Add CER, hyperparameters, and training logs to README

README.md

@@ -46,6 +46,30 @@ It achieves the following results on the evaluation set:
 - Cer: 7.3593
 - Real Time Factor: 0.0520
 
+## Quick Inference
+
+
+```python
+import torch
+import librosa
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+processor = WhisperProcessor.from_pretrained("andrewbawitlung/whisper-medium-mizonal3-E2-lus-v2026.06")
+model = WhisperForConditionalGeneration.from_pretrained("andrewbawitlung/whisper-medium-mizonal3-E2-lus-v2026.06").to(device)
+
+audio, sr = librosa.load("your_audio.wav", sr=16000)
+input_features = processor(audio, sampling_rate=16000, return_tensors="pt").input_features.to(device)
+
+with torch.no_grad():
+    predicted_ids = model.generate(input_features, max_new_tokens=256)
+
+transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+print(transcription)
+```
+
+
 ## Model description
 
 ### Experiment Configurations
@@ -65,16 +89,6 @@ This repository is part of a series of experiments. The different configurations
 | **E4 (SpecAug)** | [andrewbawitlung/whisper-medium-mizonal3-E4-lus-v2026.06](https://huggingface.co/andrewbawitlung/whisper-medium-mizonal3-E4-lus-v2026.06) |
 | **E5 (Combined)** | [andrewbawitlung/whisper-medium-mizonal3-E5-lus-v2026.06](https://huggingface.co/andrewbawitlung/whisper-medium-mizonal3-E5-lus-v2026.06) |
 
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
 ### Training hyperparameters
 
 The following hyperparameters were used during training: