Update README.md

README.md

@@ -11,10 +11,101 @@ language:
 - en
 ---
 
-# Uploaded  model
+# Use Model
 
-- **Developed by:** AhmedZaky1
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/whisper-small
 
+```
+import torch
+import numpy as np
+import re
+import jiwer
+import librosa
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+
+
+# TEXT NORMALIZATION
+def normalize_text(text):
+    text = text.lower()
+    text = re.sub(r"[^\w\s]", "", text)
+    text = " ".join(text.split())
+    return text
+
+
+# METRICS
+def calculate_metrics(ref, hyp):
+    ref_n = normalize_text(ref)
+    hyp_n = normalize_text(hyp)
+
+    return {
+        "reference": ref_n,
+        "hypothesis": hyp_n,
+        "wer": jiwer.wer(ref_n, hyp_n),
+        "cer": jiwer.cer(ref_n, hyp_n)
+    }
+
+
+# LOAD MODEL FROM HF
+def load_whisper(model_name, device="cuda"):
+    print(f"Loading Whisper model from HuggingFace: {model_name}")
+
+    processor = WhisperProcessor.from_pretrained(model_name)
+    model = WhisperForConditionalGeneration.from_pretrained(model_name)
+
+    return processor, model.to(device).eval()
+
+
+# TRANSCRIBE AUDIO
+def transcribe_audio(audio_path, processor, model, device="cuda"):
+    waveform, sr = librosa.load(audio_path, sr=16000)
+
+    inputs = processor(
+        waveform,
+        sampling_rate=sr,
+        return_tensors="pt"
+    ).input_features.to(device)
+
+    with torch.no_grad():
+        predicted_ids = model.generate(inputs)
+
+    transcription = processor.batch_decode(
+        predicted_ids,
+        skip_special_tokens=True
+    )[0]
+
+    return transcription
+
+
+# MAIN
+def main():
+    # SET YOUR AUDIO FILE & REFERENCE TEXT HERE
+    audio_path = ""  
+    reference_text = ""  
+
+    # 2️ YOUR MODEL NAME
+    model_name = "AhmedZaky1/whisper-small-v1"
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    # Load model
+    processor, model = load_whisper(model_name, device)
+
+    # Transcribe
+    print("\nTranscribing audio...")
+    prediction = transcribe_audio(audio_path, processor, model, device)
+
+    # Compute metrics
+    metrics = calculate_metrics(reference_text, prediction)
+
+    print("\n==============================")
+    print("REFERENCE:", metrics["reference"])
+    print("PREDICTION:", metrics["hypothesis"])
+    print(f"WER: {metrics['wer'] * 100:.2f}%")
+    print(f"CER: {metrics['cer'] * 100:.2f}%")
+    print("==============================")
+
+
+# Run script
+if __name__ == "__main__":
+    main()
+```