Create app.py

app.py

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+from engine.speech2text import Speech2Text
+from engine.ner_engine import NERInfer
+from engine.audio_chunk import AudioChunk
+import glob
+import time
+import gradio as gr
+import os
+
+audio_chunk = AudioChunk()
+speech2text = Speech2Text()
+ner_tag = NERInfer()
+
+text = ""
+def streaming_process(streaming_audio_file) -> str:
+    global text 
+    text = speech2text.process_streaming(streaming_audio_file)
+    # return text
+
+def output_streaming(text_streaming,text01)-> str:
+    text_streaming+=text01
+    return text_streaming
+
+def recorded_process(recorded_audio_file) -> str:
+    """ 
+    to get both input 
+    and use speech2text for get text
+    """
+    text = speech2text.process_microphone(recorded_audio_file)
+    result = ner_tag.infer(text)
+    return {"text": text, "entities": result}
+
+
+def uploaded_process(uploaded_audio_file) -> str:
+    audio_chunk.chunks_audio(uploaded_audio_file)
+    text = speech2text.process_audio()
+    result = ner_tag.infer(text)      
+    return {"text": text, "entities": result}
+
+
+def clear_inputs_and_outputs() -> list:
+    """
+    Clears all inputs and outputs when the user clicks "Clear" button
+    """
+    audio_chunk.remove_chunk()
+    return [None, None, None, None]
+
+text_streaming = ""
+
+with gr.Blocks() as demo:
+    """
+    buld gradio app
+
+    """
+    gr.Markdown(
+        """
+        ![logo](/file=./image/clicknext_logo2x.png)
+
+        # Automatic Speech Recognition
+         
+        #####  Experience real-time, accurate, and multilingual speech-to-text conversion with our cutting-edge ASR technology.
+        """
+    )
+
+    with gr.Tab("Record File"):
+        with gr.Row():  
+            with gr.Column():
+                mic_input = gr.Microphone( type="filepath",label="Record voice")
+                with gr.Row():
+                    clr_btn = gr.Button(value="Clear", variant="secondary")
+                    sub_btn = gr.Button(value="submit")
+            with gr.Column():
+                lbl_output = gr.HighlightedText(label="Result")
+
+            clr_btn.click(
+                fn=clear_inputs_and_outputs,
+                    inputs=[],
+                    outputs=[mic_input, lbl_output]
+            )
+
+            sub_btn.click(
+                fn=recorded_process,
+                inputs=[mic_input],
+                outputs=[lbl_output]
+            )
+
+    with gr.Tab("streaming"):
+        gr.Interface(
+                    fn=streaming_process,
+                    inputs=[
+                        gr.Microphone(type="filepath", streaming=True)],
+                    outputs=[
+                    #     # gr.HighlightedText(label="Result"),
+                        gr.Textbox(type ="text", label="Result",)],
+                    live=True,
+                    allow_flagging="never"
+                    )
+        with gr.Row():  
+            with gr.Column():
+                print(text)
+                text_streaming = output_streaming(text_streaming,text)
+                gr.Textbox(value=text, label="Result", autofocus=True)
+
+    with gr.Tab("Upload File"):
+        with gr.Row():  
+            with gr.Column():
+                upl_input = gr.Audio( type="filepath", label="Upload a file")   
+                with gr.Row():
+                        clr_btn = gr.Button(value="Clear", variant="secondary")
+                        sub_btn = gr.Button(value="submit")
+                gr.Examples(examples=[
+                    os.path.join(os.path.dirname(__file__),"examples/politics.mp3"),
+                    os.path.join(os.path.dirname(__file__),"examples/law1.mp3"),
+                    os.path.join(os.path.dirname(__file__),"examples/law2.mp3"),
+                    os.path.join(os.path.dirname(__file__),"examples/law3.mp3"),
+                    os.path.join(os.path.dirname(__file__),"examples/economy.mp3"),
+                    os.path.join(os.path.dirname(__file__),"examples/general.mp3")
+                    ],
+                    inputs = upl_input)
+            with gr.Column():
+                lbl_output = gr.HighlightedText(label="Result")
+
+            clr_btn.click(
+                fn=clear_inputs_and_outputs,
+                inputs=[],
+                outputs=[upl_input, lbl_output]
+            )
+            sub_btn.click(
+                fn=uploaded_process,
+                inputs=[upl_input],
+                outputs=[lbl_output]
+            )
+
+demo.launch(favicon_path = "./image/fe_logo.png", server_name="0.0.0.0", server_port=8085)