Audio Classification
Transformers
PyTorch
Safetensors
English
wav2vec2
speech
audio
emotion-recognition
Instructions to use audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim")# Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim") model = Wav2Vec2ForSpeechClassification.from_pretrained("audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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