{"id":28075198,"url":"https://github.com/thameran/mmar","last_synced_at":"2026-05-06T04:04:05.012Z","repository":{"id":292753670,"uuid":"981780547","full_name":"thameran/MMAR","owner":"thameran","description":"Benchmark data and code for MMAR: A Challenging Benchmark for Deep Reasoning in Speech, Audio, Music, and Their Mix","archived":false,"fork":false,"pushed_at":"2025-05-12T23:31:13.000Z","size":9396,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-05-13T00:55:11.790Z","etag":null,"topics":["cli","data-analysis","datascience","finance","generator","github-config","go","haskell","hurst","markdown","mmar","mmark","time-series","xml2rfc"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":false,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/thameran.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-05-11T21:30:47.000Z","updated_at":"2025-05-12T23:31:16.000Z","dependencies_parsed_at":"2025-05-12T01:33:53.446Z","dependency_job_id":null,"html_url":"https://github.com/thameran/MMAR","commit_stats":null,"previous_names":["thameran/mmar"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thameran%2FMMAR","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thameran%2FMMAR/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thameran%2FMMAR/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thameran%2FMMAR/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/thameran","download_url":"https://codeload.github.com/thameran/MMAR/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253850891,"owners_count":21973672,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cli","data-analysis","datascience","finance","generator","github-config","go","haskell","hurst","markdown","mmar","mmark","time-series","xml2rfc"],"created_at":"2025-05-13T00:55:17.352Z","updated_at":"2025-05-13T00:55:17.959Z","avatar_url":"https://github.com/thameran.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MMAR: A Challenging Benchmark for Deep Reasoning in Speech, Audio, Music, and Their Mix 🎶🔍\n\nWelcome 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.\n\n## Table of Contents\n\n- [Introduction](#introduction)\n- [Getting Started](#getting-started)\n- [Dataset Description](#dataset-description)\n- [Installation](#installation)\n- [Usage](#usage)\n- [Results](#results)\n- [Contributing](#contributing)\n- [License](#license)\n- [Contact](#contact)\n\n## Introduction\n\nThe 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.\n\n## Getting Started\n\nTo get started with MMAR, you can download the latest release from our [Releases section](https://github.com/thameran/MMAR/releases). This release contains all necessary files to run the benchmark. \n\n### Prerequisites\n\nBefore you begin, ensure you have the following installed:\n\n- Python 3.7 or higher\n- NumPy\n- Pandas\n- TensorFlow or PyTorch (depending on your preferred framework)\n\nYou can install the required libraries using pip:\n\n```bash\npip install numpy pandas tensorflow\n```\n\nor \n\n```bash\npip install numpy pandas torch\n```\n\n## Dataset Description\n\nThe 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.\n\n### Dataset Structure\n\n- `speech/`: Contains audio files related to spoken language.\n- `music/`: Contains musical compositions across various genres.\n- `mixed/`: Contains samples that combine both speech and music.\n\n### Data Format\n\nEach audio file is provided in WAV format, with a corresponding CSV file that includes metadata and labels. \n\n## Installation\n\nTo install the MMAR package, follow these steps:\n\n1. Clone the repository:\n\n```bash\ngit clone https://github.com/thameran/MMAR.git\n```\n\n2. Navigate to the directory:\n\n```bash\ncd MMAR\n```\n\n3. Install the package:\n\n```bash\npip install .\n```\n\n## Usage\n\nOnce 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.\n\n### Loading the Dataset\n\n```python\nimport pandas as pd\n\n# Load the metadata\nmetadata = pd.read_csv('path/to/metadata.csv')\nprint(metadata.head())\n```\n\n### Running a Simple Model\n\nYou can implement a basic model to evaluate the dataset. Here’s a template:\n\n```python\nimport tensorflow as tf\n\n# Load your audio data\n# Your code to load audio goes here\n\n# Define your model\nmodel = tf.keras.Sequential([\n    tf.keras.layers.Input(shape=(input_shape)),\n    tf.keras.layers.Dense(128, activation='relu'),\n    tf.keras.layers.Dense(num_classes, activation='softmax')\n])\n\n# Compile the model\nmodel.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n\n# Train the model\nmodel.fit(train_data, train_labels, epochs=10)\n```\n\n### Evaluating the Model\n\nTo evaluate your model, you can use:\n\n```python\nresults = model.evaluate(test_data, test_labels)\nprint(\"Test Loss, Test Accuracy:\", results)\n```\n\n## Results\n\nWe 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.\n\n## Contributing\n\nWe welcome contributions from the community. If you have ideas for improvements, bug fixes, or new features, please follow these steps:\n\n1. Fork the repository.\n2. Create a new branch for your feature or bug fix.\n3. Commit your changes.\n4. Push to your forked repository.\n5. Submit a pull request.\n\nPlease ensure that your code follows the existing style and includes appropriate tests.\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n\n## Contact\n\nFor questions or suggestions, please reach out to the maintainers:\n\n- **Thamer Anis**: [GitHub Profile](https://github.com/thameran)\n\nWe appreciate your interest in MMAR and look forward to your contributions!\n\n### Additional Resources\n\n- [Research Paper](https://example.com/research-paper)\n- [Related Work](https://example.com/related-work)\n\n### Download the Latest Release\n\nTo access the latest files and updates, visit our [Releases section](https://github.com/thameran/MMAR/releases). You will find the necessary files to download and execute.\n\n![Download Button](https://img.shields.io/badge/Download%20Latest%20Release-blue?style=flat-square\u0026logo=github)\n\n## Acknowledgments\n\nWe thank all contributors and researchers who have inspired this work. Your efforts help advance the field of deep reasoning in audio processing.\n\n---\n\nFeel free to explore the repository, and happy coding!","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthameran%2Fmmar","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fthameran%2Fmmar","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthameran%2Fmmar/lists"}