├── .gitattributes ├── LICENSE ├── PLCMOS ├── models │ ├── plcmos_v0.onnx │ ├── plcmos_v1_intrusive.onnx │ └── plcmos_v1_nonintrusive.onnx └── plc_mos.py ├── README.md ├── audio_samples ├── sample_1 │ ├── FRN_enhanced.wav │ ├── TFGAN_enhanced.wav │ ├── clean.wav │ ├── lossy.wav │ └── tPLC_enhanced.wav ├── sample_2 │ ├── FRN_enhanced.wav │ ├── TFGAN_enhanced.wav │ ├── clean.wav │ ├── lossy.wav │ └── tPLC_enhanced.wav └── sample_3 │ ├── FRN_enhanced.wav │ ├── TFGAN_enhanced.wav │ ├── clean.wav │ ├── lossy.wav │ └── tPLC_enhanced.wav ├── config.py ├── css └── styles.css ├── data └── vctk │ ├── test.txt │ └── train.txt ├── dataset.py ├── index.html ├── inference_onnx.py ├── lightning_logs ├── predictor │ ├── checkpoints │ │ └── predictor.ckpt │ └── hparams.yaml └── version_0 │ ├── checkpoints │ ├── frn-epoch=65-val_loss=0.2290.ckpt │ └── frn.onnx │ └── hparams.yaml ├── loss.py ├── main.py ├── models ├── __init__.py ├── blocks.py └── frn.py ├── requirements.txt └── utils ├── __init__.py ├── stft.py ├── tblogger.py └── utils.py /.gitattributes: -------------------------------------------------------------------------------- 1 | lightning_logs/version_0/checkpoints/frn-epoch=65-val_loss=0.2290.ckpt filter=lfs diff=lfs merge=lfs -text 2 | lightning_logs/version_0/checkpoints/frn.onnx filter=lfs diff=lfs merge=lfs -text 3 | lightning_logs/predictor/checkpoints/predictor.ckpt filter=lfs diff=lfs merge=lfs -text 4 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | 2 | Attribution-NonCommercial 4.0 International 3 | 4 | ======================================================================= 5 | 6 | Creative Commons Corporation ("Creative Commons") is not a law firm and 7 | does not provide legal services or legal advice. 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For 397 | the avoidance of doubt, this paragraph does not form part of the 398 | public licenses. 399 | 400 | Creative Commons may be contacted at creativecommons.org. 401 | -------------------------------------------------------------------------------- /PLCMOS/models/plcmos_v0.onnx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Crystalsound/FRN/1b6445ded23f41bd76aef8e22ef12d4e2983397c/PLCMOS/models/plcmos_v0.onnx -------------------------------------------------------------------------------- /PLCMOS/models/plcmos_v1_intrusive.onnx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Crystalsound/FRN/1b6445ded23f41bd76aef8e22ef12d4e2983397c/PLCMOS/models/plcmos_v1_intrusive.onnx -------------------------------------------------------------------------------- /PLCMOS/models/plcmos_v1_nonintrusive.onnx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Crystalsound/FRN/1b6445ded23f41bd76aef8e22ef12d4e2983397c/PLCMOS/models/plcmos_v1_nonintrusive.onnx -------------------------------------------------------------------------------- /PLCMOS/plc_mos.py: -------------------------------------------------------------------------------- 1 | import math 2 | import os 3 | 4 | import librosa 5 | import numpy as np 6 | import onnxruntime as ort 7 | from numpy.fft import rfft 8 | from numpy.lib.stride_tricks import as_strided 9 | 10 | 11 | class PLCMOSEstimator(): 12 | def __init__(self, model_version=1): 13 | """ 14 | Initialize a PLC-MOS model of a given version. There are currently three models available, v0 (intrusive) 15 | and v1 (both non-intrusive and intrusive available). The default is to use the v1 models. 16 | """ 17 | 18 | self.model_version = model_version 19 | model_paths = [ 20 | # v0 model: 21 | [("models/plcmos_v0.onnx", 999999999999), (None, 0)], 22 | 23 | # v1 models: 24 | [("models/plcmos_v1_intrusive.onnx", 768), 25 | ("models/plcmos_v1_nonintrusive.onnx", 999999999999)], 26 | ] 27 | self.sessions = [] 28 | self.max_lens = [] 29 | options = ort.SessionOptions() 30 | options.intra_op_num_threads = 8 31 | options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL 32 | for path, max_len in model_paths[model_version]: 33 | if not path is None: 34 | file_dir = os.path.dirname(os.path.realpath(__file__)) 35 | self.sessions.append(ort.InferenceSession( 36 | os.path.join(file_dir, path), options)) 37 | self.max_lens.append(max_len) 38 | else: 39 | self.sessions.append(None) 40 | self.max_lens.append(0) 41 | 42 | def logpow_dns(self, sig, floor=-30.): 43 | """ 44 | Compute log power of complex spectrum. 45 | 46 | Floor any -`np.inf` value to (nonzero minimum + `floor`) dB. 47 | If all values are 0s, floor all values to -80 dB. 48 | """ 49 | log10e = np.log10(np.e) 50 | pspec = sig.real ** 2 + sig.imag ** 2 51 | zeros = pspec == 0 52 | logp = np.empty_like(pspec) 53 | if np.any(~zeros): 54 | logp[~zeros] = np.log(pspec[~zeros]) 55 | logp[zeros] = np.log(pspec[~zeros].min()) + floor / 10 / log10e 56 | else: 57 | logp.fill(-80 / 10 / log10e) 58 | 59 | return logp 60 | 61 | def hop2hsize(self, wind, hop): 62 | """ 63 | Convert hop fraction to integer size if necessary. 64 | """ 65 | if hop >= 1: 66 | assert type(hop) == int, "Hop size must be integer!" 67 | return hop 68 | else: 69 | assert 0 < hop < 1, "Hop fraction has to be in range (0,1)!" 70 | return int(len(wind) * hop) 71 | 72 | def stana(self, sig, sr, wind, hop, synth=False, center=False): 73 | """ 74 | Short term analysis by windowing 75 | """ 76 | ssize = len(sig) 77 | fsize = len(wind) 78 | hsize = self.hop2hsize(wind, hop) 79 | if synth: 80 | sstart = hsize - fsize # int(-fsize * (1-hfrac)) 81 | elif center: 82 | sstart = -int(len(wind) / 2) # odd window centered at exactly n=0 83 | else: 84 | sstart = 0 85 | send = ssize 86 | 87 | nframe = math.ceil((send - sstart) / hsize) 88 | 89 | # Calculate zero-padding sizes 90 | zpleft = -sstart 91 | zpright = (nframe - 1) * hsize + fsize - zpleft - ssize 92 | if zpleft > 0 or zpright > 0: 93 | sigpad = np.zeros(ssize + zpleft + zpright, dtype=sig.dtype) 94 | sigpad[zpleft:len(sigpad) - zpright] = sig 95 | else: 96 | sigpad = sig 97 | 98 | return as_strided(sigpad, shape=(nframe, fsize), 99 | strides=(sig.itemsize * hsize, sig.itemsize)) * wind 100 | 101 | def stft(self, sig, sr, wind, hop, nfft): 102 | """ 103 | Compute STFT: window + rfft 104 | """ 105 | frames = self.stana(sig, sr, wind, hop, synth=True) 106 | return rfft(frames, n=nfft) 107 | 108 | def stft_transform(self, audio, dft_size=512, hop_fraction=0.5, sr=16000): 109 | """ 110 | Compute STFT parameters, then compute STFT 111 | """ 112 | window = np.hamming(dft_size + 1) 113 | window = window[:-1] 114 | amp = np.abs(self.stft(audio, sr, window, hop_fraction, dft_size)) 115 | feat = self.logpow_dns(amp, floor=-120.) 116 | return feat / 20. 117 | 118 | def run(self, audio_degraded, audio_clean=None, combined=False): 119 | """ 120 | Run the PLCMOS model and return the MOS for the given audio. If a clean audio file is passed and the 121 | selected model version has an intrusive version, that version will be used, otherwise, the nonintrusive 122 | model will be used. If combined is set to true (default), the mean of intrusive and nonintrusive models 123 | results will be returned, when both are available 124 | 125 | For intrusive models, the clean reference should be the unprocessed audio file the degraded audio is 126 | based on. It is not required to be aligned with the degraded audio. 127 | 128 | Audio data should be 16kHz, mono, [-1, 1] range. 129 | """ 130 | audio_features_degraded = np.float32(self.stft_transform(audio_degraded))[ 131 | np.newaxis, np.newaxis, ...] 132 | assert len( 133 | audio_features_degraded) <= self.max_lens[0], "Maximum input length exceeded" 134 | 135 | if audio_clean is None: 136 | combined = False 137 | 138 | mos = 0 139 | 140 | session = self.sessions[0] 141 | assert not session is None, "Intrusive model not available for this model version." 142 | audio_features_clean = np.float32(self.stft_transform(audio_clean))[ 143 | np.newaxis, np.newaxis, ...] 