Add CER, hyperparameters, and training logs to README

README.md

@@ -46,6 +46,40 @@ It achieves the following results on the evaluation set:
 - Cer: 4.2170
 - Real Time Factor: 0.0697
 
+## Quick Inference
+
+
+```python
+import torch
+import librosa
+from transformers import AutoProcessor, Qwen2AudioForConditionalGeneration
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+processor = AutoProcessor.from_pretrained("andrewbawitlung/qwen3-asr-0.6b-mizonal3-E3-lus-v2026.06")
+model = Qwen2AudioForConditionalGeneration.from_pretrained("andrewbawitlung/qwen3-asr-0.6b-mizonal3-E3-lus-v2026.06").to(device)
+
+audio, sr = librosa.load("your_audio.wav", sr=16000)
+
+conversation = [
+    {"role": "user", "content": [
+        {"type": "audio", "audio_url": "your_audio.wav"},
+        {"type": "text", "text": "Transcribe the audio:"}
+    ]}
+]
+text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
+inputs = processor(text=text, audios=[audio], return_tensors="pt", padding=True)
+inputs.input_ids = inputs.input_ids.to(device)
+
+with torch.no_grad():
+    generate_ids = model.generate(**inputs, max_length=256)
+
+generate_ids = generate_ids[:, inputs.input_ids.size(1):]
+transcription = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+print(transcription)
+```
+
+
 ## Model description
 
 ### Experiment Configurations
@@ -65,16 +99,6 @@ This repository is part of a series of experiments. The different configurations
 | **E4 (SpecAug)** | [andrewbawitlung/qwen3-asr-0.6b-mizonal3-E4-lus-v2026.06](https://huggingface.co/andrewbawitlung/qwen3-asr-0.6b-mizonal3-E4-lus-v2026.06) |
 | **E5 (Combined)** | [andrewbawitlung/qwen3-asr-0.6b-mizonal3-E5-lus-v2026.06](https://huggingface.co/andrewbawitlung/qwen3-asr-0.6b-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: