon": -10.224139685412252}, {"Trumpet": 5.939763951566752, "Violin": 5.957894962709236}, {"Trumpet": 3.0868434340069455, "Violin": 4.642075483548619}, {"Flute": 6.259745551692517, "Clarinet": 6.818697549020795}, {"Flute": 4.941118922955765, "Saxophone": 0.1954592801971419}, {"Bassoon": -3.448178985167047, "Oboe": -0.27161265937604895}, {"Trumpet": 1.2723559004955045, "Trombone": 2.3506275151405003}, {"Trumpet": 2.740302664280981, "Horn": 11.871646489756532}, {"Tuba": 14.886345187021178, "Trumpet": 13.26696557903243}, {"Trumpet": 1.5628933516633805, "Trombone": 3.276492702484322}, {"Trumpet": 9.442530332209113, "Horn": 12.705992978512022}, {"Cello": 9.137813694083905, "Oboe": 4.823742745856226}, {"Trumpet": 12.682455929463304, "Tuba": 12.919274738450994}, {"Trumpet": 5.582079973697156, "Trombone": -1.856029538771877}, {"Horn": 6.1622367098751285, "Trumpet": 2.945647568426861}, {"Trumpet": 6.2673377917483775, "Horn": 13.575113452101357}, {"Trumpet": 6.152750484815411, "Trombone": 5.147797855183857}]}, "MSI-S": {"separation": [{"Violin": 7.843305068898716, "Saxophone": 11.929191243917652}, {"Violin": 11.966525037474744, "Double_Bass": 6.953270915241092}, {"Cello": 7.99183965297512, "Viola": 8.784201514471224}, {"Violin": 11.863988477785286, "Double_Bass": 9.647516816581131}, {"Violin": 11.61399832787309, "Cello": 13.681708381172886}, {"Viola": 4.695670230946728, "Clarinet": 5.425892433455645}, {"Flute": 11.457398633516359, "Violin": 10.825109962905238}, {"Saxophone": -1.0970764928261632, "Clarinet": 2.2864066634822864}, {"Saxophone": 10.715997355229979, "Tuba": 7.28990878475631}, {"Trombone": 9.454668995454501, "Saxophone": 9.67125970610323}, {"Oboe": 6.911185742493357, "Saxophone": 8.05445637932222}, {"Oboe": 6.445197425179235, "Viola": 11.101000867905714}, {"Flute": 9.448056963711478, "Cello": 13.591085361388993}, {"Flute": 12.648575125646753, "Trombone": 10.071433827411067}, {"Clarinet": 7.820966719407945, "Horn": 11.666964306059898}, {"Clarinet": 10.737167531779043, "Bassoon": 7.641218293749594}, {"Trumpet": 7.425035449903609, "Violin": 3.0107595965766714}, {"Trumpet": 4.385935545620114, "Violin": 3.9094550435989115}, {"Flute": 15.978379782076061, "Clarinet": 9.550151237463139}, {"Flute": 10.587900116515016, "Saxophone": 5.126442933388766}, {"Bassoon": 12.42846602342926, "Oboe": -1.4857967442847158}, {"Trumpet": 6.281325663604525, "Trombone": 7.519311618295204}, {"Trumpet": 14.580846573807111, "Horn": 15.242704647130925}, {"Tuba": 10.676579735080168, "Trumpet": 13.508922484162513}, {"Trumpet": 5.573236423848283, "Trombone": 8.413966273693276}, {"Trumpet": 13.99350522687913, "Horn": 13.242641617389108}, {"Cello": 9.144709678550118, "Oboe": 11.358703796489953}, {"Trumpet": 13.442816644984163, "Tuba": 13.565610990043393}, {"Trumpet": 7.333466654352797, "Trombone": 7.801098855812278}, {"Horn": 7.869435886175751, "Trumpet": 5.078572594050752}, {"Trumpet": 13.35644524716921, "Horn": 14.13488797378907}, {"Trumpet": 6.8030524763534785, "Trombone": 6.300163096039944}]}} -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # A Unified Model for Zero-shot Music Source Separation, Transcription and Synthesis 2 | This is the code repository for the paper: A Unified Model for Zero-shot Music Source Separation, Transcription and Synthesis. The paper is available [here](https://arxiv.org/abs/2108.03456). 3 | 4 | ## Introduction 5 | We propose a unified model for three inter-related tasks: 6 | - to *separate* individual sound sources from a mixed music audio; 7 | - to *transcribe* each sound source to MIDI notes; 8 | - to *synthesize* new pieces based on the timber of separated sources. 9 | 10 | The model is inspired by the fact that when humans listen to music, our minds can not only separate the sounds of different instruments, but also at the same time perceive high-level representations such as score and timbre. 11 | 12 | ## Model architecture 13 | ### - Components of the proposed model 14 | The proposed model comprises four components: 15 | - a query-by-example (QBE) network 16 | - a pitch-timber disentanglement module 17 | - a transcriptor 18 | - an audio encoder-decoder network 19 | 20 | ![](https://github.com/Kikyo-16/A-unified-model-for-zero-shot-musical-source-separation-transcription-and-synthesis/blob/main/imgs/model-fig-1-ab.png) 21 | >The baseline models and the proposed model. In the left figure, the large orange and gray box indicate a QBE 22 | transcription-only and QBE separation-only model respectively. The whole figure indicates a QBE multi-task model. 23 | 24 | 25 | ### - Training losses 26 | The model is trained with separatiopn loss, transcription loss and contrastive loss. See details in [our paper](https://arxiv.org/abs/2108.03456). 27 | 28 | ### - Pitch-translation Invariance Loss 29 | To further improve the timbre disentanglement performance, we propose a *pitch-translation invariance loss*. We term the model without pitch-transformation invariance loss `multi-task informed (MSI) model`. And we term MSI model with further disentanglement via pitch-transformation invariance loss `MSI-DIS model`. 30 | 31 | ### - Detailed hyper-parameters of the proposed model 32 | ![](https://github.com/Kikyo-16/A-unified-model-for-zero-shot-musical-source-separation-transcription-and-synthesis/blob/main/imgs/model-fig-3.png) 33 | 34 | ## Experimental results 35 | 36 | | Model|MSS-only| Multi-task| MSI (ours)| MSI-DIS (ours)| 37 | | ----| ----| ----| ----| ----| 38 | | Seen| 4.69 ± 0.31| 3.32 ± 0.1| **6.33 ± 0.17**| 5.04 ± 0.16| 39 | | Unseen| **6.20 ± 0.26**| 4.63 ± 0.34| 5.53 ± 0.11| 3.99 ± 0.22| 40 | | **Overall**| 5.07 ± 0.22| 3.65 ± 0.22| **6.13 ± 0.15**| 4.77 ± 0.14| 41 | 42 | 43 | ## Demos 44 | The initial version of the demo page is available [here](https://kikyo-16.github.io/demo-page-of-a-unified-model-for-separation-transcriptiion-synthesis/). New demo page with more demos will be updated soon. 45 | 46 | ## Quick start 47 | 48 | ### Requirements 49 | You will need at least Python 3.6 and Pytorch . See requirements.txt for requirements. Install dependencies with pip: 50 | ``` 51 | pip install -r requirements.txt 52 | ``` 53 | 54 | ### Data preparation 55 | 1. Download URMP Dataset from [URMP homepage](http://www2.ece.rochester.edu/projects/air/projects/URMP.html). 56 | 2. Run the following command to generate your feature and annotations. 57 | ``` 58 | python src/dataset/urmp/urmp_feature.py --dataset_dir=ur_unzipped_dataset_folder --feature_dir=dataset/hdf5s/urmp --process_num=1 59 | ``` 60 | **NOTE** that `ur_unzipped_dataset_folder` is your unzipped data folder and it should contain directories of songs: 61 | > . 62 | ├── `ur_unzipped_dataset_folder` 63 |         ├── `0_song0` 64 |         ├── `1_song1` 65 |         ├── ... 66 |         ... 