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from transformers import pipeline
import gradio as gr
import os
# Load the ASR model from Hugging Face Hub
asr = pipeline(task="automatic-speech-recognition", model="openai/whisper-small")
# Define the transcription function
def transcribe_long_form(filepath):
if filepath is None:
return "No audio file provided, please upload a file or record one."
output = asr(filepath)
return output['text']
# Custom CSS to improve the interface
css = """
body { font-family: Arial, sans-serif; }
button { background-color: #4CAF50; color: white; border: none; padding: 10px 20px; }
"""
# Set up the Gradio interface
with gr.Blocks(css=css) as demo:
gr.Markdown("### Audio Transcription Service")
gr.Markdown("Upload an audio file or use your microphone to record one. Then press the 'Transcribe' button to see the transcription.")
with gr.Tab("Transcribe Audio"):
with gr.Row():
audio_input = gr.Audio(sources=["microphone", "upload"], type="filepath", label="Upload or Record Audio")
submit_button = gr.Button("Transcribe")
transcription_output = gr.Textbox(label="Transcription", lines=10, placeholder="Your transcription will appear here...")
submit_button.click(
transcribe_long_form,
inputs=[audio_input],
outputs=[transcription_output]
)
# Launch the Gradio app
demo.launch(share=True, server_port=int(os.environ.get('PORT1', 7860))) # Default port 7860 if PORT1 is not set
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