144 | assert len( 145 | audio_features_clean) <= self.max_lens[0], "Maximum input length exceeded" 146 | onnx_inputs = {"degraded_audio": audio_features_degraded, 147 | "clean_audio": audio_features_clean} 148 | mos = float(session.run(None, onnx_inputs)[0]) 149 | 150 | session = self.sessions[1] 151 | assert not session is None, "Nonintrusive model not available for this model version." 152 | onnx_inputs = {"degraded_audio": audio_features_degraded} 153 | mos_2 = float(session.run(None, onnx_inputs)[0]) 154 | mos = [mos, mos_2] 155 | return mos 156 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # FRN - Full-band Recurrent Network Official Implementation 2 | 3 | **Improving performance of real-time full-band blind packet-loss concealment with predictive network - ICASSP 2023** 4 | 5 | [![Generic badge](https://img.shields.io/badge/arXiv-2211.04071-brightgreen.svg?style=flat-square)](https://arxiv.org/abs/2211.04071) 6 | [![Generic badge](https://img.shields.io/github/stars/Crystalsound/FRN?color=yellow&label=FRN&logo=github&style=flat-square)](https://github.com/Crystalsound/FRN/) 7 | [![Generic badge](https://img.shields.io/badge/GitHub.io-Audio_Samples-blue?color=blue&label=GitHub.io&style=flat-square)](https://crystalsound.github.io/FRN/) 8 | [![Generic badge](https://img.shields.io/badge/Demo-HuggingFace-yellow?color=yellow&label=Demo&style=flat-square)](https://huggingface.co/spaces/anhnv125/FRN) 9 | 10 | ## License and citation 11 | 12 | This repository is released under the CC-BY-NC 4.0. license as found in the LICENSE file. 13 | 14 | If you use our software, please cite as below. 15 | For future queries, please contact [anh.nguyen@namitech.io](mailto:anh.nguyen@namitech.io). 16 | 17 | Copyright © 2022 NAMI TECHNOLOGY JSC, Inc. All rights reserved. 18 | 19 | ``` 20 | @misc{Nguyen2022ImprovingPO, 21 | title={Improving performance of real-time full-band blind packet-loss concealment with predictive network}, 22 | author={Viet-Anh Nguyen and Anh H. T. Nguyen and Andy W. H. Khong}, 23 | year={2022}, 24 | eprint={2211.04071}, 25 | archivePrefix={arXiv}, 26 | primaryClass={cs.LG} 27 | } 28 | ``` 29 | 30 | # 1. Results 31 | 32 | Our model achieved a significant gain over baselines. Here, we include the predicted packet loss concealment 33 | mean-opinion-score (PLCMOS) using Microsoft's [PLCMOS](https://github.com/microsoft/PLC-Challenge/tree/main/PLCMOS) 34 | service. Please refer to our paper for more benchmarks. 35 | 36 | | Model | PLCMOS | 37 | |---------|-----------| 38 | | Input | 3.517 | 39 | | tPLC | 3.463 | 40 | | TFGAN | 3.645 | 41 | | **FRN** | **3.655** | 42 | 43 | We also provide several audio samples for 44 | comparison at [github.io](https://crystalsound.github.io/FRN/), and a demo application at [HuggingFace Space](https://huggingface.co/spaces/anhnv125/FRN). 45 | 46 | # 2. Installation 47 | 48 | ## Setup 49 | 50 | ### Clone the repo 51 | 52 | ``` 53 | $ git clone https://github.com/Crystalsound/FRN.git 54 | $ cd FRN 55 | ``` 56 | 57 | ### Install dependencies 58 | 59 | * Our implementation requires the `libsndfile` libraries for the Python packages `soundfile`. On Ubuntu, they can be 60 | easily installed using `apt-get`: 61 | ``` 62 | $ apt-get update && apt-get install libsndfile-dev 63 | ``` 64 | * Create a Python 3.8 environment. Conda is recommended: 65 | ``` 66 | $ conda create -n frn python=3.8 67 | $ conda activate frn 68 | ``` 69 | 70 | * Install the requirements: 71 | ``` 72 | $ pip install -r requirements.txt 73 | ``` 74 | 75 | # 3. Data preparation 76 | 77 | In our paper, we conduct experiments on the [VCTK](https://datashare.ed.ac.uk/handle/10283/3443) dataset. 78 | 79 | * Download and extract the datasets: 80 | ``` 81 | $ wget http://www.udialogue.org/download/VCTK-Corpus.tar.gz -O data/vctk/VCTK-Corpus.tar.gz 82 | $ tar -zxvf data/vctk/VCTK-Corpus.tar.gz -C data/vctk/ --strip-components=1 83 | ``` 84 | 85 | After extracting the datasets, your `./data` directory should look like this: 86 | 87 | ``` 88 | . 89 | |--data 90 | |--vctk 91 | |--wav48 92 | |--p225 93 | |--p225_001.wav 94 | ... 95 | |--train.txt 96 | |--test.txt 97 | ``` 98 | * In order to load the datasets, text files that contain training and testing audio paths are required. We have 99 | prepared `train.txt` and `test.txt` files in `./data/vctk` directory. 100 | 101 | # 4. Run the code 102 | 103 | ## Configuration 104 | 105 | `config.py` is the most important file. Here, you can find all the configurations related to experiment setups, 106 | datasets, models, training, testing, etc. Although the config file has been explained thoroughly, we recommend reading 107 | our paper to fully understand each parameter. 108 | 109 | ## Training 110 | 111 | * Adjust training hyperparameters in `config.py`. We provide the pretrained predictor in `lightning_logs/predictor` as stated in our paper. The FRN model can be trained entirely from scratch and will work as well. In this case, initiate `PLCModel(..., pred_ckpt_path=None)`. 112 | 113 | * Run `main.py`: 114 | ``` 115 | $ python main.py --mode train 116 | ``` 117 | * Each run will create a version in `./lightning_logs`, where the model checkpoint and hyperparameters are saved. In 118 | case you want to continue training from one of these versions, just set the argument `--version` of the above command 119 | to your desired version number. For example: 120 | ``` 121 | # resume from version 0 122 | $ python main.py --mode train --version 0 123 | ``` 124 | * To monitor the training curves as well as inspect model output visualization, run the tensorboard: 125 | ``` 126 | $ tensorboard --logdir=./lightning_logs --bind_all 127 | ``` 128 | ![image.png](https://images.viblo.asia/eb2246f9-2747-43b9-8f78-d6c154144716.png) 129 | 130 | ## Evaluation 131 | 132 | In our paper, we evaluated with 2 masking methods: simulation using Markov Chain and employing real traces in PLC 133 | Challenge. 134 | 135 | * Get the blind test set with loss traces: 136 | ``` 137 | $ wget http://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/blind.tar.gz 138 | $ tar -xvf blind.tar.gz -C test_samples 139 | ``` 140 | * Modify `config.py` to change evaluation setup if necessary. 141 | * Run `main.py` with a version number to be evaluated: 142 | ``` 143 | $ python main.py --mode eval --version 0 144 | ``` 145 | During the evaluation, several output samples are saved to `CONFIG.LOG.sample_path` for sanity testing. 146 | 147 | ## Configure a new dataset 148 | 149 | Our implementation currently works with the VCTK dataset but can be easily extensible to a new one. 150 | 151 | * Firstly, you need to prepare `train.txt` and `test.txt`. See `./data/vctk/train.txt` and `./data/vctk/test.txt` for 152 | example. 153 | * Secondly, add a new dictionary to `CONFIG.DATA.data_dir`: 154 | ``` 155 | { 156 | 'root': 'path/to/data/directory', 157 | 'train': 'path/to/train.txt', 158 | 'test': 'path/to/test.txt' 159 | } 160 | ``` 161 | **Important:** Make sure each line in `train.txt` and `test.txt` joining with `'root'` is a valid path to its 162 | corresponding audio file. 163 | 164 | # 5. Audio generation 165 | 166 | * In order to generate output audios, you need to modify `CONFIG.TEST.in_dir` to your input directory. 167 | * Run `main.py`: 168 | ``` 169 | python main.py --mode test --version 0 170 | ``` 171 | The generated audios are saved to `CONFIG.TEST.out_dir`. 172 | 173 | ## ONNX inferencing 174 | We provide ONNX inferencing scripts and the best ONNX model (converted from the best checkpoint) 175 | at `lightning_logs/best_model.onnx`. 176 | * Convert a checkpoint to an ONNX model: 177 | ``` 178 | python main.py --mode onnx --version 0 179 | ``` 180 | The converted ONNX model will be saved to `lightning_logs/version_0/checkpoints`. 