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.env
View file @ 4a689e82
PROXY_PORT=8888
DATA_VOLUME=./data
LOGS_VOLUME=./logs
AUDIO_ANALYSER_PORT=50051
VIDEO_ANALYSER_PORT=50052
TAPE_IRREGULARITY_PORT=50053
TAPE_AUDIO_RESTORATION_PORT=50054
PROXY_PORT=8888
DATA_VOLUME=./data
LOGS_VOLUME=./logs
AUDIO_ANALYSER_PORT=50051
VIDEO_ANALYSER_PORT=50052
TAPE_IRREGULARITY_PORT=50053
TAPE_AUDIO_RESTORATION_PORT=50054
PACKAGER_PORT=50055
\ No newline at end of file
.gitignore
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/data
/logs
/opencv_source
/eq_detection
\ No newline at end of file
/data
/logs
/opencv_source
/eq_detection
**/__pycache__
\ No newline at end of file
.gitmodules
View file @ 4a689e82
[submodule "packager"]
path = packager
url = https://gitlab.dei.unipd.it/mpai/packager.git
[submodule "tape-audio-restoration"]
path = tape-audio-restoration
url = https://gitlab.dei.unipd.it/mpai/tape-audio-restoration.git
[submodule "tape-irregularity-classifier"]
path = tape-irregularity-classifier
url = https://gitlab.dei.unipd.it/mpai/tape-irregularity-classifier.git
[submodule "video-analyzer"]
path = video-analyzer
url = https://gitlab.dei.unipd.it/mpai/video-analyzer.git
[submodule "audio-analyzer"]
path = audio-analyzer
url = https://gitlab.dei.unipd.it/mpai/audio-analyzer.git
[submodule "packager"]
path = packager
url = https://gitlab.dei.unipd.it/mpai/packager.git
[submodule "tape-audio-restoration"]
path = tape-audio-restoration
url = https://gitlab.dei.unipd.it/mpai/tape-audio-restoration.git
[submodule "tape-irregularity-classifier"]
path = tape-irregularity-classifier
url = https://gitlab.dei.unipd.it/mpai/tape-irregularity-classifier.git
[submodule "video-analyzer"]
path = video-analyzer
url = https://gitlab.dei.unipd.it/mpai/video-analyzer.git
[submodule "audio-analyzer"]
path = audio-analyzer
url = https://gitlab.dei.unipd.it/mpai/audio-analyzer.git
README.md
View file @ 4a689e82
# MPAI-CAE ARP Workflow
[![MPAI-CAE ARP](https://img.shields.io/badge/MPAI%20CAE--ARP-gray?style=for-the-badge&logo=AppleMusic&logoColor=cyan&link=https://mpai.community/standards/mpai-cae/about-mpai-cae/)](https://mpai.community/standards/mpai-cae/about-mpai-cae/)
This is the repository containing the MPAI-CAE ARP workflow implementation. The workflow is composed of the following steps:
1. Audio and video analysis;
2. Video irregularity classification;
3. Tape audio restoration;
4. Packaging.
The workflow is implemented as a set of docker containers, each one performing a specific task. The containers are orchestrated by docker-compose. Once the composition is started, the containers will communicate with each other through a shared volume, which will be used to store the intermediate results of the workflow. The starting point of the workflow is a client application, which will specify the input path. The client and the workflow containers communicate via gRPC.
## Requirements
The following software is required to run the workflow:
* Docker;
* Docker-compose;
* Python 3.10 or higher.
* poetry
* git
## Installation
To install the workflow, clone the repository with (you may need to install git first, and request access to the repository):
```bash
git clone https://gitlab.dei.unipd.it/mpai/workflow.git
cd workflow
```
Then, since the workflow has been developed in many repositories (one for each step), you need to clone the submodules with:
```bash
git submodule update --init --recursive
```
Once the repository is cloned, you need to build the docker images. To do so start docker and run:
```bash
docker compose up -d
```
On the first run, docker will download the base images and build the custom ones. This may take a while.
