├── .gitignore ├── LICENSE ├── README.md ├── egs ├── daps │ ├── config.json │ └── run.py ├── edinburgh_tts │ ├── config.json │ └── run.py └── wsj0-2mix │ ├── chimera │ ├── evaluate.py │ ├── msa │ │ ├── RESULT │ │ ├── config.json │ │ └── run.py │ └── psa │ │ ├── RESULT │ │ ├── config.json │ │ └── run.py │ ├── deep_clustering │ ├── RESULT │ ├── config.json │ ├── evaluate.py │ └── run.py │ ├── phase-net │ ├── config.json │ └── run.py │ └── tasnet │ ├── conv-tasnet │ ├── config.json │ └── run.py │ ├── evaluate.py │ └── lstm-tasnet │ ├── config.json │ └── run.py └── onssen ├── __init__.py ├── data ├── __init__.py ├── daps_enhance.py ├── edinburgh_tts.py ├── feature_utils.py └── wsj0_2mix.py ├── evaluate ├── __init__.py └── sdr.py ├── loss ├── __init__.py ├── loss_chimera.py ├── loss_dc.py ├── loss_e2e.py ├── loss_mask.py ├── loss_phase.py └── loss_util.py ├── nn ├── __init__.py ├── chimera.py ├── deep_clustering.py ├── enhancement.py ├── phase_network.py ├── tasnet.py └── uPIT-LSTM.py └── utils ├── __init__.py ├── basic.py ├── test.py └── train.py /.gitignore: -------------------------------------------------------------------------------- 1 | *.pyc 2 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ONSSEN: An Open-source Speech Separation and Enhancement Library 2 | ====== 3 | Onssen, pronounced as おんせん(温泉, Japanese hot spring), is a PyTorch-based library for speech separation, speech enhancement, or speech style transformation. 4 | 5 | Development plan: 6 | ------ 7 | * [ ] Provide template classes for data, model, and evaluation 8 | * [ ] Move models to separate folders (i.e. Kaldi style) 9 | * [ ] Reproduce scores and upload pretrained models 10 | * [ ] Finish inference method for online separation 11 | 12 | 2020-04-20 Updates: 13 | ----- 14 | + Add evaluation method for deep clustering 15 | + Use W_{MR} weight in deep clustering 16 | + Minor changes 17 | 18 | 19 | Supported Models 20 | ------ 21 | 22 | + Deep Clustering 23 | + Chimera Net 24 | + Chimera++ 25 | + Phase Estimation Network 26 | + Speech Enhancement with Restoration Layers 27 | 28 | 29 | Supported Dataset 30 | ------ 31 | 32 | + Wsj0-2mix (http://www.merl.com/demos/deep-clustering) 33 | + Daps (https://archive.org/details/daps_dataset) 34 | + Edinburgh-TTS (https://datashare.is.ed.ac.uk/handle/10283/2791) 35 | 36 | Requirements 37 | ------ 38 | + PyTorch 39 | + LibRosa 40 | + NumPy 41 | 42 | Usage 43 | ------ 44 | You can simply use the existing config JSON file or customize your config file to train the enhancement or separation model. 45 | under the egs/wsj0-2mix/deep_clustering/ directory: 46 | ``` 47 | python run.py -c config.json 48 | ``` 49 | 50 | 51 | Citing 52 | ------ 53 | 54 | If you use onssen for your research project, please cite one of the following bibtex citations: 55 | 56 | @article{ni2019onssen, 57 | title={Onssen: an open-source speech separation and enhancement library}, 58 | author={Ni, Zhaoheng and Mandel, Michael I}, 59 | journal={arXiv preprint arXiv:1911.00982}, 60 | year={2019} 61 | } 62 | 63 | @Misc{onssen, 64 | author = {Zhaoheng Ni and Michael Mandel}, 65 | title = "ONSSEN: An Open-source Speech Separation and Enhancement Library", 66 | howpublished = {\url{https://github.com/speechLabBcCuny/onssen}}, 67 | year = {2019} 68 | } 69 | -------------------------------------------------------------------------------- /egs/daps/config.json: -------------------------------------------------------------------------------- 1 | { 2 | "model_name": "enhance", 3 | "dataset": "daps", 4 | "feature_options": { 5 | "data_path": "/home/data/daps", 6 | "batch_size": 8, 7 | "frame_length": 400, 8 | "sampling_rate": 44100, 9 | "window_size": 2048, 10 | "hop_size": 441 11 | }, 12 | "optimizer_options": { 13 | "name": "adam", 14 | "lr": 0.001 15 | }, 16 | "model_options": { 17 | "input_dim": 1025, 18 | "output_dim": 1025, 19 | "hidden_dim": 300, 20 | "num_layers": 3, 21 | "dropout": 0.3 22 | }, 23 | "device": "cpu", 24 | "num_speaker": 2, 25 | "num_epoch": 200, 26 | "resume_from_checkpoint": "False", 27 | "checkpoint_path": "./checkpoint/" 28 | } 29 | -------------------------------------------------------------------------------- /egs/daps/run.py: -------------------------------------------------------------------------------- 1 | from onssen import data, loss, nn, utils 2 | from attrdict import AttrDict 3 | import torch 4 | import json 5 | 6 | 7 | def main(): 8 | parser = argparse.ArgumentParser(description='Parse the config path') 9 | parser.add_argument("-c", "--config", dest="path", 10 | help='The path to the config file. e.g. python run.py --config dc_config.json') 11 | 12 | config = parser.parse_args() 13 | with open(config.path) as f: 14 | args = json.load(f) 15 | args = AttrDict(args) 16 | device = torch.device(args.device) 17 | args.model = onssen.nn.enhance(args.model_options) 18 | args.model.to(device) 19 | args.train_loader = data.daps_enhance_dataloader(args.train_num_batch, args.feature_options, 'train', args.cuda_option, self.device) 20 | args.valid_loader = data.daps_enhance_dataloader(args.vaildate_num_batch, args.feature_options, 'validation', args.cuda_option, self.device) 21 | args.optimizer = utils.build_optimizer(args.model.parameters(), args.optimizer_options) 22 | args.loss_fn = loss.loss_mask_msa 23 | trainer = onssen.utils.trainer(args) 24 | trainer.run() 25 | 26 | tester = onssen.utils.tester(args) 27 | tester.eval() 28 | 29 | 30 | if __name__ == "__main__": 31 | main() 32 | -------------------------------------------------------------------------------- /egs/edinburgh_tts/config.json: -------------------------------------------------------------------------------- 1 | { 2 | "model_name": "chimera++", 3 | "dataset": "edinburgh_tts", 4 | "feature_options": { 5 | "data_path": "/home/data/Edinburgh_TTS/", 6 | "batch_size": 16, 7 | "frame_length": 400, 8 | "sampling_rate": 16000, 9 | "window_size": 1024, 10 | "hop_size": 256, 11 | "db_threshold": 40 12 | }, 13 | "optimizer_options": { 14 | "name": "adam", 15 | "lr": 0.001 16 | }, 17 | "model_options": { 18 | "input_dim": 513, 19 | "hidden_dim": 600, 20 | "embedding_dim": 40, 21 | "num_layers": 3, 22 | "dropout": 0.3 23 | }, 24 | "device": "cpu", 25 | "num_speaker": 2, 26 | "num_epoch": 200, 27 | "resume_from_checkpoint": "False", 28 | "checkpoint_path": "./checkpoint/" 29 | } 30 | -------------------------------------------------------------------------------- /egs/edinburgh_tts/run.py: -------------------------------------------------------------------------------- 1 | from onssen import data, loss, nn, utils 2 | from attrdict import AttrDict 3 | import torch 4 | import json 5 | 6 | 7 | def main(): 8 | parser = argparse.ArgumentParser(description='Parse the config path') 9 | parser.add_argument("-c", "--config", dest="path", 10 | help='The path to the config file. e.g. python run.py --config dc_config.json') 11 | 12 | config = parser.parse_args() 13 | with open(config.path) as f: 14 | args = json.load(f) 15 | args = AttrDict(args) 16 | device = torch.device(args.device) 17 | args.model = onssen.nn.chimera(args.model_options) 18 | args.model.to(device) 19 | args.train_loader = data.edinburgh_tts_dataloader(args.model_name, args.feature_options, 'train', args.cuda_option, self.device) 20 | args.valid_loader = data.edinburgh_tts_dataloader(args.model_name, args.feature_options, 'validation', args.cuda_option, self.device) 21 | args.optimizer = utils.build_optimizer(args.model.parameters(), args.optimizer_options) 22 | args.loss_fn = loss.loss_chimera_psa 23 | trainer = onssen.utils.trainer(args) 