In this article I will show how we can extract music sources: bass, drums, vocals and other accompaniments using neural networks.
Before you will continue reading please watch short introduction:
Separation of individual instruments from arranged music is another area where machine learning
algorithms could help. Demucs solves this problem using neural networks.
The trained model (https://arxiv.org/pdf/1909.01174v1.pdf) use U-NET architecture which contains two parts encoder and decoder.
On the encoder input we put the original track and after processing we get bass, drums, vocals and other accompaniments at the decoder output.
The encoder, is connected to the decoder,
through additional LSTM layer,
as well as residual connections between subsequent layers.
Ok, we have neural network architecture but what about the training data ?
This is another difficulty which can be handled by the unlabeled data remixing pipeline.
We start with another classifier, which can find the parts of music,
which do not contain the specific instruments, for example drums.
Then, we mix it with well known drums signal, and separate the tracks
using the model.
Now we can compare, the separation results, with known drums track and mixture of other instruments.
According to this, we can calculate the loss (L1 loss), and use it during the training.
Additionally, we set different loss weights, for known track and the other.
The whole UI is kept in the docker image thus you can simply try it:
#for CPU
docker run --name aiaudioseparation -it -p 8000:8000 -v $(pwd)/checkpoints:/root/.cache/torch/hub/checkpoints --rm qooba/aimusicseparation
#for GPU
docker run --name aiaudioseparation --gpus all -it -p 8000:8000 -v $(pwd)/checkpoints:/root/.cache/torch/hub/checkpoints --rm qooba/aimusicseparation
In this article I will show how we can prepare and perform calculations on quantum computers
using OpenFermion, Cirq and PySCF.
Before you will continue reading please watch short introduction:
Currently, there are many supercomputing centers, where we can run complicated simulations.
However, there are still problems that are beyond the capabilities of classical computers,
which can be addressed by quantum computers.
Quantum chemistry and materials science problems which that are described by the laws of
quantum mechanics can be mapped to the quantum computers and projected to qubits.
OpenFermion is the library which can help to perform such calculations on a quantum computer.
Additionally we will use the PySCF package which will help to perform initial structure optimization (if you are interested in PySCF package I have shared the example DFT based band structure calculation of the single layer graphene structure pyscf_graphene.ipynb).
In our example we will investigate [latex]H_2[/latex] molecule for simplicity. We will use the PySCF package to find optimal bond length of the molecule.
Thanks to the OpenFermion-PySCF plugin we can smoothly use the molecule initial state obtained from PySCF package run in OpenFermion library (openfermionpyscf_h2.ipynb).
Now it is time to compile the molecule to the representation readable by the quantum computer
using OpenFermion and Cirq library.
Currently you can use several methods to achieve this:
Using one of this methods we get optimized quantum circuit.
In our case the quantum cirquit for [latex]H_2[/latex] system will be represented by 4 qubits and operations that act on them (moment is collection of operations that act at the same abstract time slice).
Before you will continue reading please watch short introduction:
In the last article (TinyML with Arduino) I have shown the example TinyML model which will classify
jelly bears using RGB sensor.
The next step, will be to build a process that will simplify, the model versions management, and the deployment.
–device=/dev/ttyACM0 - is arduino device connected using USB
AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY - are minio credentials
MLFLOW_S3_ENDPOINT_URL - is minio url
MLFLOW_TRACKING_URI - is mlflow url
${RUN_ID} - is run id of model saved in MLflow registry
Additionally we have several command line options:
ARDUINO MLOPS
Syntax: docker run -it qooba/tinyml-arduino:mlops -h [-r MLFLOW_RUN_ID] [-s ARDUINO_SERIAL] [-c ARDUINO_CORE] [-m ARDUINO_MODEL]
options:
-h|--help Print help
-r|--run MLflow run id
-s|--serial Arduino device serial (default: /dev/ttyACM0)
-c|--core Arduino core (default: arduino:mbed_nano)
-m|--model Arduino model (default: arduino:mbed_nano:nano33ble)
After running the code the docker image qooba/tinyml-arduino:mlops
will fetch the model for indicated RUN_ID from MLFlow.
Then it will install required dependencies using the file requirements.ino.txt.
It will compile the model and the Arduino code.
And finally upload it to the device.
Thanks to this, we can more easily manage subsequent versions of models, and automate the deployment process.
In this article I will show how to build Tensorflow Lite based jelly bears classifier using Arduino Nano 33 BLE Sense.
