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language:
- en
- hi
- or
- bn
- ta
- te
- kn
- ml
- mr
- gu
- pa
- as
license: apache-2.0
pipeline_tag: audio-classification
library_name: transformers
tags:
- language-identification
- indian-languages
- multilingual
- speech
- asr-preprocessing
- callcenter-ai
- speech-analytics
- audio-classification
- wav2vec2
- transformers
- pytorch
- huggingface
---
# **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`](https://huggingface.co/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**
```bash
pip install transformers torchaudio
```
---
## **Usage**
```python
from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "onecxi/vakgyata-base" # You can replace with tiny/small/mini variants
processor = AutoFeatureExtractor.from_pretrained(model_id)
model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id).to(device)
```
---
## **Inference Example**
```python
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-base}
}
```
--- |