| | ---
|
| | license: mit
|
| | tags:
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| | - audio-classification
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| | - baby-cry-detection
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| | - pytorch
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| | - hubert
|
| | language:
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| | - en
|
| | library_name: transformers
|
| | pipeline_tag: audio-classification
|
| | ---
|
| |
|
| | # Baby Cry Classification Model
|
| |
|
| | Classifies baby cries into 5 categories to help parents understand their baby's needs.
|
| |
|
| | ## Categories
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| | - 🍼 **Hungry**: Baby needs feeding
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| | - 😴 **Tired**: Baby needs sleep
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| | - 😣 **Belly Pain**: Digestive discomfort
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| | - 💨 **Burping**: Needs burping
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| | - 😢 **Discomfort**: General discomfort (diaper, temperature, etc.)
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| |
|
| | ## Model Details
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| | - **Base Model**: HuBERT (facebook/hubert-base-ls960)
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| | - **Architecture**: HuBERT + Custom Classification Head
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| | - **Sample Rate**: 16kHz
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| | - **Input Duration**: 5 seconds
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| | - **Framework**: PyTorch + Transformers
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| |
|
| | ## Usage
|
| | ```python
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| | import requests
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| | import numpy as np
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| |
|
| | # Load audio file
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| | audio_data = open("baby_cry.wav", "rb").read()
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| |
|
| | # Call API
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| | API_URL = "https://api-inference.huggingface.co/models/dontcryai/baby-cry-classifier"
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| | headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
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| |
|
| | response = requests.post(API_URL, headers=headers, data=audio_data)
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| | result = response.json()
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| |
|
| | print(result)
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| | # [{'label': 'hungry', 'score': 0.85}, ...]
|
| | ```
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| |
|
| | ## Training
|
| | Trained on custom baby cry dataset with data augmentation.
|
| |
|
| | ## License
|
| | MIT License - Free for commercial and personal use |
