Update app.py

app.py

@@ -12,14 +12,7 @@ model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id).to(device)
 processor = AutoProcessor.from_pretrained(model_id)
 
 # Create pipeline with correct parameter
-whisper_pipe = pipeline(
-    "automatic-speech-recognition",
-    model=model,
-    tokenizer=processor.tokenizer,
-    feature_extractor=processor.feature_extractor,
-    device=0 if torch.cuda.is_available() else -1,
-    generate_kwargs={"input_features": None},  # Ensure correct input handling
-)
+pipe = pipeline("automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, device=0 if torch.cuda.is_available() else -1)
 
 # Convert audio to WAV format
 def convert_to_wav(audio_path):
@@ -48,7 +41,7 @@ def transcribe_long_audio(audio_path):
     transcription = ""
     
     for chunk in chunk_paths:
-        result = whisper_pipe(chunk)  # No longer uses deprecated `inputs`
+        result = pipe({"path": chunk})  # FIXED: Pass chunk as dict
         transcription += result["text"] + "\n"
         os.remove(chunk)  # Remove processed chunk
     
@@ -58,6 +51,8 @@ def transcribe_long_audio(audio_path):
 
 # Gradio interface
 def transcribe_interface(audio_file):
+    if not audio_file:
+        return "No file uploaded."
     return transcribe_long_audio(audio_file)
 
 iface = gr.Interface(