181 | * Put test audios in `test_samples` and inference with the converted ONNX model (see `inference_onnx.py` for more 182 | details): 183 | ``` 184 | python inference_onnx.py --onnx_path lightning_logs/version_0/frn.onnx 185 | ``` 186 | -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /config.py: -------------------------------------------------------------------------------- 1 | class CONFIG: 2 | gpus = "0,1" # List of gpu devices 3 | 4 | class TRAIN: 5 | batch_size = 90 # number of audio files per batch 6 | lr = 1e-4 # learning rate 7 | epochs = 150 # max training epochs 8 | workers = 12 # number of dataloader workers 9 | val_split = 0.1 # validation set proportion 10 | clipping_val = 1.0 # gradient clipping value 11 | patience = 3 # learning rate scheduler's patience 12 | factor = 0.5 # learning rate reduction factor 13 | 14 | # Model config 15 | class MODEL: 16 | enc_layers = 4 # number of MLP blocks in the encoder 17 | enc_in_dim = 384 # dimension of the input projection layer in the encoder 18 | enc_dim = 768 # dimension of the MLP blocks 19 | pred_dim = 512 # dimension of the LSTM in the predictor 20 | pred_layers = 1 # number of LSTM layers in the predictor 21 | 22 | # Dataset config 23 | class DATA: 24 | dataset = 'vctk' # dataset to use 25 | ''' 26 | Dictionary that specifies paths to root directories and train/test text files of each datasets. 27 | 'root' is the path to the dataset and each line of the train.txt/test.txt files should contains the path to an 28 | audio file from 'root'. 29 | ''' 30 | data_dir = {'vctk': {'root': 'data/vctk/wav48', 31 | 'train': "data/vctk/train.txt", 32 | 'test': "data/vctk/test.txt"}, 33 | } 34 | 35 | assert dataset in data_dir.keys(), 'Unknown dataset.' 36 | sr = 48000 # audio sampling rate 37 | audio_chunk_len = 122880 # size of chunk taken in each audio files 38 | window_size = 960 # window size of the STFT operation, equivalent to packet size 39 | stride = 480 # stride of the STFT operation 40 | 41 | class TRAIN: 42 | packet_sizes = [256, 512, 768, 960, 1024, 43 | 1536] # packet sizes for training. All sizes should be divisible by 'audio_chunk_len' 44 | transition_probs = ((0.9, 0.1), (0.5, 0.1), (0.5, 0.5)) # list of trainsition probs for Markow Chain 45 | 46 | class EVAL: 47 | packet_size = 960 # 20ms 48 | transition_probs = [(0.9, 0.1)] # (0.9, 0.1) ~ 10%; (0.8, 0.2) ~ 20%; (0.6, 0.4) ~ 40% 49 | masking = 'gen' # whether using simulation or real traces from Microsoft to generate masks 50 | assert masking in ['gen', 'real'] 51 | trace_path = 'test_samples/blind/lossy_singals' # must be clarified if masking = 'real' 52 | 53 | class LOG: 54 | log_dir = 'lightning_logs' # checkpoint and log directory 55 | sample_path = 'audio_samples' # path to save generated audio samples in evaluation. 56 | 57 | class TEST: 58 | in_dir = 'test_samples/blind/lossy_signals' # path to test audio inputs 59 | out_dir = 'test_samples/blind/lossy_signals_out' # path to generated outputs 60 | -------------------------------------------------------------------------------- /css/styles.css: -------------------------------------------------------------------------------- 1 | body { 2 | margin: 60px auto; 3 | width: 70%; 4 | } 5 | 6 | nav ul, footer ul { 7 | font-family: 'Helvetica', 'Arial', 'Sans-Serif'; 8 | padding: 0px; 9 | list-style: none; 10 | font-weight: bold; 11 | } 12 | 13 | nav ul li, footer ul li { 14 | display: inline; 15 | margin-right: 20px; 16 | } 17 | 18 | a { 19 | text-decoration: none; 20 | color: #999; 21 | } 22 | 23 | a:hover { 24 | text-decoration: underline; 25 | } 26 | 27 | h1 { 28 | font-size: 2em; 29 | font-family: 'Helvetica', 'Arial', 'Sans-Serif'; 30 | } 31 | 32 | p { 33 | font-size: 1.2em; 34 | line-height: 1.4em; 35 | color: #333; 36 | } 37 | 38 | footer { 39 | border-top: 1px solid #d5d5d5; 40 | font-size: .8em; 41 | } 42 | 43 | ul.posts { 44 | margin: 20px auto 40px; 45 | font-size: 1.5em; 46 | } 47 | 48 | ul.posts li { 49 | list-style: none; 50 | } 51 | -------------------------------------------------------------------------------- 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p374/p374_365.wav 3507 | p374/p374_075.wav 3508 | p374/p374_414.wav 3509 | p374/p374_362.wav 3510 | p374/p374_009.wav 3511 | p374/p374_246.wav 3512 | p374/p374_180.wav 3513 | p374/p374_151.wav 3514 | p374/p374_297.wav 3515 | p374/p374_234.wav 3516 | p374/p374_123.wav 3517 | p374/p374_007.wav 3518 | p374/p374_310.wav 3519 | p374/p374_248.wav 3520 | p374/p374_299.wav 3521 | p374/p374_317.wav 3522 | p374/p374_189.wav 3523 | p374/p374_158.wav 3524 | p374/p374_233.wav 3525 | p374/p374_124.wav 3526 | p374/p374_322.wav 3527 | p374/p374_035.wav 3528 | p374/p374_098.wav 3529 | p374/p374_049.wav 3530 | p374/p374_111.wav 3531 | p374/p374_206.wav 3532 | p374/p374_359.wav 3533 | p374/p374_388.wav 3534 | p374/p374_116.wav 3535 | p374/p374_201.wav 3536 | p374/p374_325.wav 3537 | p374/p374_032.wav 3538 | p374/p374_091.wav 3539 | p374/p374_357.wav 3540 | p374/p374_421.wav 3541 | p374/p374_386.wav 3542 | p374/p374_040.wav 3543 | p374/p374_118.wav 3544 | p374/p374_164.wav 3545 | p374/p374_273.wav 3546 | p374/p374_163.wav 3547 | p374/p374_274.wav 3548 | p374/p374_096.wav 3549 | p374/p374_350.wav 3550 | p374/p374_381.wav 3551 | p374/p374_047.wav 3552 | p374/p374_208.wav -------------------------------------------------------------------------------- /dataset.py: -------------------------------------------------------------------------------- 1 | import glob 2 | import os 3 | import random 4 | 5 | import librosa 6 | import numpy as np 7 | import soundfile as sf 8 | import torch 9 | from numpy.random import default_rng 10 | from pydtmc import MarkovChain 11 | from sklearn.model_selection import train_test_split 12 | from torch.utils.data import Dataset 13 | 14 | from config import CONFIG 15 | 16 | np.random.seed(0) 17 | rng = default_rng() 18 | 19 | 20 | def load_audio( 21 | path, 22 | sample_rate: int = 16000, 23 | chunk_len=None, 24 | ): 25 | with sf.SoundFile(path) as f: 26 | sr = f.samplerate 27 | audio_len = f.frames 28 | 29 | if chunk_len is not None and chunk_len < audio_len: 30 | start_index = torch.randint(0, audio_len - chunk_len, (1,))[0] 31 | 32 | frames = f._prepare_read(start_index, start_index + chunk_len, -1) 33 | audio = f.read(frames, always_2d=True, dtype="float32") 34 | 35 | else: 36 | audio = f.read(always_2d=True, dtype="float32") 37 | 38 | if sr != sample_rate: 39 | audio = librosa.resample(np.squeeze(audio), sr, sample_rate)[:, np.newaxis] 40 | 41 | return audio.T 42 | 43 | 44 | def pad(sig, length): 45 | if sig.shape[1] < length: 46 | pad_len = length - sig.shape[1] 47 | sig = torch.hstack((sig, torch.zeros((sig.shape[0], pad_len)))) 48 | 49 | else: 50 | start = random.randint(0, sig.shape[1] - length) 51 | sig = sig[:, start:start + length] 52 | return sig 53 | 54 | 55 | class MaskGenerator: 56 | def __init__(self, is_train=True, probs=((0.9, 0.1), (0.5, 0.1), (0.5, 0.5))): 57 | ''' 58 | is_train: if True, mask generator for training otherwise for evaluation 59 | probs: a list of transition probability (p_N, p_L) for Markov Chain. Only allow 1 tuple if 'is_train=False' 60 | ''' 61 | self.is_train = is_train 62 | self.probs = probs 63 | self.mcs = [] 64 | if self.is_train: 65 | for prob in probs: 66 | self.mcs.append(MarkovChain([[prob[0], 1 - prob[0]], [1 - prob[1], prob[1]]], ['1', '0'])) 67 | else: 68 | assert len(probs) == 1 69 | prob = self.probs[0] 70 | self.mcs.append(MarkovChain([[prob[0], 1 - prob[0]], [1 - prob[1], prob[1]]], ['1', '0'])) 71 | 72 | def gen_mask(self, length, seed=0): 73 | if self.is_train: 74 | mc = random.choice(self.mcs) 75 | else: 76 | mc = self.mcs[0] 77 | mask = mc.walk(length - 1, seed=seed) 78 | mask = np.array(list(map(int, mask))) 79 | return mask 80 | 81 | 82 | class TestLoader(Dataset): 83 | def __init__(self): 84 | dataset_name = CONFIG.DATA.dataset 85 | self.mask = CONFIG.DATA.EVAL.masking 86 | 87 | self.target_root = CONFIG.DATA.data_dir[dataset_name]['root'] 88 | txt_list = CONFIG.DATA.data_dir[dataset_name]['test'] 89 | self.data_list = self.load_txt(txt_list) 90 | if self.mask == 'real': 91 | trace_txt = glob.glob(os.path.join(CONFIG.DATA.EVAL.trace_path, '*.txt')) 92 | trace_txt.sort() 93 | self.trace_list = [1 - np.array(list(map(int, open(txt, 'r').read().strip('\n').split('\n')))) for txt in 94 | trace_txt] 95 | else: 96 | self.mask_generator = MaskGenerator(is_train=False, probs=CONFIG.DATA.EVAL.transition_probs) 