{"id":22039386,"url":"https://github.com/mpolinowski/yolo-listen","last_synced_at":"2025-10-13T21:41:32.197Z","repository":{"id":234831451,"uuid":"695583409","full_name":"mpolinowski/yolo-listen","owner":"mpolinowski","description":"Use an image classifier to predict audio file labels.","archived":false,"fork":false,"pushed_at":"2023-09-23T15:59:53.000Z","size":2097,"stargazers_count":5,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-10-04T10:39:41.641Z","etag":null,"topics":["audio-labeling","docker","image-classifier","pytorch","yolov8n"],"latest_commit_sha":null,"homepage":"https://mpolinowski.github.io/docs/IoT-and-Machine-Learning/ML/2023-09-23--yolo8-listen/2023-09-23","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":" \n# Audio Classification with Computer Vision\n\nUse an PyTorch image classifier to predict audio file labels for the following dataset.\n\n## Dataset\n\n\u003e The [ESC-50 dataset](https://github.com/karolpiczak/ESC-50) is a labeled collection of 2000 environmental audio recordings suitable for benchmarking methods of environmental sound classification.\n\u003e\n\u003e The dataset consists of 5-second-long recordings organized into 50 semantical classes (with 40 examples per class) loosely arranged into the following categories:\n\n| class | instances |\n| -- | -- |\n| dog | 40 |\n| glass_breaking | 40 |\n| drinking_sipping | 40 |\n| rain | 40 |\n| insects | 40 |\n| laughing | 40 |\n| hen | 40 |\n| engine | 40 |\n| breathing | 40 |\n| crying_baby | 40 |\n| hand_saw | 40 |\n| coughing | 40 |\n| snoring | 40 |\n| chirping_birds | 40 |\n| toilet_flush | 40 |\n| pig | 40 |\n| washing_machine | 40 |\n| clock_tick | 40 |\n| sneezing | 40 |\n| rooster | 40 |\n| sea_waves | 40 |\n| siren | 40 |\n| cat | 40 |\n| door_wood_creaks | 40 |\n| helicopter | 40 |\n| crackling_fire | 40 |\n| car_horn | 40 |\n| brushing_teeth | 40 |\n| vacuum_cleaner | 40 |\n| thunderstorm | 40 |\n| door_wood_knock | 40 |\n| can_opening | 40 |\n| crow | 40 |\n| clapping | 40 |\n| fireworks | 40 |\n| chainsaw | 40 |\n| airplane | 40 |\n| mouse_click | 40 |\n| pouring_water | 40 |\n| train | 40 |\n| sheep | 40 |\n| water_drops | 40 |\n| church_bells | 40 |\n| clock_alarm | 40 |\n| keyboard_typing | 40 |\n| wind | 40 |\n| footsteps | 40 |\n| frog | 40 |\n| cow | 40 |\n| crickets | 40 |\n\n\n## Data Preprocessing\n\nDownload the dataset all `*.wav` files to `dataset/ESC-50/audio` and run the pre-processing scripts to generate the corresponding spectrograms. The __Train/Val-Split__ will then copy all image files to `./data`:\n\n```bash\n├── data\n│   ├── test\n│   ├── train\n│   ├── val\n├── dataset\n│   └── ESC-50\n│       ├── audio\n│       └── spectrogram\n```\n\n### Spectrograms\n\n![Audio Classification with Computer Vision](./assets/class_label_crow.webp)\n\n\n![Audio Classification with Computer Vision](./assets/class_label_toilet_flush.webp)\n\n\n## Model Training\n\nRun the YOLO model inside the a PyTorch container image with [Jupyter Notebooks](https://github.com/mpolinowski/pytorch-jupyter):\n\n\n\n```bash\ndocker run --ipc=host --gpus all -ti --rm \\\n    -v $(pwd):/opt/app -p 8888:8888 \\\n    --name pytorch-jupyter \\\n    pytorch-jupyter:latest\n```\n\n\n![Audio Classification with Computer Vision](./assets/confusion_matrix_normalized.webp)\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmpolinowski%2Fyolo-listen","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmpolinowski%2Fyolo-listen","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmpolinowski%2Fyolo-listen/lists"}