If everithing went fine, you should be to see the containers running with:
```bash
docker ps
```
Create and copy your digital tape files in the following directories:
```bash
mkdir data
mkdir data/PreservationAudioFile
mkdir data/PreservationAudioVisualFile
```
To install the client application, you need to install poetry with:
```bash
pip install poetry
```
Then, you can install the client application by fisrt moving to the client directory:
```bash
cd client
```
And then running:
```bash
poetry install
```
## Usage
If the containers are not running, you can start them with:
```bash
docker compose up -d
```
To run the client application, you need to move to the client directory:
```bash
cd client
```
And then run:
```bash
poetry run mpai-cae-arp -h
```
It will show the help message with some instructions on how to use the client application:
```
usage: mpai-cae-arp [-h] -f FILES_NAME [-s HOST]
The MPAI-CAE ARP client.
options:
-h, --help show this help message and exit
-f FILES_NAME, --files-name FILES_NAME
Specify the name of the Preservation files (without extension)
-s HOST, --host HOST Specify the host of the server (default is localhost)
```
The application expects to find the input files in the `data` directory, that is shared with the containers and should have the following structure:
```
data
├── PreservationAudioFile
│   ├── BERIO052.wav
│   └── BERIO100.wav
└── PreservationAudioVisualFile
    ├── BERIO052.mov
   └── BERIO100.mov
```
To execute this program for file `BERIO100` prompt:
```
poetry run mpai-cae-arp -f BERIO100
```
It will take a while. When you see the message "Success", you can open the data folder where you will find the restoration files.
## Support
If you require additional information or have any problem running the software, please contact us at:
* Matteo Spanio (dev2@audioinnova.com)
## Authors and acknowledgment
This project was developed by:
* Nadir Dalla Pozza (University of Padova);
* Niccolò Pretto (University of Padova);
* Sergio Canazza (University of Padova).
## License
This project is licensed with [GNU GPL v3.0](https://www.gnu.org/licenses/gpl-3.0.html).
# MPAI-CAE ARP Workflow
[![MPAI-CAE ARP](https://img.shields.io/badge/MPAI%20CAE--ARP-gray?style=for-the-badge&logo=AppleMusic&logoColor=cyan&link=https://mpai.community/standards/mpai-cae/about-mpai-cae/)](https://mpai.community/standards/mpai-cae/about-mpai-cae/)
This is the repository containing the MPAI-CAE ARP workflow implementation. The workflow is composed of the following steps:
1. Audio and video analysis;
2. Video irregularity classification;
3. Tape audio restoration;
4. Packaging.
The workflow is implemented as a set of docker containers, each one performing a specific task. The containers are orchestrated by docker-compose. Once the composition is started, the containers will communicate with each other through a shared volume, which will be used to store the intermediate results of the workflow. The starting point of the workflow is a client application, which will specify the input path. The client and the workflow containers communicate via gRPC.
## Requirements
The following software is required to run the workflow:
* Docker;
* Docker-compose;
* Python 3.10 or higher.
* poetry
* git
## Installation
To install the workflow, clone the repository with (you may need to install git first, and request access to the repository):
```bash
git clone https://gitlab.dei.unipd.it/mpai/workflow.git
cd workflow
```
Then, since the workflow has been developed in many repositories (one for each step), you need to clone the submodules with:
```bash
git submodule update --init --recursive
```
Once the repository is cloned, you need to build the docker images. To do so start docker and run:
```bash
docker compose up -d
```
On the first run, docker will download the base images and build the custom ones. This may take a while.
If everithing went fine, you should be to see the containers running with:
```bash
docker ps
```
Create and copy your digital tape files in the following directories:
```bash
mkdir data
mkdir data/PreservationAudioFile
mkdir data/PreservationAudioVisualFile
```
To install the client application, you need to install poetry with:
```bash
pip install poetry
```
Then, you can install the client application by fisrt moving to the client directory:
```bash
cd client
```
And then running:
```bash
poetry install
```
## Usage
If the containers are not running, you can start them with:
```bash
docker compose up -d
```
To run the client application, you need to move to the client directory:
```bash
cd client
```
And then run:
```bash
poetry run mpai-cae-arp -h
```
It will show the help message with some instructions on how to use the client application:
```
usage: mpai-cae-arp [-h] -f FILES_NAME [-s HOST]
The MPAI-CAE ARP client.
options:
-h, --help show this help message and exit
-f FILES_NAME, --files-name FILES_NAME
Specify the name of the Preservation files (without extension)
-s HOST, --host HOST Specify the host of the server (default is localhost)
```
The application expects to find the input files in the `data` directory, that is shared with the containers and should have the following structure:
```
data
├── PreservationAudioFile
│   ├── BERIO052.wav
│   └── BERIO100.wav
└── PreservationAudioVisualFile
    ├── BERIO052.mov
   └── BERIO100.mov
```
To execute this program for file `BERIO100` prompt:
```
poetry run mpai-cae-arp -f BERIO100
```
It will take a while. When you see the message "Success", you can open the data folder where you will find the restoration files.
