| import gradio as gr | |
| from transformers import pipeline | |
| # Load the model separately | |
| model = pipeline("automatic-speech-recognition", model="speechbrain/mtl-mimic-voicebank") | |
| # Define a function to make predictions using the loaded model | |
| def transcribe(audio): | |
| return model(audio)["text"] | |
| # Define a CSS string to hide the footer | |
| custom_css = """ | |
| footer {visibility: hidden;} | |
| """ | |
| # Create the Gradio interface | |
| demo = gr.Interface( | |
| fn=transcribe, # Function to process input | |
| inputs=gr.Audio(source="microphone", type="filepath"), # Take audio input | |
| outputs="text", # Display output as text | |
| css=custom_css # Hide the Gradio footer | |
| ) | |
| # Launch the interface | |
| demo.launch() |