Instructions to use hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned1.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned1.1")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned1.1") model = AutoModelForAudioClassification.from_pretrained("hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned1.1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ace3a2bcfc53d1155cd127b5ced2bea275f0897b218ff4febc2ad185e13b9688
- Size of remote file:
- 4.86 kB
- SHA256:
- ac5e13f5c6c6764b94a420982aab8c376fbf460abb50d886df1d91d52be21698
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.