67 | 68 | ### Training 69 | Run the following command to train the proposed MSI-DIS Model: 70 | ``` 71 | python train.py --model_name=MSI_DIS --model_folder=folder_to_store_model_weights --epoch=200 72 | ``` 73 | 74 | ### Evaluation 75 | Download models weights [here](https://drive.google.com/drive/folders/1fT3Fva5JywhpYnOhsORbDkLQ9Vnhv_Lj?usp=sharing). 76 | Run the following command to evaluate the proposed MSI-DIS Model on the test set: 77 | ``` 78 | python evaluate.py --model_name=MSI_DIS --model_path=path_of_model_weights --evaluation_folder=folder_to_store_evaluation_results --epoch=199 79 | ``` 80 | **NOTE:** Since we do not divide a validation set to chose the bestperformance model among all the training epochs, we report average results with a 95% confidence interval (CI) of models at the last 10 epochs. 81 | Therefore, if you want to reproduce the results of our paper, please 82 | 1. Evaluate all last-10-epoch models. 83 | 2. Run the following command to print experimental result tables: 84 | ``` 85 | python src/analyze/draw_table.py --evaluation_folder=`folder_to_store_evaluation_results 86 | ``` 87 | 88 | ### Synthesis 89 | Run the following command to synthesize audios using the given midi, the test set, and the proposed MSI-DIS Model: 90 | ``` 91 | python synthesis.py --model_name=MSI-DIS --model_path=path_of_model_weights --evaluation_folder=folder_to_store_synthesis_results 92 | ``` 93 | 94 | ## Citation 95 | Please cite our work as: 96 | 97 | >@inproceedings{lin2021unified, 98 | >title={A Unified Model for Zero-shot Music Source Separation, Transcription and Synthesis}, 99 | >author={Liwei Lin and Qiuqiang Kong and Junyan Jiang and Gus Xia}, 100 | >booktitle = {Proceedings of 22st International Conference on Music Information Retrieval, {ISMIR}}, 101 | >year = {2021} 102 | >} 103 | 104 | ## License 105 | This code is released under the MIT license as found in the LICENSE file. 106 | -------------------------------------------------------------------------------- /src/utils/utilities.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import time 4 | import numpy as np 5 | import configparser 6 | import json 7 | 8 | et = 1e-8 9 | 10 | def load_json(path): 11 | with open(path,'r') as load_f: 12 | load_dict = json.load(load_f) 13 | return load_dict 14 | 15 | def save_json(path, data): 16 | with open(path,'w') as f: 17 | json.dump(data,f) 18 | 19 | def print_dict(x): 20 | for key in x: 21 | print(key, x[key]) 22 | 23 | def factorized_fft(fft, onset_offset): 24 | st = -1 25 | curve_fft = np.zeros_like(fft) 26 | mean_fft = np.zeros_like(fft) 27 | for i in range(fft.shape[-1]): 28 | if onset_offset[i] == 1 and st == -1: 29 | st = i 30 | elif not onset_offset[i] == 0: 31 | if st == -1: 32 | out_fft[i] = 0 33 | mean_fft = fft[i] 34 | else: 35 | ave = np.mean(fft[st : i + 1]) 36 | std = np.std(fft[st : i + 1]) 37 | mean_fft[st : i + 1] = ave 38 | curve_fft[st : i + 1] = (fft[st : i + 1] - ave) / (std + et) 39 | 40 | if onset_offset[i] == 2: 41 | st = -1 42 | 43 | return curve_fft, mean_fft 44 | 45 | 46 | 47 | def compute_time(event, pre_time): 48 | cur_time = time.time() 49 | print(f'{event} use', cur_time - pre_time) 50 | return cur_time 51 | 52 | def encode_mu_law(x, mu=256): 53 | mu = mu - 1 54 | fx = np.sign(x) * np.log(1 + mu * np.abs(x)) / np.log(1 + mu) 55 | return np.floor((fx + 1) / 2 * mu + 0.5).astype(np.int64) 56 | 57 | 58 | def decode_mu_law(y, mu=256): 59 | mu = mu - 1 60 | fx = (y - 0.5) / mu * 2 - 1 61 | x = np.sign(fx) / mu * ((1 + mu) ** np.abs(fx) - 1) 62 | return x 63 | 64 | 65 | def read_config(config_path, name): 66 | config = configparser.ConfigParser() 67 | config.read(config_path) 68 | return config[name] 69 | 70 | 71 | def dict2str(dic, pre): 72 | res = '' 73 | for i, d in enumerate(dic): 74 | if i == 0: 75 | res += pre 76 | res += d + ' :' 77 | val = dic[d] 78 | if type(val) is dict: 79 | res += '\n' + dict2str(val, pre + '\t') + '\n' 80 | else: 81 | res += f'\t{val}\t' 82 | 83 | return res 84 | 85 | def save_score(path, score): 86 | mkdir(path, is_file=True) 87 | res = dict2str(score, '') 88 | write_lst(path, [res]) 89 | return res 90 | 91 | def get_process_groups(audio_num, process_num): 92 | assert audio_num > 0 and process_num > 0 93 | if process_num > audio_num: 94 | process_num = audio_num 95 | audio_num_per_process = (audio_num + process_num - 1) // process_num 96 | 97 | reduce_id = process_num - (audio_num_per_process * process_num - audio_num) 98 | 99 | groups = [] 100 | cur = 0 101 | for i in range(process_num): 102 | if i == reduce_id: 103 | audio_num_per_process -= 1 104 | groups += [[cur, cur + audio_num_per_process]] 105 | cur += audio_num_per_process 106 | return groups 107 | 108 | 109 | def mkdir(fd, is_file=False): 110 | fd = fd.split('/') 111 | fd = fd[:-1] if is_file else fd 112 | ds = [] 113 | for d in fd: 114 | ds.append(d) 115 | d = "/".join(ds) 116 | if not d == "" and not os.path.exists(d): 117 | os.makedirs(d) 118 | 119 | 120 | def get_filename(path): 121 | path = os.path.realpath(path) 122 | na_ext = path.split('/')[-1] 123 | na = os.path.splitext(na_ext)[0] 124 | return na 125 | 126 | 127 | def traverse_folder(folder): 128 | paths = [] 129 | names = [] 130 | 131 | for root, dirs, files in os.walk(folder): 132 | for name in files: 133 | filepath = os.path.join(root, name) 134 | names.append(name) 135 | paths.append(filepath) 136 | 137 | return names, paths 138 | 139 | 140 | def note_to_freq(piano_note): 141 | return 2 ** ((piano_note - 39) / 12) * 440 142 | 143 | 144 | def create_logging(log_dir, filemode): 145 | mkdir(log_dir) 146 | i1 = 0 147 | 148 | while os.path.isfile(os.path.join(log_dir, '{:04d}.log'.format(i1))): 149 | i1 += 1 150 | 151 | log_path = os.path.join(log_dir, '{:04d}.log'.format(i1)) 152 | logging.basicConfig( 153 | level=logging.DEBUG, 154 | format='%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s', 155 | datefmt='%a, %d %b %Y %H:%M:%S', 156 | filename=log_path, 157 | filemode=filemode) 158 | 159 | # Print to console 160 | console = logging.StreamHandler() 161 | console.setLevel(logging.INFO) 162 | formatter = logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s') 163 | console.setFormatter(formatter) 164 | logging.getLogger('').addHandler(console) 165 | 166 | return logging 167 | 168 | 169 | def float32_to_int16(x): 170 | x = np.clip(x, -1, 1) 171 | assert np.max(np.abs(x)) <= 1. 