Welcome to the MMAR repository! This project provides benchmark data and code designed to evaluate deep reasoning capabilities in the domains of speech, audio, music, and their combinations. We aim to push the boundaries of what machines can understand in these complex areas.
- Introduction
- Getting Started
- Dataset Description
- Installation
- Usage
- Results
- Contributing
- License
- Contact
The MMAR benchmark challenges researchers and developers to create models that can effectively reason about audio-related tasks. By offering a diverse dataset, we encourage innovation and exploration in machine learning and artificial intelligence.
To get started with MMAR, you can download the latest release from our Releases section. This release contains all necessary files to run the benchmark.
Before you begin, ensure you have the following installed:
- Python 3.7 or higher
- NumPy
- Pandas
- TensorFlow or PyTorch (depending on your preferred framework)
You can install the required libraries using pip:
pip install numpy pandas tensorflowor
pip install numpy pandas torchThe MMAR dataset consists of various audio samples categorized into speech, music, and mixed categories. Each sample is annotated with labels that indicate the complexity of reasoning required to interpret the content.
speech/: Contains audio files related to spoken language.music/: Contains musical compositions across various genres.mixed/: Contains samples that combine both speech and music.
Each audio file is provided in WAV format, with a corresponding CSV file that includes metadata and labels.
To install the MMAR package, follow these steps:
- Clone the repository:
git clone https://github.com/thameran/MMAR.git- Navigate to the directory:
cd MMAR- Install the package:
pip install .Once you have installed the package, you can start using the benchmark for your experiments. Here is a simple example of how to load the dataset and run a basic evaluation.
import pandas as pd
# Load the metadata
metadata = pd.read_csv('path/to/metadata.csv')
print(metadata.head())You can implement a basic model to evaluate the dataset. Here’s a template:
import tensorflow as tf
# Load your audio data
# Your code to load audio goes here
# Define your model
model = tf.keras.Sequential([
tf.keras.layers.Input(shape=(input_shape)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(num_classes, activation='softmax')
])
# Compile the model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# Train the model
model.fit(train_data, train_labels, epochs=10)To evaluate your model, you can use:
results = model.evaluate(test_data, test_labels)
print("Test Loss, Test Accuracy:", results)We encourage users to share their results and findings. Please document your experiments and submit them as pull requests. This way, we can collectively improve the benchmark and learn from each other’s work.
We welcome contributions from the community. If you have ideas for improvements, bug fixes, or new features, please follow these steps:
- Fork the repository.
- Create a new branch for your feature or bug fix.
- Commit your changes.
- Push to your forked repository.
- Submit a pull request.
Please ensure that your code follows the existing style and includes appropriate tests.
This project is licensed under the MIT License. See the LICENSE file for details.
For questions or suggestions, please reach out to the maintainers:
- Thamer Anis: GitHub Profile
We appreciate your interest in MMAR and look forward to your contributions!
To access the latest files and updates, visit our Releases section. You will find the necessary files to download and execute.
We thank all contributors and researchers who have inspired this work. Your efforts help advance the field of deep reasoning in audio processing.
Feel free to explore the repository, and happy coding!