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import gradio as gr | |
from transformers import pipeline | |
import torchaudio | |
import time | |
# Load Whisper ASR model | |
transcriber = pipeline(model="openai/whisper-base") | |
# Load summarization model | |
summarization_model = pipeline("summarization") | |
def translate_audio(audio): | |
# Step 1: Transcribe audio to text | |
transcription = transcriber(audio) | |
print('transcription', transcription) | |
# Step 2: Translate text to Hindi | |
summary = summarization_model(transcription['text']) | |
print('summary', summary) | |
return transcription['text'], summary[0]['summary_text'] | |
# Create Gradio interface | |
with gr.Blocks() as iface: | |
gr.Markdown("# Audio Translator, Summarizer") | |
with gr.Row(): | |
audio_input = gr.Audio(type="filepath", label="Upload Audio") | |
transcription_output = gr.Textbox( | |
label="Transcribed Text", | |
info="Initial text") | |
translation_output = gr.Textbox( | |
label="Summary", | |
info="Meeting minute") | |
translate_button = gr.Button("Translate Audio") | |
translate_button.click( | |
translate_audio, | |
inputs=[audio_input], | |
outputs=[transcription_output, translation_output] | |
) | |
# Launch the app | |
iface.launch(share=True) # 'share=True' to get a public link |