97 | 98 | self.sr = CONFIG.DATA.sr 99 | self.stride = CONFIG.DATA.stride 100 | self.window_size = CONFIG.DATA.window_size 101 | self.audio_chunk_len = CONFIG.DATA.audio_chunk_len 102 | self.p_size = CONFIG.DATA.EVAL.packet_size # 20ms 103 | self.hann = torch.sqrt(torch.hann_window(self.window_size)) 104 | 105 | def __len__(self): 106 | return len(self.data_list) 107 | 108 | def load_txt(self, txt_list): 109 | target = [] 110 | with open(txt_list) as f: 111 | for line in f: 112 | target.append(os.path.join(self.target_root, line.strip('\n'))) 113 | target = list(set(target)) 114 | target.sort() 115 | return target 116 | 117 | def __getitem__(self, index): 118 | target = load_audio(self.data_list[index], sample_rate=self.sr) 119 | target = target[:, :(target.shape[1] // self.p_size) * self.p_size] 120 | 121 | sig = np.reshape(target, (-1, self.p_size)).copy() 122 | if self.mask == 'real': 123 | mask = self.trace_list[index % len(self.trace_list)] 124 | mask = np.repeat(mask, np.ceil(len(sig) / len(mask)), 0)[:len(sig)][:, np.newaxis] 125 | else: 126 | mask = self.mask_generator.gen_mask(len(sig), seed=index)[:, np.newaxis] 127 | sig *= mask 128 | sig = torch.tensor(sig).reshape(-1) 129 | 130 | target = torch.tensor(target).squeeze(0) 131 | 132 | sig_wav = sig.clone() 133 | target_wav = target.clone() 134 | 135 | target = torch.stft(target, self.window_size, self.stride, window=self.hann, 136 | return_complex=False).permute(2, 0, 1) 137 | sig = torch.stft(sig, self.window_size, self.stride, window=self.hann, return_complex=False).permute(2, 0, 1) 138 | return sig.float(), target.float(), sig_wav, target_wav 139 | 140 | 141 | class BlindTestLoader(Dataset): 142 | def __init__(self, test_dir): 143 | self.data_list = glob.glob(os.path.join(test_dir, '*.wav')) 144 | self.sr = CONFIG.DATA.sr 145 | self.stride = CONFIG.DATA.stride 146 | self.chunk_len = CONFIG.DATA.window_size 147 | self.hann = torch.sqrt(torch.hann_window(self.chunk_len)) 148 | 149 | def __len__(self): 150 | return len(self.data_list) 151 | 152 | def __getitem__(self, index): 153 | sig = load_audio(self.data_list[index], sample_rate=self.sr) 154 | sig = torch.from_numpy(sig).squeeze(0) 155 | sig = torch.stft(sig, self.chunk_len, self.stride, window=self.hann, return_complex=False).permute(2, 0, 1) 156 | return sig.float() 157 | 158 | 159 | class TrainDataset(Dataset): 160 | 161 | def __init__(self, mode='train'): 162 | dataset_name = CONFIG.DATA.dataset 163 | self.target_root = CONFIG.DATA.data_dir[dataset_name]['root'] 164 | 165 | txt_list = CONFIG.DATA.data_dir[dataset_name]['train'] 166 | self.data_list = self.load_txt(txt_list) 167 | 168 | if mode == 'train': 169 | self.data_list, _ = train_test_split(self.data_list, test_size=CONFIG.TRAIN.val_split, random_state=0) 170 | 171 | elif mode == 'val': 172 | _, self.data_list = train_test_split(self.data_list, test_size=CONFIG.TRAIN.val_split, random_state=0) 173 | 174 | self.p_sizes = CONFIG.DATA.TRAIN.packet_sizes 175 | self.mode = mode 176 | self.sr = CONFIG.DATA.sr 177 | self.window = CONFIG.DATA.audio_chunk_len 178 | self.stride = CONFIG.DATA.stride 179 | self.chunk_len = CONFIG.DATA.window_size 180 | self.hann = torch.sqrt(torch.hann_window(self.chunk_len)) 181 | self.mask_generator = MaskGenerator(is_train=True, probs=CONFIG.DATA.TRAIN.transition_probs) 182 | 183 | def __len__(self): 184 | return len(self.data_list) 185 | 186 | def load_txt(self, txt_list): 187 | target = [] 188 | with open(txt_list) as f: 189 | for line in f: 190 | target.append(os.path.join(self.target_root, line.strip('\n'))) 191 | target = list(set(target)) 192 | target.sort() 193 | return target 194 | 195 | def fetch_audio(self, index): 196 | sig = load_audio(self.data_list[index], sample_rate=self.sr, chunk_len=self.window) 197 | while sig.shape[1] < self.window: 198 | idx = torch.randint(0, len(self.data_list), (1,))[0] 199 | pad_len = self.window - sig.shape[1] 200 | if pad_len < 0.02 * self.sr: 201 | padding = np.zeros((1, pad_len), dtype=np.float) 202 | else: 203 | padding = load_audio(self.data_list[idx], sample_rate=self.sr, chunk_len=pad_len) 204 | sig = np.hstack((sig, padding)) 205 | return sig 206 | 207 | def __getitem__(self, index): 208 | sig = self.fetch_audio(index) 209 | 210 | sig = sig.reshape(-1).astype(np.float32) 211 | 212 | target = torch.tensor(sig.copy()) 213 | p_size = random.choice(self.p_sizes) 214 | 215 | sig = np.reshape(sig, (-1, p_size)) 216 | mask = self.mask_generator.gen_mask(len(sig), seed=index)[:, np.newaxis] 217 | sig *= mask 218 | sig = torch.tensor(sig.copy()).reshape(-1) 219 | 220 | target = torch.stft(target, self.chunk_len, self.stride, window=self.hann, 221 | return_complex=False).permute(2, 0, 1).float() 222 | sig = torch.stft(sig, self.chunk_len, self.stride, window=self.hann, return_complex=False) 223 | sig = sig.permute(2, 0, 1).float() 224 | return sig, target 225 | -------------------------------------------------------------------------------- /index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | Full-band Recurrent Network 7 | 8 | 9 | 18 |
19 |
20 |

Audio samples

21 |

Improving performance of real-time full-band blind packet-loss concealment with predictive network 22 |

23 |

Viet-Anh Nguyen1, Anh H. T. Nguyen1, and Andy W. H. Khong2 24 |
1Crystalsound Team, NamiTech JSC, Ho Chi Minh City, Vietnam 25 |
2Nanyang Technological University, Singapore 26 |
{vietanh.nguyen, anh.nguyen}@namitech.io, andykhong@ntu.edu.sg 27 |

28 |
29 |

Audio samples of our full-band recurrent network (FRN) versus TFGAN and tPLCNet for blind packet loss concealment 30 | (PLC)

31 | Audio files are at 48 kHz sampling rate with packet size of 20 ms. Our FRN is a causal and blind PLC model while TFGAN 32 | is non-causal and tPLC is an informed PLC model. 33 |

34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 52 | 57 | 62 | 67 | 72 | 73 | 74 | 75 | 80 | 85 | 90 | 95 | 100 | 101 | 102 | 103 | 108 | 113 | 118 | 123 | 128 | 129 | 130 | 131 | 132 |
Clean targetLossy inputTFGANtPLCNetFRN (Ours)
48 | 51 | 53 | 56 | 58 | 61 | 63 | 66 | 68 | 71 |
76 | 79 | 81 | 84 | 86 | 89 | 91 | 94 | 96 | 99 |
104 | 107 | 109 | 112 | 114 | 117 | 119 | 122 | 124 | 127 |
133 | 138 | 139 | 140 | 141 | -------------------------------------------------------------------------------- /inference_onnx.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import glob 3 | import os 4 | 5 | import librosa 6 | import numpy as np 7 | import onnx 8 | import onnxruntime 9 | import soundfile as sf 10 | import torch 11 | import tqdm 12 | 13 | from config import CONFIG 14 | 15 | parser = argparse.ArgumentParser() 16 | 17 | parser.add_argument('--onnx_path', default=None, 18 | help='path to onnx') 19 | args = parser.parse_args() 20 | 21 | if __name__ == '__main__': 22 | path = args.onnx_path 23 | window = CONFIG.DATA.window_size 24 | stride = CONFIG.DATA.stride 25 | onnx_model = onnx.load(path) 26 | options = onnxruntime.SessionOptions() 27 | options.intra_op_num_threads = 8 28 | options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL 29 | session = onnxruntime.InferenceSession(path, options) 30 | input_names = [x.name for x in session.get_inputs()] 31 | output_names = [x.name for x in session.get_outputs()] 32 | print(input_names) 33 | print(output_names) 34 | 35 | audio_files = glob.glob(os.path.join(CONFIG.TEST.in_dir, '*.wav')) 36 | hann = torch.sqrt(torch.hann_window(window)) 37 | os.makedirs(CONFIG.TEST.out_dir, exist_ok=True) 38 | for file in tqdm.tqdm(audio_files, total=len(audio_files)): 39 | sig, _ = librosa.load(file, sr=48000) 40 | sig = torch.tensor(sig) 41 | re_im = torch.stft(sig, window, stride, window=hann, return_complex=False).permute(1, 0, 2).unsqueeze( 42 | 1).numpy().astype(np.float32) 43 | 44 | inputs = {input_names[i]: np.zeros([d.dim_value for d in _input.type.tensor_type.shape.dim], 45 | dtype=np.float32) 46 | for i, _input in enumerate(onnx_model.graph.input) 47 | } 48 | 49 | output_audio = [] 50 | for t in range(re_im.shape[-1]): 51 | inputs[input_names[0]] = re_im[t] 52 | out, prev_mag, predictor_state, mlp_state = session.run(output_names, inputs) 53 | inputs[input_names[1]] = prev_mag 54 | inputs[input_names[2]] = predictor_state 55 | inputs[input_names[3]] = mlp_state 56 | output_audio.append(out) 57 | 58 | output_audio = torch.tensor(np.concatenate(output_audio, 0)) 59 | output_audio = output_audio.permute(1, 0, 2).contiguous() 60 | output_audio = torch.view_as_complex(output_audio) 61 | output_audio = torch.istft(output_audio, window, stride, window=hann) 62 | sf.write(os.path.join(CONFIG.TEST.out_dir, os.path.basename(file)), output_audio, samplerate=48000, 63 | subtype='PCM_16') 64 | -------------------------------------------------------------------------------- /lightning_logs/predictor/checkpoints/predictor.ckpt: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:1f3679c9431666575eb7899e556d040073aa74956c48f122b16b30b9efa2e93b 3 | size 14985163 4 | -------------------------------------------------------------------------------- /lightning_logs/predictor/hparams.yaml: -------------------------------------------------------------------------------- 1 | batch_size: 90 2 | input: mag 3 | lstm_dim: 512 4 | lstm_layers: 1 5 | output: mag 6 | window_size: 960 7 | -------------------------------------------------------------------------------- /lightning_logs/version_0/checkpoints/frn-epoch=65-val_loss=0.2290.ckpt: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:4061bb0f6e669315e00878009440dab749f60f823d5bf863bfa4b8172d96d073 3 | size 109184745 4 | -------------------------------------------------------------------------------- /lightning_logs/version_0/checkpoints/frn.onnx: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:e373d149496278e2368fb371802878f22ed650ce65f4bf13f73446c9df6ac56e 3 | size 36527867 4 | -------------------------------------------------------------------------------- /lightning_logs/version_0/hparams.yaml: -------------------------------------------------------------------------------- 1 | batch_size: 90 2 | cnn_dim: 64 3 | cnn_layers: 5 4 | lstm_dim: 512 5 | lstm_layers: 1 6 | window_size: 960 7 | -------------------------------------------------------------------------------- /loss.py: -------------------------------------------------------------------------------- 1 | import librosa 2 | import pytorch_lightning as pl 3 | import torch 4 | from auraloss.freq import STFTLoss, MultiResolutionSTFTLoss, apply_reduction, SpectralConvergenceLoss, STFTMagnitudeLoss 5 | 6 | from config import CONFIG 7 | 8 | 9 | class STFTLossDDP(STFTLoss): 10 | def __init__(self, 11 | fft_size=1024, 12 | hop_size=256, 13 | win_length=1024, 14 | window="hann_window", 15 | w_sc=1.0, 16 | w_log_mag=1.0, 17 | w_lin_mag=0.0, 18 | w_phs=0.0, 19 | sample_rate=None, 20 | scale=None, 21 | n_bins=None, 22 | scale_invariance=False, 23 | eps=1e-8, 24 | output="loss", 25 | reduction="mean", 26 | device=None): 27 | super(STFTLoss, self).__init__() 28 | self.fft_size = fft_size 29 | self.hop_size = hop_size 30 | self.win_length = win_length 31 | self.window = getattr(torch, window)(win_length) 32 | self.w_sc = w_sc 33 | self.w_log_mag = w_log_mag 34 | self.w_lin_mag = w_lin_mag 35 | self.w_phs = w_phs 36 | self.sample_rate = sample_rate 37 | self.scale = scale 38 | self.n_bins = n_bins 39 | self.scale_invariance = scale_invariance 40 | self.eps = eps 41 | self.output = output 42 | self.reduction = reduction 43 | self.device = device 44 | 45 | self.spectralconv = SpectralConvergenceLoss() 46 | self.logstft = STFTMagnitudeLoss(log=True, reduction=reduction) 47 | self.linstft = STFTMagnitudeLoss(log=False, reduction=reduction) 48 | 49 | # setup mel filterbank 50 | if self.scale == "mel": 51 | assert (sample_rate is not None) # Must set sample rate to use mel scale 52 | assert (n_bins <= fft_size) # Must be more FFT bins than Mel bins 53 | fb = librosa.filters.mel(sample_rate, fft_size, n_mels=n_bins) 54 | self.fb = torch.tensor(fb).unsqueeze(0) 55 | elif self.scale == "chroma": 56 | assert (sample_rate is not None) # Must set sample rate to use chroma scale 57 | assert (n_bins <= fft_size) # Must be more FFT bins than chroma bins 58 | fb = librosa.filters.chroma(sample_rate, fft_size, n_chroma=n_bins) 59 | self.fb = torch.tensor(fb).unsqueeze(0) 60 | 61 | if scale is not None and device is not None: 62 | self.fb = self.fb.to(self.device) # move filterbank to device 63 | 64 | def compressed_loss(self, x, y, alpha=None): 65 | self.window = self.window.to(x.device) 66 | x_mag, x_phs = self.stft(x.view(-1, x.size(-1))) 67 | y_mag, y_phs = self.stft(y.view(-1, y.size(-1))) 68 | 69 | if alpha is not None: 70 | x_mag = x_mag ** alpha 71 | y_mag = y_mag ** alpha 72 | 73 | # apply relevant transforms 74 | if self.scale is not None: 75 | x_mag = torch.matmul(self.fb.to(x_mag.device), x_mag) 76 | y_mag = torch.matmul(self.fb.to(y_mag.device), y_mag) 77 | 78 | # normalize scales 79 | if self.scale_invariance: 80 | alpha = (x_mag * y_mag).sum([-2, -1]) / ((y_mag ** 2).sum([-2, -1])) 81 | y_mag = y_mag * alpha.unsqueeze(-1) 82 | 83 | # compute loss terms 84 | sc_loss = self.spectralconv(x_mag, y_mag) if self.w_sc else 0.0 85 | mag_loss = self.logstft(x_mag, y_mag) if self.w_log_mag else 0.0 86 | lin_loss = self.linstft(x_mag, y_mag) if self.w_lin_mag else 0.0 87 | 88 | # combine loss terms 89 | loss = (self.w_sc * sc_loss) + (self.w_log_mag * mag_loss) + (self.w_lin_mag * lin_loss) 90 | loss = apply_reduction(loss, reduction=self.reduction) 91 | return loss 92 | 93 | def forward(self, x, y): 94 | return self.compressed_loss(x, y, 0.3) 95 | 96 | 97 | class MRSTFTLossDDP(MultiResolutionSTFTLoss): 98 | def __init__(self, 99 | fft_sizes=(1024, 2048, 512), 100 | hop_sizes=(120, 240, 50), 101 | win_lengths=(600, 1200, 240), 102 | window="hann_window", 103 | w_sc=1.0, 104 | w_log_mag=1.0, 105 | w_lin_mag=0.0, 106 | w_phs=0.0, 107 | sample_rate=None, 108 | scale=None, 109 | n_bins=None, 110 | scale_invariance=False, 111 | **kwargs): 112 | super(MultiResolutionSTFTLoss, self).__init__() 113 | assert len(fft_sizes) == len(hop_sizes) == len(win_lengths) # must define all 114 | self.stft_losses = torch.nn.ModuleList() 115 | for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths): 116 | self.stft_losses += [STFTLossDDP(fs, 117 | ss, 118 | wl, 119 | window, 120 | w_sc, 121 | w_log_mag, 122 | w_lin_mag, 123 | w_phs, 124 | sample_rate, 125 | scale, 126 | n_bins, 127 | scale_invariance, 128 | **kwargs)] 129 | 130 | 131 | class Loss(pl.LightningModule): 132 | def __init__(self): 133 | super(Loss, self).__init__() 134 | self.stft_loss = MRSTFTLossDDP(sample_rate=CONFIG.DATA.sr, device="cpu", w_log_mag=0.0, w_lin_mag=1.0) 135 | self.window = torch.sqrt(torch.hann_window(CONFIG.DATA.window_size)) 136 | 137 | def forward(self, x, y): 138 | x = x.permute(0, 2, 3, 1) 139 | y = y.permute(0, 2, 3, 1) 140 | wave_x = torch.istft(torch.view_as_complex(x.contiguous()), CONFIG.DATA.window_size, CONFIG.DATA.stride, 141 | window=self.window.to(x.device)) 142 | wave_y = torch.istft(torch.view_as_complex(y.contiguous()), CONFIG.DATA.window_size, CONFIG.DATA.stride, 143 | window=self.window.to(y.device)) 144 | loss = self.stft_loss(wave_x, wave_y) 145 | return loss 146 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | 4 | import pytorch_lightning as pl 5 | import soundfile as sf 6 | import torch 7 | from pytorch_lightning.callbacks import ModelCheckpoint 8 | from pytorch_lightning.utilities.model_summary import summarize 9 | from torch.utils.data import DataLoader 10 | 11 | from config import CONFIG 12 | from dataset import TrainDataset, TestLoader, BlindTestLoader 13 | from models.frn import PLCModel, OnnxWrapper 14 | from utils.tblogger import TensorBoardLoggerExpanded 15 | from utils.utils import mkdir_p 16 | 17 | parser = argparse.ArgumentParser() 18 | 19 | parser.add_argument('--version', default=None, 20 | help='version to resume') 21 | parser.add_argument('--mode', default='train', 22 | help='training or testing mode') 23 | 24 | args = parser.parse_args() 25 | os.environ["CUDA_VISIBLE_DEVICES"] = str(CONFIG.gpus) 26 | assert args.mode in ['train', 'eval', 'test', 'onnx'], "--mode should be 'train', 'eval', 'test' or 'onnx'" 27 | 28 | 29 | def resume(train_dataset, val_dataset, version): 30 | print("Version", version) 31 | model_path = os.path.join(CONFIG.LOG.log_dir, 'version_{}/checkpoints/'.format(str(version))) 32 | config_path = os.path.join(CONFIG.LOG.log_dir, 'version_{}/'.format(str(version)) + 'hparams.yaml') 33 | model_name = [x for x in os.listdir(model_path) if x.endswith(".ckpt")][0] 34 | ckpt_path = model_path + model_name 35 | checkpoint = PLCModel.load_from_checkpoint(ckpt_path, 36 | strict=True, 37 | hparams_file=config_path, 38 | train_dataset=train_dataset, 39 | val_dataset=val_dataset, 40 | window_size=CONFIG.DATA.window_size) 41 | 42 | return checkpoint 43 | 44 | 45 | def train(): 46 | train_dataset = TrainDataset('train') 47 | val_dataset = TrainDataset('val') 48 | checkpoint_callback = ModelCheckpoint(monitor='val_loss', mode='min', verbose=True, 49 | filename='frn-{epoch:02d}-{val_loss:.4f}', save_weights_only=False) 50 | gpus = CONFIG.gpus.split(',') 51 | logger = TensorBoardLoggerExpanded(CONFIG.DATA.sr) 52 | if args.version is not None: 53 | model = resume(train_dataset, val_dataset, args.version) 54 | else: 55 | model = PLCModel(train_dataset, 56 | val_dataset, 57 | window_size=CONFIG.DATA.window_size, 58 | enc_layers=CONFIG.MODEL.enc_layers, 59 | enc_in_dim=CONFIG.MODEL.enc_in_dim, 60 | enc_dim=CONFIG.MODEL.enc_dim, 61 | pred_dim=CONFIG.MODEL.pred_dim, 62 | pred_layers=CONFIG.MODEL.pred_layers) 63 | 64 | trainer = pl.Trainer(logger=logger, 65 | gradient_clip_val=CONFIG.TRAIN.clipping_val, 66 | gpus=len(gpus), 67 | max_epochs=CONFIG.TRAIN.epochs, 68 | accelerator="gpu" if len(gpus) > 1 else None, 69 | callbacks=[checkpoint_callback] 70 | ) 71 | 72 | print(model.hparams) 73 | print( 74 | 'Dataset: {}, Train files: {}, Val files {}'.format(CONFIG.DATA.dataset, len(train_dataset), len(val_dataset))) 75 | trainer.fit(model) 76 | 77 | 78 | def to_onnx(model, onnx_path): 79 | model.eval() 80 | 81 | model = OnnxWrapper(model) 82 | 83 | torch.onnx.export(model, 84 | model.sample, 85 | onnx_path, 86 | export_params=True, 87 | opset_version=12, 88 | input_names=model.input_names, 89 | output_names=model.output_names, 90 | do_constant_folding=True, 91 | verbose=False) 92 | 93 | 94 | if __name__ == '__main__': 95 | 96 | if args.mode == 'train': 97 | train() 98 | else: 99 | model = resume(None, None, args.version) 100 | print(model.hparams) 101 | print(summarize(model)) 102 | 103 | model.eval() 104 | model.freeze() 105 | if args.mode == 'eval': 106 | model.cuda(device=0) 107 | trainer = pl.Trainer(accelerator='gpu', devices=1, enable_checkpointing=False, logger=False) 108 | testset = TestLoader() 109 | test_loader = DataLoader(testset, batch_size=1, num_workers=4) 110 | trainer.test(model, test_loader) 111 | print('Version', args.version) 112 | masking = CONFIG.DATA.EVAL.masking 113 | prob = CONFIG.DATA.EVAL.transition_probs[0] 114 | loss_percent = (1 - prob[0]) / (2 - prob[0] - prob[1]) * 100 115 | print('Evaluate with real trace' if masking == 'real' else 116 | 'Evaluate with generated trace with {:.2f}% packet loss'.format(loss_percent)) 117 | elif args.mode == 'test': 118 | model.cuda(device=0) 119 | testset = BlindTestLoader(test_dir=CONFIG.TEST.in_dir) 120 | test_loader = DataLoader(testset, batch_size=1, num_workers=4) 121 | trainer = pl.Trainer(accelerator='gpu', devices=1, enable_checkpointing=False, logger=False) 122 | preds = trainer.predict(model, test_loader, return_predictions=True) 123 | mkdir_p(CONFIG.TEST.out_dir) 124 | for idx, path in enumerate(test_loader.dataset.data_list): 125 | out_path = os.path.join(CONFIG.TEST.out_dir, os.path.basename(path)) 126 | sf.write(out_path, preds[idx], samplerate=CONFIG.DATA.sr, subtype='PCM_16') 127 | 128 | else: 129 | onnx_path = 'lightning_logs/version_{}/checkpoints/frn.onnx'.format(str(args.version)) 130 | to_onnx(model, onnx_path) 131 | print('ONNX model saved to', onnx_path) 132 | -------------------------------------------------------------------------------- /models/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Crystalsound/FRN/1b6445ded23f41bd76aef8e22ef12d4e2983397c/models/__init__.py -------------------------------------------------------------------------------- /models/blocks.py: -------------------------------------------------------------------------------- 1 | import librosa 2 | import pytorch_lightning as pl 3 | import torch 4 | from einops.layers.torch import Rearrange 5 | from torch import nn 6 | 7 | 8 | class Aff(nn.Module): 9 | def __init__(self, dim): 10 | super().__init__() 11 | 12 | self.alpha = nn.Parameter(torch.ones([1, 1, dim])) 13 | self.beta = nn.Parameter(torch.zeros([1, 1, dim])) 14 | 15 | def forward(self, x): 16 | x = x * self.alpha + self.beta 17 | return x 18 | 19 | 20 | class FeedForward(nn.Module): 21 | def __init__(self, dim, hidden_dim, dropout=0.): 22 | super().__init__() 23 | self.net = nn.Sequential( 24 | nn.Linear(dim, hidden_dim), 25 | nn.GELU(), 26 | nn.Dropout(dropout), 27 | nn.Linear(hidden_dim, dim), 28 | nn.Dropout(dropout) 29 | ) 30 | 31 | def forward(self, x): 32 | return self.net(x) 33 | 34 | 35 | class MLPBlock(nn.Module): 36 | 37 | def __init__(self, dim, mlp_dim, dropout=0., init_values=1e-4): 38 | super().__init__() 39 | 40 | self.pre_affine = Aff(dim) 41 | self.inter = nn.LSTM(input_size=dim, hidden_size=dim, num_layers=1, 42 | bidirectional=False, batch_first=True) 43 | self.ff = nn.Sequential( 44 | FeedForward(dim, mlp_dim, dropout), 45 | ) 46 | self.post_affine = Aff(dim) 47 | self.gamma_1 = nn.Parameter(init_values * torch.ones(dim), requires_grad=True) 48 | self.gamma_2 = nn.Parameter(init_values * torch.ones(dim), requires_grad=True) 49 | 50 | def forward(self, x, state=None): 51 | x = self.pre_affine(x) 52 | if state is None: 53 | inter, _ = self.inter(x) 54 | else: 55 | inter, state = self.inter(x, (state[0], state[1])) 56 | x = x + self.gamma_1 * inter 57 | x = self.post_affine(x) 58 | x = x + self.gamma_2 * self.ff(x) 59 | if state is None: 60 | return x 61 | state = torch.stack(state, 0) 62 | return x, state 63 | 64 | 65 | class Encoder(nn.Module): 66 | 67 | def __init__(self, in_dim, dim, depth, mlp_dim): 68 | super().__init__() 69 | self.in_dim = in_dim 70 | self.dim = dim 71 | self.depth = depth 72 | self.mlp_dim = mlp_dim 73 | self.to_patch_embedding = nn.Sequential( 74 | Rearrange('b c f t -> b t (c f)'), 75 | nn.Linear(in_dim, dim), 76 | nn.GELU() 77 | ) 78 | 79 | self.mlp_blocks = nn.ModuleList([]) 80 | 81 | for _ in range(depth): 82 | self.mlp_blocks.append(MLPBlock(self.dim, mlp_dim, dropout=0.15)) 83 | 84 | self.affine = nn.Sequential( 85 | Aff(self.dim), 86 | nn.Linear(dim, in_dim), 87 | Rearrange('b t (c f) -> b c f t', c=2), 88 | ) 89 | 90 | def forward(self, x_in, states=None): 91 | x = self.to_patch_embedding(x_in) 92 | if states is not None: 93 | out_states = [] 94 | for i, mlp_block in enumerate(self.mlp_blocks): 95 | if states is None: 96 | x = mlp_block(x) 97 | else: 98 | x, state = mlp_block(x, states[i]) 99 | out_states.append(state) 100 | x = self.affine(x) 101 | x = x + x_in 102 | if states is None: 103 | return x 104 | else: 105 | return x, torch.stack(out_states, 0) 106 | 107 | 108 | class Predictor(pl.LightningModule): # mel 109 | def __init__(self, window_size=1536, sr=48000, lstm_dim=256, lstm_layers=3, n_mels=64): 110 | super(Predictor, self).