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--- |
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language: |
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- en |
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- hi |
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- or |
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- bn |
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- ta |
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- te |
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- kn |
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- ml |
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- mr |
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- gu |
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- pa |
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- as |
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license: apache-2.0 |
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pipeline_tag: audio-classification |
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library_name: transformers |
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tags: |
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- language-identification |
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- indian-languages |
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- multilingual |
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- speech |
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- asr-preprocessing |
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- callcenter-ai |
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- speech-analytics |
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- audio-classification |
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- wav2vec2 |
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- transformers |
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- pytorch |
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- huggingface |
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--- |
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# **Vakgyata** |
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**Language Identification for Indian Languages from Speech** |
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--- |
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## **Model Overview** |
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`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. |
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--- |
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## **Variants and Model Sizes** |
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| Variant | Parameters | Accuracy | |
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| ---------------- | ---------- | -------- | |
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| `vakgyata-base` | 95M | 95.88% | |
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| `vakgyata-small` | 52M | 95.06% | |
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| `vakgyata-mini` | 38M | 95.06% | |
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| `vakgyata-tiny` | 24M | 93.63% | |
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--- |
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## **Supported Languages** |
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| Language | Code | |
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| --------------- | ----- | |
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| English (India) | en-IN | |
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| Hindi | hi-IN | |
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| Odia | or-IN | |
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| Bengali | bn-IN | |
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| Tamil | ta-IN | |
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| Telugu | te-IN | |
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| Kannada | kn-IN | |
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| Malayalam | ml-IN | |
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| Marathi | mr-IN | |
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| Gujarati | gu-IN | |
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| Punjabi | pa-IN | |
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| Assamese | as-IN | |
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--- |
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## **Specifications** |
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* **Supported Sampling Rate:** 16000 Hz |
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* **Recommended Audio Format:** 16kHz, 16bit PCM (Mono) |
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--- |
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## **Installation** |
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```bash |
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pip install transformers torchaudio |
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``` |
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--- |
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## **Usage** |
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```python |
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from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor |
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import torch |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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model_id = "onecxi/vakgyata-tiny" |
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processor = AutoFeatureExtractor.from_pretrained(model_id) |
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model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id).to(device) |
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``` |
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--- |
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## **Inference Example** |
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```python |
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import torchaudio |
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# Load the audio (ensure it's 16kHz mono) |
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audio, sr = torchaudio.load("path/to/audio.wav") |
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# Preprocess |
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inputs = processor(audio.squeeze(), sampling_rate=sr, return_tensors="pt").to(device) |
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# Inference |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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# Softmax to get probabilities |
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probs = logits.softmax(dim=-1).cpu().numpy() |
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# Predicted language |
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language = model.config.id2label.get(probs.argmax()) |
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print("Predicted Language:", language) |
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``` |
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--- |
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## **Citation** |
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If you use this model in your research or application, please consider citing the model and its base source: |
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``` |
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@misc{vakgyata2024, |
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title={vakgyata: Language Identification for Indian Speech}, |
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author={OneCXI}, |
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year={2024}, |
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url={https://huggingface.co/onecxi/vakgyata-tiny} |
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} |
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``` |
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--- |
