Audio Classification
Transformers
ONNX
Safetensors
PyTorch
wav2vec2
language-identification
indian-languages
multilingual
speech
asr-preprocessing
callcenter-ai
speech-analytics
huggingface
Instructions to use onecxi/vakgyata-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use onecxi/vakgyata-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="onecxi/vakgyata-base")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("onecxi/vakgyata-base") model = AutoModelForAudioClassification.from_pretrained("onecxi/vakgyata-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e5f793556ed3171fd6d47bfe146f882bb2bfb11d9182d3e7e6dcc8ddb2df396
- Size of remote file:
- 381 MB
- SHA256:
- 5f10668607c452d27eab588188d55ad7d83733a75b5419d2fd067328d9968fcb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.