Update app.py

app.py

@@ -25,3 +25,32 @@ demo = gr.Interface(
 )
 
 demo.launch()
+
+
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+import torch
+from datasets import load_dataset
+
+# Load model and processor
+processor = WhisperProcessor.from_pretrained("openai/whisper-small")
+model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
+
+# Optional: Use GPU if available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device)
+
+# Load sample audio (here using a dummy dataset, aap apni audio file bhi use kar sakte hain)
+ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+sample = ds[0]["audio"]
+
+# Prepare audio input
+input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
+input_features = input_features.to(device)
+
+# Generate transcription
+predicted_ids = model.generate(input_features)
+
+# Decode transcription
+transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
+print(transcription)
+