Update README.md

README.md

@@ -1,23 +1,23 @@
----
-base_model: openai/whisper-small
-library_name: peft
-tags:
-- whisper
-- lora
-- transformers
-- speech-to-text
-- persian
-- stt
-- fine-tune
-- adapter
-datasets:
-- vhdm/persian-voice-v1
-language:
-- fa
-metrics:
-- cer
-- wer
----
+---
+base_model: openai/whisper-small
+library_name: peft
+tags:
+- whisper
+- lora
+- transformers
+- speech-to-text
+- persian
+- stt
+- fine-tune
+- adapter
+datasets:
+- vhdm/persian-voice-v1
+language:
+- fa
+metrics:
+- cer
+- wer
+---
 
 # Whisper-Small Persian STT — LoRA Fine-Tuned
 
@@ -77,25 +77,3 @@ Users should:
 - Provide clean 16kHz mono WAV audio
 - Use domain-specific fine-tuning if necessary
 - Validate outputs before critical use
-
----
-
-## How to Get Started
-
-```python
-from transformers import WhisperProcessor, WhisperForConditionalGeneration
-from peft import PeftModel
-
-processor = WhisperProcessor.from_pretrained("Mehdipoladrag/REPO_NAME")
-model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
-
-# Load LoRA adapter
-model = PeftModel.from_pretrained(model, "Mehdipoladrag/REPO_NAME")
-
-audio = ...  # your 16kHz mono waveform
-
-inputs = processor(audio, sampling_rate=16000, return_tensors="pt")
-pred_ids = model.generate(inputs["input_features"])
-text = processor.batch_decode(pred_ids, skip_special_tokens=True)[0]
-
-print(text)