24 | trainer.run() 25 | 26 | tester = onssen.utils.tester(args) 27 | tester.eval() 28 | 29 | 30 | if __name__ == "__main__": 31 | main() 32 | -------------------------------------------------------------------------------- /egs/wsj0-2mix/chimera/evaluate.py: -------------------------------------------------------------------------------- 1 | import sys 2 | sys.path.append('../../../../../onssen/') 3 | 4 | from onssen import utils 5 | from sklearn.cluster import KMeans 6 | import librosa 7 | import numpy as np 8 | import torch 9 | 10 | 11 | class tester_chimera(utils.tester): 12 | def get_est_sig(self, input, label, output): 13 | """ 14 | args: 15 | feature_mix: batch x frame x frequency 16 | embedding: batch x frame x frequency x embedding_dim 17 | stft_r_mix: batch x frame x frequency 18 | stft_i_mix: batch x frame x frequency 19 | sig_ref: batch x num_spk x nsample 20 | return: 21 | sig_est: batch x num_spk x nsample 22 | """ 23 | feature_mix, = input 24 | embedding, mask_A, mask_B = output 25 | stft_r_mix, stft_i_mix, sig_ref = label 26 | 27 | stft_r_mix = stft_r_mix.detach().cpu().numpy() 28 | stft_i_mix = stft_i_mix.detach().cpu().numpy() 29 | embedding = embedding.detach().cpu().numpy() 30 | feature_mix = feature_mix.detach().cpu().numpy() 31 | mask_A = mask_A.detach().cpu().numpy() 32 | mask_B = mask_B.detach().cpu().numpy() 33 | 34 | stft_mix = stft_r_mix + 1j * stft_i_mix 35 | batch, frame, frequency = feature_mix.shape 36 | batch, num_spk, nsample = sig_ref.shape 37 | mask = np.zeros((num_spk, frame, frequency)) 38 | mask[0, :, :] = mask_A[0] 39 | mask[1, :, :] = mask_B[0] 40 | stft_est = stft_mix * mask 41 | sig_est = np.zeros((batch, num_spk, nsample)) 42 | for i in range(num_spk): 43 | sig_est[0, i] = librosa.core.istft(stft_est[i].T, hop_length=64, length=nsample) 44 | sig_est = torch.tensor(sig_est).to(self.device) 45 | return sig_est, sig_ref 46 | 47 | -------------------------------------------------------------------------------- /egs/wsj0-2mix/chimera/msa/RESULT: -------------------------------------------------------------------------------- 1 | SI-SDR: 10.33 2 | -------------------------------------------------------------------------------- /egs/wsj0-2mix/chimera/msa/config.json: -------------------------------------------------------------------------------- 1 | { 2 | "model_name": "chimera", 3 | "feature_options": { 4 | "data_path": "/home/data/wsj0-mix/2speakers/", 5 | "batch_size": 8, 6 | "frame_length": 400, 7 | "sampling_rate": 8000, 8 | "window_size": 256, 9 | "hop_size": 64, 10 | "db_threshold": 40 11 | }, 12 | "optimizer_options": { 13 | "name": "adam", 14 | "lr": 0.001 15 | }, 16 | "model_options": { 17 | "input_dim": 129, 18 | "hidden_dim": 300, 19 | "embedding_dim": 20, 20 | "num_layers": 4 21 | }, 22 | "device": "cuda", 23 | "num_speaker": 2, 24 | "num_epoch": 200, 25 | "resume_from_checkpoint": "False", 26 | "checkpoint_path": "./checkpoint/" 27 | } 28 | -------------------------------------------------------------------------------- /egs/wsj0-2mix/chimera/msa/run.py: -------------------------------------------------------------------------------- 1 | import sys 2 | sys.path.append('../../../../../onssen/') 3 | sys.path.append('../') 4 | from onssen import data, loss, nn, utils 5 | from attrdict import AttrDict 6 | import torch 7 | import json 8 | from evaluate import tester_chimera 9 | 10 | def main(): 11 | config_path = './config.json' 12 | with open(config_path) as f: 13 | args = json.load(f) 14 | args = AttrDict(args) 15 | device = torch.device(args.device) 16 | args.model = nn.chimera(**(args['model_options'])) 17 | args.model.to(device) 18 | args.train_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'tr', device) 19 | args.valid_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'cv', device) 20 | args.test_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'tt', device) 21 | args.optimizer = utils.build_optimizer(args.model.parameters(), args.optimizer_options) 22 | args.loss_fn = loss.loss_chimera_msa 23 | trainer = utils.trainer(args) 24 | trainer.run() 25 | tester = tester_chimera(args) 26 | tester.eval() 27 | 28 | 29 | if __name__ == "__main__": 30 | main() 31 | -------------------------------------------------------------------------------- /egs/wsj0-2mix/chimera/psa/RESULT: -------------------------------------------------------------------------------- 1 | SI-SDR: 10.93 2 | -------------------------------------------------------------------------------- /egs/wsj0-2mix/chimera/psa/config.json: -------------------------------------------------------------------------------- 1 | { 2 | "model_name": "chimera++", 3 | "feature_options": { 4 | "data_path": "/home/data/wsj0-mix/2speakers/", 5 | "batch_size": 8, 6 | "frame_length": 400, 7 | "sampling_rate": 8000, 8 | "window_size": 256, 9 | "hop_size": 64, 10 | "db_threshold": 40 11 | }, 12 | "optimizer_options": { 13 | "name": "adam", 14 | "lr": 0.001 15 | }, 16 | "model_options": { 17 | "input_dim": 129, 18 | "hidden_dim": 600, 19 | "embedding_dim": 20, 20 | "num_layers": 4 21 | }, 22 | "device": "cuda", 23 | "num_speaker": 2, 24 | "num_epoch": 200, 25 | "resume_from_checkpoint": "False", 26 | "checkpoint_path": "./checkpoint/" 27 | } 28 | -------------------------------------------------------------------------------- /egs/wsj0-2mix/chimera/psa/run.py: -------------------------------------------------------------------------------- 1 | import sys 2 | sys.path.append('../../../../../onssen/') 3 | sys.path.append('../') 4 | from onssen import data, loss, nn, utils 5 | from attrdict import AttrDict 6 | import torch 7 | import json 8 | from evaluate import tester_chimera 9 | 10 | 11 | def main(): 12 | config_path = './config.json' 13 | with open(config_path) as f: 14 | args = json.load(f) 15 | args = AttrDict(args) 16 | device = torch.device(args.device) 17 | args.model = nn.chimera(**(args['model_options'])) 18 | args.model.to(device) 19 | args.train_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'tr', device) 20 | args.valid_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'cv', device) 21 | args.test_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'tt', device) 22 | args.optimizer = utils.build_optimizer(args.model.parameters(), args.optimizer_options) 23 | args.loss_fn = loss.loss_chimera_psa 24 | trainer = utils.trainer(args) 25 | trainer.run() 26 | tester = tester_chimera(args) 27 | tester.eval() 28 | 29 | 30 | if __name__ == "__main__": 31 | main() 32 | -------------------------------------------------------------------------------- /egs/wsj0-2mix/deep_clustering/RESULT: -------------------------------------------------------------------------------- 1 | SI-SDR: 8.858 2 | -------------------------------------------------------------------------------- /egs/wsj0-2mix/deep_clustering/config.json: -------------------------------------------------------------------------------- 1 | { 2 | "model_name": "dc", 3 | "feature_options": { 4 | "data_path": "/home/data/wsj0-mix/2speakers/", 5 | "batch_size": 16, 6 | "frame_length": 400, 7 | "sampling_rate": 8000, 8 | "window_size": 256, 9 | "hop_size": 64, 10 | "db_threshold": 40 11 | }, 12 | "optimizer_options": { 13 | "name": "adam", 14 | "lr": 0.001 15 | }, 16 | "model_options": { 17 | "input_dim": 129, 18 | "hidden_dim": 600, 19 | "embedding_dim": 20, 20 | "num_layers": 3 21 | }, 22 | "device": "cuda:0", 23 | "num_speaker": 2, 24 | "num_epoch": 200, 25 | "resume_from_checkpoint": "False", 26 | "checkpoint_path": "./checkpoint/" 27 | } 28 | -------------------------------------------------------------------------------- /egs/wsj0-2mix/deep_clustering/evaluate.py: -------------------------------------------------------------------------------- 1 | import sys 2 | sys.path.append('/home/near/onssen/') 3 | from onssen import utils 4 | from sklearn.cluster import KMeans 5 | import librosa 6 | import numpy as np 7 | import torch 8 | 9 | 10 | class tester_dc(utils.tester): 11 | def get_est_sig(self, input, label, output): 12 | """ 13 | args: 14 | feature_mix: batch x frame x frequency 15 | embedding: batch x frame x frequency x embedding_dim 16 | stft_r_mix: batch x frame x frequency 17 | stft_i_mix: batch x frame x frequency 18 | sig_ref: batch x num_spk x nsample 19 | return: 20 | sig_est: batch x num_spk x nsample 21 | """ 22 | feature_mix, = input 23 | embedding, = output 24 | stft_r_mix, stft_i_mix, sig_ref = label 25 | 26 | stft_r_mix = stft_r_mix.detach().cpu().numpy() 27 | stft_i_mix = stft_i_mix.detach().cpu().numpy() 28 | embedding = embedding.detach().cpu().numpy() 29 | feature_mix = feature_mix.detach().cpu().numpy() 30 | 31 | stft_mix = stft_r_mix + 1j * stft_i_mix 32 | batch, frame, frequency = feature_mix.shape 33 | batch, num_spk, nsample = sig_ref.shape 34 | feature_mix = feature_mix.reshape(frame, frequency) 35 | embedding = embedding.reshape(frame, frequency, -1) 36 | m = np.max(feature_mix) - 40/20 37 | emb = embedding[feature_mix>=m,:] 38 | label = KMeans(n_clusters=num_spk, random_state=0).fit_predict(emb) 39 | mask = np.zeros((num_spk, frame, frequency)) 40 | mask[0, feature_mix>=m] = label 41 | mask[1, feature_mix>=m] = 1-label 42 | stft_est = stft_mix * mask 43 | sig_est = np.zeros((batch, num_spk, nsample)) 44 | for i in range(num_spk): 45 | sig_est[0, i] = librosa.core.istft(stft_est[i].T, hop_length=64, length=nsample) 46 | sig_est = torch.tensor(sig_est).to(self.device) 47 | return sig_est, sig_ref 48 | -------------------------------------------------------------------------------- /egs/wsj0-2mix/deep_clustering/run.py: -------------------------------------------------------------------------------- 1 | 2 | import sys 3 | sys.path.append('../../../../onssen/') 4 | 5 | from onssen import data, loss, nn, utils 6 | from evaluate import tester_dc 7 | from attrdict import AttrDict 8 | import argparse 9 | import torch 10 | import json 11 | 12 | 13 | def main(): 14 | parser = argparse.ArgumentParser(description='Parse the config path') 15 | parser.add_argument("-c", "--config", dest="path", 16 | help='The path to the config file. e.g. python run.py --config onfig.json') 17 | 18 | config = parser.parse_args() 19 | with open(config.path) as f: 20 | args = json.load(f) 21 | args = AttrDict(args) 22 | device = torch.device(args.device) 23 | args.model = nn.deep_clustering(**(args['model_options'])) 24 | args.model.to(device) 25 | args.train_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'tr', device) 26 | args.valid_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'cv', device) 27 | args.test_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'tt', device) 28 | args.optimizer = utils.build_optimizer(args.model.parameters(), args.optimizer_options) 29 | args.loss_fn = loss.loss_dc 30 | trainer = utils.trainer(args) 31 | trainer.run() 32 | 33 | tester = tester_dc(args) 34 | tester.eval() 35 | 36 | 37 | if __name__ == "__main__": 38 | main() 39 | -------------------------------------------------------------------------------- /egs/wsj0-2mix/phase-net/config.json: -------------------------------------------------------------------------------- 1 | { 2 | "model_name": "phase", 3 | "dataset": "wsj0-2mix", 4 | "feature_options": { 5 | "data_path": "/home/data/wsj0-2mix/", 6 | "batch_size": 16, 7 | "frame_length": 400, 8 | "sampling_rate": 8000, 9 | "window_size": 256, 10 | "hop_size": 64, 11 | "db_threshold": 40 12 | }, 13 | "optimizer_options": { 14 | "name": "adam", 15 | "lr": 0.001 16 | }, 17 | "model_options": { 18 | "input_dim": 129, 19 | "hidden_dim": 300, 20 | "embedding_dim": 20, 21 | "num_layers": 3 22 | }, 23 | "loss_option": "loss_phase", 24 | "num_speaker": 2, 25 | "num_epoch": 200, 26 | "output_path": "./output/", 27 | "cuda_option": "True" 28 | } 29 | -------------------------------------------------------------------------------- /egs/wsj0-2mix/phase-net/run.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/speechLabBcCuny/onssen/179cff94451918601f648d17c76ed0788fc5295c/egs/wsj0-2mix/phase-net/run.py -------------------------------------------------------------------------------- /egs/wsj0-2mix/tasnet/conv-tasnet/config.json: -------------------------------------------------------------------------------- 1 | { 2 | "model_name": "conv-tasnet", 3 | "feature_options": { 4 | "data_path": "/home/data/wsj0-mix/2speakers/", 5 | "batch_size": 3, 6 | "sampling_rate": 8000, 7 | "chunk_size": 32000 8 | }, 9 | "optimizer_options": { 10 | "name": "adam", 11 | "lr": 0.001 12 | }, 13 | "model_options": { 14 | "N": 512, 15 | "L": 16, 16 | "B": 128, 17 | "H": 512, 18 | "P": 3, 19 | "X": 8, 20 | "R": 3, 21 | "norm": "gln", 22 | "num_spks": 2, 23 | "activate": "sigmoid", 24 | "causal": false 25 | }, 26 | "device": "cuda:1", 27 | "num_epoch": 200, 28 | "resume_from_checkpoint": "False", 29 | "checkpoint_path": "./checkpoint/" 30 | } 31 | -------------------------------------------------------------------------------- /egs/wsj0-2mix/tasnet/conv-tasnet/run.py: -------------------------------------------------------------------------------- 1 | import sys 2 | sys.path.append('../../../../../onssen/') 3 | sys.path.append('../') 4 | from onssen import data, loss, nn, utils 5 | from attrdict import AttrDict 6 | import torch 7 | import json 8 | from evaluate import tester_tasnet 9 | 10 | 11 | def main(): 12 | config_path = './config.json' 13 | with open(config_path) as f: 14 | args = json.load(f) 15 | args = AttrDict(args) 16 | device = torch.device(args.device) 17 | args.device = device 18 | args.model = nn.ConvTasNet(**args["model_options"]) 19 | args.model.to(device) 20 | args.train_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'tr', device) 21 | args.valid_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'cv', device) 22 | args.test_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'tt', device) 23 | args.optimizer = utils.build_optimizer(args.model.parameters(), args.optimizer_options) 24 | args.loss_fn = loss.si_snr_loss 25 | trainer = utils.trainer(args) 26 | trainer.run() 27 | tester = tester_tasnet(args) 28 | tester.eval() 29 | 30 | 31 | if __name__ == "__main__": 32 | main() 33 | -------------------------------------------------------------------------------- /egs/wsj0-2mix/tasnet/evaluate.py: -------------------------------------------------------------------------------- 1 | import sys 2 | sys.path.append('../../../../../onssen/') 3 | 4 | from onssen import utils 5 | from sklearn.cluster import KMeans 6 | import librosa 7 | import numpy as np 8 | import torch 9 | 10 | 11 | class tester_tasnet(utils.tester): 12 | def get_est_sig(self, input, label, output): 13 | """ 14 | args: 15 | feature_mix: batch x frame x frequency 16 | embedding: batch x frame x frequency x embedding_dim 17 | stft_r_mix: batch x frame x frequency 18 | stft_i_mix: batch x frame x frequency 19 | sig_ref: batch x num_spk x nsample 20 | return: 21 | sig_est: batch x num_spk x nsample 22 | """ 23 | feature_mix, = input 24 | sig_ref, = label 25 | batch, num_spk, N = sig_ref.shape 26 | sig_est = torch.zeros((batch, num_spk, N), device=self.device) 27 | for i in range(num_spk): 28 | sig_est[:, i, :] = output[i][0:N] 29 | return sig_est, sig_ref 30 | 31 | -------------------------------------------------------------------------------- /egs/wsj0-2mix/tasnet/lstm-tasnet/config.json: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/speechLabBcCuny/onssen/179cff94451918601f648d17c76ed0788fc5295c/egs/wsj0-2mix/tasnet/lstm-tasnet/config.json -------------------------------------------------------------------------------- /egs/wsj0-2mix/tasnet/lstm-tasnet/run.