Instructions to use Aniemore/wav2vec2-emotion-russian-resd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aniemore/wav2vec2-emotion-russian-resd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Aniemore/wav2vec2-emotion-russian-resd")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Aniemore/wav2vec2-emotion-russian-resd") model = AutoModelForAudioClassification.from_pretrained("Aniemore/wav2vec2-emotion-russian-resd") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 39eb2b55a93c3047d11fe3d7193893db6058c8a9b422a2c341b1882580696602
- Size of remote file:
- 1.27 GB
- SHA256:
- 9168e14378d37af83d2a65b47c9bc9d946298cb02114a188483439898b76fdf3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.