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:
- 9c09e8c112d08a33d8a337da1dbe5bc7a6bd39bf4d4936cdd863ba1fec040b0f
- Size of remote file:
- 3.38 kB
- SHA256:
- b19e5d57926cf4a2a3707f9b4f4871fcffe80b7405debaf0f3eb903f056575dd
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