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/speechLabBcCuny/onssen/179cff94451918601f648d17c76ed0788fc5295c/egs/wsj0-2mix/tasnet/lstm-tasnet/run.py -------------------------------------------------------------------------------- /onssen/__init__.py: -------------------------------------------------------------------------------- 1 | import onssen.data 2 | import onssen.loss 3 | import onssen.evaluate 4 | import onssen.nn 5 | import onssen.utils 6 | -------------------------------------------------------------------------------- /onssen/data/__init__.py: -------------------------------------------------------------------------------- 1 | from .daps_enhance import daps_enhance_dataloader 2 | from .edinburgh_tts import edinburgh_tts_dataloader 3 | from .wsj0_2mix import wsj0_2mix_dataloader 4 | -------------------------------------------------------------------------------- /onssen/data/daps_enhance.py: -------------------------------------------------------------------------------- 1 | """ 2 | We need to define a batch size for training the deep clustering model. 3 | Each batch has a shape (batch_size, 100/400, feature_dim) 4 | 5 | For STFT: 6 | 8kHz fs 7 | 32 ms window length 32*8 = 256 8 | 8 ms window shift = 64 9 | 10 | 44kHz fs 11 | 128 ms window length 128*8 = 1024 12 | 32 ms window shift = 256 13 | """ 14 | 15 | from torch.utils.data.dataset import Dataset 16 | from torch.utils.data import DataLoader 17 | from .feature_utils import * 18 | import glob,librosa, numpy as np, os, random, torch 19 | 20 | """ 21 | what I want from the DataLoader? 22 | Each batch should contain different speakers 23 | There should be 400 * 513 tensor for each sample 24 | We have a list of files, each contains N times of 400 frames 25 | To make full use of them, we need to generate something 26 | We have 20 speakers, each will contains N X 400 X 513 tensors 27 | 28 | """ 29 | 30 | def daps_enhance_dataloader(num_batch, feature_options, partition, device=None): 31 | return DataLoader( 32 | daps_dataset(num_batch, feature_options, partition, device=device), 33 | batch_size=feature_options.batch_size, 34 | shuffle=True, 35 | ) 36 | 37 | 38 | class daps_dataset(Dataset): 39 | def __init__(self, num_batch, feature_options, partition, device=None): 40 | """ 41 | The arguments: 42 | feature_options: a dictionary containing the feature params 43 | partition: can be "train", "validation" 44 | num_batch: Each training epoch uses num_batch * batch_size * frame_length data 45 | e.g. 46 | "feature_options": { 47 | "data_path": "/home/data/wsj0-2mix", 48 | "batch_size": 16, 49 | "frame_length": 400, 50 | "sampling_rate": 8000, 51 | "window_size": 256, 52 | "hop_size": 64, 53 | "db_threshold": 40 54 | } 55 | The returns: 56 | input: a tuple which follows the requirement of the loss 57 | label: a tuple which follows the requirement of the loss 58 | e.g. 59 | for dc loss: 60 | input: (feature_mix) 61 | label: (one_hot_label) 62 | for chimera loss: 63 | input: (feature_mix) 64 | label: (one_hot_label, mag_mix, mag_s1, mag_s2) 65 | """ 66 | self.sampling_rate = feature_options.sampling_rate 67 | self.window_size = feature_options.window_size 68 | self.hop_size = feature_options.hop_size 69 | self.frame_length = feature_options.frame_length 70 | self.num_batch = num_batch 71 | self.batch_size = feature_options.batch_size 72 | self.file_list = [] 73 | self.base_path = feature_options.data_path 74 | self.partition = partition 75 | self.length_remaining = 0 76 | self.get_item_list() 77 | if device is None: 78 | self.device = torch.device('cpu') 79 | else: 80 | self.device = device 81 | 82 | 83 | def get_item_list(self): 84 | f = open(self.base_path+'/'+self.partition) 85 | self.file_list = [line.replace('\n','') for line in f] 86 | random.shuffle(self.file_list) 87 | 88 | 89 | def __getitem__(self, index): 90 | if self.length_remaining < self.frame_length: 91 | if len(self.file_list)==0: 92 | self.get_item_list() 93 | # add one more file, delete the index from the list 94 | index = index % len(self.file_list) 95 | f_noisy = self.file_list.pop(index) 96 | base_names = os.path.basename(f_noisy).split("_") 97 | f_clean = self.base_path + "/clean/" + base_names[0] + "_" + base_names[1] + "_clean.wav" 98 | stft_noisy = get_stft(f_noisy, self.sampling_rate, self.window_size, self.hop_size) 99 | stft_clean = get_stft(f_clean, self.sampling_rate, self.window_size, self.hop_size) 100 | 101 | feature = get_log_magnitude(stft_noisy) 102 | #feature = get_log_mel_spectrogram(f_noisy, self.sampling_rate, self.window_size, self.hop_size) 103 | # one_hot_label 104 | mag_noisy = np.abs(stft_noisy) 105 | mag_clean = np.abs(stft_clean) 106 | cos_diff = get_cos_difference(stft_noisy, stft_clean) 107 | input, label = [feature, mag_noisy], [mag_clean, cos_diff] 108 | 109 | input = [torch.tensor(ele).to(self.device) for ele in input] 110 | label = [torch.tensor(ele).to(self.device) for ele in label] 111 | 112 | self.input = input 113 | self.label = label 114 | return self.cutoff_feature() 115 | else: 116 | return self.cutoff_feature() 117 | 118 | def cutoff_feature(self): 119 | input, label = [ele[0:self.frame_length] for ele in self.input], [ele[0:self.frame_length] for ele in self.label] 120 | self.input = [ele[self.frame_length:] for ele in self.input] 121 | self.label = [ele[self.frame_length:] for ele in self.label] 122 | self.length_remaining = self.input[0].shape[0] 123 | return input, label 124 | 125 | def __len__(self): 126 | return self.num_batch*self.batch_size 127 | -------------------------------------------------------------------------------- /onssen/data/edinburgh_tts.py: -------------------------------------------------------------------------------- 1 | from torch.utils.data.dataset import Dataset 2 | from torch.utils.data import DataLoader 3 | from .feature_utils import * 4 | import glob 5 | import numpy as np 6 | import random 7 | import torch 8 | 9 | 10 | def edinburgh_tts_dataloader(model_name, feature_options, partition, device=None): 11 | return DataLoader( 12 | edinburgh_tts_dataset(model_name, feature_options, partition, device=device), 13 | batch_size=feature_options.batch_size, 14 | shuffle=True, 15 | ) 16 | 17 | 18 | class edinburgh_tts_dataset(Dataset): 19 | def __init__(self, model_name, feature_options, partition, device=None): 20 | """ 21 | The arguments: 22 | feature_options: a dictionary containing the feature params 23 | partition: can be "train", "validation" 24 | model_name: can be "dc", "chimera", "chimera++", "phase" 25 | e.g. 26 | "feature_options": { 27 | "data_path": "/home/data/Edinburg_tts", 28 | "batch_size": 16, 29 | "frame_length": 400, 30 | "sampling_rate": 16000, 31 | "window_size": 512, 32 | "hop_size": 128, 33 | "db_threshold": 40 34 | } 35 | The returns: 36 | input: a tuple which follows the requirement of the loss 37 | label: a tuple which follows the requirement of the loss 38 | e.g. 39 | for dc loss: 40 | input: (feature_mix) 41 | label: (one_hot_label) 42 | for chimera loss: 43 | input: (feature_mix) 44 | label: (one_hot_label, mag_mix, mag_s1, mag_s2) 