__init__() 111 | self.window_size = window_size 112 | self.hop_size = window_size // 2 113 | self.lstm_dim = lstm_dim 114 | self.n_mels = n_mels 115 | self.lstm_layers = lstm_layers 116 | 117 | fb = librosa.filters.mel(sr=sr, n_fft=self.window_size, n_mels=self.n_mels)[:, 1:] 118 | self.fb = torch.from_numpy(fb).unsqueeze(0).unsqueeze(0) 119 | self.lstm = nn.LSTM(input_size=self.n_mels, hidden_size=self.lstm_dim, bidirectional=False, 120 | num_layers=self.lstm_layers, batch_first=True) 121 | self.expand_dim = nn.Linear(self.lstm_dim, self.n_mels) 122 | self.inv_mel = nn.Linear(self.n_mels, self.hop_size) 123 | 124 | def forward(self, x, state=None): # B, 2, F, T 125 | 126 | self.fb = self.fb.to(x.device) 127 | x = torch.log(torch.matmul(self.fb, x) + 1e-8) 128 | B, C, F, T = x.shape 129 | x = x.reshape(B, F * C, T) 130 | x = x.permute(0, 2, 1) 131 | if state is None: 132 | x, _ = self.lstm(x) 133 | else: 134 | x, state = self.lstm(x, (state[0], state[1])) 135 | x = self.expand_dim(x) 136 | x = torch.abs(self.inv_mel(torch.exp(x))) 137 | x = x.permute(0, 2, 1) 138 | x = x.reshape(B, C, -1, T) 139 | if state is None: 140 | return x 141 | else: 142 | return x, torch.stack(state, 0) 143 | -------------------------------------------------------------------------------- /models/frn.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | import librosa 4 | import pytorch_lightning as pl 5 | import soundfile as sf 6 | import torch 7 | from torch import nn 8 | from torch.utils.data import DataLoader 9 | from torchmetrics.audio.pesq import PerceptualEvaluationSpeechQuality as PESQ 10 | from torchmetrics.audio.stoi import ShortTimeObjectiveIntelligibility as STOI 11 | 12 | from PLCMOS.plc_mos import PLCMOSEstimator 13 | from config import CONFIG 14 | from loss import Loss 15 | from models.blocks import Encoder, Predictor 16 | from utils.utils import visualize, LSD 17 | 18 | plcmos = PLCMOSEstimator() 19 | 20 | 21 | class PLCModel(pl.LightningModule): 22 | def __init__(self, train_dataset=None, val_dataset=None, window_size=960, enc_layers=4, enc_in_dim=384, enc_dim=768, 23 | pred_dim=512, pred_layers=1, pred_ckpt_path='lightning_logs/predictor/checkpoints/predictor.ckpt'): 24 | super(PLCModel, self).__init__() 25 | self.window_size = window_size 26 | self.hop_size = window_size // 2 27 | self.learning_rate = CONFIG.TRAIN.lr 28 | self.hparams.batch_size = CONFIG.TRAIN.batch_size 29 | 30 | self.enc_layers = enc_layers 31 | self.enc_in_dim = enc_in_dim 32 | self.enc_dim = enc_dim 33 | self.pred_dim = pred_dim 34 | self.pred_layers = pred_layers 35 | self.train_dataset = train_dataset 36 | self.val_dataset = val_dataset 37 | self.stoi = STOI(48000) 38 | self.pesq = PESQ(16000, 'wb') 39 | 40 | if pred_ckpt_path is not None: 41 | self.predictor = Predictor.load_from_checkpoint(pred_ckpt_path) 42 | else: 43 | self.predictor = Predictor(window_size=self.window_size, lstm_dim=self.pred_dim, 44 | lstm_layers=self.pred_layers) 45 | self.joiner = nn.Sequential( 46 | nn.Conv2d(3, 48, kernel_size=(9, 1), stride=1, padding=(4, 0), padding_mode='reflect', 47 | groups=3), 48 | nn.LeakyReLU(0.2), 49 | nn.Conv2d(48, 2, kernel_size=1, stride=1, padding=0, groups=2), 50 | ) 51 | 52 | self.encoder = Encoder(in_dim=self.window_size, dim=self.enc_in_dim, depth=self.enc_layers, 53 | mlp_dim=self.enc_dim) 54 | 55 | self.loss = Loss() 56 | self.window = torch.sqrt(torch.hann_window(self.window_size)) 57 | self.save_hyperparameters('window_size', 'enc_layers', 'enc_in_dim', 'enc_dim', 'pred_dim', 'pred_layers') 58 | 59 | def forward(self, x): 60 | """ 61 | Input: real-imaginary; shape (B, F, T, 2); F = hop_size + 1 62 | Output: real-imaginary 63 | """ 64 | 65 | B, C, F, T = x.shape 66 | 67 | x = x.permute(3, 0, 1, 2).unsqueeze(-1) 68 | prev_mag = torch.zeros((B, 1, F, 1), device=x.device) 69 | predictor_state = torch.zeros((2, self.predictor.lstm_layers, B, self.predictor.lstm_dim), device=x.device) 70 | mlp_state = torch.zeros((self.encoder.depth, 2, 1, B, self.encoder.dim), device=x.device) 71 | result = [] 72 | for step in x: 73 | feat, mlp_state = self.encoder(step, mlp_state) 74 | prev_mag, predictor_state = self.predictor(prev_mag, predictor_state) 75 | feat = torch.cat((feat, prev_mag), 1) 76 | feat = self.joiner(feat) 77 | feat = feat + step 78 | result.append(feat) 79 | prev_mag = torch.linalg.norm(feat, dim=1, ord=1, keepdims=True) # compute magnitude 80 | output = torch.cat(result, -1) 81 | return output 82 | 83 | def forward_onnx(self, x, prev_mag, predictor_state=None, mlp_state=None): 84 | prev_mag, predictor_state = self.predictor(prev_mag, predictor_state) 85 | feat, mlp_state = self.encoder(x, mlp_state) 86 | 87 | feat = torch.cat((feat, prev_mag), 1) 88 | feat = self.joiner(feat) 89 | prev_mag = torch.linalg.norm(feat, dim=1, ord=1, keepdims=True) 90 | feat = feat + x 91 | return feat, prev_mag, predictor_state, mlp_state 92 | 93 | def train_dataloader(self): 94 | return DataLoader(self.train_dataset, shuffle=False, batch_size=self.hparams.batch_size, 95 | num_workers=CONFIG.TRAIN.workers, persistent_workers=True) 96 | 97 | def val_dataloader(self): 98 | return DataLoader(self.val_dataset, shuffle=False, batch_size=self.hparams.batch_size, 99 | num_workers=CONFIG.TRAIN.workers, persistent_workers=True) 100 | 101 | def training_step(self, batch, batch_idx): 102 | x_in, y = batch 103 | f_0 = x_in[:, :, 0:1, :] 104 | x = x_in[:, :, 1:, :] 105 | 106 | x = self(x) 107 | x = torch.cat([f_0, x], dim=2) 108 | 109 | loss = self.loss(x, y) 110 | self.log('train_loss', loss, logger=True) 111 | return loss 112 | 113 | def validation_step(self, val_batch, batch_idx): 114 | x, y = val_batch 115 | f_0 = x[:, :, 0:1, :] 116 | x_in = x[:, :, 1:, :] 117 | 118 | pred = self(x_in) 119 | pred = torch.cat([f_0, pred], dim=2) 120 | 121 | loss = self.loss(pred, y) 122 | self.window = self.window.to(pred.device) 123 | pred = torch.view_as_complex(pred.permute(0, 2, 3, 1).contiguous()) 124 | pred = torch.istft(pred, self.window_size, self.hop_size, window=self.window) 125 | y = torch.view_as_complex(y.permute(0, 2, 3, 1).contiguous()) 126 | y = torch.istft(y, self.window_size, self.hop_size, window=self.window) 127 | 128 | self.log('val_loss', loss, on_step=False, on_epoch=True, logger=True, prog_bar=True, sync_dist=True) 129 | 130 | if batch_idx == 0: 131 | i = torch.randint(0, x.shape[0], (1,)).item() 132 | x = torch.view_as_complex(x.permute(0, 2, 3, 1).contiguous()) 133 | x = torch.istft(x[i], self.window_size, self.hop_size, window=self.window) 134 | 135 | self.trainer.logger.log_spectrogram(y[i], x, pred[i], self.current_epoch) 136 | self.trainer.logger.log_audio(y[i], x, pred[i], self.current_epoch) 137 | 138 | def test_step(self, test_batch, batch_idx): 139 | inp, tar, inp_wav, tar_wav = test_batch 140 | inp_wav = inp_wav.squeeze() 141 | tar_wav = tar_wav.squeeze() 142 | f_0 = inp[:, :, 0:1, :] 143 | x = inp[:, :, 1:, :] 144 | pred = self(x) 145 | pred = torch.cat([f_0, pred], dim=2) 146 | pred = torch.istft(pred.squeeze(0).permute(1, 2, 0), self.window_size, self.hop_size, 147 | window=self.window.to(pred.device)) 148 | stoi = self.stoi(pred, tar_wav) 149 | 150 | tar_wav = tar_wav.cpu().numpy() 151 | inp_wav = inp_wav.cpu().numpy() 152 | pred = pred.detach().cpu().numpy() 153 | lsd, _ = LSD(tar_wav, pred) 154 | 155 | if batch_idx in [5, 7, 9]: 156 | sample_path = os.path.join(CONFIG.LOG.sample_path) 157 | path = os.path.join(sample_path, 'sample_' + str(batch_idx)) 158 | visualize(tar_wav, inp_wav, pred, path) 159 | sf.write(os.path.join(path, 'enhanced_output.wav'), pred, samplerate=CONFIG.DATA.sr, subtype='PCM_16') 160 | sf.write(os.path.join(path, 'lossy_input.wav'), inp_wav, samplerate=CONFIG.DATA.sr, subtype='PCM_16') 161 | sf.write(os.path.join(path, 'target.wav'), tar_wav, samplerate=CONFIG.DATA.sr, subtype='PCM_16') 162 | if CONFIG.DATA.sr != 16000: 163 | pred = librosa.resample(pred, orig_sr=48000, target_sr=16000) 164 | tar_wav = librosa.resample(tar_wav, orig_sr=48000, target_sr=16000, res_type='kaiser_fast') 165 | ret = plcmos.run(pred, tar_wav) 166 | pesq = self.pesq(torch.tensor(pred), torch.tensor(tar_wav)) 167 | metrics = { 168 | "Intrusive": ret[0], 169 | "Non-intrusive": ret[1], 170 | 'LSD': lsd, 171 | 'STOI': stoi, 172 | 'PESQ': pesq, 173 | } 174 | self.log_dict(metrics) 175 | return metrics 176 | 177 | def predict_step(self, batch, batch_idx: int, dataloader_idx: int = 0): 178 | f_0 = batch[:, :, 0:1, :] 179 | x = batch[:, :, 1:, :] 180 | pred = self(x) 181 | pred = torch.cat([f_0, pred], dim=2) 182 | pred = torch.istft(pred.squeeze(0).permute(1, 2, 0), self.window_size, self.hop_size, 183 | window=self.window.to(pred.device)) 184 | return pred 185 | 186 | def configure_optimizers(self): 187 | optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate) 188 | lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=CONFIG.TRAIN.patience, 189 | factor=CONFIG.TRAIN.factor, verbose=True) 190 | 191 | scheduler = { 192 | 'scheduler': lr_scheduler, 193 | 'reduce_on_plateau': True, 194 | 'monitor': 'val_loss' 195 | } 196 | return [optimizer], [scheduler] 197 | 198 | 199 | class OnnxWrapper(pl.LightningModule): 200 | def __init__(self, model, *args, **kwargs): 201 | super().