Audio Classification
Transformers
Safetensors
English
wav2vec2
emotion
audio
classification
music
facebook
Instructions to use prithivMLmods/Speech-Emotion-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Speech-Emotion-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="prithivMLmods/Speech-Emotion-Classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Speech-Emotion-Classification") model = AutoModelForAudioClassification.from_pretrained("prithivMLmods/Speech-Emotion-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-3750/optimizer.pt from prithivMLmods/Speech-Emotion-Classification: direct link, hf CLI and curl.
- Browser
- Download file 723 MB
-
https://huggingface.co/prithivMLmods/Speech-Emotion-Classification/resolve/main/checkpoint-3750/optimizer.pt
- Command line
-
hf download hf://prithivMLmods/Speech-Emotion-Classification/checkpoint-3750/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/prithivMLmods/Speech-Emotion-Classification/resolve/main/checkpoint-3750/optimizer.pt
723 MB
- Xet hash:
- b8635ade92eb270446391dc0594e5851187457724729d16321fbcd1e6375f06f
- Size of remote file:
- 723 MB
- SHA256:
- ac41701489dec80c540f22f0fe343445a05fc4189909b5cffc3d408392ef6ed9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.