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Running
on
CPU Upgrade
ZennyKenny
commited on
Update app.py
Browse files
app.py
CHANGED
@@ -2,38 +2,43 @@ import gradio as gr
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import torch
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from transformers import pipeline
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#
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# Function to process audio
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def process_audio(audio_file):
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# Gradio Interface with Horizontal Layout
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with gr.Blocks() as interface:
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with gr.Row():
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# Upload button on the left
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with gr.Column():
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audio_input = gr.Audio(type="filepath", label="Upload Audio File")
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process_button = gr.Button("Process Audio")
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# Output text box on the right
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with gr.Column():
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transcription_output = gr.Textbox(label="Full Transcription", lines=10)
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summary_output = gr.Textbox(label="Summary", lines=5)
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# Link the button to the function
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process_button.click(
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process_audio,
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inputs=[audio_input],
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outputs=[transcription_output, summary_output]
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)
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# Launch the interface with
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interface.launch
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import torch
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from transformers import pipeline
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# Check if GPU is available; fallback to CPU if not
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device = 0 if torch.cuda.is_available() else -1
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try:
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# Load models with error handling
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transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)
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summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
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except Exception as e:
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print(f"Error loading models: {e}")
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raise
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# Function to process audio
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def process_audio(audio_file):
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try:
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# Transcribe the audio
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transcription = transcriber(audio_file)["text"]
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# Summarize the transcription
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summary = summarizer(transcription, max_length=50, min_length=10, do_sample=False)[0]["summary_text"]
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return transcription, summary
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except Exception as e:
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return f"Error processing audio: {e}", ""
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# Gradio Interface with Horizontal Layout
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with gr.Blocks() as interface:
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with gr.Row():
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with gr.Column():
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audio_input = gr.Audio(type="filepath", label="Upload Audio File")
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process_button = gr.Button("Process Audio")
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with gr.Column():
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transcription_output = gr.Textbox(label="Full Transcription", lines=10)
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summary_output = gr.Textbox(label="Summary", lines=5)
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process_button.click(
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process_audio,
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inputs=[audio_input],
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outputs=[transcription_output, summary_output]
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)
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# Launch the interface with public sharing and SSR disabled
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interface.launch(share=True, ssr=False)
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