45 | """ 46 | self.sampling_rate = feature_options.sampling_rate 47 | self.window_size = feature_options.window_size 48 | self.hop_size = feature_options.hop_size 49 | self.frame_length = feature_options.frame_length 50 | self.db_threshold = feature_options.db_threshold 51 | self.model_name = model_name 52 | self.data_path = feature_options.data_path 53 | self.partition = partition 54 | self.file_list = [] 55 | self.get_file_list() 56 | if device is None: 57 | self.device = torch.device('cpu') 58 | else: 59 | self.device = device 60 | 61 | def get_file_list(self): 62 | with open(self.data_path+'/'+self.partition,'r') as f: 63 | for line in f: 64 | self.file_list.append(self.data_path+'/noisy_trainset_28spk_wav/'+line.replace('\n','')) 65 | random.shuffle(self.file_list) 66 | 67 | 68 | def get_feature(self,fn): 69 | stft_mix = get_stft(fn, self.sampling_rate, self.window_size, self.hop_size) 70 | stft_s1 = get_stft(fn.replace('/noisy_trainset_28spk_wav','/clean_trainset_28spk_wav'), self.sampling_rate, self.window_size, self.hop_size) 71 | stft_s2 = get_stft_from_subtraction(fn, fn.replace('/noisy_trainset_28spk_wav','/clean_trainset_28spk_wav'), self.sampling_rate, self.window_size, self.hop_size) 72 | 73 | if stft_mix.shape[0]<=self.frame_length: 74 | #pad in a double-copy fashion 75 | times = self.frame_length // stft_mix.shape[0]+1 76 | stft_mix = np.concatenate([stft_mix]*times, axis=0) 77 | stft_s1 = np.concatenate([stft_s1]*times, axis=0) 78 | stft_s2 = np.concatenate([stft_s2]*times, axis=0) 79 | 80 | stft_mix = stft_mix[:self.frame_length] 81 | stft_s1 = stft_s1[:self.frame_length] 82 | stft_s2 = stft_s2[:self.frame_length] 83 | # base feature 84 | feature_mix = get_log_magnitude(stft_mix) 85 | # one_hot_label 86 | mag_mix = np.abs(stft_mix) 87 | mag_s1 = np.abs(stft_s1) 88 | mag_s2 = np.abs(stft_s2) 89 | one_hot_label = get_one_hot(feature_mix, mag_s1, mag_s2, self.db_threshold) 90 | 91 | if self.model_name == "dc": 92 | input, label = [feature_mix], [one_hot_label] 93 | 94 | if self.model_name == "chimera": 95 | input, label = [feature_mix], [one_hot_label, mag_mix, mag_s1, mag_s2] 96 | 97 | if self.model_name == "chimera++": 98 | cos_s1 = get_cos_difference(stft_mix, stft_s1) 99 | cos_s2 = get_cos_difference(stft_mix, stft_s2) 100 | input, label = [feature_mix], [one_hot_label, mag_mix, mag_s1, mag_s2, cos_s1, cos_s2] 101 | 102 | if self.model_name == "phase": 103 | phase_mix = get_phase(stft_mix) 104 | phase_s1 = get_phase(stft_s1) 105 | phase_s2 = get_phase(stft_s2) 106 | input, label = [feature_mix, phase_mix], [one_hot_label, mag_mix, mag_s1, mag_s2, phase_s1, phase_s2] 107 | 108 | input = [torch.tensor(ele).to(self.device) for ele in input] 109 | label = [torch.tensor(ele).to(self.device) for ele in label] 110 | 111 | return input, label 112 | 113 | 114 | def __getitem__(self, index): 115 | file_name_mix = self.file_list[index] 116 | return self.get_feature(file_name_mix) 117 | 118 | 119 | def __len__(self): 120 | return len(self.file_list) 121 | -------------------------------------------------------------------------------- /onssen/data/feature_utils.py: -------------------------------------------------------------------------------- 1 | import librosa 2 | import numpy as np 3 | 4 | 5 | def get_stft(fn, sampling_rate, window_size, hop_size): 6 | """ 7 | fn: the absolute path of the wav file 8 | sampling_rate: in Hz 9 | window_size: window size for fft 10 | hop_size: the hop size for shifting the window 11 | 12 | return: 13 | stft: frame * frequency numpy array 14 | """ 15 | sig, fs = librosa.load(fn, sr = None) 16 | if fs != sampling_rate: 17 | # print("WARNING!!! The sampling rate provided is different from the data") 18 | # print("Resample the audio...") 19 | sig = librosa.core.resample(sig, fs, sampling_rate) 20 | stft = np.transpose(librosa.core.stft(sig, n_fft=window_size, hop_length=hop_size)) 21 | return stft 22 | 23 | 24 | def get_stft_from_subtraction(f_mix, f_clean, sampling_rate, window_size, hop_size): 25 | sig_mix, fs = librosa.load(f_mix, sr = None) 26 | sig_clean, fs = librosa.load(f_clean, sr = None) 27 | sig_noise = sig_mix - sig_clean 28 | if fs != sampling_rate: 29 | # print("WARNING!!! The sampling rate provided is different from the data") 30 | # print("Resample the audio...") 31 | sig_noise = librosa.core.resample(sig_noise, fs, sampling_rate) 32 | stft = np.transpose(librosa.core.stft(sig_noise, n_fft=window_size, hop_length=hop_size)) 33 | return stft 34 | 35 | 36 | def get_log_mel_spectrogram(fn, sampling_rate, window_size, hop_size, epsilon=1e-7): 37 | sig, fs = librosa.load(fn, sr = None) 38 | assert sampling_rate == fs 39 | mel_spectra = librosa.feature.melspectrogram( 40 | sig, 41 | sr=sampling_rate, 42 | n_fft=window_size, 43 | hop_length=hop_size 44 | ) 45 | mel_spectra = np.transpose(np.log10(mel_spectra + epsilon)) 46 | return mel_spectra 47 | 48 | 49 | def get_log_magnitude(stft, epsilon=1e-7): 50 | feature = np.log10(np.abs(stft) + epsilon) 51 | return feature 52 | 53 | 54 | def get_phase(stft): 55 | """ 56 | stft: frame * frequency complex numpy array 57 | return: 58 | phase: frame * frequency * 2 real numpy array 59 | """ 60 | real = np.real(stft) 61 | imag = np.imag(stft) 62 | phase = np.array([real, imag]) 63 | phase = np.transpose(phase, (1,2,0)) 64 | return phase 65 | 66 | 67 | def get_angle(stft): 68 | """ 69 | stft: frame * frequency complex numpy array 70 | return: 71 | angle: the angle of the STFT 72 | """ 73 | angle = np.angle(stft) 74 | return angle 75 | 76 | 77 | def get_cos_difference(stft_1, stft_2): 78 | angle_1 = get_angle(stft_1) 79 | angle_2 = get_angle(stft_2) 80 | return np.cos(angle_1 - angle_2) 81 | 82 | 83 | def get_one_hot(feature_mix, mag_s1, mag_s2, db_threshold): 84 | specs = np.asarray([mag_s1, mag_s2]) 85 | vals = np.argmax(specs, axis=0) 86 | Y = np.zeros(mag_s1.shape+(2,)) 87 | for i in range(2): 88 | temp = np.zeros((2)) 89 | temp[i]=1 90 | Y[vals == i] = temp 91 | #label the silence part 92 | m = np.max(feature_mix) - db_threshold/20 93 | temp = np.zeros((2)) 94 | Y[feature_mix < m] = temp 95 | return Y 96 | -------------------------------------------------------------------------------- /onssen/data/wsj0_2mix.py: -------------------------------------------------------------------------------- 1 | """ 2 | We need to define a batch size for training the deep clustering model. 3 | Each batch has a shape (batch_size, 100/400, feature_dim) 4 | 5 | For STFT: 6 | 8kHz fs 7 | 32 ms window length 32*8 = 256 8 | 8 ms window shift = 64 9 | 10 | 44kHz fs 11 | 128 ms window length 128*8 = 1024 12 | 32 ms window shift = 256 13 | """ 14 | 15 | from torch.utils.data.dataset import Dataset 16 | from torch.utils.data import DataLoader 17 | from .feature_utils import * 18 | import glob 19 | import numpy as np 20 | import random 21 | import torch 22 | import torchaudio 23 | import torch.nn.functional as F 24 | 25 | 26 | def wsj0_2mix_dataloader(model_name, feature_options, partition, device=None): 27 | if partition == "tr" or partition == "cv": 28 | return DataLoader( 29 | wsj0_2mix_dataset(model_name, feature_options, partition, device=device), 30 | batch_size=feature_options.batch_size, 31 | shuffle=True, 32 | ) 33 | elif partition == "tt": 34 | return DataLoader( 35 | wsj0_2mix_eval_dataset(model_name, feature_options, partition, device=device), 36 | batch_size=1, 37 | ) 38 | 39 | 40 | class wsj0_2mix_dataset(Dataset): 41 | def __init__(self, model_name, feature_options, partition, device=None): 42 | """ 43 | The arguments: 44 | feature_options: a