Use an PyTorch image classifier to predict audio file labels for the following dataset.
The ESC-50 dataset is a labeled collection of 2000 environmental audio recordings suitable for benchmarking methods of environmental sound classification.
The dataset consists of 5-second-long recordings organized into 50 semantical classes (with 40 examples per class) loosely arranged into the following categories:
| class | instances |
|---|---|
| dog | 40 |
| glass_breaking | 40 |
| drinking_sipping | 40 |
| rain | 40 |
| insects | 40 |
| laughing | 40 |
| hen | 40 |
| engine | 40 |
| breathing | 40 |
| crying_baby | 40 |
| hand_saw | 40 |
| coughing | 40 |
| snoring | 40 |
| chirping_birds | 40 |
| toilet_flush | 40 |
| pig | 40 |
| washing_machine | 40 |
| clock_tick | 40 |
| sneezing | 40 |
| rooster | 40 |
| sea_waves | 40 |
| siren | 40 |
| cat | 40 |
| door_wood_creaks | 40 |
| helicopter | 40 |
| crackling_fire | 40 |
| car_horn | 40 |
| brushing_teeth | 40 |
| vacuum_cleaner | 40 |
| thunderstorm | 40 |
| door_wood_knock | 40 |
| can_opening | 40 |
| crow | 40 |
| clapping | 40 |
| fireworks | 40 |
| chainsaw | 40 |
| airplane | 40 |
| mouse_click | 40 |
| pouring_water | 40 |
| train | 40 |
| sheep | 40 |
| water_drops | 40 |
| church_bells | 40 |
| clock_alarm | 40 |
| keyboard_typing | 40 |
| wind | 40 |
| footsteps | 40 |
| frog | 40 |
| cow | 40 |
| crickets | 40 |
Download 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:
├── data
│ ├── test
│ ├── train
│ ├── val
├── dataset
│ └── ESC-50
│ ├── audio
│ └── spectrogram
Run the YOLO model inside the a PyTorch container image with Jupyter Notebooks:
docker run --ipc=host --gpus all -ti --rm \
-v $(pwd):/opt/app -p 8888:8888 \
--name pytorch-jupyter \
pytorch-jupyter:latest