## Support
If you require additional information or have any problem running the software, please contact us at:
* Matteo Spanio (dev2@audioinnova.com)
## Authors and acknowledgment
This project was developed by:
* Nadir Dalla Pozza (University of Padova);
* Niccolò Pretto (University of Padova);
* Sergio Canazza (University of Padova).
## License
This project is licensed with [GNU GPL v3.0](https://www.gnu.org/licenses/gpl-3.0.html).
client/poetry.lock
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client/pyproject.toml
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[tool.poetry]
name = "client"
version = "0.1.0"
description = ""
authors = ["Matteo Spanio <dev2@audioinnova.com>"]
readme = "README.md"
packages = [
{ include = "src" },
]
[tool.poetry.dependencies]
python = "^3.10"
rich = "^13.3.3"
grpcio-tools = "^1.53.0"
mpai-cae-arp = "^0.4.1"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
[tool.poetry.scripts]
mpai-cae-arp = 'src.app:run'
[tool.poetry]
name = "client"
version = "0.1.0"
description = ""
authors = ["Matteo Spanio <dev2@audioinnova.com>"]
readme = "README.md"
packages = [
{ include = "src" },
]
[tool.poetry.dependencies]
python = "^3.10"
rich = "^13.3.3"
grpcio-tools = "^1.53.0"
mpai-cae-arp = "^0.4.1"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
[tool.poetry.scripts]
mpai-cae-arp = 'src.app:run'
client/src/app.py
View file @ 4a689e82
from rich.console import Console
import os
from argparse import ArgumentParser
from time import sleep
import grpc
from mpai_cae_arp.network import arp_pb2
from mpai_cae_arp.network import arp_pb2_grpc
channels = {
"AudioAnalyser": grpc.insecure_channel("localhost:50051"),
"VideoAnalyser": grpc.insecure_channel("localhost:50052"),
"TapeIrregularityClassifier": grpc.insecure_channel("localhost:50053"),
"TapeAudioRestoration": grpc.insecure_channel("localhost:50054"),
"Packager": grpc.insecure_channel("localhost:50055"),
}
def get_args() -> tuple[str, str]:
parser = ArgumentParser(
prog="mpai-cae-arp",
description="The MPAI-CAE ARP client.",
)
parser.add_argument("-f", "--files-name", help="Specify the name of the Preservation files (without extension)", required=True)
parser.add_argument("-s", "--host", help="Specify the host of the server (default is localhost)", default="localhost")
args = parser.parse_args()
return args.files_name, args.host
def run():
console = Console()
files_name, host = get_args()
if host != "localhost":
console.print("[red bold]Warning![/] :warning:")
console.print("[italic red]You are not using the default host. Make sure you are using the same host as the server.[/]")
exit(os.EX_USAGE)
audio_analyser = arp_pb2_grpc.AIMStub(channels["AudioAnalyser"])
video_analyser = arp_pb2_grpc.AIMStub(channels["VideoAnalyser"])
tape_irreg_classifier = arp_pb2_grpc.AIMStub(channels["TapeIrregularityClassifier"])
tape_audio_restoration = arp_pb2_grpc.AIMStub(channels["TapeAudioRestoration"])
packager = arp_pb2_grpc.AIMStub(channels["Packager"])
request = arp_pb2.InfoRequest()
try:
for aim in [audio_analyser, video_analyser, tape_irreg_classifier, tape_audio_restoration, packager]:
response = aim.getInfo(request)
console.print("[bold]{}[/], v{}".format(response.title, response.version))
sleep(0.3)
except grpc.RpcError as e:
console.print("[bold red]Error![/] :boom:")
console.print(f"[italic red]{e.details()}[/]")
exit(69)
request = arp_pb2.JobRequest(
working_dir="/data",
files_name=files_name,
index=1,
)
with console.status("[bold]Computing AudioAnalyser IrregularityFile 1...", spinner="bouncingBall"):
for result in audio_analyser.work(request):
if result.status == "error":
console.print("[bold red]Error![/] :boom:")
console.print(f"[italic red]{result.message}")
for channel in channels.values():
channel.close()
exit(os.EX_SOFTWARE)
console.print(result.message)
request.files_name = f"{files_name}.mov"
with console.status("[bold]Computing VideoAnalyser IrregularityFiles...", spinner="bouncingBall"):
for result in video_analyser.work(request):
if result.status == "error":
console.print("[bold red]Error![/] :boom:")