172 | return (x * 32767.).astype(np.int16) 173 | 174 | 175 | def int16_to_float32(x): 176 | return (x / 32767.).astype(np.float32) 177 | 178 | 179 | def pad_truncate_sequence(x, max_len): 180 | if len(x) < max_len: 181 | return np.concatenate((x, np.zeros(max_len - len(x)))) 182 | else: 183 | return x[0 : max_len] 184 | 185 | def read_lst(lst_path): 186 | with open(lst_path) as f: 187 | data = f.readlines() 188 | data = [d.rstrip() for d in data] 189 | return data 190 | 191 | def write_lst(lst_path, lst): 192 | lst = [str(l) for l in lst] 193 | with open(lst_path, 'w') as f: 194 | f.writelines('\n'.join(lst)) 195 | 196 | def freq2note(freq): 197 | freq = float(freq) 198 | note = round(12 * np.log2(freq / 440)) + 48 199 | return note 200 | 201 | def note2freq(note): 202 | note = float(note) 203 | freq = (2**((note - 48) / 12)) * 440 204 | return freq 205 | 206 | 207 | def parse_frameroll2annotation(frame_roll, frames_per_second=100, notes_num=88): 208 | pre = notes_num 209 | st = -1 210 | est = [] 211 | preds = np.pad(frame_roll,(0,1), 'constant', constant_values=(0, notes_num)) 212 | for i in range(frame_roll.shape[0]): 213 | if not frame_roll[i] == pre: 214 | if st > -1 and not pre == notes_num: 215 | est.append(\ 216 | '%f\t%f\t%d' % (st * 1.0 / frames_per_second, i * 1.0 / frames_per_second, pre)) 217 | st = i 218 | pre = frame_roll[i] 219 | return est 220 | -------------------------------------------------------------------------------- /src/dataset/urmp/urmp_generate_dataset.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import random 4 | import argparse 5 | 6 | sys.path.insert(1, os.path.join(sys.path[0], '../..')) 7 | from utils.utilities import (mkdir, write_lst) 8 | 9 | random.seed(1234) 10 | 11 | instr_tags = "vn,vc,va,fl,cl,sax,tpt,tbn,bn,hn,tba,db,ob" 12 | instrs = "Violin,Cello,Viola,Flute,Clarinet,Saxophone,Trumpet,Trombone,Bassoon,Horn,Tuba,Double_Bass,Oboe" 13 | tag2instr = {} 14 | 15 | seen = "Violin,Cello,Viola,Flute,Clarinet,Saxophone,Trumpet,Trombone" 16 | unseen = "Horn,Tuba,Double_Bass,Bassoon,Oboe" 17 | 18 | skips = "" 19 | 20 | instr_tags = instr_tags.split(',') 21 | instrs = instrs.split(',') 22 | seen = seen.split(',') 23 | unseen = unseen.split(',') 24 | skips = skips.split(',') 25 | 26 | for i, tag in enumerate(instr_tags): 27 | tag2instr[tag] = instrs[i] 28 | 29 | def get_all_audios(folder): 30 | audios = {} 31 | tracks_num = 0 32 | sample_folders = os.listdir(folder) 33 | for sample in sample_folders: 34 | sample_path = os.path.join(folder, sample) 35 | tracks = os.listdir(sample_path) 36 | if len(sample.split('_')) < 2: 37 | continue 38 | sampleName = sample.split('_')[1] 39 | sample_instrs = sample.split('_')[2:] 40 | if sampleName not in audios: 41 | audios[sampleName] = {} 42 | for track in tracks: 43 | if not str.endswith(track, "ref.txt"): 44 | continue 45 | track = str.replace(track, "_ref.txt", ".h5") 46 | #track = str.replace(track, "_TRAIN.h5", "_TEST.h5") 47 | track_path = os.path.join(sample_path, track) 48 | track_name = track.split("_")[1] 49 | instr = tag2instr[track.split("_")[2]] 50 | if instr not in audios[sampleName]: 51 | audios[sampleName][instr] = {} 52 | if track_name not in audios[sampleName][instr]: 53 | tracks_num += 1 54 | audios[sampleName][instr][track_name] = track_path 55 | seen_audios = [] 56 | unseen_audios = [] 57 | for songName in audios: 58 | for instr in audios[songName]: 59 | if instr in seen: 60 | seen_audios.append(songName) 61 | else: 62 | unseen_audios.append(songName) 63 | 64 | 65 | train_lst = {} 66 | test_lst = {} 67 | 68 | for songName in audios: 69 | if songName in unseen_audios: 70 | instrs = {} 71 | instrs_num = 0 72 | for instr in audios[songName]: 73 | if instr not in instrs: 74 | instrs[instr] = [] 75 | for track in audios[songName][instr]: 76 | instrs[instr].append(audios[songName][instr][track]) 77 | instrs_num += len(instrs[instr]) 78 | instrs = sorted(instrs.items(), key=lambda d: -len(d[1])) 79 | show = [{instr[0]:len(instr[1])} for instr in instrs] 80 | print(show) 81 | data_lst = [] 82 | for instr in instrs: 83 | if len(instr[1]) > instrs_num // 2: 84 | print("aaaaaaaaaaaaaaaaaaaaaaaah") 85 | for track in instr[1]: 86 | data_lst.append([instr[0], track]) 87 | 88 | total = len(data_lst) 89 | pairs = [] 90 | for i, track in enumerate(data_lst): 91 | j = total - 1- i 92 | if j == i: 93 | j = 0 94 | pairs.append([track[0], data_lst[j][0], track[1],data_lst[j][1]]) 95 | if i + 1 >= (total + 1)// 2: 96 | break 97 | test_lst[songName] = {"test" : pairs, "query" : []} 98 | 99 | else: 100 | for instr in audios[songName]: 101 | if instr not in train_lst: 102 | train_lst[instr] = [] 103 | for track in audios[songName][instr]: 104 | train_lst[instr].append(str.replace(audios[songName][instr][track], "_TEST.h5", "h5")) 105 | 106 | 107 | 108 | print("\nseen:\n") 109 | compute_instr_samples(audios, songNames=None, skipNames=unseen_audios) 110 | print("\nunseen:\n") 111 | compute_instr_samples(audios, songNames=unseen_audios) 112 | 113 | print("\nall:\n") 114 | compute_instr_samples(audios) 115 | 116 | 117 | query_lst = [] 118 | 119 | songs_lst = [] 120 | songs_num = len(test_lst) 121 | for test in test_lst: 122 | songs_lst.append(test) 123 | 124 | for i, test in enumerate(test_lst): 125 | for pair in test_lst[test]["test"]: 126 | query = [] 127 | query += pair[:2] 128 | for j in range(2): 129 | path = None 130 | while path is None: 131 | song_id = random.randint(0, songs_num - 1) 132 | if song_id == i: 133 | continue 134 | query_pairs = test_lst[songs_lst[song_id]]["test"] 135 | for query_pair in query_pairs: 136 | for k in range(2): 137 | if query_pair[k] == pair[j] and not query_pair[k + 2] == pair[j + 2]: 138 | path = query_pair[k + 2] 139 | query.append(path) 140 | break 141 | if path is not None: 142 | break 143 | test_lst[test]["query"] += [query] 144 | 145 | return audios, train_lst, test_lst 146 | 147 | def compute_instr_samples(audios, songNames=None, skipNames=None): 148 | samples = {} 149 | num = 0 150 | for songName in audios: 151 | if songNames is not None and songName not in songNames: 152 | continue 153 | if skipNames is not None and songName in skipNames: 154 | continue 155 | for instr in audios[songName]: 156 | if instr not in samples: 157 | samples[instr] = 0 158 | num += len(audios[songName][instr]) 159 | samples[instr] += len(audios[songName][instr]) 160 | 161 | total_num = 0 162 | for instr in samples: 163 | total_num += samples[instr] 164 | print(instr, samples[instr]) 165 | print(total_num, num) 166 | return samples 167 | 168 | def save_train_lst(data, output_folder): 169 | for instr in data: 170 | instr_folder = os.path.join(output_folder, instr) 171 | mkdir(instr_folder) 172 | path = os.path.join(instr_folder, "train.lst") 173 | write_lst(path, data[instr]) 174 | 175 | def save_test_lst(data, output_folder): 176 | testset_folder = os.path.join(output_folder, "testset") 177 | mkdir(testset_folder) 178 | test_lst = [] 179 | query_lst = [] 180 | for songName in data: 181 | test_lst += data[songName]["test"] 182 | query_lst += data[songName]["query"] 183 | test_lst = [f"{t[0]},{t[1]}\t{t[2]},{t[3]}" for t in test_lst] 184 | query_lst = [f"{t[0]},{t[1]}\t{t[2]},{t[3]}" for t in query_lst] 185 | print("test set", len(test_lst)) 186 | test_lst_path = os.path.join(testset_folder, "test.lst") 187 | query_lst_path = os.path.join(testset_folder, "query.lst") 188 | write_lst(test_lst_path, test_lst) 189 | write_lst(query_lst_path, query_lst) 190 | 191 | 192 | if __name__=="__main__": 193 | parser = argparse.ArgumentParser(description='') 194 | parser.add_argument('--feature_dir', type=str, required=True, help='Directory of generated dataset.') 