| | --- |
| | datasets: |
| | - SHD-2 |
| | tags: |
| | - audio |
| | - audio-classificaiton |
| | - shower detection |
| | metrics: |
| | - Accuracy |
| |
|
| | --- |
| | |
| | **Context** |
| |
|
| | Most of our great brilliant ideas happen in periods of relaxation, like taking a |
| | shower, however, once we leave the shower, we forget the brilliant idea. What if |
| | we do not forget, and collect your ideas in the shower? |
| |
|
| | **What is the Shower Ideas concept?** |
| |
|
| | This is an app that detects when someone is taking a shower (douche) and asks |
| | “do you have any idea?”, and the person will speak while taking the shower telling |
| | the idea. And also will ask questions after taking a shower. |
| |
|
| | **Abstract about the model** |
| |
|
| | This model was trained based on *facebook/wav2vec2-base-960h* (which is a pretrained model on 960 hours of Librispeech on 16kHz sampled speech audio.) in order to classify the audio input into shower or no_shower. |
| | |
| | **Dataset** |
| | |
| | The SHD-2 dataset is a labeled collection of 2260 audio recordings of shower and no shower sounds. |
| | |
| | The dataset consists of 6-second-long recordings organized into 2 classes (with 1130 examples per class). |
| | |
| | # Usage |
| | In order to use the model in your Python script just copy the following code: |
| | ```python |
| | from transformers import pipeline |
| | |
| | audio_input = 'example.wav' |
| | classifier = pipeline("audio-classification", model="abdelhalim/Shower_Sound_Recognition") |
| | labels = classifier(audio_input) |
| | labels |
| | |
| | ``` |