dictionary containing the feature params 45 | partition: can be "tr", "cv" 46 | model_name: can be "dc", "chimera", "chimera++", "phase" 47 | e.g. 48 | "feature_options": { 49 | "data_path": "/home/data/wsj0-2mix", 50 | "batch_size": 16, 51 | "frame_length": 400, 52 | "sampling_rate": 8000, 53 | "window_size": 256, 54 | "hop_size": 64, 55 | "db_threshold": 40 56 | } 57 | The returns: 58 | input: a tuple which follows the requirement of the loss 59 | label: a tuple which follows the requirement of the loss 60 | e.g. 61 | for dc loss: 62 | input: (feature_mix) 63 | label: (one_hot_label) 64 | for chimera loss: 65 | input: (feature_mix) 66 | label: (one_hot_label, mag_mix, mag_s1, mag_s2) 67 | """ 68 | self.model_name = model_name 69 | self.sampling_rate = feature_options.sampling_rate 70 | if self.model_name in ["lstm-tasnet", "conv-tasnet"]: 71 | self.chunk_size = feature_options.chunk_size 72 | else: 73 | self.window_size = feature_options.window_size 74 | self.hop_size = feature_options.hop_size 75 | self.frame_length = feature_options.frame_length 76 | self.db_threshold = feature_options.db_threshold 77 | self.file_list = [] 78 | full_path = feature_options.data_path+'/wav8k/min/'+partition+'/mix/*.wav' 79 | self.file_list = glob.glob(full_path) 80 | if device is None: 81 | self.device = torch.device('cpu') 82 | else: 83 | self.device = device 84 | 85 | 86 | def get_tr_sigs(self, fn, sr): 87 | sig, rate = torchaudio.load(fn) 88 | assert(rate==sr) 89 | sig_s1, rate = torchaudio.load(fn.replace('/mix','/s1')) 90 | sig_s2, rate = torchaudio.load(fn.replace('/mix','/s2')) 91 | if sig.shape[1] < self.chunk_size: 92 | gap = self.chunk_size- sig.shape[1] 93 | sig = F.pad(sig, (0, gap), mode='constant') 94 | sig_s1 = F.pad(sig_s1, (0, gap), mode='constant') 95 | sig_s2 = F.pad(sig_s2, (0, gap), mode='constant') 96 | else: 97 | random_start = random.randint(0, sig.shape[1]-self.chunk_size) 98 | sig = sig[:, random_start:self.chunk_size+random_start] 99 | sig_s1 = sig_s1[:, random_start:self.chunk_size+random_start] 100 | sig_s2 = sig_s2[:, random_start:self.chunk_size+random_start] 101 | return sig, sig_s1, sig_s2 102 | 103 | def get_feature(self,fn): 104 | if self.model_name in ["lstm-tasnet", "conv-tasnet"]: 105 | sig_mix, sig_s1, sig_s2 = self.get_tr_sigs(fn, self.sampling_rate) 106 | sig_mix = sig_mix.reshape(-1,) 107 | sig_s1 = sig_s1.reshape(-1,) 108 | sig_s2 = sig_s2.reshape(-1,) 109 | input, label = [sig_mix], [sig_s1, sig_s2] 110 | input = [ele.to(self.device) for ele in input] 111 | label = [ele.to(self.device) for ele in label] 112 | return input, label 113 | 114 | stft_mix = get_stft(fn, self.sampling_rate, self.window_size, self.hop_size) 115 | stft_s1 = get_stft(fn.replace('/mix','/s1'), self.sampling_rate, self.window_size, self.hop_size) 116 | stft_s2 = get_stft(fn.replace('/mix','/s2'), self.sampling_rate, self.window_size, self.hop_size) 117 | 118 | if stft_mix.shape[0]<=self.frame_length: 119 | #pad in a double-copy fashion 120 | times = self.frame_length // stft_mix.shape[0]+1 121 | stft_mix = np.concatenate([stft_mix]*times, axis=0) 122 | stft_s1 = np.concatenate([stft_s1]*times, axis=0) 123 | stft_s2 = np.concatenate([stft_s2]*times, axis=0) 124 | 125 | random_index = np.random.randint(stft_mix.shape[0]-self.frame_length) 126 | stft_mix = stft_mix[random_index:random_index+self.frame_length] 127 | stft_s1 = stft_s1[random_index:random_index+self.frame_length] 128 | stft_s2 = stft_s2[random_index:random_index+self.frame_length] 129 | # base feature 130 | feature_mix = get_log_magnitude(stft_mix) 131 | # one_hot_label 132 | mag_mix = np.abs(stft_mix) 133 | mag_s1 = np.abs(stft_s1) 134 | mag_s2 = np.abs(stft_s2) 135 | one_hot_label = get_one_hot(feature_mix, mag_s1, mag_s2, self.db_threshold) 136 | 137 | if self.model_name == "dc": 138 | input, label = [feature_mix], [one_hot_label, mag_mix] 139 | 140 | if self.model_name == "chimera": 141 | input, label = [feature_mix], [one_hot_label, mag_mix, mag_s1, mag_s2] 142 | 143 | if self.model_name == "chimera++": 144 | cos_s1 = get_cos_difference(stft_mix, stft_s1) 145 | cos_s2 = get_cos_difference(stft_mix, stft_s2) 146 | input, label = [feature_mix], [one_hot_label, mag_mix, mag_s1, mag_s2, cos_s1, cos_s2] 147 | 148 | if self.model_name == "phase": 149 | phase_mix = get_phase(stft_mix) 150 | phase_s1 = get_phase(stft_s1) 151 | phase_s2 = get_phase(stft_s2) 152 | input, label = [feature_mix, phase_mix], [one_hot_label, mag_mix, mag_s1, mag_s2, phase_s1, phase_s2] 153 | 154 | 155 | input = [torch.tensor(ele).to(self.device) for ele in input] 156 | label = [torch.tensor(ele).to(self.device) for ele in label] 157 | 158 | return input, label 159 | 160 | 161 | def __getitem__(self, index): 162 | file_name_mix = self.file_list[index] 163 | return self.get_feature(file_name_mix) 164 | 165 | 166 | def __len__(self): 167 | return len(self.file_list) 168 | 169 | 170 | class wsj0_2mix_eval_dataset(Dataset): 171 | def __init__(self, model_name, feature_options, partition, device=None): 172 | """ 173 | The arguments: 174 | feature_options: a dictionary containing the feature params 175 | partition: can be "tr", "cv" 176 | model_name: can be "dc", "chimera", "chimera++", "phase" 177 | e.g. 178 | "feature_options": { 179 | "data_path": "/home/data/wsj0-2mix", 180 | "batch_size": 16, 181 | "frame_length": 400, 182 | "sampling_rate": 8000, 183 | "window_size": 256, 184 | "hop_size": 64, 185 | "db_threshold": 40 186 | } 187 | The returns: 188 | input: a tuple which follows the requirement of the loss 189 | label: a tuple which follows the requirement of the loss 190 | e.g. 191 | for dc loss: 192 | input: (feature_mix) 193 | label: (one_hot_label) 194 | for chimera loss: 195 | input: (feature_mix) 196 | label: (one_hot_label, mag_mix, mag_s1, mag_s2) 197 | """ 198 | self.model_name = model_name 199 | self.sampling_rate = feature_options.sampling_rate 200 | if self.model_name in ["lstm-tasnet", "conv-tasnet"]: 201 | self.chunk_size = feature_options.chunk_size 202 | else: 203 | self.window_size = feature_options.window_size 204 | self.hop_size = feature_options.hop_size 205 | self.frame_length = feature_options.frame_length 206 | self.db_threshold = feature_options.db_threshold 207 | self.file_list = [] 208 | full_path = feature_options.data_path+'/wav8k/min/'+partition+'/mix/*.wav' 209 | self.file_list = glob.glob(full_path) 210 | if device is None: 211 | self.device = torch.device('cpu') 212 | else: 213 | self.device = device 214 | 215 | 216 | def get_sigs(self, fn, sr): 217 | sig_mix, rate = torchaudio.load(fn) 218 | assert(rate==sr) 219 | sig_s1, rate = torchaudio.load(fn.replace('tt/mix/','tt/s1/')) 220 | sig_s2, rate = torchaudio.load(fn.replace('tt/mix/','tt/s2/')) 221 | N = sig_mix.shape[1] 222 | gap = 32- N % 32 223 | sig_mix = F.pad(sig_mix, (0, gap), mode='constant') 224 | sig_s1 = F.pad(sig_s1, (0, gap), mode='constant') 225 | sig_s2 = F.pad(sig_s2, (0, gap), mode='constant') 226 | sig_ref = torch.cat((sig_s1, sig_s2), dim=0) 227 | sig_mix = sig_mix.reshape(-1,) 228 | return sig_mix, sig_ref 229 | 230 | 231 | def get_feature(self,fn): 232 | if self.model_name in ["lstm-tasnet", "conv-tasnet"]: 233 | sig_mix, sig_ref = self.get_sigs(fn, self.sampling_rate) 234 | input, label = [sig_mix.to(self.device)], [sig_ref.to(self.device)] 235 | else: 236 | stft_mix = get_stft(fn, self.sampling_rate, self.window_size, self.hop_size) 237 | stft_r_mix = np.real(stft_mix) 238 | stft_i_mix = np.imag(stft_mix) 239 | feature_mix = get_log_magnitude(stft_mix) 240 | sig_ref = self.get_ref_sig(fn) 241 | input, label = [feature_mix], [stft_r_mix, stft_i_mix, sig_ref] 242 | input = [torch.tensor(ele).to(self.device) for ele in input] 243 | label = [torch.tensor(ele).to(self.device) for ele in label] 244 | 245 | return input, label 246 | 247 | 248 | def __getitem__(self, index): 249 | file_name_mix = self.file_list[index] 250 | return self.get_feature(file_name_mix) 251 | 252 | 253 | def __len__(self): 254 | return len(self.file_list) 255 | -------------------------------------------------------------------------------- /onssen/evaluate/__init__.py: -------------------------------------------------------------------------------- 1 | from .sdr import batch_SDR_torch 2 | 3 | -------------------------------------------------------------------------------- /onssen/evaluate/sdr.py: -------------------------------------------------------------------------------- 1 | ### Forked from https://github.com/yluo42/TAC/blob/master/utility/sdr.py 2 | import numpy as np 3 | from itertools import permutations 4 | from torch.autograd import Variable 5 | 6 | import scipy,time,numpy 7 | 8 | import torch 9 | 10 | # Pytorch implementation with batch processing 11 | def calc_sdr_torch(estimation, origin, mask=None): 12 | """ 13 | batch-wise SDR caculation for one audio file on pytorch Variables. 14 | estimation: (batch, nsample) 15 | origin: (batch, nsample) 16 | mask: an optional mask for sequence masking. This is for cases where zero-padding was applied at the end and should not be consider for SDR calculation. 17 | """ 18 | 19 | if mask is not None: 20 | origin = origin * mask 21 | estimation = estimation * mask 22 | 23 | def calculate(estimation, origin): 24 | origin_power = torch.pow(origin, 2).sum(1, keepdim=True) + 1e-8 # (batch, 1) 25 | scale = torch.sum(origin*estimation, 1, keepdim=True) / origin_power # (batch, 1) 26 | 27 | est_true = scale * origin # (batch, nsample) 28 | est_res = estimation - est_true # (batch, nsample) 29 | 30 | true_power = torch.pow(est_true, 2).sum(1) + 1e-8 31 | res_power = torch.pow(est_res, 2).sum(1) + 1e-8 32 | 33 | return 10*torch.log10(true_power) - 10*torch.log10(res_power) # (batch, ) 34 | 35 | best_sdr = calculate(estimation, origin) 36 | 37 | return best_sdr 38 | 39 | 40 | def batch_SDR_torch(estimation, origin, mask=None, return_perm=False): 41 | """ 42 | batch-wise SDR caculation for multiple audio files. 43 | estimation: (batch, nsource, nsample) 44 | origin: (batch, nsource, nsample) 45 | mask: optional, (batch, nsample), binary 46 | return_perm: bool, whether to return the permutation index. Default is false. 47 | """ 48 | 49 | batch_size_est, nsource_est, nsample_est = estimation.size() 50 | batch_size_ori, nsource_ori, nsample_ori = origin.size() 51 | 52 | assert batch_size_est == batch_size_ori, "Estimation and original sources should have same shape." 53 | assert nsource_est == nsource_ori, "Estimation and original sources should have same shape." 54 | assert nsample_est == nsample_ori, "Estimation and original sources should have same shape." 55 | 56 | assert nsource_est < nsample_est, "Axis 1 should be the number of sources, and axis 2 should be the signal." 57 | 58 | batch_size = batch_size_est 59 | nsource = nsource_est 60 | 61 | # zero mean signals 62 | estimation = estimation - torch.mean(estimation, 2, keepdim=True).expand_as(estimation) 63 | origin = origin - torch.mean(origin, 2, keepdim=True).expand_as(estimation) 64 | 65 | # SDR for each permutation 66 | SDR = torch.zeros((batch_size, nsource, nsource)).type(estimation.type()) 67 | for i in range(nsource): 68 | for j in range(nsource): 69 | SDR[:,i,j] = calc_sdr_torch(estimation[:,i], origin[:,j], mask) 70 | 71 | # choose the best permutation 72 | SDR_max = [] 73 | SDR_perm = [] 74 | perm = sorted(list(set(permutations(np.arange(nsource))))) 75 | for permute in perm: 76 | sdr = [] 77 | for idx in range(len(permute)): 78 | sdr.append(SDR[:,idx,permute[idx]].view(batch_size,-1)) 79 | sdr = torch.sum(torch.cat(sdr, 1), 1) 80 | SDR_perm.append(sdr.view(batch_size, 1)) 81 | SDR_perm = torch.cat(SDR_perm, 1) 82 | SDR_max, SDR_idx = torch.max(SDR_perm, dim=1) 83 | 84 | if not return_perm: 85 | return SDR_max / nsource 86 | else: 87 | return SDR_max / nsource, SDR_idx 88 | -------------------------------------------------------------------------------- /onssen/loss/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | For every new loss function added, please import it here and add it to loss_fns 3 | The loss function should take two arguments: 4 | output: a tuple from the network 5 | label: a tuple which is from dataloader 6 | You need to assert the format of the output and label in the loss function! 7 | """ 8 | from .loss_dc import loss_dc 9 | from .loss_chimera import loss_chimera_msa, loss_chimera_psa 10 | from .loss_mask import loss_mask_msa, loss_mask_psa 11 | from .loss_phase import loss_phase 12 | from .loss_e2e import SI_SNR, permute_SI_SNR, sisnr, si_snr_loss 13 | from .loss_util import T, norm, norm_1d 14 | 15 | 16 | __all__ = [ 17 | 'loss_dc', 'loss_chimera_msa', 'loss_chimera_psa', 18 | 'loss_mask_msa', 'loss_mask_psa', 19 | 'loss_phase', 20 | 'SI_SNR', 'permute_SI_SNR', 'sisnr', 'si_snr_loss', 21 | ] 22 | -------------------------------------------------------------------------------- /onssen/loss/loss_chimera.py: -------------------------------------------------------------------------------- 1 | from .loss_util import T, norm, norm_1d 2 | from .loss_dc import loss_dc 3 | import torch 4 | import torch.nn.functional as F 5 | 6 | def loss_chimera_msa(output, label): 7 | """ 8 | output: 9 | noisy_mag: batch_size X T X F tensor 10 | masks: batch_size X T X F X num_speaker tensor 11 | clean_mags: batch_size X T X F X num_speaker tensor 12 | label: 13 | one_hot_label: the label for deep clustering 14 | mag_mix: the magnitude of mix speech 15 | mag_s1: the magnitude of clean speech s1 16 | mag_s2: the magnitude of clean speech s2 17 | """ 18 | [embedding, mask_A, mask_B] = output 19 | [one_hot_label, mag_mix, mag_s1, mag_s2] = label 20 | batch_size, frame, frequency = mask_A.shape 21 | # compute the loss of embedding part 22 | loss_embedding = loss_dc([embedding], [one_hot_label, mag_mix]) 23 | 24 | #compute the loss of mask part 25 | loss_mask1 = norm_1d(mask_A*mag_mix - mag_s1)\ 26 | + norm_1d(mask_B*mag_mix - mag_s2) 27 | loss_mask2 = norm_1d(mask_B*mag_mix - mag_s1)\ 28 | + norm_1d(mask_A*mag_mix - mag_s2) 29 | loss_mask = torch.min(loss_mask1, loss_mask2) 30 | 31 | return loss_embedding*0.975 + loss_mask*0.025 32 | 33 | def loss_chimera_psa(output, label): 34 | """ 35 | output: 36 | noisy_mag: batch_size X T X F tensor 37 | masks: batch_size X T X F X num_speaker tensor 38 | clean_mags: batch_size X T X F X num_speaker tensor 39 | label: 40 | one_hot_label: the label for deep clustering 41 | mag_mix: the magnitude of mix speech 42 | mag_s1: the magnitude of clean speech s1 43 | mag_s2: the magnitude of clean speech s2 44 | cos_s1: the cosine of phase difference between mix and s1 45 | cos_s2: the cosine of phase difference between mix and s2 46 | """ 47 | [embedding, mask_A, mask_B] = output 48 | [one_hot_label, mag_mix, mag_s1, mag_s2, cos_s1, cos_s2] = label 49 | batch_size, frame, frequency = mask_A.shape 50 | # compute the loss of embedding part 51 | loss_embedding = loss_dc([embedding], [one_hot_label, mag_mix]) 52 | #compute the loss of mask part 53 | loss_mask1 = norm_1d(mask_A*mag_mix - torch.min(mag_mix,F.relu(mag_s1*cos_s1)))\ 54 | + norm_1d(mask_B*mag_mix - torch.min(mag_mix,F.relu(mag_s2*cos_s2))) 55 | loss_mask2 = norm_1d(mask_B*mag_mix - torch.min(mag_mix,F.relu(mag_s1*cos_s1)))\ 56 | + norm_1d(mask_A*mag_mix - torch.min(mag_mix,F.relu(mag_s2*cos_s2))) 57 | loss_mask = torch.min(loss_mask1, loss_mask2) 58 | 59 | return loss_embedding*0.975 + loss_mask*0.025 60 | -------------------------------------------------------------------------------- /onssen/loss/loss_dc.