Audio Classification
Transformers
PyTorch
Safetensors
English
wav2vec2
speech
audio
emotion-recognition
Instructions to use muqtadar/voice_emo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use muqtadar/voice_emo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="muqtadar/voice_emo")# Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("muqtadar/voice_emo") model = Wav2Vec2ForSpeechClassification.from_pretrained("muqtadar/voice_emo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from muqtadar/voice_emo: direct link, hf CLI and curl.
- Browser
- Download file 661 MB
-
https://huggingface.co/muqtadar/voice_emo/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://muqtadar/voice_emo/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/muqtadar/voice_emo/resolve/main/pytorch_model.bin
661 MB
- Xet hash:
- a5746da0b7cbed49b3292535c8b97d5d0136d3d7bd745348bfc4474f64562e28
- Size of remote file:
- 661 MB
- SHA256:
- 176d9d1ce29a8bddbab44068b9c1c194c51624c7f1812905e01355da58b18816
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