__init__(*args, **kwargs) 202 | self.model = model 203 | batch_size = 1 204 | pred_states = torch.zeros((2, 1, batch_size, model.predictor.lstm_dim)) 205 | mlp_states = torch.zeros((model.encoder.depth, 2, 1, batch_size, model.encoder.dim)) 206 | mag = torch.zeros((batch_size, 1, model.hop_size, 1)) 207 | x = torch.randn(batch_size, model.hop_size + 1, 2) 208 | self.sample = (x, mag, pred_states, mlp_states) 209 | self.input_names = ['input', 'mag_in_cached_', 'pred_state_in_cached_', 'mlp_state_in_cached_'] 210 | self.output_names = ['output', 'mag_out_cached_', 'pred_state_out_cached_', 'mlp_state_out_cached_'] 211 | 212 | def forward(self, x, prev_mag, predictor_state=None, mlp_state=None): 213 | x = x.permute(0, 2, 1).unsqueeze(-1) 214 | f_0 = x[:, :, 0:1, :] 215 | x = x[:, :, 1:, :] 216 | 217 | output, prev_mag, predictor_state, mlp_state = self.model.forward_onnx(x, prev_mag, predictor_state, mlp_state) 218 | output = torch.cat([f_0, output], dim=2) 219 | output = output.squeeze(-1).permute(0, 2, 1) 220 | return output, prev_mag, predictor_state, mlp_state 221 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | auraloss==0.3.0 2 | einops==0.6.0 3 | librosa==0.9.2 4 | matplotlib==3.5.3 5 | numpy==1.22.3 6 | onnxruntime==1.13.1 7 | pandas==1.5.3 8 | pydtmc==7.0.0 9 | pytorch_lightning==1.9.0 10 | scikit_learn==1.2.1 11 | soundfile==0.11.0 12 | torch==1.13.1 13 | torchmetrics==0.11.0 14 | tqdm==4.64.0 15 | pystoi==0.3.3 16 | pesq==0.0.4 17 | onnx==1.13.0 -------------------------------------------------------------------------------- /utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Crystalsound/FRN/1b6445ded23f41bd76aef8e22ef12d4e2983397c/utils/__init__.py -------------------------------------------------------------------------------- /utils/stft.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | 5 | class STFTMag(nn.Module): 6 | def __init__(self, 7 | nfft=1024, 8 | hop=256): 9 | super().__init__() 10 | self.nfft = nfft 11 | self.hop = hop 12 | self.register_buffer('window', torch.hann_window(nfft), False) 13 | 14 | # x: [B,T] or [T] 15 | @torch.no_grad() 16 | def forward(self, x): 17 | stft = torch.stft(x.cpu(), 18 | self.nfft, 19 | self.hop, 20 | window=self.window, 21 | ) # return_complex=False) #[B, F, TT,2] 22 | mag = torch.norm(stft, p=2, dim=-1) # [B, F, TT] 23 | return mag 24 | -------------------------------------------------------------------------------- /utils/tblogger.py: -------------------------------------------------------------------------------- 1 | from os import path 2 | 3 | import librosa as rosa 4 | import matplotlib 5 | import matplotlib.pyplot as plt 6 | import numpy as np 7 | from pytorch_lightning.loggers import TensorBoardLogger 8 | from pytorch_lightning.utilities import rank_zero_only 9 | 10 | from utils.stft import STFTMag 11 | 12 | matplotlib.use('Agg') 13 | 14 | 15 | class TensorBoardLoggerExpanded(TensorBoardLogger): 16 | def __init__(self, sr=16000): 17 | super().__init__(save_dir='lightning_logs', default_hp_metric=False, name='') 18 | self.sr = sr 19 | self.stftmag = STFTMag() 20 | 21 | def fig2np(self, fig): 22 | data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') 23 | data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) 24 | return data 25 | 26 | def plot_spectrogram_to_numpy(self, y, y_low, y_recon, step): 27 | name_list = ['y', 'y_low', 'y_recon'] 28 | fig = plt.figure(figsize=(9, 15)) 29 | fig.suptitle(f'Epoch_{step}') 30 | for i, yy in enumerate([y, y_low, y_recon]): 31 | if yy.dim() == 1: 32 | yy = self.stftmag(yy) 33 | ax = plt.subplot(3, 1, i + 1) 34 | ax.set_title(name_list[i]) 35 | plt.imshow(rosa.amplitude_to_db(yy.numpy(), 36 | ref=np.max, top_db=80.), 37 | # vmin = -20, 38 | vmax=0., 39 | aspect='auto', 40 | origin='lower', 41 | interpolation='none') 42 | plt.colorbar() 43 | plt.xlabel('Frames') 44 | plt.ylabel('Channels') 45 | plt.tight_layout() 46 | 47 | fig.canvas.draw() 48 | data = self.fig2np(fig) 49 | 50 | plt.close() 51 | return data 52 | 53 | @rank_zero_only 54 | def log_spectrogram(self, y, y_low, y_recon, epoch): 55 | y, y_low, y_recon = y.detach().cpu(), y_low.detach().cpu(), y_recon.detach().cpu() 56 | spec_img = self.plot_spectrogram_to_numpy(y, y_low, y_recon, epoch) 57 | self.experiment.add_image(path.join(self.save_dir, 'result'), 58 | spec_img, 59 | epoch, 60 | dataformats='HWC') 61 | self.experiment.flush() 62 | return 63 | 64 | @rank_zero_only 65 | def log_audio(self, y, y_low, y_recon, epoch): 66 | y, y_low, y_recon = y.detach().cpu(), y_low.detach().cpu(), y_recon.detach().cpu(), 67 | name_list = ['y', 'y_low', 'y_recon'] 68 | for n, yy in zip(name_list, [y, y_low, y_recon]): 69 | self.experiment.add_audio(n, yy, epoch, self.sr) 70 | self.experiment.flush() 71 | return 72 | -------------------------------------------------------------------------------- /utils/utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | import librosa 4 | import librosa.display 5 | import matplotlib.pyplot as plt 6 | import numpy as np 7 | from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas 8 | 9 | from config import CONFIG 10 | 11 | 12 | def mkdir_p(mypath): 13 | """Creates a directory. equivalent to using mkdir -p on the command line""" 14 | 15 | from errno import EEXIST 16 | from os import makedirs, path 17 | 18 | try: 19 | makedirs(mypath) 20 | except OSError as exc: # Python >2.5 21 | if exc.errno == EEXIST and path.isdir(mypath): 22 | pass 23 | else: 24 | raise 25 | 26 | 27 | def visualize(target, input, recon, path): 28 | sr = CONFIG.DATA.sr 29 | window_size = 1024 30 | window = np.hanning(window_size) 31 | 32 | stft_hr = librosa.core.spectrum.stft(target, n_fft=window_size, hop_length=512, window=window) 33 | stft_hr = 2 * np.abs(stft_hr) / np.sum(window) 34 | 35 | stft_lr = librosa.core.spectrum.stft(input, n_fft=window_size, hop_length=512, window=window) 36 | stft_lr = 2 * np.abs(stft_lr) / np.sum(window) 37 | 38 | stft_recon = librosa.core.spectrum.stft(recon, n_fft=window_size, hop_length=512, window=window) 39 | stft_recon = 2 * np.abs(stft_recon) / np.sum(window) 40 | 41 | fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharey=True, sharex=True, figsize=(16, 10)) 42 | ax1.title.set_text('Target signal') 43 | ax2.title.set_text('Lossy signal') 44 | ax3.title.set_text('Reconstructed signal') 45 | 46 | canvas = FigureCanvas(fig) 47 | p = librosa.display.specshow(librosa.amplitude_to_db(stft_hr), ax=ax1, y_axis='linear', x_axis='time', sr=sr) 48 | p = librosa.display.specshow(librosa.amplitude_to_db(stft_lr), ax=ax2, y_axis='linear', x_axis='time', sr=sr) 49 | p = librosa.display.specshow(librosa.amplitude_to_db(stft_recon), ax=ax3, y_axis='linear', x_axis='time', sr=sr) 50 | mkdir_p(path) 51 | fig.savefig(os.path.join(path, 'spec.png')) 52 | 53 | 54 | def get_power(x, nfft): 55 | S = librosa.stft(x, n_fft=nfft) 56 | S = np.log(np.abs(S) ** 2 + 1e-8) 57 | return S 58 | 59 | 60 | def LSD(x_hr, x_pr): 61 | S1 = get_power(x_hr, nfft=2048) 62 | S2 = get_power(x_pr, nfft=2048) 63 | lsd = np.mean(np.sqrt(np.mean((S1 - S2) ** 2 + 1e-8, axis=-1)), axis=0) 64 | S1 = S1[-(len(S1) - 1) // 2:, :] 65 | S2 = S2[-(len(S2) - 1) // 2:, :] 66 | lsd_high = np.mean(np.sqrt(np.mean((S1 - S2) ** 2 + 1e-8, axis=-1)), axis=0) 67 | return lsd, lsd_high 68 | --------------------------------------------------------------------------------