console.print(f"[italic red]{result.message}")
for channel in channels.values():
channel.close()
exit(os.EX_SOFTWARE)
console.print(result.message)
request.index = 2
request.files_name = files_name
with console.status("[bold]Computing AudioAnalyser IrregularityFile 2...", spinner="bouncingBall"):
for result in audio_analyser.work(request):
if result.status == "error":
console.print("[bold red]Error![/] :boom:")
console.print(f"[italic red]{result.message}")
for channel in channels.values():
channel.close()
exit(os.EX_SOFTWARE)
console.print(result.message)
with console.status("[bold]Computing TapeIrregularityClassifier...", spinner="bouncingBall"):
for result in tape_irreg_classifier.work(request):
if result.status == "error":
console.print("[bold red]Error![/] :boom:")
console.print(f"[italic red]{result.message}")
for channel in channels.values():
channel.close()
exit(os.EX_SOFTWARE)
console.print(result.message)
with console.status("[bold]Computing TapeAudioRestoration...", spinner="bouncingBall"):
for result in tape_audio_restoration.work(request):
if result.status == "error":
console.print("[bold red]Error![/] :boom:")
console.print(f"[italic red]{result.message}")
for channel in channels.values():
channel.close()
exit(os.EX_SOFTWARE)
console.print(result.message)
with console.status("[bold]Packaging...", spinner="bouncingBall"):
for result in packager.work(request):
if result.status == "error":
console.print("[bold red]Error![/] :boom:")
console.print(f"[italic red]{result.message}")
for channel in channels.values():
channel.close()
exit(os.EX_SOFTWARE)
console.print(result.message)
channels["AudioAnalyser"].close()
channels["VideoAnalyser"].close()
channels["TapeIrregularityClassifier"].close()
channels["TapeAudioRestoration"].close()
channels["Packager"].close()
console.print("[bold green]Success![/] :tada:")
if __name__ == '__main__':
run()
from rich.console import Console
import os
from argparse import ArgumentParser
from time import sleep
import grpc
from mpai_cae_arp.network import arp_pb2
from mpai_cae_arp.network import arp_pb2_grpc
channels = {
"AudioAnalyser": grpc.insecure_channel("localhost:50051"),
"VideoAnalyser": grpc.insecure_channel("localhost:50052"),
"TapeIrregularityClassifier": grpc.insecure_channel("localhost:50053"),
"TapeAudioRestoration": grpc.insecure_channel("localhost:50054"),
"Packager": grpc.insecure_channel("localhost:50055"),
}
def get_args() -> tuple[str, str]:
parser = ArgumentParser(
prog="mpai-cae-arp",
description="The MPAI-CAE ARP client.",
)
parser.add_argument("-f", "--files-name", help="Specify the name of the Preservation files (without extension)", required=True)
parser.add_argument("-s", "--host", help="Specify the host of the server (default is localhost)", default="localhost")
args = parser.parse_args()
return args.files_name, args.host
def run():
console = Console()
files_name, host = get_args()
if host != "localhost":
console.print("[red bold]Warning![/] :warning:")
console.print("[italic red]You are not using the default host. Make sure you are using the same host as the server.[/]")
exit(os.EX_USAGE)
audio_analyser = arp_pb2_grpc.AIMStub(channels["AudioAnalyser"])
video_analyser = arp_pb2_grpc.AIMStub(channels["VideoAnalyser"])
tape_irreg_classifier = arp_pb2_grpc.AIMStub(channels["TapeIrregularityClassifier"])
tape_audio_restoration = arp_pb2_grpc.AIMStub(channels["TapeAudioRestoration"])
packager = arp_pb2_grpc.AIMStub(channels["Packager"])
request = arp_pb2.InfoRequest()
try:
for aim in [audio_analyser, video_analyser, tape_irreg_classifier, tape_audio_restoration, packager]:
response = aim.getInfo(request)
console.print("[bold]{}[/], v{}".format(response.title, response.version))
sleep(0.3)
except grpc.RpcError as e:
console.print("[bold red]Error![/] :boom:")
console.print(f"[italic red]{e.details()}[/]")
exit(69)
request = arp_pb2.JobRequest(
working_dir="/data",
files_name=files_name,
index=1,
)
with console.status("[bold]Computing AudioAnalyser IrregularityFile 1...", spinner="bouncingBall"):
