Upload Live_Recording.py

App_Function_Libraries/Gradio_UI/Live_Recording.py

@@ -2,11 +2,13 @@
 # Description: Gradio UI for live audio recording and transcription.
 #
 # Import necessary modules and functions
+import logging
 import os
 # External Imports
 import gradio as gr
 # Local Imports
-from App_Function_Libraries.Audio_Transcription_Lib import (record_audio, speech_to_text, save_audio_temp)
+from App_Function_Libraries.Audio_Transcription_Lib import (record_audio, speech_to_text, save_audio_temp,
+                                                            stop_recording)
 from App_Function_Libraries.DB.DB_Manager import add_media_to_database
 #
 #######################################################################################################################
@@ -25,15 +27,27 @@ def create_live_recording_tab():
                 whisper_models_input = gr.Dropdown(choices=whisper_models, value="medium", label="Whisper Model")
                 vad_filter = gr.Checkbox(label="Use VAD Filter")
                 save_recording = gr.Checkbox(label="Save Recording")
-                save_to_db = gr.Checkbox(label="Save Transcription to Database")
+                save_to_db = gr.Checkbox(label="Save Transcription to Database(Must be checked to save - can be checked afer transcription)", value=False)
                 custom_title = gr.Textbox(label="Custom Title (for database)", visible=False)
-                record_button = gr.Button("Record and Transcribe")
+                record_button = gr.Button("Start Recording")
+                stop_button = gr.Button("Stop Recording")
             with gr.Column():
                 output = gr.Textbox(label="Transcription", lines=10)
                 audio_output = gr.Audio(label="Recorded Audio", visible=False)
 
-        def record_and_transcribe(duration, whisper_model, vad_filter, save_recording):
-            audio_data = record_audio(duration)
+        recording_state = gr.State(value=None)
+
+        def start_recording(duration):
+            p, stream, audio_queue, stop_event, audio_thread = record_audio(duration)
+            return (p, stream, audio_queue, stop_event, audio_thread)
+
+        def end_recording_and_transcribe(recording_state, whisper_model, vad_filter, save_recording, save_to_db, custom_title):
+            if recording_state is None:
+                return "Recording hasn't started yet.", None
+
+            p, stream, audio_queue, stop_event, audio_thread = recording_state
+            audio_data = stop_recording(p, stream, audio_queue, stop_event, audio_thread)
+
             temp_file = save_audio_temp(audio_data)
             segments = speech_to_text(temp_file, whisper_model=whisper_model, vad_filter=vad_filter)
             transcription = "\n".join([segment["Text"] for segment in segments])
@@ -48,23 +62,47 @@ def create_live_recording_tab():
             if custom_title.strip() == "":
                 custom_title = "Self-recorded Audio"
 
-            add_media_to_database(
-                url="self_recorded",
-                info_dict={"title": custom_title, "uploader": "self-recorded"},
-                segments=[{"Text": transcription}],
-                summary="",
-                keywords="self-recorded,audio",
-                custom_prompt_input="",
-                whisper_model="self-recorded"
-            )
-            return "Transcription saved to database successfully."
+            try:
+                url = "self_recorded"
+                info_dict = {
+                    "title": custom_title,
+                    "uploader": "self-recorded",
+                    "webpage_url": url
+                }
+                segments = [{"Text": transcription}]
+                summary = ""
+                keywords = ["self-recorded", "audio"]
+                custom_prompt_input = ""
+                whisper_model = "self-recorded"
+                media_type = "audio"
+
+                result = add_media_to_database(
+                    url=url,
+                    info_dict=info_dict,
+                    segments=segments,
+                    summary=summary,
+                    keywords=keywords,
+                    custom_prompt_input=custom_prompt_input,
+                    whisper_model=whisper_model,
+                    media_type=media_type
+                )
+                return f"Transcription saved to database successfully. {result}"
+            except Exception as e:
+                logging.error(f"Error saving transcription to database: {str(e)}")
+                return f"Error saving transcription to database: {str(e)}"
 
         def update_custom_title_visibility(save_to_db):
             return gr.update(visible=save_to_db)
 
         record_button.click(
-            fn=record_and_transcribe,
-            inputs=[duration, whisper_models_input, vad_filter, save_recording],
+            fn=start_recording,
+            inputs=[duration],
+            outputs=[recording_state]
+        )
+
+        stop_button.click(
+            fn=end_recording_and_transcribe,
+            inputs=[recording_state, whisper_models_input, vad_filter, save_recording, save_to_db, custom_title],
             outputs=[output, audio_output]
         )