Update README.md

README.md

@@ -26,12 +26,29 @@ model-index:
 library_name: transformers
 ---
 
-## Malwhisper-v1-small
+# Malwhisper-v1-small
 
 This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) fine-tuned on [IMASc dataset](https://www.kaggle.com/datasets/thennal/imasc). 
 
+## About Dataset
+
 IMaSC is a Malayalam text and speech corpus made available by ICFOSS for the purpose of developing speech technology for Malayalam, particularly text-to-speech. The corpus contains 34,473 text-audio pairs of Malayalam sentences spoken by 8 speakers, totalling in approximately 50 hours of audio.
 
+## Training
+
+[Script Used for training](https://github.com/kurianbenoy/Keyword_generator_project/blob/main/Whisper_IMASC_final_e2eofficerun.ipynb)
+
+[Training run](https://wandb.ai/hello34/wandb_whisper_e2e/runs/q2xlvbw5)
+
+[Experiment Tracking with Weights and Biases](https://wandb.ai/hello34/wandb_whisper_e2e)
+
+- GPUs used: A100 and 80 GB
+
+- Training Time: 16 hours
+
+- This project was build with A100 80GB GPU provided by [E2E during their open hack day](https://www.eventbrite.com/e/open-hack-day-tickets-783582435157)
+
+## Evaluation
 The fine-tuned model on evaluating in the following dataset:
 
 **In Mozilla CommonVoice 11.0 dataset (Malayalam subset):**
@@ -47,16 +64,4 @@ WER - 70.49
 CER - 17.0
 
 
-## Training
-
-[Script Used for training](https://github.com/kurianbenoy/Keyword_generator_project/blob/main/Whisper_IMASC_final_e2eofficerun.ipynb)
 
-[Training run](https://wandb.ai/hello34/wandb_whisper_e2e/runs/q2xlvbw5)
-
-[Experiment Tracking with Weights and Biases](https://wandb.ai/hello34/wandb_whisper_e2e)
-
-- GPUs used: A100 and 80 GB
-
-- Training Time: 16 hours
-
-- This project was build with A100 80GB GPU provided by [E2E during their open hack day](https://www.eventbrite.com/e/open-hack-day-tickets-783582435157)