metadata

title: DockerFRN
emoji: 📉
colorFrom: gray
colorTo: red
app_file: app.py
sdk: docker
app_port: 8501
pinned: false

FRN - Full-band Recurrent Network Official Implementation

Improving performance of real-time full-band blind packet-loss concealment with predictive network - ICASSP 2023

License and citation

This repository is released under the CC-BY-NC 4.0. license as found in the LICENSE file.

If you use our software, please cite as below. For future queries, please contact anh.nguyen@namitech.io.

@misc{Nguyen2022ImprovingPO,
  title={Improving performance of real-time full-band blind packet-loss concealment with predictive network},
  author={Viet-Anh Nguyen and Anh H. T. Nguyen and Andy W. H. Khong},
  year={2022},
  eprint={2211.04071},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}

1. Results

Our model achieved a significant gain over baselines. Here, we include the predicted packet loss concealment mean-opinion-score (PLCMOS) using Microsoft's PLCMOS service. Please refer to our paper for more benchmarks.

Model	PLCMOS
Input	3.517
tPLC	3.463
TFGAN	3.645
FRN	3.655

We also provide several audio samples in https://crystalsound.github.io/FRN/ for comparison.

2. Installation

Setup

Clone the repo

$ git clone https://github.com/Crystalsound/FRN.git
$ cd FRN

Install dependencies

Our implementation requires the libsndfile libraries for the Python packages soundfile. On Ubuntu, they can be easily installed using apt-get:
```
$ apt-get update && apt-get install libsndfile-dev
```

Create a Python 3.8 environment. Conda is recommended:

$ conda create -n frn python=3.8
$ conda activate frn

Install the requirements:
```
$ pip install -r requirements.txt 
```

3. Data preparation

In our paper, we conduct experiments on the VCTK dataset.

Download and extract the datasets:

$ wget http://www.udialogue.org/download/VCTK-Corpus.tar.gz -O data/vctk/VCTK-Corpus.tar.gz
$ tar -zxvf data/vctk/VCTK-Corpus.tar.gz -C data/vctk/ --strip-components=1

After extracting the datasets, your ./data directory should look like this:

.
|--data
    |--vctk
        |--wav48
            |--p225
                |--p225_001.wav
                ...
        |--train.txt   
        |--test.txt

In order to load the datasets, text files that contain training and testing audio paths are required. We have prepared train.txt and test.txt files in ./data/vctk directory.

4. Run the code

Configuration

config.py is the most important file. Here, you can find all the configurations related to experiment setups, datasets, models, training, testing, etc. Although the config file has been explained thoroughly, we recommend reading our paper to fully understand each parameter.

Training

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).
Run main.py:
```
$ python main.py --mode train
```
Each run will create a version in ./lightning_logs, where the model checkpoint and hyperparameters are saved. In case you want to continue training from one of these versions, just set the argument --version of the above command to your desired version number. For example:
```
# resume from version 0
$ python main.py --mode train --version 0
```
To monitor the training curves as well as inspect model output visualization, run the tensorboard:
```
$ tensorboard --logdir=./lightning_logs --bind_all
```

Evaluation

In our paper, we evaluated with 2 masking methods: simulation using Markov Chain and employing real traces in PLC Challenge.

Get the blind test set with loss traces:

$ wget http://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/blind.tar.gz
$ tar -xvf blind.tar.gz -C test_samples

Modify config.py to change evaluation setup if necessary.
Run main.py with a version number to be evaluated:
```
$ python main.py --mode eval --version 0
```
During the evaluation, several output samples are saved to CONFIG.LOG.sample_path for sanity testing.

Configure a new dataset

Our implementation currently works with the VCTK dataset but can be easily extensible to a new one.

Firstly, you need to prepare train.txt and test.txt. See ./data/vctk/train.txt and ./data/vctk/test.txt for example.
Secondly, add a new dictionary to CONFIG.DATA.data_dir:
```
{
'root': 'path/to/data/directory',
'train': 'path/to/train.txt',
'test': 'path/to/test.txt'
}
```
Important: Make sure each line in train.txt and test.txt joining with 'root' is a valid path to its corresponding audio file.

5. Audio generation

In order to generate output audios, you need to modify CONFIG.TEST.in_dir to your input directory.
Run main.py:
```
python main.py --mode test --version 0
```
The generated audios are saved to CONFIG.TEST.out_dir.

ONNX inferencing

We provide ONNX inferencing scripts and the best ONNX model (converted from the best checkpoint) at lightning_logs/best_model.onnx.
- Convert a checkpoint to an ONNX model:
```
python main.py --mode onnx --version 0
```
  The converted ONNX model will be saved to lightning_logs/version_0/checkpoints.
- Put test audios in test_samples and inference with the converted ONNX model (see inference_onnx.py for more details):
```
  python inference_onnx.py --onnx_path lightning_logs/version_0/frn.onnx
```