195 | parser.add_argument('--data_dir', type=str, required=True, help='Directory to store generated files.') 196 | 197 | args = parser.parse_args() 198 | 199 | folder = args.feature_dir 200 | output_folder = args.data_dir 201 | audios, train_lst, test_lst = get_all_audios(folder) 202 | save_train_lst(train_lst, output_folder) 203 | save_test_lst(test_lst, output_folder) 204 | instr_samples = compute_instr_samples(audios) 205 | 206 | 207 | -------------------------------------------------------------------------------- /src/models/layers.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import torch.nn as nn 4 | import torch.nn.functional as F 5 | import sys 6 | import numpy as np 7 | import time 8 | import h5py 9 | import math 10 | 11 | EPS = 1e-8 12 | 13 | 14 | def init_layer(layer): 15 | """Initialize a Linear or Convolutional layer. """ 16 | nn.init.xavier_uniform_(layer.weight) 17 | 18 | if hasattr(layer, 'bias'): 19 | if layer.bias is not None: 20 | layer.bias.data.fill_(0.) 21 | 22 | 23 | def init_bn(bn): 24 | """Initialize a Batchnorm layer. """ 25 | bn.bias.data.fill_(0.) 26 | bn.weight.data.fill_(1.) 27 | 28 | 29 | 30 | class ConvBlock(nn.Module): 31 | """A Convolutional Layer Followed by a Batchnorm Layer and a ReLU Activation Layer. 32 | 33 | Input : [B x in_channels x T x F] 34 | Output : [B x out_chanels x T x F] 35 | 36 | Parameters 37 | ----------- 38 | in_channels : int 39 | out_channels : int 40 | momentum : float 41 | 42 | """ 43 | def __init__(self, in_channels, out_channels, momentum=0.01): 44 | super(ConvBlock, self).__init__() 45 | 46 | self.conv = nn.Conv2d(in_channels=in_channels, 47 | out_channels=out_channels, 48 | kernel_size=(3, 3), stride=(1, 1), 49 | padding=(1, 1), bias=False) 50 | 51 | self.bn = nn.BatchNorm2d(out_channels, momentum=momentum) 52 | 53 | self.init_weights() 54 | 55 | def init_weights(self): 56 | init_layer(self.conv) 57 | init_bn(self.bn) 58 | 59 | 60 | def forward(self, input): 61 | """ 62 | Parameters 63 | ---------- 64 | input : [B x in_channels x T x F] 65 | 66 | Returns 67 | ------- 68 | x : [B x out_chanels x T x F] 69 | 70 | """ 71 | x = input 72 | x = F.relu_(self.bn(self.conv(x))) 73 | return x 74 | 75 | 76 | class DeepConvBlock(nn.Module): 77 | """2 Convolutional Layers, each of which is followed by a Batchnorm Layer and a ReLU Activation Layer. 78 | 79 | Input : [B x in_channels x T x F] 80 | Output : [B x out_chanels x T x F] 81 | 82 | Parameters 83 | ----------- 84 | in_channels : int 85 | out_channels : int 86 | momentum : float 87 | 88 | """ 89 | 90 | def __init__(self, in_channels, out_channels, momentum=0.01): 91 | super(DeepConvBlock, self).__init__() 92 | 93 | self.conv1 = ConvBlock(in_channels, out_channels, momentum) 94 | self.conv2 = ConvBlock(out_channels, out_channels, momentum) 95 | 96 | 97 | def forward(self, input): 98 | """ 99 | 100 | Parameters 101 | ---------- 102 | input : [B x in_channels x T x F] 103 | 104 | Returns 105 | ------- 106 | : [B x out_chanels x T x F] 107 | """ 108 | x = input 109 | return self.conv2(self.conv1(x)) 110 | 111 | 112 | class LinearBlock2D(nn.Module): 113 | """1 2D 1x1 Convolutional Layer with bias. 114 | 115 | Input : [B x in_channels x T x F] 116 | Output : [B x out_chanels x T x F] 117 | 118 | Parameters 119 | ----------- 120 | in_channels : int 121 | out_channels : int 122 | 123 | """ 124 | 125 | def __init__(self, in_channels, out_channels): 126 | super(LinearBlock2D, self).__init__() 127 | self.conv = nn.Conv2d(in_channels=in_channels, 128 | out_channels=out_channels, 129 | kernel_size=(1, 1), stride=(1, 1), bias=True) 130 | 131 | self.init_weights() 132 | 133 | 134 | def init_weights(self): 135 | init_layer(self.conv) 136 | 137 | def forward(self, input): 138 | """ 139 | Parameters 140 | ---------- 141 | input : [B x in_channels x T x F] 142 | 143 | Returns 144 | ------- 145 | x : [B x out_chanels x T x F] 146 | """ 147 | x = input 148 | x = self.conv(x) 149 | return x 150 | 151 | class LinearBlock1D(nn.Module): 152 | """1 1D 1x1 Convolutional Layer. 153 | 154 | Input : [B x in_channels x T] 155 | Output : [B x out_chanels x T] 156 | 157 | Parameters 158 | ----------- 159 | in_channels : int 160 | out_channels : int 161 | bias : boolean 162 | default : true 163 | has bias if true 164 | 165 | """ 166 | 167 | def __init__(self, in_channels, out_channels, bias=True): 168 | super(LinearBlock1D, self).__init__() 169 | self.conv = nn.Conv1d(in_channels=in_channels, 170 | out_channels=out_channels, 171 | kernel_size=1, stride=1, bias=bias) 172 | 173 | self.init_weights() 174 | 175 | 176 | def init_weights(self): 177 | init_layer(self.conv) 178 | 179 | 180 | def forward(self, input): 181 | """ 182 | Parameters 183 | ----------- 184 | input : [B x in_channels x T] 185 | 186 | Returns 187 | ----------- 188 | x : [B x out_chanels x T] 189 | """ 190 | 191 | x = input 192 | x = self.conv(x) 193 | return x 194 | 195 | class EncoderBlock(nn.Module): 196 | """A Convolutional Layer Followed by a Batchnorm Layer and a ReLU Activation Layer. 197 | 198 | Look details of the description at `ConvBlock`. 199 | 200 | """ 201 | 202 | def __init__(self, in_channels, out_channels, momentum = 0.01): 203 | super(EncoderBlock, self).__init__() 204 | 205 | self.conv_block = ConvBlock(in_channels, out_channels, momentum) 206 | 207 | def forward(self, input): 208 | x = input 209 | x = self.conv_block(x) 210 | #x_pool = F.avg_pool2d(x, kernel_size=self.downsample) 211 | return x 212 | 213 | class DecoderBlock(nn.Module): 214 | """A Deconv Block (a 2D 3x3 Deconvolutional Layer Followed by a Batchnorm Layer and a ReLU Activation Layer) followed by a `DeepConvBlock` or `ConvBlock`. 215 | 216 | Input: [B x in_channels x T x F], [B x (out_channels // 2) x (T* strides[0]) x (F * strides[1])] 217 | Output：[B x out_channels x (T* strides[0]) x (F * strides[1])] 218 | (ummmm... stride other than (2, 2) might require extra consideration of padding operation) 219 | 220 | Parameters 221 | ---------- 222 | in_channels : int 223 | out_channels : int 224 | strides : tuple 225 | momentum : float 226 | deep : boolean 227 | default: False 228 | the Deconv Block is followed by a `DeepConvBlock` if true else `ConvBlock` 229 | 230 | """ 231 | 232 | def __init__(self, in_channels, out_channels, strides, momentum=0.01, deep=False): 233 | super(DecoderBlock, self).