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Browse files- app.py +23 -0
- requirements.txt +3 -0
app.py
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import gradio as gr
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import torch
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from transformers import pipeline
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# Load the Whisper pipeline
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transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base") # Choose your Whisper size
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def transcribe_audio(audio_file):
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if audio_file is not None:
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text = transcriber(audio_file)["text"]
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return text
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else:
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return "No audio file uploaded"
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with gr.Blocks() as demo:
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gr.Markdown("## Audio Transcription with Whisper")
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audio_input = gr.Audio(source="upload", type="filepath", label="Upload Audio File")
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text_output = gr.Textbox(label="Transcription")
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btn = gr.Button("Transcribe")
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btn.click(transcribe_audio, inputs=audio_input, outputs=text_output)
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demo.launch()
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requirements.txt
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transformers
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gradio
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torch
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