onecxi/vakgyata-tiny
By onecxi · ↓ 2 · ♥ 2 · Updated 2025-07-23
What's in this repo
This page lists the files in onecxi/vakgyata-tiny, including model_quantized.onnx.
- 1 ONNX models (.onnx files)
- 6 files, including 2 .json, 1 .md, 1 .safetensors, 1 .onnx
- Last updated 2025-07-23
ONNX files
| File | Size | Download |
|---|---|---|
| onnx/model_quantized.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
Vakgyata
Language Identification for Indian Languages from Speech
Model Overview
vakgyata is an open-source language identification model specifically designed to classify Indian languages from raw speech audio. It is built upon the pretrained Harveenchadha/wav2vec2-pretrained-clsril-23-10k with additional Layer Normalization integrated to improve stability and performance for audio classification tasks.
Variants and Model Sizes
| Variant | Parameters | Accuracy |
|---|---|---|
vakgyata-base |
95M | 95.88% |
vakgyata-small |
52M | 95.06% |
vakgyata-mini |
38M | 95.06% |
vakgyata-tiny |
24M | 93.63% |
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 |
| Punjabi | pa-IN |
| Assamese | as-IN |
Specifications
- Supported Sampling Rate: 16000 Hz
- Recommended Audio Format: 16kHz, 16bit PCM (Mono)
Installation
pip install transformers torchaudio
Usage
from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "onecxi/vakgyata-tiny"
processor = AutoFeatureExtractor.from_pretrained(model_id)
model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id).to(device)
Inference Example
import torchaudio
# Load the audio (ensure it's 16kHz mono)
audio, sr = torchaudio.load("path/to/audio.wav")
# Preprocess
inputs = processor(audio.squeeze(), sampling_rate=sr, return_tensors="pt").to(device)
# Inference
with torch.no_grad():
logits = model(**inputs).logits
# Softmax to get probabilities
probs = logits.softmax(dim=-1).cpu().numpy()
# Predicted language
language = model.config.id2label.get(probs.argmax())
print("Predicted Language:", 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/vakgyata-tiny}
}
Model info
Created: 2025-07-23
Updated: 2025-07-23
View on Hugging Face