ShiweiHe0713 / Guitar-Chords-recognition

An application that predicts the chords when melspectrograms of guitar sound is fed into a CNN.

Guitar-Chords-recognition

An application that predicts the chords when melspectrograms of guitar sound is fed into a CNN.

Setting up the project

1. Clone the repo

$ git clone https://github.com/ayushkumarshah/Guitar-Chords-recognition.git
$ cd Guitar-Chords-recognition

If you are using linux system, switch to linux branch:

$ git checkout linux

2. Installation

You can install the required packages using any one of the 3 options below. However, Option 1 is recommended.

Option 1: Using Conda - Recommended

• Download and install miniconda or anaconda if you don't have conda installed in your system.

• Create a new environment 'tf' using the following command:

  $ conda env create -f environment.yml

  If an error like the one shown below occurs:

  ResolvePackageNotFound:
      - appnope=0.1.0
      - libcxx=4.0.1

  Just remove those packages from the file environment.yml and rerun the above command. Then activate the environment by

  $ conda activate tf

Option 2: Using setup.sh

• If you face trouble installing packages, you may install using setup.sh. If you use bash shell instead of zsh, edit line 2 of setup.sh by replacing zsh with bash. i.e.

  eval "$(conda shell.zsh hook)"

  Then run the following commands

  $ chmod +x setup.sh
  $ ./setup.sh

Option 3: Using Virtual Environment

• Install virtualenv using pip and create a virtual environment '.venv'

  $ pip install virtualenv
  $ virtualenv .venv

• Activate the virtual environment '.venv' and install required packages

  $ source .venv/bin/activate
  $ pip install -r requirements.txt

Configuring the Chords Classifier App (classifier.py)

It uses the trained model models/model.json to predict a recorded guitar chord.

• First, check the info of the audio recording device of your system by running

  $ python -m src.sound

  You will receive output something like this:

  pygame 1.9.6
  Hello from the pygame community. https://www.pygame.org/contribute.html
  src.sound - INFO - List of System's Audio Devices configurations:
  src.sound - INFO - Number of audio devices: 2
  src.sound - INFO - [('index', 0), ('name', 'MacBook Pro Microphone'), ('maxInputChannels', 1), ('defaultSampleRate', 44100.0)]
  src.sound - INFO - [('index', 1), ('name', 'MacBook Pro Speakers'), ('maxInputChannels', 0), ('defaultSampleRate', 44100.0)]
  
  src.sound - INFO - Audio device configurations currently used
  src.sound - INFO - Default input device index = 0
  src.sound - INFO - Max input channels = 1
  src.sound - INFO - Default samplerate = 44100

• Check if the index, maxInputChannels and defaultSampleRate of your recording device or microphone (eg. MacBook Pro Microphone) matches with the device configurations currently used (both displayed in the output). The configurations for my recording device is:

  index = 0
  maxInputChannels = 1
  defaultSampleRate = 44100.0

• Open settings.py and modify the values accordingly in line numbers 38 to 40

  # Audio configurations
  INPUT_DEVICE = 0
  MAX_INPUT_CHANNELS = 1  # Max input channels
  DEFAULT_SAMPLE_RATE = 44100   # Default sample rate of microphone or recording device

Running the Guitar Chords Recognition Webapp

• Execute the python file app.py using streamlit

  $ streamlit run app.py

• The webapp is launched in your browser and opened automatically as shown below. You may also open it by visiting http://localhost:8501

  

  Click the above video to to go to YouTube and hear the sound as well.

• Click Record and play a chord in your guitar. It records for 3 seconds and saves the output wav file to recording/recorded.wav.

• Click Play to listen to the recorded sound.

• Click Classify to view the predicted chord along with the melspectrogram of the recorded chord.

• Click Display Spectrogram to display the spectrogram of the recorded chord.

Training the model (Optional)

If you want to experiment by training the model yourself with your own data or the data used currently, follow the steps below:

1. Download the Dataset or Use your own dataset

The chords dataset was collected from MONTEFIORE RESEARCH GROUP of University of Liège - Montefiore Institute (Montefiore.ulg.ac.be, 2019). The chords dataset consists of 10 types of chords with 200 audio files of each chord.

Run download_data.sh to download the dataset:

$ chmod +x download_data.sh
$ ./download_data.sh

2. Run train.py

$ python -m src.train

You can view the training logs in the file logs/info.log

To view the real-time tensorboard logging, type the command:

$ tensorboard --logdir logs/training

Then open your browser and goto localhost:6006 to visualize the training.

You will see something like this:

3. Run test.py

You can test the performance of your model after the training by running:

$ python -m src.test

Report Issues

If you have any issues with the app, please report it here: Issues

License

Guitar-Chords-recognition is licensed under the GNU GPLv3 license.