added gradio app

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+from transformers import pipeline
+
+
+from datasets import DatasetDict, Dataset, load_dataset, Audio
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+
+def transcribe(audio):
+    # load model and processor
+    processor = WhisperProcessor.from_pretrained("openai/whisper-medium")
+    model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-medium")
+    
+    ds = Dataset.from_dict({"audio": [audio]}).cast_column("audio", Audio())
+    ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
+    input_speech = next(iter(ds))["audio"]["array"]
+    
+    input_features = processor(input_speech, return_tensors="pt").input_features 
+    forced_decoder_ids = processor.get_decoder_prompt_ids(language = "no", task = "transcribe")
+
+    predicted_ids = model.generate(input_features, forced_decoder_ids = forced_decoder_ids)
+    transcription = processor.batch_decode(predicted_ids, skip_special_tokens = True)
+
+    return transcription
+
+
+gr.Interface(
+    title = "OpenAI Whisper ASR Gradio Norwegian Web UI",
+    fn=transcribe,
+    inputs=[
+        gr.inputs.Audio(type="filepath")
+    ],
+    outputs=[
+        "textbox"
+    ]
+).launch()