Audio Classification
Transformers
PyTorch
multilingual
wav2vec2
voice
classification
vocalization
speech
audio
Instructions to use padmalcom/wav2vec2-large-nonverbalvocalization-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use padmalcom/wav2vec2-large-nonverbalvocalization-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="padmalcom/wav2vec2-large-nonverbalvocalization-classification")# Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("padmalcom/wav2vec2-large-nonverbalvocalization-classification") model = Wav2Vec2ForSpeechClassification.from_pretrained("padmalcom/wav2vec2-large-nonverbalvocalization-classification") - Notebooks
- Google Colab
- Kaggle
This language indendent wav2vec2 classification model is based on this dataset.
Sound classes are:
- teeth-chattering
- teeth-grinding
- tongue-clicking
- nose-blowing
- coughing
- yawning
- throat clearing
- sighing
- lip-popping
- lip-smacking
- panting
- crying
- laughing
- sneezing
- moaning
- screaming
inference.py shows, how the model can be used.
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