__init__() 234 | 235 | self.conv = torch.nn.ConvTranspose2d(in_channels=in_channels, 236 | out_channels=out_channels, kernel_size=(3, 3), stride=strides, 237 | padding=(0, 0), output_padding=(0, 0), bias=False) 238 | 239 | self.bn = nn.BatchNorm2d(out_channels, momentum=momentum) 240 | self.conv_block = DeepConvBlock(out_channels * 2, out_channels, momentum) if deep else ConvBlock(out_channels * 2, out_channels, momentum) 241 | 242 | self.init_weights() 243 | 244 | self.prune_temporal = (not strides[-2] == 1) 245 | 246 | def init_weights(self): 247 | init_layer(self.conv) 248 | init_bn(self.bn) 249 | 250 | def prune(self, x): 251 | """Prune the shape of x after transpose convolution. 252 | """ 253 | if self.prune_temporal: 254 | x = x[:, :, : - 1, : - 1] 255 | else: 256 | x = x[:, :, 1 : -1, : -1] 257 | return x 258 | 259 | 260 | def forward(self, input_tensor, concat_tensor): 261 | """ 262 | 263 | Parameters 264 | ---------- 265 | input_tensor : tensor 266 | [B x in_channels x T x F] 267 | concat_tensor : tensor 268 | [B x (out_channels // 2) x (T* strides[0]) x (F * strides[1])] 269 | 270 | Returns 271 | --------- 272 | x : tensor 273 | [B x out_channels x (T* strides[0]) x (F * strides[1])] 274 | 275 | """ 276 | 277 | x = input_tensor 278 | x = F.relu_(self.bn(self.conv(x))) 279 | x = self.prune(x) 280 | x = torch.cat((x, concat_tensor), dim=1) 281 | x = self.conv_block(x) 282 | return x 283 | 284 | 285 | 286 | -------------------------------------------------------------------------------- /src/dataset/urmp/urmp_feature.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import argparse 3 | import csv 4 | import os 5 | import time 6 | import h5py 7 | import librosa 8 | import multiprocessing 9 | import sys 10 | 11 | sys.path.insert(1, os.path.join(sys.path[0], '../..')) 12 | 13 | from utils.utilities import (mkdir, float32_to_int16, freq2note, get_filename, get_process_groups, read_lst, write_lst) 14 | from utils.target_process import TargetProcessor 15 | 16 | from conf.feature import * 17 | 18 | et = 1e-8 19 | 20 | 21 | def remove_empty_segment(wav, frame_roll, sample_rate): 22 | segments = [] 23 | samples_per_frame = sample_rate * 1. / FRAMES_PER_SEC 24 | for i in range(frame_roll.shape[-1]): 25 | if not frame_roll[i] == NOTES_NUM_EXCLUDE_SILENCE: 26 | st = int(i * samples_per_frame) 27 | ed = int((i + 1)* samples_per_frame) 28 | if ed > wav.shape[-1]: 29 | ed = wav.shape[-1] 30 | segments.append(wav[st : ed]) 31 | if ed == wav.shape[-1]: 32 | break 33 | return np.concatenate(segments, -1) 34 | 35 | 36 | def pack_urmp_dataset_to_hdf5(args): 37 | 38 | dataset_dir = args.dataset_dir 39 | feature_dir = args.feature_dir 40 | process_num = args.process_num 41 | 42 | mkdir(feature_dir) 43 | 44 | meta_dict = {} 45 | meta_dict['audio_filename'] = [] 46 | audios_num = 0 47 | 48 | for folder in os.listdir(dataset_dir): 49 | if str.startswith(folder, "._"): 50 | continue 51 | meta_data = folder.split('_') 52 | if len(meta_data) < 4: 53 | continue 54 | audios_num += 1 55 | id = meta_data[0] 56 | name = meta_data[1] 57 | sources = meta_data[2:] 58 | audio = {} 59 | audio['mix'] = os.path.join(folder, f'AuMix_{folder}.wav') 60 | audio['separated_sources'] = [] 61 | audio['note_annotations'] = [] 62 | for j, s in enumerate(sources): 63 | audio['separated_sources'] += [os.path.join(folder, f'AuSep_{j + 1}_{s}_{id}_{name}.wav')] 64 | audio['note_annotations'] += [os.path.join(folder, f'Notes_{j + 1}_{s}_{id}_{name}.txt')] 65 | 66 | meta_dict['audio_filename'] += [audio] 67 | 68 | feature_time = time.time() 69 | print(f"The total number of the mixture audio is {audios_num}") 70 | def process_unit(n): 71 | 72 | name = meta_dict['audio_filename'][n]['mix'] 73 | print(name) 74 | audio_path = os.path.join(dataset_dir, name) 75 | (audio, _) = librosa.core.load(audio_path, sr=SAMPLE_RATE, mono=True) 76 | packed_hdf5_path = os.path.join(feature_dir, '{}.h5'.format(os.path.splitext(name)[0])) 77 | mkdir(os.path.dirname(packed_hdf5_path)) 78 | with h5py.File(packed_hdf5_path, 'w') as hf: 79 | #hf.attrs.create('midi_filename', data=meta_dict['midi_filename'][n].encode(), dtype='S100') 80 | hf.create_dataset(name='waveform', data=float32_to_int16(audio), dtype=np.int16) 81 | 82 | for i, name in enumerate(meta_dict['audio_filename'][n]['separated_sources']): 83 | audio_path = os.path.join(dataset_dir, name) 84 | 85 | (audio, _) = librosa.core.load(audio_path, sr=SAMPLE_RATE, mono=True) 86 | (hq_audio, _) = librosa.core.load(audio_path, sr=SAMPLE_RATE * 2, mono=True) 87 | 88 | note_annotations_path = os.path.join(dataset_dir, meta_dict['audio_filename'][n]['note_annotations'][i]) 89 | note_annotations = read_lst(note_annotations_path) 90 | note_annotations = [notes.split('\t\t') for notes in note_annotations] 91 | note_annotations = [[notes[0], float(notes[2]) + float(notes[0]), float(freq2note(notes[1]))] for notes in note_annotations] 92 | note_annotations = np.array(note_annotations, dtype = np.float32) 93 | note_annotations_lst = ['%s\t%s\t%s' % (notes[0], str(notes[1]), str(notes[2])) for notes in note_annotations] 94 | ref_path = os.path.join(feature_dir, '{}_ref.txt'.format(os.path.splitext(name)[0])) 95 | mkdir(os.path.dirname(packed_hdf5_path)) 96 | write_lst(ref_path, note_annotations_lst) 97 | 98 | duration = (audio.shape[-1] + SAMPLE_RATE - 1) // SAMPLE_RATE 99 | target_processor = TargetProcessor(duration, FRAMES_PER_SEC, BEGIN_NOTE, NOTES_NUM_EXCLUDE_SILENCE) 100 | target_dict = target_processor.process(0, note_annotations) 101 | frame_roll = np.array(target_dict['frame_roll'], dtype=np.int16) 102 | 103 | 104 | train_packed_hdf5_path = os.path.join(feature_dir, '{}._TRAIN.h5'.format(os.path.splitext(name)[0])) 105 | test_packed_hdf5_path = os.path.join(feature_dir, '{}._TEST.h5'.format(os.path.splitext(name)[0])) 106 | 107 | scale = 9 108 | dense_audio = remove_empty_segment(audio, frame_roll, SAMPLE_RATE) 109 | dense_hq_audio = remove_empty_segment(hq_audio, frame_roll, SAMPLE_RATE * 2) 110 | 111 | for i in range(scale): 112 | shift_pitch = i - (scale // 2) 113 | packed_hdf5_path = os.path.join(feature_dir, '{}._TRAIN_shift_pitch_{}.h5'.format(os.path.splitext(name)[0], shift_pitch)) 114 | if os.path.exists(packed_hdf5_path): 115 | continue 116 | 117 | if shift_pitch == 0: 118 | shift_audio = audio 119 | shift_dense_audio = dense_audio 120 | else: 121 | shift_audio = librosa.effects.pitch_shift(hq_audio, SAMPLE_RATE * 2, n_steps=shift_pitch) 122 | shift_audio = librosa.core.resample(shift_audio, SAMPLE_RATE * 2, SAMPLE_RATE) 123 | shift_dense_audio = librosa.effects.pitch_shift(dense_hq_audio, SAMPLE_RATE * 2, n_steps=shift_pitch) 