Model Name: open-vakgyata
Model Overview: open-vakgyata is an open-source language identification model capable of detecting and classifying indian languages from speech inputs.
Supported Languages:
| Language | Code |
|---|---|
| English (India) | en-IN |
| Hindi | hi-IN |
| Odia | or-IN |
| Bengali | bn-IN |
| Tamil | ta-IN |
| Telugu | te-IN |
| Kannada | kn-IN |
| Malayalam | ml-IN |
| Marathi | mr-IN |
| Gujarati | gu-IN |
Specification
- Supported Sampling Rate: 16000
- Recomonded Audio Format: 16kHz, 16bit PCM
Usage:
from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor
import torch
device = "cpu" # "cuda"
model_id = "onecxi/open-vakgyata"
processor = AutoFeatureExtractor.from_pretrained(model_id)
model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id).to(device)
Inference:
import torchaudio
audio, sr = torchaudio.load("path/to/audio.wav")
# Process the waveform and move to the appropriate device
inputs = processor(audio.flatten(), sampling_rate=sr, return_tensors="pt").to(device)
# Perform inference
with torch.no_grad():
logits = model(**inputs).logits
# Get language probabilities
probs = logits.softmax(dim=-1).cpu().numpy()
language = model.config.id2label.get(probs.argmax())
print(language)
Citation
If you use this model in your research or application, please consider citing the model and its base source:
@misc{vakgyata2024,
title={vakgyata: Language Identification for Indian Speech},
author={OneCXI},
year={2024},
url={https://huggingface.co/onecxi/open-vakgyata}
}