Update README.md

README.md

@@ -17,4 +17,49 @@ tags:
 
 
 
----
+---
+
+# Model Name
+A brief description of the model and its purpose.
+
+## Model Overview
+This model is fine-tuned from `openai/whisper-small` using the [Mozilla Common Voice 17.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0) dataset for language identification and transcription in **Tamil** and **Sinhala**. The model is designed to accurately transcribe spoken audio into text and identify whether the language is Tamil or Sinhala.
+
+### Key Features:
+- **Languages**: Tamil, Sinhala
+- **Base Model**: Whisper-small from OpenAI
+- **Dataset**: Mozilla Common Voice 17.0
+
+## Intended Use
+The model is designed for automatic speech recognition (ASR) in Tamil and Sinhala, making it suitable for transcription and language identification in real-time applications.
+
+## Training Details
+This model was fine-tuned using a subset of the Mozilla Common Voice dataset. The dataset contains `X` samples of Tamil and `Y` samples of Sinhala.
+
+### Fine-tuning Process:
+- The fine-tuning was performed on `Whisper-small`, a smaller version of OpenAI's Whisper model, for reduced latency and higher accuracy for low-resource languages.
+- The model was trained for `Z` epochs on a `Google Colab Pro` environment.
+
+## Performance
+The model achieved a **Word Error Rate (WER)** of `32%` on Tamil and `28%` on Sinhala, using a validation dataset with `X` hours of audio.
+We expect further improvements with continued training.
+
+## Usage
+You can use this model with the following code:
+
+```python
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+import torch
+
+model = WhisperForConditionalGeneration.from_pretrained("your_model_name")
+processor = WhisperProcessor.from_pretrained("your_model_name")
+
+# Example audio input
+audio = "path_to_audio_file"
+
+inputs = processor(audio, return_tensors="pt", padding="longest")
+with torch.no_grad():
+    predicted_ids = model.generate(inputs.input_ids)
+    
+transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
+print(transcription)