124 | shift_dense_audio = librosa.core.resample(shift_dense_audio, SAMPLE_RATE * 2, SAMPLE_RATE) 125 | 126 | shift_frame_roll = frame_roll.copy() + shift_pitch 127 | shift_frame_roll[shift_frame_roll == NOTES_NUM_EXCLUDE_SILENCE + shift_pitch] = NOTES_NUM_EXCLUDE_SILENCE 128 | shift_frame_roll = np.clip(shift_frame_roll, 0, NOTES_NUM_EXCLUDE_SILENCE) 129 | 130 | with h5py.File(packed_hdf5_path, 'w') as hf: 131 | hf.create_dataset(name='shift_waveform', data=float32_to_int16(shift_audio), dtype=np.int16) 132 | hf.create_dataset(name='shift_dense_waveform', data=float32_to_int16(shift_dense_audio), dtype=np.int16) 133 | hf.create_dataset(name='frame_roll', data=shift_frame_roll, dtype=np.int16) 134 | 135 | with h5py.File(train_packed_hdf5_path, 'w') as hf: 136 | hf.create_dataset(name='waveform', data=float32_to_int16(audio), dtype=np.int16) 137 | hf.create_dataset(name='frame_roll', data=frame_roll, dtype=np.int16) 138 | 139 | with h5py.File(test_packed_hdf5_path, 'w') as hf: 140 | hf.create_dataset(name='waveform', data=float32_to_int16(audio), dtype=np.int16) 141 | hf.create_dataset(name='waveform_path', data=[audio_path.encode()], dtype='S200') 142 | hf.create_dataset(name='note_annotations_txt', data=[ref_path.encode()], dtype='S200') 143 | hf.create_dataset(name='frame_roll', data=frame_roll, dtype=np.int16) 144 | 145 | def process_group(st, ed, total_num, pid): 146 | print(f"process {pid + 1} starts") 147 | for n in range(st, ed): 148 | process_unit(n) 149 | print(f"process {pid + 1} : {n + 1}/{total_num} done.") 150 | print(f"process {pid + 1} ends") 151 | 152 | 153 | audio_groups = get_process_groups(audios_num, process_num) 154 | for pid, (st, ed) in enumerate(audio_groups): 155 | p = multiprocessing.Process(target = process_group, args = (st, ed, audios_num, pid)) 156 | p.start() 157 | 158 | if __name__ == '__main__': 159 | 160 | parser = argparse.ArgumentParser(description='') 161 | parser.add_argument('--dataset_dir', type=str, required=True, help='Directory of dataset.') 162 | parser.add_argument('--feature_dir', type=str, required=True, help='Directory to store generated files.') 163 | parser.add_argument('--process_num', type=int, required=True, help='Number of processes.') 164 | 165 | args = parser.parse_args() 166 | pack_urmp_dataset_to_hdf5(args) 167 | 168 | -------------------------------------------------------------------------------- /src/dataset/urmp/urmp_sample.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import numpy as np 4 | import torch 5 | from torch.utils.data import Dataset 6 | from torch.utils.data.dataloader import default_collate 7 | import random 8 | import h5py 9 | from prefetch_generator import BackgroundGenerator 10 | 11 | from utils.utilities import (read_lst, read_config, int16_to_float32, encode_mu_law) 12 | from utils.audio_utilities import write_audio 13 | 14 | from conf.feature import * 15 | from conf.sample import * 16 | from conf.urmp import * 17 | 18 | 19 | shuffle_rng = np.random.RandomState(1234) 20 | sample_rng = np.random.RandomState(1234) 21 | 22 | 23 | class UrmpDataset(): 24 | def __init__(self, instr_name): 25 | self._file_lst = read_lst(str.replace(TRAINING_FILE_LST_PATH, "INSTR_NAME", instr_name)) 26 | audios_num = len(self._file_lst) 27 | self._data = [None] * audios_num 28 | self._tracks_id = np.arange(audios_num) 29 | self._audios_num = audios_num 30 | self._current_id = 0 31 | self.tag = -1 32 | 33 | 34 | 35 | def __get_next_track_id__(self, pos): 36 | audios_num = self._audios_num 37 | current_id = pos % audios_num 38 | shuffle_rng.shuffle(self._tracks_id) 39 | nid = self._tracks_id[current_id] 40 | return nid 41 | 42 | def next_sample(self, pos=None, is_query=False): 43 | 44 | def is_silence(x): 45 | return x.shape[-1] * 88 == x.sum() 46 | 47 | def frame_roll_mask(x, y): 48 | mask = np.ones_like(x) 49 | mask[x == 88] = 0 50 | mask[y == 88] = 1 51 | return mask 52 | 53 | def load_file(pos, track_id, shift_pitch): 54 | if self._data[track_id] is None: 55 | hdf5_path = self._file_lst[track_id] 56 | datas = [] 57 | for i in range(POS_SHIFT_SEMITONE): 58 | data = {} 59 | train_hdf5_path = str.replace(hdf5_path, '.h5', f'._TRAIN_shift_pitch_{i - SHIFT_SEMITONE}.h5') 60 | hf = h5py.File(train_hdf5_path, 'r') 61 | data = {'shift_waveform': int16_to_float32(hf['shift_waveform'][:])[None, :], 62 | 'shift_dense_waveform' : int16_to_float32(hf['shift_dense_waveform'][:])[None, :], 63 | 'frame_roll': hf['frame_roll'][:].astype(np.int)} 64 | datas.append(data) 65 | self._data[track_id] = datas 66 | return self._data[track_id][shift_pitch + SHIFT_SEMITONE] 67 | 68 | def load_cache_data(pos, track_id, other_nid, another_nid, is_query): 69 | 70 | if is_query: 71 | shift_pitch = sample_rng.randint(0, POS_SHIFT_SEMITONE) - SHIFT_SEMITONE 72 | hf = load_file(pos, other_nid, shift_pitch) 73 | shift_dense_waveform = hf['shift_dense_waveform'] 74 | st = sample_rng.randint(0, shift_dense_waveform.shape[1] - SAMPLE_DURATION) 75 | query_waveform = shift_dense_waveform[:, st : st + SAMPLE_DURATION].copy() 76 | 77 | shift_pitch = sample_rng.randint(0, POS_SHIFT_SEMITONE) - SHIFT_SEMITONE 78 | hf = load_file(pos, another_nid, shift_pitch) 79 | shift_dense_waveform = hf['shift_dense_waveform'] 80 | st = sample_rng.randint(0, shift_dense_waveform.shape[1] - SAMPLE_DURATION) 81 | another_query_waveform = shift_dense_waveform[:, st : st + SAMPLE_DURATION].copy() 82 | 83 | return query_waveform, another_query_waveform 84 | 85 | else: 86 | 87 | shift_pitch = sample_rng.randint(0, POS_SHIFT_SEMITONE) - SHIFT_SEMITONE 88 | hf = load_file(pos, track_id, shift_pitch) 89 | waveform = hf['shift_waveform'] 90 | frame_roll = hf['frame_roll'] 91 | 92 | shift_pitch = sample_rng.randint(0, POS_SHIFT_SEMITONE) - SHIFT_SEMITONE 93 | hf = load_file(pos, track_id, shift_pitch) 94 | strong_waveform = hf['shift_waveform'] 95 | another_frame_roll = hf['frame_roll'] 96 | 97 | start_time = sample_rng.randint(0, int((waveform.shape[-1] - SAMPLE_DURATION) / SAMPLE_RATE)) 98 | st = start_time * SAMPLE_RATE 99 | frame_roll_st = int(start_time * FRAMES_PER_SEC) 100 | ed = frame_roll_st + FRAME_DURATION + 1 101 | obj_frame_roll = frame_roll[frame_roll_st : ed].copy() 102 | 103 | another_start_time = sample_rng.randint(0, int((waveform.shape[-1] - SAMPLE_DURATION) / SAMPLE_RATE)) if is_silence(obj_frame_roll) else start_time 104 | another_st = another_start_time * SAMPLE_RATE 105 | another_frame_roll_st = int(another_start_time * FRAMES_PER_SEC) 106 | another_ed = another_frame_roll_st + FRAME_DURATION + 1 107 | another_frame_roll = another_frame_roll[another_frame_roll_st : another_ed].copy() 108 | 109 | ori_waveform = waveform[:, st : st + SAMPLE_DURATION].copy() 110 | strong_waveform = strong_waveform[:, another_st : another_st + SAMPLE_DURATION].copy() 111 | 112 | return (ori_waveform, strong_waveform, obj_frame_roll, another_frame_roll) 113 | 114 | def get_next_track(pos=None, is_query=False): 115 | nid = self.