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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() |