Audio Classification
Transformers
PyTorch
Safetensors
Bengali
whisper
emotion-classification
sentiment-classification
Instructions to use shhossain/whisper-tiny-bn-emo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shhossain/whisper-tiny-bn-emo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="shhossain/whisper-tiny-bn-emo")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("shhossain/whisper-tiny-bn-emo") model = AutoModelForAudioClassification.from_pretrained("shhossain/whisper-tiny-bn-emo") - Notebooks
- Google Colab
- Kaggle
whisper-tiny-bn-emo
This model is a fine-tuned on shhossain/whisper-tiny-bn for emotion classification. It achieves the following results on the evaluation set:
- Loss: 0.1842
- Accuracy: 0.9357
Model Info
It detects 7 basic human emotions on Bengali Language.
ANGRYDISGUSTFEARHAPPYNEUTRALSADSURPRISE
Usage
from transformers import pipeline
pipe = pipeline("audio-classification", model="shhossain/whisper-tiny-bn-emo")
pipe("audio_file.wav")
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