__get_next_track_id__(pos) 116 | other_nid = self.__get_next_track_id__(pos + 1) 117 | another_nid = self.__get_next_track_id__(pos + 2) 118 | return load_cache_data(pos, nid, other_nid, another_nid, is_query) 119 | 120 | tracks = get_next_track(pos, is_query) 121 | return tracks 122 | 123 | def get_samples_num(self): 124 | return len(self._file_lst) 125 | 126 | 127 | 128 | class UrmpSample(Dataset): 129 | def __init__(self): 130 | super(UrmpSample, self).__init__() 131 | 132 | datasets = {} 133 | for instr in SEEN_INSTRUMENTS: 134 | datasets[instr] = UrmpDataset(instr) 135 | 136 | self._datasets = datasets 137 | datasets_index = [] 138 | datasets_samples_num = [0] 139 | for d in datasets: 140 | datasets_index.append(d) 141 | n = datasets[d].get_samples_num() 142 | datasets_samples_num.append(n + datasets_samples_num[-1]) 143 | 144 | self._datasets_index = datasets_index 145 | self.datasets_samples_num = datasets_samples_num 146 | 147 | def __iter__(self): 148 | return BackgroundGenerator(super().__iter__()) 149 | 150 | def __get_train_sample__(self, index, instr_indexs, is_query): 151 | input_samples = [] 152 | datasets = self._datasets 153 | datasets_index = self._datasets_index 154 | 155 | for instr in instr_indexs: 156 | dataset = datasets[datasets_index[instr]] 157 | inputs = dataset.next_sample(index, is_query) 158 | for i, input in enumerate(inputs): 159 | if len(input_samples) == i: 160 | input_samples.append([]) 161 | input = np.expand_dims(input, 0) 162 | input_samples[i].append(input) 163 | 164 | for i, input in enumerate(input_samples): 165 | input_samples[i] = np.concatenate(input_samples[i], 0) 166 | 167 | return input_samples 168 | 169 | 170 | def __sample_seen_instruments__(self): 171 | instruments_ratio = self.datasets_samples_num 172 | index = sample_rng.randint(instruments_ratio[-1]) 173 | for i in range(len(instruments_ratio) - 1): 174 | if index < instruments_ratio[i + 1]: 175 | return i 176 | 177 | assert False 178 | 179 | def __getitem__(self, index = 0): 180 | up_bound = SEEN_INSTRUMENTS_NUM if SEEN_INSTRUMENTS_NUM < UP_BOUND else UP_BOUND 181 | selected_ids = [] 182 | while len(selected_ids) < up_bound: 183 | id = self.__sample_seen_instruments__() 184 | if not id in selected_ids: 185 | selected_ids.append(id) 186 | 187 | (separated, strong_separated, target, another_target) = self.__get_train_sample__(index, selected_ids[ :SOURCES_NUM_OF_MIXTURE], is_query=False) 188 | (query_separated, another_query_separated) = self.__get_train_sample__(index, selected_ids, is_query=True) 189 | mix = torch.from_numpy(separated).float().sum(0) 190 | strong_mix = torch.from_numpy(strong_separated).float().sum(0) 191 | separated = torch.from_numpy(separated).float() 192 | query_separated = torch.from_numpy(query_separated).float() 193 | another_query_separated = torch.from_numpy(another_query_separated).float() 194 | target = torch.from_numpy(target).long() 195 | another_target = torch.from_numpy(another_target).long() 196 | batch = (separated, query_separated, another_query_separated, target, another_target) 197 | return mix, strong_mix, batch 198 | 199 | def __len__(self): 200 | return SAMPLES_NUM 201 | 202 | def get_len(self): 203 | return self.__len__() 204 | 205 | def get_collate_fn(self): 206 | return default_collate 207 | 208 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import time 4 | import numpy as np 5 | import random 6 | import argparse 7 | import torch 8 | import torch.nn as nn 9 | import torch.nn.functional as F 10 | 11 | import src 12 | from dataset.urmp.urmp_sample import UrmpSample 13 | from models.model_factory import ModelFactory 14 | from utils.utilities import (compute_time, save_score, mkdir) 15 | from utils.multiEpochsDataLoader import MultiEpochsDataLoader as DataLoader 16 | from conf.sample import * 17 | from conf.feature import * 18 | 19 | def seed_torch(seed=1234): 20 | os.environ['PYTHONHASHSEED'] = str(seed) 21 | torch.manual_seed(seed) 22 | torch.cuda.manual_seed_all(seed) 23 | np.random.seed(seed) 24 | random.seed(seed) 25 | torch.cuda.manual_seed_all(seed) 26 | torch.backends.cudnn.deterministic = True 27 | torch.backends.cudnn.benchmark = False 28 | 29 | def mae(input, target): 30 | return torch.mean(torch.abs(input - target)) 31 | 32 | def align(a, b, dim): 33 | return a.transpose(0, dim)[:b.shape[dim]].transpose(0, dim) 34 | 35 | def onehot(x, dim, classes_num): 36 | x = x.unsqueeze(dim) 37 | shape = list(x.shape) 38 | shape[dim] = classes_num 39 | y = torch.zeros(shape).to(x.device).scatter_(dim, x, 1) 40 | return y 41 | 42 | def move_data2cuda(urmp_batch): 43 | mix, another_mix, batch = urmp_batch 44 | separated, query, another_query, pitch_target, another_pitch_target = batch 45 | batch = [separated, query, another_query, pitch_target, another_pitch_target] 46 | for i, b in enumerate(batch): 47 | batch[i] = b.cuda() 48 | mix = mix.cuda() 49 | another_mix = another_mix.cuda() 50 | return mix, another_mix, batch 51 | 52 | def train_step(network, urmp_batch, mode, adv_id=0): 53 | mix, another_mix, batch = urmp_batch 54 | separated, query, another_query, pitch_target, another_pitch_target = batch 55 | 56 | a = 1./ 8. 57 | if mode == 'query': 58 | 59 | #contrastive loss 60 | 61 | latent_vectors = [] 62 | hQuery = [] 63 | for i in range(query.shape[1]): 64 | query_spec = network(query[:, i], 'wav2spec') 65 | another_query_spec = network(another_query[:, i], 'wav2spec') 66 | h = network(query_spec, 'query') 67 | hc = network(another_query_spec, 'query') 68 | latent_vectors.append([h, hc]) 69 | sim = 0. 70 | sep_num = query.shape[1] 71 | batch_size = query.shape[0] 72 | for i in range(sep_num): 73 | next_i = (i + 1) % sep_num 74 | sim += torch.mean((latent_vectors[i][0] - latent_vectors[i][1])**2, dim=-1) + \ 75 | torch.relu(a - torch.mean((latent_vectors[i][0] - latent_vectors[next_i][1])**2, dim=-1)) 76 | sim_loss = sim.mean() / sep_num 77 | return sim_loss, f'{sim_loss.item()}' 78 | 79 | 80 | elif mode == 'AMT': 81 | 82 | # transcription loss for AMT-only baseline 83 | 84 | pitch_transcription = [] 85 | mix_spec = network(mix, 'wav2spec') 86 | for i in range(separated.shape[1]): 87 | query_spec = network(query[:, i], 'wav2spec') 88 | hQuery = network(query_spec, "query") 89 | args = (mix_spec, hQuery) 90 | prob = network(args, 'transcribe') 91 | pitch_transcription.append(prob) 92 | 93 | transcription = torch.stack(pitch_transcription, 2) 94 | pitch_loss = nn.CrossEntropyLoss()(transcription, align(pitch_target, transcription, -1)) 95 | return pitch_loss, f'{pitch_loss.item()}' 96 | 97 | elif mode == 