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from engine.speech2text import Speech2Text |
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from engine.ner_engine import NERInfer |
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from engine.audio_chunk import AudioChunk |
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import glob |
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import time |
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import gradio as gr |
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import os |
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audio_chunk = AudioChunk() |
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speech2text = Speech2Text() |
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ner_tag = NERInfer() |
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text = "" |
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def streaming_process(streaming_audio_file) -> str: |
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global text |
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text = speech2text.process_streaming(streaming_audio_file) |
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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 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 = speech2text.process_microphone(recorded_audio_file) |
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result = ner_tag.infer(text) |
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return {"text": text, "entities": result} |
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def uploaded_process(uploaded_audio_file) -> str: |
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audio_chunk.chunks_audio(uploaded_audio_file) |
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text = speech2text.process_audio() |
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result = ner_tag.infer(text) |
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return {"text": text, "entities": result} |
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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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""" |
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buld gradio app |
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""" |
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gr.Markdown( |
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""" |
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 |
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# Automatic Speech Recognition |
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##### Experience real-time, accurate, and multilingual speech-to-text conversion with our cutting-edge ASR technology. |
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""" |
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) |
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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.HighlightedText(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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with gr.Tab("Upload File"): |
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with gr.Row(): |
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with gr.Column(): |
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upl_input = gr.Audio( type="filepath", label="Upload a file") |
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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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gr.Examples(examples=[ |
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os.path.join(os.path.dirname(__file__),"examples/politics.mp3"), |
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os.path.join(os.path.dirname(__file__),"examples/law1.mp3"), |
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os.path.join(os.path.dirname(__file__),"examples/law2.mp3"), |
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os.path.join(os.path.dirname(__file__),"examples/law3.mp3"), |
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os.path.join(os.path.dirname(__file__),"examples/economy.mp3"), |
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os.path.join(os.path.dirname(__file__),"examples/general.mp3") |
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], |
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inputs = upl_input) |
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with gr.Column(): |
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lbl_output = gr.HighlightedText(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=[upl_input, lbl_output] |
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) |
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sub_btn.click( |
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fn=uploaded_process, |
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inputs=[upl_input], |
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outputs=[lbl_output] |
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) |
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demo.launch(favicon_path = "./image/fe_logo.png", server_name="0.0.0.0", server_port=8085) |
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