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README.md

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+---
+language:
+  - en
+tags:
+  - audio
+  - language-identification
+  - speech
+  - indian-languages
+datasets:
+  - hmsolanki/indian-languages-audio-dataset
+metrics:
+  - accuracy
+  - f1
+---
+
+# Indian Language Identification Model
+
+This model identifies the language spoken in an audio clip from a set of 10 Indian languages.
+
+## Model Details
+
+- **Model Type:** Audio Language Classifier
+- **Languages Supported:** Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Punjabi, Tamil, Telugu, Urdu
+- **Framework:** PyTorch
+- **Training Dataset:** [Indian Languages Audio Dataset](https://www.kaggle.com/datasets/hmsolanki/indian-languages-audio-dataset/)
+- **Audio Sampling Rate:** 16kHz
+
+## Performance
+
+- **Accuracy:** 0.8465
+- **Precision:** 0.8457
+- **Recall:** 0.8465
+- **F1 Score:** 0.8452
+
+## Usage
+
+```python
+import torch
+import torchaudio
+import json
+from transformers import pipeline
+
+# Load the model
+pipe = pipeline("audio-classification", model="prithvirajjadhav2266/indian-language-identifier")
+
+# Or use it directly
+waveform, sample_rate = torchaudio.load("path/to/audio.wav")
+if sample_rate != 16000:
+    resampler = torchaudio.transforms.Resample(sample_rate, 16000)
+    waveform = resampler(waveform)
+
+# Get prediction
+prediction = pipe(waveform)
+print(f"Detected language: {prediction[0]['label']}")
+```
+
+## Limitations
+
+- Works best with clear audio without background noise
+- Audio should be sampled at 16kHz for optimal performance
+
+## Training Details
+
+This model was trained on a dataset of Indian language audio samples. The model architecture combines CNN layers for feature extraction with transformer layers for classification.
+
+## Confusion Matrix
+
+![Confusion Matrix](/confusion_matrix.png)