Before you will continue reading please watch short introduction:
Currently a machine learning solution can be deployed not only on very powerful machines containing GPU cards but also on a really small devices. Of course such a devices has a some limitation eg. memory etc. To deploy ML model we need to prepare it. The Tensorflow framework allows you to convert neural networks to Tensorflow Lite which can be installed on the edge devices eg. Arduino Nano.
Arduino Nano 33 BLE Sense is equipped with many sensors that allow for the implementation of many projects eg.:
Digital microphone
Digital proximity, ambient light, RGB and gesture sensor
3D magnetometer, 3D accelerometer, 3D gyroscope
Capacitive digital sensor for relative humidity and temperature
Examples which I have used in this project can be found here.
To simplify device usage I have build Arduino Lab project where you can test and investigate listed sensors directly on the web browser.
The project dependencies are packed into docker image to simplify usage.
Before you start the project you will need to connect Arduino through USB (the Arduino will communicate with docker container through /dev/ttyACM0)
git clone https://github.com/qooba/tinyml-arduino.git
cd tinyml-arduino
./run.server.sh
# in another terminal tab
./run.nginx.sh
# go inside server container
docker exec -it arduino /bin/bash
./start.sh
For each sensor type you can click Prepare button which will build and deploy appropriate Arduino code.
NOTE:
Sometimes you will have to deploy to arduino manually to do this you will need to
go to arduino container
docker exec -it arduino /bin/bash
cd /arduino
make rgb
Here you have complete Makefile with all types of implemented sensors.
You can start observations using Watch button.
Now we will build TinyML solution.
In the first step we will capture training data:
The training data will be saved in the csv format. You will need to repeat the proces for each class you will detect.
Captured data will be uploaded to the Colab Notebook.
Here I fully base on the project Fruit identification using Arduino and TensorFlow.
In the notebook we train the model using Tensorflow then convert it to Tensorflow Lite and finally encode to hex format (model.h header file) which is readable by Arduino.
Now we compile and upload model.h header file using drag and drop mechanism.
Finally we can classify the jelly bears by the color:
To better visualize the whole process we will use the Propensity to buy example where I base on the Kaggle examples and data.
We start in Jupyter Notebook where we prepare Feast feature store schema which is kept in S3.
We can simply inspect the Feast schema in Jupyter Notebook:
from feast import FeatureStore
from IPython.core.display import display, HTML
import json
from json2html import *
import warnings
warnings.filterwarnings('ignore')
class FeastSchema:
def __init__(self, repo_path: str):
self.store = FeatureStore(repo_path=repo_path)
def show_schema(self, skip_meta: bool= False):
feast_schema=self.__project_show_schema(skip_meta)
display(HTML(json2html.convert(json = feast_schema)))
def show_table_schema(self, table: str, skip_meta: bool= False):
feasture_tables_dictionary=self.__project_show_schema(skip_meta)
display(HTML(json2html.convert(json = {table:feasture_tables_dictionary[table]})))
def __project_show_schema(self, skip_meta: bool= False):
entities_dictionary={}
feast_entities=self.store.list_entities()
for entity in feast_entities:
entity_dictionary=entity.to_dict()
entity_spec=entity_dictionary['spec']
entities_dictionary[entity_spec['name']]=entity_spec
feasture_tables_dictionary={}
feast_feature_tables=self.store.list_feature_views()
for feature_table in feast_feature_tables:
feature_table_dict=json.loads(str(feature_table))
feature_table_spec=feature_table_dict['spec']
feature_table_name=feature_table_spec['name']
feature_table_spec.pop('name',None)
if 'entities' in feature_table_spec:
feature_table_entities=[]
for entity in feature_table_spec['entities']:
feature_table_entities.append(entities_dictionary[entity])
feature_table_spec['entities']=feature_table_entities
if not skip_meta:
feature_table_spec['meta']=feature_table_dict['meta']
else:
feature_table_spec.pop('input',None)
feature_table_spec.pop('ttl',None)
feature_table_spec.pop('online',None)
feasture_tables_dictionary[feature_table_name]=feature_table_spec
return feasture_tables_dictionary
FeastSchema(".").show_schema()
#FeastSchema(".").show_schema(skip_meta=True)
#FeastSchema(".").show_table_schema('driver_hourly_stats')
#FeastSchema().show_tables()
In our case we store the data in Apache Parquet files in S3 bucket.
Using the Feast we can fetch the historical features and train the model using Scikit-learn library
Using MLflow we can simply deploy model as a microservice in k8s.
In our case we want to deploy the model models:/propensity_model/Production
which is currently assigned for Production. During start the MLflow will automatically fetch the proper model from S3:
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