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Create app.py
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app.py
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import gradio as gr
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from transformers import pipeline
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pipe2 = pipeline("automatic-speech-recognition", model="distil-whisper/distil-small.en")
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pipe3 = pipeline("automatic-speech-recognition", model="antony66/whisper-large-v3-russian")
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demo = gr.Blocks()
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def transcribe_speech_english(filepath):
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if filepath is None:
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gr.Warning("No audio found, please retry.")
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return ""
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output = pipe2(filepath)
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return output["text"]
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def transcribe_speech_russian(filepath):
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if filepath is None:
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gr.Warning("No audio found, please retry.")
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return ""
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output = pipe3(filepath)
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return output["text"]
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mic_transcribe_english = gr.Interface(
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fn=transcribe_speech_english,
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inputs=gr.Audio(sources="microphone",
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type="filepath"),
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outputs=gr.Textbox(label="Transcription",
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lines=3),
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allow_flagging="never")
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mic_transcribe_russian = gr.Interface(
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fn=transcribe_speech_russian,
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inputs=gr.Audio(sources="microphone",
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type="filepath"),
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outputs=gr.Textbox(label="Transcription",
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lines=3),
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allow_flagging="never")
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file_transcribe_english = gr.Interface(
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fn=transcribe_speech_english,
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inputs=gr.Audio(sources="upload",
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type="filepath"),
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outputs=gr.Textbox(label="Transcription",
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lines=3),
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allow_flagging="never",
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)
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file_transcribe_russian = gr.Interface(
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fn=transcribe_speech_russian,
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inputs=gr.Audio(sources="upload",
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type="filepath"),
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outputs=gr.Textbox(label="Transcription",
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lines=3),
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allow_flagging="never",
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)
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with demo:
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gr.TabbedInterface(
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[mic_transcribe_english,
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file_transcribe_english,
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mic_transcribe_russian,
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file_transcribe_russian],
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["Transcribe Microphone English",
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"Transcribe Audio File English",
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"Transcribe Microphone Russian",
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"Transcribe Audio File Russian"],
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)
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demo.launch()
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