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
- Xet hash:
- 36df3cf6ebf534944e2406794f944fc6e5fac62bda31692cafc430525fbd83ba
- Size of remote file:
- 1.27 GB
- SHA256:
- 56a09bf8bb4a628bdf32d6d96d37e15c9b3f490bda8f48c08441f5cb258e28e9
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