Create app.py

app.py

@@ -0,0 +1,32 @@
+!pip install datasets>=2.6.1
+!pip install git+https://github.com/huggingface/transformers
+!pip install librosa
+!pip install evaluate>=0.30
+!pip install jiwer
+!pip install gradio
+!pip install transformers
+
+import gradio as gr
+from transformers import pipeline
+from huggingface_hub import login
+
+login("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", add_to_git_credential=True)
+
+
+pipe = pipeline(model="SebLih/whisper-SV")  # change to "your-username/the-name-you-picked"
+
+def transcribe(audio):
+    text = pipe(audio)["text"]
+    print("Transcribed text:")
+    print(text)
+    return text
+
+iface = gr.Interface(
+    fn=transcribe, 
+    inputs=gr.Audio(source="microphone", type="filepath"), 
+    outputs="text",
+    title="Whisper Small Swedish",
+    description="Realtime demo for Swedish speech recognition using a fine-tuned Whisper small model.",
+)
+
+iface.launch(share=True)