'MSS': 98 | 99 | # separation loss for MSS-only baseline 100 | 101 | spec_losses = [] 102 | mix_spec = network(mix, 'wav2spec') 103 | for i in range(separated.shape[1]): 104 | query_spec = network(query[:, i], 'wav2spec') 105 | hQuery = network(query_spec, "query") 106 | source_spec = network(separated[:, i], 'wav2spec') 107 | args = (mix_spec, hQuery) 108 | est_spec = network(args, 'separate') 109 | spec_loss = torch.abs(est_spec - align(source_spec, est_spec, -2)) 110 | spec_losses.append(spec_loss) 111 | 112 | spec_loss = torch.stack(spec_losses, 1) 113 | spec_loss = spec_loss.mean() 114 | return spec_loss, f'{spec_loss.item()}' 115 | 116 | 117 | elif mode == 'MSS-AMT': 118 | 119 | # separation and transcription loss for muli-task baseline and multi-task score-informed (MSI) model 120 | 121 | spec_losses = [] 122 | pitch_transcription = [] 123 | mix_spec = network(mix, 'wav2spec') 124 | for i in range(separated.shape[1]): 125 | source_spec = network(separated[:, i], 'wav2spec') 126 | query_spec = network(query[:, i], 'wav2spec') 127 | hQuery = network(query_spec, "query") 128 | args = (mix_spec, hQuery) 129 | est_spec, prob = network(args, 'multiTask') 130 | pitch_transcription.append(prob) 131 | spec_loss = torch.abs(est_spec - align(source_spec, est_spec, -2)) 132 | spec_losses.append(spec_loss) 133 | 134 | transcription = torch.stack(pitch_transcription, 2) 135 | pitch_loss = nn.CrossEntropyLoss()(transcription, align(pitch_target, transcription, -1)) 136 | 137 | spec_loss = torch.stack(spec_losses, 1) 138 | spec_loss = spec_loss.mean() 139 | return spec_loss + pitch_loss, f'{spec_loss.item()} {pitch_loss.item()}' 140 | 141 | 142 | 143 | elif mode == 'MSI-DIS': 144 | 145 | # transcription loss and pitch-translation invariance loss for MSI-DIS model 146 | 147 | spec_losses = [] 148 | another_mix_spec = network(another_mix, 'wav2spec') 149 | mix_spec = network(mix, 'wav2spec') 150 | target = onehot(pitch_target, 1, NOTES_NUM) 151 | another_target = onehot(another_pitch_target, 1, NOTES_NUM) 152 | 153 | pitch_transcription = [] 154 | another_pitch_transcription = [] 155 | 156 | for i in range(separated.shape[1]): 157 | source_spec = network(separated[:, i], 'wav2spec') 158 | 159 | query_spec = network(query[:, i], 'wav2spec') 160 | hQuery = network(query_spec, "query") 161 | 162 | args = (mix_spec, another_mix_spec, hQuery) 163 | est_spec, target_prob = network(args, 'transfer') 164 | 165 | pitch_transcription.append(target_prob) 166 | spec_loss = torch.abs(est_spec - align(source_spec, est_spec, -2)) 167 | spec_losses.append(spec_loss) 168 | 169 | spec_loss = torch.stack(spec_losses, 1) 170 | spec_loss = spec_loss.mean() 171 | 172 | transcription = torch.stack(pitch_transcription, 2) 173 | pitch_loss = nn.CrossEntropyLoss()(transcription, align(pitch_target, transcription, -1)) 174 | 175 | return spec_loss + pitch_loss, f'{spec_loss.item()} {pitch_loss.item()}' 176 | 177 | 178 | 179 | 180 | def train(model_name, load_epoch, epoch, model_folder): 181 | 182 | nnet = ModelFactory(model_name) 183 | nnet = nnet.cuda() 184 | 185 | learning_rate=LEARNING_RATE 186 | 187 | mkdir(model_folder) 188 | 189 | if load_epoch >=0: 190 | model_path = f'{model_folder}/params_epoch-{load_epoch}.pkl' 191 | nnet.load_state_dict(torch.load(model_path), strict=True) 192 | 193 | resume_epoch = load_epoch + 1 194 | 195 | urmp_data = UrmpSample() 196 | 197 | urmp_loader = DataLoader(urmp_data, 198 | batch_size=TRAINING_BATCH_SIZE, shuffle=False, num_workers=1, pin_memory=True, persistent_workers=False, 199 | collate_fn=urmp_data.get_collate_fn()) 200 | 201 | def get_parameters(nnet, model_name): 202 | parameters = {} 203 | parameters['query'] = list(nnet.network.parameters()) 204 | 205 | if model_name in ['MSI']: 206 | parameters['MSS-AMT'] = list(nnet.network.parameters()) 207 | if model_name in ['UNET']: 208 | parameters['MSS'] = list(nnet.network.parameters()) 209 | if model_name in ['MSI-DIS', 'AMT', 'MSS', 'MSS-AMT']: 210 | parameters[model_name] = list(nnet.network.parameters()) 211 | 212 | return parameters 213 | 214 | 215 | def get_optimizer(r_epoch, parameters): 216 | optimizers = [] 217 | for param in parameters: 218 | optimizer = torch.optim.Adam(parameters[param], lr=learning_rate / (2**(r_epoch // DECAY)), \ 219 | betas=(0.9, 0.999), eps=1e-08, weight_decay=0., amsgrad=True) 220 | optimizers.append({'mode' : param, 'opt': optimizer, 'name' : param}) 221 | return optimizers 222 | 223 | parameters = get_parameters(nnet, model_name) 224 | optimizer = get_optimizer(resume_epoch, parameters) 225 | step_per_epoch = urmp_data.get_len() // TRAINING_BATCH_SIZE 226 | 227 | pre_time = time.time() 228 | pre_time = compute_time(f'begin train...', pre_time) 229 | nnet.train() 230 | pre_time = compute_time(f'train done', pre_time) 231 | for i in range(resume_epoch, epoch): 232 | if i % DECAY == 0: 233 | pre_time = compute_time(f'begin update op...', pre_time) 234 | optimizer = get_optimizer(resume_epoch, parameters) 235 | print('learning rate', learning_rate / (2**(i // DECAY))) 236 | 237 | 238 | for i_batch, urmp_batch in enumerate(urmp_loader): 239 | urmp_batch = move_data2cuda(urmp_batch) 240 | for j in range(len(optimizer)): 241 | op = optimizer[j]['opt'] 242 | name = optimizer[j]['name'] 243 | op.zero_grad() 244 | loss, loss_text = train_step(nnet, urmp_batch, optimizer[j]['mode']) 245 | loss.backward() 246 | op.step() 247 | print(f"update {optimizer[j]['mode']} network epoch {i} loss: {i_batch}/{step_per_epoch}", loss_text) 248 | del loss 249 | torch.save(nnet.state_dict(), f"{model_folder}/params_epoch-{i}.pkl") 250 | if __name__ == "__main__": 251 | 252 | seed_torch(1234) 253 | 254 | parser = argparse.ArgumentParser(description='') 255 | parser.add_argument('--model_name', type=str, required=True, help='Model name in [`AMT` for trainscription-only baseline, \ 256 | `MSS` for separation-only baseline, \ 257 | `MSS-AMT` for multi-task baseline, \ 258 | `MSI` for the proposed multi-task score-informed model, \ 259 | `MSI-DIS` for the proposed multi-task score-informed with further disentanglement model].') 260 | parser.add_argument('--resume_epoch', type=int, default=-1, help='Epoch to resume training.') 261 | parser.add_argument('--model_folder', type=str, required=True, help='Directory to store model weights.') 262 | parser.add_argument('--epoch', type=int, default=200, help='Number of total training epochs.') 263 | 264 | args = parser.parse_args() 265 | 266 | assert args.model_name in ["AMT", "MSS", "MSS-AMT", "MSI", "MSI-DIS"] 267 | 268 | train(model_name=args.model_name, 269 | load_epoch=args.resume_epoch, 270 | epoch=args.epoch, 271 | model_folder=args.model_folder) 272 | --------------------------------------------------------------------------------