for result in audio_analyser.work(request):
if result.status == "error":
console.print("[bold red]Error![/] :boom:")
console.print(f"[italic red]{result.message}")
for channel in channels.values():
channel.close()
exit(os.EX_SOFTWARE)
console.print(result.message)
request.files_name = f"{files_name}.mov"
with console.status("[bold]Computing VideoAnalyser IrregularityFiles...", spinner="bouncingBall"):
for result in video_analyser.work(request):
if result.status == "error":
console.print("[bold red]Error![/] :boom:")
console.print(f"[italic red]{result.message}")
for channel in channels.values():
channel.close()
exit(os.EX_SOFTWARE)
console.print(result.message)
request.index = 2
request.files_name = files_name
with console.status("[bold]Computing AudioAnalyser IrregularityFile 2...", spinner="bouncingBall"):
for result in audio_analyser.work(request):
if result.status == "error":
console.print("[bold red]Error![/] :boom:")
console.print(f"[italic red]{result.message}")
for channel in channels.values():
channel.close()
exit(os.EX_SOFTWARE)
console.print(result.message)
with console.status("[bold]Computing TapeIrregularityClassifier...", spinner="bouncingBall"):
for result in tape_irreg_classifier.work(request):
if result.status == "error":
console.print("[bold red]Error![/] :boom:")
console.print(f"[italic red]{result.message}")
for channel in channels.values():
channel.close()
exit(os.EX_SOFTWARE)
console.print(result.message)
with console.status("[bold]Computing TapeAudioRestoration...", spinner="bouncingBall"):
for result in tape_audio_restoration.work(request):
if result.status == "error":
console.print("[bold red]Error![/] :boom:")
console.print(f"[italic red]{result.message}")
for channel in channels.values():
channel.close()
exit(os.EX_SOFTWARE)
console.print(result.message)
with console.status("[bold]Packaging...", spinner="bouncingBall"):
for result in packager.work(request):
if result.status == "error":
console.print("[bold red]Error![/] :boom:")
console.print(f"[italic red]{result.message}")
for channel in channels.values():
channel.close()
exit(os.EX_SOFTWARE)
console.print(result.message)
channels["AudioAnalyser"].close()
channels["VideoAnalyser"].close()
channels["TapeIrregularityClassifier"].close()
channels["TapeAudioRestoration"].close()
channels["Packager"].close()
console.print("[bold green]Success![/] :tada:")
if __name__ == '__main__':
run()
docker-compose.yml
View file @ 4a689e82
version: '3.9'
services:
audio-analyzer:
image: mpai-audio-analyzer
build: ./audio-analyzer
ports:
- "${AUDIO_ANALYSER_PORT}:50051"
volumes:
- ${DATA_VOLUME}:/data
video-analyzer:
image: mpai-video-analyzer
build: ./video-analyzer
ports:
- "${VIDEO_ANALYSER_PORT}:50051"
volumes:
- ${DATA_VOLUME}:/data
tape-irregularity-classifier:
image: mpai-tape-irregularity-classifier
build: ./tape-irregularity-classifier
ports:
- "${TAPE_IRREGULARITY_PORT}:50051"
volumes:
- ${DATA_VOLUME}:/data
tape-audio-restoration:
image: mpai-tape-audio-restoration
build: ./tape-audio-restoration
ports:
- "${TAPE_AUDIO_RESTORATION_PORT}:50051"
volumes:
- ${DATA_VOLUME}:/data
packager:
image: mpai-packager
build: ./packager
ports:
- "${PACKAGER_PORT}:50051"
volumes:
- ${DATA_VOLUME}:/data
volumes:
data:
version: '3.9'
services:
audio-analyzer:
image: mpai-audio-analyzer
build: ./audio-analyzer
ports:
- "${AUDIO_ANALYSER_PORT}:50051"
volumes:
- ${DATA_VOLUME}:/data
video-analyzer:
image: mpai-video-analyzer
build: ./video-analyzer
ports:
- "${VIDEO_ANALYSER_PORT}:50051"
volumes:
- ${DATA_VOLUME}:/data
tape-irregularity-classifier:
image: mpai-tape-irregularity-classifier
build: ./tape-irregularity-classifier
ports:
- "${TAPE_IRREGULARITY_PORT}:50051"
volumes:
- ${DATA_VOLUME}:/data
tape-audio-restoration:
image: mpai-tape-audio-restoration
build: ./tape-audio-restoration
ports:
- "${TAPE_AUDIO_RESTORATION_PORT}:50051"
volumes:
- ${DATA_VOLUME}:/data
packager:
image: mpai-packager
build: ./packager
ports:
- "${PACKAGER_PORT}:50051"
volumes:
- ${DATA_VOLUME}:/data
volumes:
data:
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