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | from .loss_util import T, norm 4 | 5 | 6 | def loss_dc(output, label): 7 | """ 8 | adopted from nussl loss function: 9 | https://github.com/interactiveaudiolab/nussl/blob/master/nussl/transformers/transformer_deep_clustering.py 10 | inputs: 11 | output: a tuple containing a batch_size X T X F X embedding_dim tensor 12 | label: a tuple containing a batch_size X T X F X num_speaker tensor 13 | outputs: 14 | loss of deep clustering model/layer 15 | """ 16 | assert len(output)==1, "Number of output must be 1 for Deep Clustering" 17 | assert len(label)==2, "Number of label must be 2 for Deep Clustering" 18 | embedding, = output 19 | label, mag_mix = label 20 | label = label.float() 21 | batch_size, frame_dim, frequency_dim, one_hot_dim = label.size() 22 | _, _, _, embedding_dim = embedding.size() 23 | 24 | embedding = embedding.view(batch_size, -1, embedding_dim) 25 | mag_mix = mag_mix.detach().view(batch_size, -1) 26 | label = label.view(batch_size, -1, one_hot_dim) 27 | 28 | # remove the loss of silence TF regions 29 | silence_mask = label.sum(2, keepdim=True) 30 | embedding = silence_mask * embedding 31 | 32 | # referred as weight WR 33 | # W_i = |x_i| / \sigma_j{|x_j|} 34 | weights = torch.sqrt(mag_mix / mag_mix.sum(1, keepdim=True)) 35 | label = label * weights.view(batch_size, frame_dim*frequency_dim, 1) 36 | embedding = embedding * weights.view(batch_size, frame_dim*frequency_dim, 1) 37 | 38 | # do batch affinity matrix computation 39 | loss_est = norm(torch.bmm(T(embedding), embedding)) 40 | loss_est_true = 2*norm(torch.bmm(T(embedding), label)) 41 | loss_true = norm(torch.bmm(T(label), label)) 42 | loss_embedding = loss_est - loss_est_true + loss_true 43 | 44 | return loss_embedding * mag_mix.sum(1, keepdim=True) 45 | -------------------------------------------------------------------------------- /onssen/loss/loss_e2e.py: -------------------------------------------------------------------------------- 1 | ### Created by Kai Li 2 | ### https://github.com/JusperLee/Conv-TasNet/blob/master/SI_SNR.py 3 | import torch 4 | from itertools import permutations 5 | 6 | 7 | def SI_SNR(_s, s, zero_mean=True): 8 | ''' 9 | Calculate the SNR indicator between the two audios. 10 | The larger the value, the better the separation. 11 | input: 12 | _s: Generated audio 13 | s: Ground Truth audio 14 | output: 15 | SNR value 16 | ''' 17 | if zero_mean: 18 | _s = _s - torch.mean(_s) 19 | s = s - torch.mean(s) 20 | s_target = sum(torch.mul(_s, s))*s/torch.pow(torch.norm(s, p=2), 2) 21 | e_noise = _s - s_target 22 | return 20*torch.log10(torch.norm(s_target, p=2)/torch.norm(e_noise, p=2)) 23 | 24 | 25 | def permute_SI_SNR(_s_lists, s_lists): 26 | ''' 27 | Calculate all possible SNRs according to 28 | the permutation combination and 29 | then find the maximum value. 30 | input: 31 | _s_lists: Generated audio list 32 | s_lists: Ground truth audio list 33 | output: 34 | max of SI-SNR 35 | ''' 36 | length = len(_s_lists) 37 | results = [] 38 | for p in permutations(range(length)): 39 | s_list = [s_lists[n] for n in p] 40 | result = sum([SI_SNR(_s, s) for _s, s in zip(_s_lists, s_list)])/length 41 | results.append(result) 42 | return max(results) 43 | 44 | 45 | def sisnr(x, s, eps=1e-8): 46 | """ 47 | calculate training loss 48 | input: 49 | x: separated signal, N x S tensor 50 | s: reference signal, N x S tensor 51 | Return: 52 | sisnr: N tensor 53 | """ 54 | 55 | def l2norm(mat, keepdim=False): 56 | return torch.norm(mat, dim=-1, keepdim=keepdim) 57 | 58 | if x.shape != s.shape: 59 | raise RuntimeError( 60 | "Dimention mismatch when calculate si-snr, {} vs {}".format( 61 | x.shape, s.shape)) 62 | x_zm = x - torch.mean(x, dim=-1, keepdim=True) 63 | s_zm = s - torch.mean(s, dim=-1, keepdim=True) 64 | t = torch.sum( 65 | x_zm * s_zm, dim=-1, 66 | keepdim=True) * s_zm / (l2norm(s_zm, keepdim=True)**2 + eps) 67 | return 20 * torch.log10(eps + l2norm(t) / (l2norm(x_zm - t) + eps)) 68 | 69 | 70 | def si_snr_loss(ests, refs): 71 | # spks x N x S 72 | num_spks = len(refs) 73 | 74 | def sisnr_loss(permute): 75 | # for one permute 76 | return sum( 77 | [sisnr(ests[s], refs[t]) 78 | for s, t in enumerate(permute)]) / len(permute) 79 | # average the value 80 | 81 | # P x N 82 | N, S = refs[0].shape 83 | sisnr_mat = torch.stack( 84 | [sisnr_loss(p) for p in permutations(range(num_spks))]) 85 | max_perutt, _ = torch.max(sisnr_mat, dim=0) 86 | # si-snr 87 | return -torch.sum(max_perutt) / N 88 | 89 | 90 | if __name__ == "__main__": 91 | a_t = torch.tensor([1, 2, 3], dtype=torch.float32) 92 | b_t = torch.tensor([1, 4, 6], dtype=torch.float32) 93 | print(permute_SI_SNR([a_t], [b_t])) 94 | -------------------------------------------------------------------------------- /onssen/loss/loss_mask.py: -------------------------------------------------------------------------------- 1 | from .loss_util import norm, norm_1d 2 | import torch 3 | import torch.nn.functional as F 4 | 5 | 6 | def loss_mask_msa(output, label): 7 | """ 8 | The loss function of Magnitude Spectrum Approximation (MSA). 9 | It is for enhancing speech in a noisy recording. 10 | output: 11 | mask: batch_size X T X F tensor 12 | label: 13 | mag_noisy: the magnitude of mix speech 14 | mag_clean: the magnitude of clean speech s1 15 | """ 16 | [clean_est] = output 17 | [mag_clean, cos_diff] = label 18 | #compute the loss of mask part 19 | # loss = nn.MSELoss()(mask * mag_noisy, mag_clean) 20 | 21 | loss = torch.nn.MSELoss()(clean_est, mag_clean) 22 | return loss 23 | 24 | 25 | def loss_mask_psa(output, label): 26 | """ 27 | The loss function of Phase-sensitive Spectrum Approximation (PSA). 28 | It is for enhancing speech in a noisy recording. 29 | output: 30 | mask: batch_size X T X F tensor 31 | label: 32 | mag_noisy: the magnitude of mix speech 33 | mag_clean: the magnitude of clean speech s1 34 | cos_diff: the cosine of phase difference between mix and clean 35 | """ 36 | [mask] = output 37 | [mag_noisy, mag_clean, cos_diff] = label 38 | #compute the loss of mask part 39 | loss = norm_1d(mask * mag_noisy - torch.min(mag_noisy,F.relu(mag_clean*cos_diff))) 40 | return loss 41 | -------------------------------------------------------------------------------- /onssen/loss/loss_phase.py: -------------------------------------------------------------------------------- 1 | from .loss_util import T, norm, norm_1d 2 | from .loss_dc import loss_dc 3 | import torch 4 | import torch.nn.functional as F 5 | 6 | def loss_phase(output, label): 7 | assert len(output) == 6, "There must be 5 tensors in the output" 8 | assert len(label) == 6, "There must be 6 tensors in the label" 9 | [embedding, mask_A, mask_B, phase_A, phase_B] = output 10 | [one_hot_label, mag_mix, mag_s1, mag_s2, phase_s1, phase_s2] = label 11 | batch_size, time_size, frequency_size = mag_mix.size() 12 | # compute the loss of embedding part 13 | loss_embedding = loss_dc([embedding, mag_mix], [one_hot_label]) 14 | 15 | #compute the loss of mask part 16 | loss_mask1 = norm_1d(mask_A*mag_mix - mag_s1)\ 17 | + norm_1d(mask_B*mag_mix - mag_s2) 18 | loss_mask2 = norm_1d(mask_B*mag_mix - mag_s1)\ 19 | + norm_1d(mask_A*mag_mix - mag_s2) 20 | 21 | amin = loss_mask1