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from transformers import pipeline |
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import gradio as gr |
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import time |
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p = pipeline( |
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task="automatic-speech-recognition", |
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model="arthoho66/model_005_2000", |
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token="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", |
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) |
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text = "" |
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def recorded_process(recorded_audio_file) -> str: |
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""" |
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to get both input |
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and use speech2text for get text |
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""" |
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text = p(recorded_audio_file)["text"] |
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return text |
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def streaming_process(streaming_audio_file) -> str: |
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global text |
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text = p(streaming_audio_file)["text"] |
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return text |
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def output_streaming(text_streaming,text01)-> str: |
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text_streaming+=text01 |
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return text_streaming |
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def clear_inputs_and_outputs() -> list: |
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""" |
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Clears all inputs and outputs when the user clicks "Clear" button |
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""" |
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audio_chunk.remove_chunk() |
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return [None, None, None, None] |
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text_streaming = "" |
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with gr.Blocks() as demo: |
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with gr.Tab("Record File"): |
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with gr.Row(): |
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with gr.Column(): |
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mic_input = gr.Microphone( type="filepath",label="Record voice") |
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with gr.Row(): |
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clr_btn = gr.Button(value="Clear", variant="secondary") |
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sub_btn = gr.Button(value="submit") |
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with gr.Column(): |
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lbl_output = gr.Textbox(label="Result") |
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clr_btn.click( |
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fn=clear_inputs_and_outputs, |
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inputs=[], |
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outputs=[mic_input, lbl_output] |
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) |
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sub_btn.click( |
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fn=recorded_process, |
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inputs=[mic_input], |
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outputs=[lbl_output] |
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) |
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with gr.Tab("streaming"): |
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gr.Interface( |
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fn=streaming_process, |
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inputs=[ |
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gr.Microphone(type="filepath", streaming=True)], |
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outputs=[ |
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gr.Textbox(type ="text", label="Result",)], |
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live=True, |
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allow_flagging="never" |
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) |
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with gr.Row(): |
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with gr.Column(): |
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print(text) |
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text_streaming = output_streaming(text_streaming,text) |
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gr.Textbox(value=text, label="Result", autofocus=True) |
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demo.launch() |
